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Paper Title:

Abstract: Epdm Rubber is produced excessively worldwide in every year. It cannot be degraded easily as its decomposition takes much time and also causes environmental pollution. One of the mostly used synthetic rubbers is Ethylene propylene diene rubber (EPDM), it is used in cold-room doors seals, electrical insulation etc. This Research shows that Epdm rubber and stone dust are added to base course, after mixing in base course CBR, Plate load and cyclic load test of pavement are investigated and acquired results are compared with regular base course. This paper reviews the use of Epdm and stone dust as supplementary material in base course of a flexible pavement. A detailed literature survey is carried out and presented here.

Keywords: Epdm Rubber, Stone Dust, Fatigue Cracking.

References:

1. Lei Gao, Hua Li, Jianguang Xie, Zengbing Yu, Stephane Charmot. Evaluation of pavement performance for reclaimed asphalt materials in different layers, CONSTRUCTION AND BUILDING MATERIALS, (2018) 561–566.
2. Iman Mohammadi, Hadi Khabbaz, Kirk Vessalas. In-depth assessment of Crumb Rubber Concrete (CRC) prepared by water-soaking treatment method for rigid pavements, CONSTRUCTION AND BUILDING MATERIALS, (2014) 456–471.
3. Suo Zhi, Wong Wing Gun , Luo Xiao Hui , Tian Bo. Evaluation of fatigue crack behavior in asphalt concrete pavements with different polymer modifiers, CONSTRUCTION AND BUILDING MATERIALS, (2012) 117–125.
4. Naman Agarwal, Ajit Kumar. Design of stone dust stabilized road, International Journal of Civil Engineering and Technology (IJCIET), Volume 6, Issue 5, May (2015), pp. 28-33.
5. Niki D.Beskou, Stephanos V.Tsinopoulos, GeorgeD.Hatzigeorgiou. Fatigue cracking failure criterion for flexible pavements under moving vehicles, SOIL DYNAMICS AND EARTH QUAKE ENGINEERING, (2016)476–479.
6. Azza MohamedElleboudy, Asser MoslehSaleh, Amany GoudaSalama. Assessment of geogrids in gravel roads under cyclic loading, Alexandria engineering journal (2017), Pages 319-326.
7. Mohd Kashif Khan, Bhanu Pratap Singh. Used Of Recycled Tyre/Rubber as Course Aggregate and Stone Dust As Fine Aggregate in Cement Concrete Works, IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) (Sep. - Oct. 2015), PP 101-107.
8. S. V. Prasad and G. V. R. Prasada Raju. Performance of waste tyre rubber on model flexible pavement, ARPN Journal of Engineering and Applied Sciences VOL. 4, NO. 6, AUGUST 2009.
9. Yash Pandey, Dr. Sangita and Dr. Vandana Tare. “Utilization of Coal Mixed Waste Aggregates available at Thermal Power Plants for GSB and Asphalt Mixtures”, Advances in Transportation Geotechnics 3 . The 3rd International Conference on Transportation Geotechnics (ICTG 2016), Procedia Engineering Volume 143, 2016, Pages 170–177.
10. Cristina Cazan, Mihaela Cosnita and Anca Duta. “Effect of PET functionalization in composites of rubber–PET–HDPE type”. Arabian Journal of Chemistry.2015.
11. G.H. Shafabakhsh, M. Sadeghnejad, Y. Sajed.”Case study of rutting performance of HMA modified with waste rubber powder”, Case Studies in Construction Materials, (2014) 69–76.

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Paper Title:

Abstract: Satellite images are used for various applications like geographical, weather and geological applications. The forecasters used the low-resolution satellite images to predict the atmospheric changes. The high resolution satellite images are used in more applications especially for object segmentation and detection. The research on the high resolution satellite image is a challenging task. Less research is performed on high resolution satellite imagery as it is a challenging task. Traffic monitoring is a challenging task in developing countries. Automatic vehicle detection is very much useful for the traffic monitoring system. Vehicle images appear in miniature size in high resolution satellite images which is very difficult to extract from the images. Many researchers are working in these areas for the past few decades and most of the research is based on various types of sensor data. In sensor images, complete road network cannot be captured. In this paper, automatic detection of vehicles from high resolution satellite is proposed. Connected component based algorithm for automatic vehicle detection in high resolution satellite images is proposed in this paper.

Keywords: Adaptive global thresholding, Connected component analysis, Morphological operator, Vehicle detection.

References:

1. Zheng, Hong, “Automatic vehicles detection from high resolution satellite imagery using morphological neural networks”, in 10^th WSEAS International Conference on COMPUTERS, Vouliagmeni, Athens, Greece, Vol.13, 2006.
2. Gerhardinger, D. Ehrlich and M. Pesaresi, “Vehicles detection from very high resolution satellite imagery”, International Archives of Photogrammetry and Remote Sensing, Vol. 36, Part 3/ W24, 2005, pp. 83-88.
3. S. Kumar and T. Mani, “Vehicle detection and classification from satellite images based on gaussian mixture model”, International Journal of Engineering Research and General Science, Vol. 3, Iss. 2, Part 2, 2015, pp. 759-766
4. Gerhardinger, D.Ehrlich and M. Pesaresi, “Vehicles detection from very high resolution satellite imagery for the development of a societal activity index”, in International Society for Photogrammetry and Remote Sensing (ISPRS) Workshop, 2005, pp. 14-16.
5. Noorpreet Kaur Gill and Anand Sharma, “Vehicle Detection from Satellite Images in Digital Image Processing”, International Journal of Computational Intelligence Research, Vol.13, 5, 2017, pp. 697-706.
6. Leitloff, S. Hinz and U. Stilla, “Vehicle detection in very high resolution satellite images of city areas”, IEEE transactions on Geoscience and remote sensing, Vol. 48, No. 7, 2010, pp. 2795-2806.
7. Qu, Shenquan, Ying Wang, Gaofeng Meng, and Chunhong Pan, "Vehicle Detection in Satellite Images by Incorporating Objectness and Convolutional Neural Network", Journal of Industrial and Intelligent Information, Vol 4, No. 2, 2016, pp. 158-162.
8. Aaron J. Heller, Yvan G. Leclerc and Quang-Tuan Luong, “A framework for robust 3-d change detection”, in International Symposium on Remote Sensing, SPIE. 2002, pp. 1-11.
9. Kembhavi, D. Harwood and L. S. Davis, “Vehicle detection using partial least squares”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 6, 2011, pp. 1250-1265.
10. Grabner, T. Nguyen, B. Gruber and H. Bischof, “On-line boosting-based car detection from aerial images”, Journal of Photogrammetry and Remote Sensing, Vol. 63, No. 3, 2008, pp. 382-396.
11. McCord, C. Merry and P. Goel, “Incorporating satellite imagery in traffic monitoring programs”, in North American Travel Monitoring Exhibition and Conference, Charlotte, NC, 1998.
12. Viangteeravat and A. Shirkhodaie, “Multiple target vehicles detection and classification with low-rank matrix decomposition”, in IEEE International Conference on System of Systems Engineering SoSE’07, 2007, pp. 1-8.
13. Kharghanian and A. Ahmadifar, “Extracting Vessel Centerlines From Retinal Images Using Topographical Properties and Directional Filters”, International Journal of Engineering-Transactions B: Applications, Vol. 25, No.4, 2012, pp. 315-324.
14. Sujatha, C., and Selvathi, D., “FPGA Implementation of Road Network Extraction using Morphological Operator”, Image Analysis & Stereology, 35, No. 2, 2016, pp. 93-103.
15. Ashourian, N. Daneshmandpour, O. Sharifi Tehrani, P. Moallem, “Real Time Implementation of a License Plate Location Recognition System Based on Adaptive Morphology” International Journal of Engineering-Transactions B: Applications, Vol.26, No. 11, 2013, pp. 1347-1356.
16. Dalla Mura, J. A. Benediktsson, B. Waske and L. Bruzzone, “Morphological attribute profiles for the analysis of very high resolution images”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, No. 10, 2010, pp. 3747-3762.
17. C. Sujatha, and D. Selvathi , “Connected component-based technique for automatic extraction of road centerline in high resolution satellite images”, EURASIP Journal on Image and Video Processing, Vol. 2015, No. 8, 2015.

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Paper Title:

Abstract: Numerous accidents are caused by sleepy drivers. To avoid such mishaps, the sluggishness acknowledgment framework is built based on the acknowledgment of eye states. The primary thought behind this exploration is to build up a drivers Safety framework by demonstrating the auspicious cautioning. This framework will screen the driver's eyes utilizing camera and by building up a calculation we can recognize indications of driver fatigue. We propose an algorithm for knowing the drivers drowsiness by checking the width and height of the eye. It helps to indicate the driver’s drowsiness by giving an alarm. A new formula has been used to check the measurements of eye and face detection. The number of eye blinking count can be measured to check the driver’s drowsiness. The warning will be deactivated manually rather than automatically. Therefore, a deactivation switch will be utilized to deactivate warning.

Keywords: auspicious cautioning, drowsiness, eye and face detection, sluggishness acknowledgment.

References:

1. Ji, Q., Zhu, Z., & Lan, P. (2004). Real-time nonintrusive monitoring and prediction of driver fatigue. IEEE transactions on vehicular technology, 53(4), 1052-1068.
2. Ueno, H., Kaneda, M., & Tsukino, M. (1994, August). Development of drowsiness detection system. In Vehicle Navigation and Information Systems Conference, 1994. Proceedings., 1994 (pp. 15-20). IEEE.
3. Hayami, T., Matsunaga, K., Shidoji, K., & Matsuki, Y. (2002). Detecting drowsiness while driving by measuring eye movement-a pilot study. In Intelligent Transportation Systems, 2002. Proceedings. The IEEE 5th International Conference on (pp. 156-161). IEEE.
4. Suzuki, M., Yamamoto, N., Yamamoto, O., Nakano, T., & Yamamoto, S. (2006, October). Measurement of driver's consciousness by image processing-a method for presuming driver's drowsiness by eye-blinks coping with individual differences. In Systems, Man and Cybernetics, 2006. SMC'06. IEEE International Conference on (Vol. 4, pp. 2891-2896). IEEE.
5. D’Orazio, T., Leo, M., Guaragnella, C., & Distante, A. (2007). A visual approach for driver inattention detection. Pattern Recognition, 40(8), 2341-2355.
6. Hsu, R. L., Abdel-Mottaleb, M., & Jain, A. K. (2002). Face detection in color images. IEEE transactions on pattern analysis and machine intelligence, 24(5), 696-706.
7. Zuraida, R., Iridiastadi, H., & Sutalaksana, I. Z. (2017). Indonesian Drivers’ Characteristics associated with road accidents. International Journal of Technology, 2, 311-319.
8. Puspasari, M. A., Muslim, E., Moch, B. N., & Aristides, A. (2015). Fatigue measurement in car driving activity using physiological, cognitive, and subjective approaches. International Journal of Technology, 6(6), 971-975.
9. Svensson, U. (2004). Blink behaviour based drowsiness detection: method development and validation. Statens väg-och transportforskningsinstitut.
10. Wilkinson, V. E., Jackson, M. L., Westlake, J., Stevens, B., Barnes, M., Swann, P., ... & Howard, M. E. (2013). The accuracy of eyelid movement parameters for drowsiness detection. Journal of clinical sleep medicine, 9(12), 1315-1324.
11. Mardi, Z., Ashtiani, S. N. M., & Mikaili, M. (2011). EEG-based drowsiness detection for safe driving using chaotic features and statistical tests. Journal of medical signals and sensors, 1(2), 130.
12. Celenk, M., Eren, H., & Poyraz, M. (2009, June). Prediction of driver head movement via Bayesian Learning and ARMA modeling. In Intelligent Vehicles Symposium, 2009 IEEE (pp. 542-547). IEEE.
13. Murata, K., Fujita, E., Kojima, S., Maeda, S., Ogura, Y., Kamei, T., ... & Suzuki, N. (2011). Noninvasive biological sensor system for detection of drunk driving. IEEE transactions on information technology in biomedicine, 15(1), 19-25.
14. Chang, T. H., & Hsu, C. S. (2007, June). Irregular vehicle behavior warning modules. In Intelligent Vehicles Symposium, 2007 IEEE (pp. 1150-1155). IEEE.

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Paper Title:

Abstract: Internet provides various services where a person interacts with each other. When a person performs any activity by internet then all the records stored on a web server. The data stored on the server called weblog data. This weblog contain lots of information about users. Now every person can get any information on a click. The huge amount of information stored on server. If we want to get the desired information from web server then it has to use some data mining techniques. Frequent pattern mining is one of the techniques for getting patterns from weblog. In this paper proposed an algorithm and framework for Pattern Analysis based on Maximum Frequency of Weblog (PAMFW) and also proposed a framework for pattern analysis.

Keywords: Data Mining, Internet, Pattern Analysis, Web Server, Weblog.

References:

1. Huang, Y. Koh, G. Dobbie, “Rare pattern mining on data streams. Data”, Warehousing and Knowledge Discovery Lecture Notes in Computer Science 7448:303-314. doi: 10.1007/978-3-642-32584-7_25, 2012.
2. Papadopoulos, T. Stamati, P. Nopparuch, “Exploring the Determinants of Knowledge Sharing Via Employee Weblogs”. International Journal of Information Management , Vol. 33(1): 133-146, 2013.
3. Bakariya, G.S. Thakur, “An Efficient Algorithm for Extracting Infrequent Itemsets”. The International Arab Journal of Information Technology (IAJIT), 16 (2), 2019.
4. Karim, C. F. Ahmed, B . Jeong, and H. Choi, “An Efficient Distributed Programming Model for Mining Useful Patterns in Big Datasets”. IETE Technical Review 30:53-63, 2013.
5. Bakariya, G.S. Thakur, “Pattern Mining Approach for Social Network Service”, National Academy Science Letters, Springer, 40(3):183–187, 2017.
6. T. Wang, A. J. T. Lee, “Mining Web Navigation Patterns with a Path Traversal Graph”. Expert Systems with Applications, Elsevier, 38:7112–7122, 2011.
7. Bakariya, G. S. Thakur, “Mining Rare Itemsets from Weblog”. National Academy Science Letters, Springer, 39(5): 359–363, 2016.
8. http: //ita.ee.lbl.gov. Accessed 12 March 2013.
9. Bakariya, G. S. Thakur, “An Efficient Algorithm for Extracting High Utility Itemsets from Web Log Data”. The Institution of Electronics and Telecommunication Engineers (IETE) Technical Review, 32(2):151-160, 2015.
10. A. Soulet, F. Rioult, “Efficiently Depth-First Minimal Pattern Mining”, Lecture Notes in Computer Science 8443, 2014.

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Paper Title:

Abstract: The self-organizing maps (SOM) is a supervised neural network(NN) studying technology but there is some data remaining to extract to analysis the neural network which has been frequently used for the analysis and organization of data files having a large size. In this same manner fuzzy c-means (FCM) is also a supervised methodology to segment the image. Here, introducing a novel path to deal with the consequent section of Magnetic Resonance (MR) imaging of the human brain into anatomical locales. This paper presents an analysis segmentation of microarray brain image in an unsupervised methodology by combines the supervised FCM and SOM methodologies.

Keywords: Image Segmentation, FCM, SOM, Microarray, MRI, FSOM.

References:

1. Arun Kumar Ray, Nilesh Bhaskarrao Bahadure, Sidheswar Routray, “A novel approach for cDNA image segmentation using SLIC based SOM methodology” International Journal of Engineering & Technology, Volume 7 No. 2.8 2018, 52-55.
2. Bhavana, Dr.V. Rajesh, D. Ravi Tej, Naveen Kishore Gattim, “Image Enhancement Technique For Uniform And Non Uniform Dark Images By DRC” International Journal of Pure and Applied Mathematics, Volume 117 No. 19 2017, 71-75.
3. Soumya Ranjan Nayak, Jibitesh Mishra and Sidheswar Routray, “Effect of Noise Determination on Estimation of Fractal Dimension of Digital Images”, International Journal of Pure and Applied Mathematics, Volume 117 No. 19 2017, 57-63.
4. Nixson, A.Aguado, “Feature Extraction and Image Processing”, Academic Press, 2008.
5. Roman, Y. Kazanovich, D. Chik, V. Tikhanoff, A. Can-gelos. “A neural model of selective attention and object segmentation in the visual scene: An approach based on partial synchronization and star-like architecture of connections”. Neural Networks, vol. 22, no. 5–6, pp. 707–719, 2009.
6. Krishnan, C. N. K. Babu, V. V. J. Rajapandian, N. R. Devaraj. “A fuzzy image segmentation using feedforward neural networks with supervised learning. InProceedings of International Conference on Cognition and Recognition”, pp. 396–400, 2009.
7. Aghajari, G. Damayanti. “Incorporating FCM and back propagation neural network for image segmentation. Inter-national Journal of Computer and Communication Technology”, vol. 2, no. 8, pp. 121–126, 2011.
8. Z. Mao, P. Zhao, P. H. Tan. “Supervised learning-based cell image segmentation for P53 immunohisto chemistry”. IEEE Transactions on Biomedical Engineering, vol. 53,no. 6, pp. 1153–1163, 2006.
9. Shirakawa, T. Nagao. “Evolutionary image segmentation based on multi objective clustering”. In Proceedings of IEEE Congress on Evolutionary Computation, IEEE, Trondheim, Norway pp. 2466–2473, 2009.
10. Yang, S. Y. Huang. “Image segmentation by fuzzy C-means clustering algorithm with a novel penalty term”. Computing and Informatics, vol. 26, no. 1, pp. 17–31, 2007.
11. M,KumudhaRaimond “MR brain image segmentation based on principle component analysis and self-organizing map” International journal for research in applied science and engineering technology vol.2 issue III,march 2014 ISSN:2321-9653.
12. Vinay Paramshwarappa,Nandish.S “A Segmented Morphological approach to detect Tumor in Brain Images” International Journal of Advanced Research in Computer
13. science and Software Engineering” Volume 4,Issue 1,January 2014 ISSN:2277 1
14. M and HimaBindu.C.H “Brain MR image segmentation using self-organizing map” International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 10, October 2013.
15. S,Moorthi.M and Madhu.M “An improved method of segmentation using fuzzy-Neuro logic” Second International conference on computer research and development 2010 ISBN 978-0-7695-4043-6.
16. P,Palanisamy.V and Purusothaman.T “Performance analysis of clustering algorithms in brain tumor detection of MR images” European journal of scientific research ISSN 1450-216X vol.62 No.3 (2011),pp.321-330.
17. AyseDemirhan, InanGuler, “Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation”,Engineering Applications of Artificial Intelligence, 24, (2011), 358–367.
18. Leonid Dorosinskiy, Tamara Lysenko “An Algorithm Of Boundaries Detection in Low-Contrast Radar Images of the Earth” International Journal of Pure and Applied Mathematics Volume 110 No. 4 2016, 657-664.
19. Nalini, C. Raghavendra, KRajendra Prasad “Comparative Observation and Performance Analysis of Multiple Algorithms on IRIS Data” International Journal of Pure and Applied Mathematics, Volume 116 No. 9 2017, 319-325.
20. Durga Prasad Kondisetty, Dr. Mohammed Ali Hussain, “A Robust Method for Reducing Image Noise in Micro-array Images” International Journal of Pure and Applied Mathematics, Volume 117 No. 19 2017, 441-447.
21. Tian, D., Fan, L., Brain A., “MR images segmentation method based on SOM neural network”. In: Proceedings of the First International Conference on Bioinformatics and Biomedical Engineering, ICBBE (2007),China, pp. 686–689.
22. Durga Prasad Kondisetty, Dr. Mohammed Ali Hussain, “A Review on Micro-array Image Segmentation Methods”. International Journal of Computer Science and Information Security (IJCSIS), Vol. 14, No. 12, December 2016.

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Paper Title:

Abstract: Swarm intelligence algorithms are based on the behavior and intelligence of living organisms that exist in nature. Amidst living organisms, Bacteria is a micro-organism that exhibit intelligent behavior by the development of biofilms to overcome harsh and adverse environment such as antibiotics and other bacteria. This survival behavior of bacteria forms the basis for the design of Biofilm (Bifi) algorithm in this paper. Biofilm algorithm uses three important characteristics of biofilm forming bacteria viz., conjugation, transformation and quorum sensing for solving real-world optimization problems. Biofilm algorithm is applied to global numerical benchmark test functions and compared with known state-of-the-art optimization algorithms.

Keywords: Bacteria behavior, Biofilm, Swarm intelligence algorithm, Single objective optimization.

References:

1. Amirthalingam and G. Radhamani, “New chaff point based fuzzy vault for multimodal biometric cryptosystem using particle swarm optimization,” J. King Saud Univ. - Comput. Inf. Sci., vol. 28, no. 4, pp. 381–394, 2016.
2. M. Passino, “Bacterial Foraging Optimization,” International
3. Journal of Swarm Intelligence Research, vol. 1, no. 1. pp. 1–16, 2010.
4. H. and S. Lackner, “Modeling of Biofilm Systems: A Review,”
5. Biochem. Eng. Biotechnol., vol. 146, no. July 2015, pp. 127–141, 2014.
6. Poli, J. Kennedy, and T. Blackwell, “Particle swarm optimization An overview,” Swarm Intell., no. 1, pp. 33–57, 2007.
7. D. Narooei, R. Ramli, M. Nizam, A. Rahman, F. Iberahim, and J. A. Qudeiri, “Tool Routing Path Optimization for Multi-Hole Drilling Based on Ant Colony Optimization,” World Appl. Sci. J., vol. 32, no. 9, pp. 1894–1898, 2014.
8. Yang, X.-S., and Deb, “Engineering Optimisation by Cuckoo Search,” Int. J. Math. Model. Numer. Optim., vol. 1, no. 4, pp. 330–343, 2010.
9. S. Yang, “A new metaheuristic Bat-inspired Algorithm,” Stud. Comput. Intell., vol. 284, pp. 65–74, 2010.
10. V. Devi and S. S. Sathya, “Monkey Behavior Based Algorithms - A Survey,” Int. J. Intell. Syst. Appl., vol. 9, no. 12, pp. 67–86, 2017.
11. Das, A. Biswas, S. Dasgupta, and A. Abraham, “Bacterial Foraging Optimization Algorithm : Theoretical Foundations , Analysis , and Applications,” Found. Comput. Intell. Vol. 3, vol. 3, pp. 23–55, 2009.
12. Fouad and X. Z. Gao, “A novel modified flower pollination algorithm for global optimization,” Neural Computing and Applications, pp. 1–34, 2018.
13. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–61, 2014.
14. Mirjalili and A. Lewis, “The Whale Optimization Algorithm,” Adv. Eng. Softw., vol. 95, pp. 51–67, 2016.
15. Y. ¸AK Hasan MAKAS, “Balancing exploration and exploitation by using sequential execution cooperation between artificial bee colony and migrating birds optimization algorithms.pdf,” Turk J Elec Eng Comp Sci, pp. 4935–4956, 2016.
16. M. Abd-Elazim and E. S. Ali, “A hybrid Particle Swarm Optimization and Bacterial Foraging for optimal Power System Stabilizers design,” Int. J. Electr. Power Energy Syst., vol. 46, no. 1, pp. 334–341, 2013.
17. Kora and S. R. Kalva, “Hybrid Bacterial Foraging and Particle Swarm Optimization for detecting Bundle Branch Block,” Springerplus, vol. 4, no. 1, 2015.
18. El Moustaid, “Mathematical Modelling of Bacterial Attachment to Surfaces : Biofilm Initiation,” no. September, pp. 6–8, 2011.
19. M. Derbyshire and T. A. Gray, “Distributive Conjugal Transfer: New Insights into Horizontal Gene Transfer and Genetic Exchange in Mycobacteria Keith,” vol. 2, no. 1, pp. 1–32, 2014.
20. B. H. Najafi and P. Pezeshki, “Bacterial Mutation ; Types , mechanisms and mutant detection methods: A Review,” vol. 4, no. December, pp. 628–638, 2013.
21. “Matlab software,” Matlab, high-performance numeric computation and visualization software: reference guide, 1992. [Online]. Available: https://in.mathworks.com/downloads/web_downloads/?s_iid=hp_ff_t_downloads.
22. Jamil and X. Y. Blekinge, “A Literature Survey of Benchmark Functions For Global Optimization Problems,” pp. 1–47, 2013.

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Paper Title:

Abstract: AISI 1050 is utilized in the creation of landing Gear, actuators and other aviation parts yet their application is constrained because of erosion protection from the high quality. In any metal cutting task the highlights of Tools, input work materials, machine parameter settings will impact the procedure proficiency and yield quality attributes. A huge enhancement in process productivity might be acquired by the process parameter improvement that distinguishes and decides the areas of basic process control factors prompting wanted yields or reactions with satisfactory varieties guaranteeing a lower cost of assembling. This test thinks about clarifies the issues and machinability issues like failure of tools and precision are found while machining and less yield in machining. In the present investigation of Different Heat treatment, for example, Annealing, Normalizing and Spherodizing was explored in turning of AISI 1050 under the thought of a few turning process Constraints. The anticipated outcomes were observed to be in great concurrence with the test.

Keywords: AISI 1050, Heat treatment, Turning Operation & Process Parameter, Ranking Algorithm

References:

1. Naderi, V. Uthaisangsuk, U. Prahl, W. Bleck, A Numerical and Experimental Investigation into Hot Stamping of Boron Alloyed Heat Treated Steels, steel research int. 79 (2008) No. 2.
2. Aziz, S.N. Aqida, Optimization of quenching process in hot press forming of 22MnB5 steel for high strength properties , Materials Science and Engineering 50 (2013).
3. Hu, D. Shi, L. Ying, G. Shen, W. Liu, The finite element analysis of ductile damage during hot stamping of 22MnB5 steel, Materials and Design 69 (2015) 141–152.
4. -L. Zhao, “Section capacity of very high strength (VHS) circular tubes under compression,” Thin Walled Structures, vol. 37, no. 3, pp. 223-240, 2000.
5. Wang, Y. Yan, J. Li, J. Huang, Y. Su, and L. Qiao, “Hydrogen embrittlement assessment of ultra-high strength steel 30CrMnSiNi 2,” Corrosion Science, vol. 77, pp. 273-280, 2013.
6. Chang, and H. Bhadeshia, “Carbon content of austenite in isothermally transformed 300M steel,” Materials Science and Engineering: A, vol. 184, no. 1, pp. L17-L19, 1994.
7. Y. Sirin, K. Sirin, and E. Kaluc, “Effect of the ion nitriding surface hardening process on fatigue behavior of AISI 4340 steel,” Materials Characterization, vol. 59, no. 4, pp. 351-358, 2008.
8. Yang, X. Sun, Z. Li, X. Li, and Q. Yong, “Effects of vanadium on the microstructure and mechanical properties of a high strength low alloy martensite steel,” Materials & Design, vol. 50, pp. 102- 107, 2013.
9. K. D. H. Bhadeshia, Bainite in steels: Inst. of Metals, 1992.
10. SIVAM, S. P. Sundar Singh et al.”Multi Response Optimization of Setting Input Variables for Getting Better Product Quality in Machining of Magnesium AM60 by Grey Relation Analysis and ANOVA." Periodica Polytechnica Mechanical Engineering, [S.l.], 2017. ISSN 1587-379X. https://doi.org/10.3311/PPme.11034
11. Sivam, S.P.S.S.,, Gopal , S.Venkatasamy, Siddhartha Singh, An Experimental Investigation And Optimisation Of Ecological Machining Parameters On Aluminium 6063 In Its Annealed And Unannealed Form, Journal Of Chemical And Pharmaceutical Sciences. Page No Page (46 – 53), 2015.
12. Sivam, S.P.S.S.,, M.Gopal, S.Venkatasamy, Siddhartha Singh 2015, “Application of Forming Limit Diagram and Yield Surface Diagram to Study Anisotropic Mechanical Properties of Annealed and Unannealed SPRC 440E Steels”. Journal of Chemical and Pharmaceutical Sciences. ISSN: 0974-2115, Page No (15 – 22).
13. P. Sundar Singh Sivam, Abburi Lakshman Kumar, K. Sathiya Moorthy and Rajendrakumar, “Investigation Exploration Outcome of Heat Treatment on Corrosion Resistance of AA 5083 in Marine Application”, Journal of Science and Technology.14 (S2), 2016, 453-460. ISSN 0972-768X.
14. Sivam, S.P.S.S., Umasekar, V.G., Mishra, A., Mishra, S. and Mondal, A. (2016) ‘Orbital cold forming technology – combining high quality forming with cost effectiveness – a review’, Indian Journal of Science and Technology, October, Vol. 9, No. 38, DOI: 10.17485/ijst/2016/ v9i38/91426.
15. Sivam, S.P.S.S., UmaSekar, V.G., Saravanan, K., RajendraKumar, S., Karthikeyan, P. and SathiyaMoorthy, K. (2016) ‘frequently used anisotropic yield criteria for sheet metal applications: a review’, Indian Journal of Science and Technology, December, Vol. 9, No. 47, DOI: 10.17485/ijst/2015/v8i1/92107.
16. P. Sundar Singh Sivam, A. Rajasekaran, S. RajendraKumar, K. SathiyaMoorthy & M. Gopal (2019) A study of cooling time, copper reduction and effects of alloying elements on the microstructure and mechanical properties of SG iron casting during machining, Australian Journal of Mechanical Engineering, DOI: 10.1080/14484846.2018.1560679
17. P. Sundar Singh Sivam, Durai Kumaran, Krishnaswamy Saravanan, Venugopal Guruswamy Umasekar, Sankarapandian Rajendrakumar, Karuppiah Sathiya Moorthy (2018) "THICKNESS DISTRIBUTION AND NUMERICAL MODELLING OF CONVENTIONAL SUPERPLASTIC FORMING IN AA2024 ALLOY", International Journal of Modern Manufacturing Technologies, ISSN 2067–3604,76,85, Vol. X, No. 2 / 2018
18. Sivam, S. P. S. S., Saravanan, K., Pradeep, N., Moorthy, K. and Rajendrakumar, S. “The Grey Relational Analysis and Anova to Determine the Optimum Process Parameters for Friction Stir Welding of Ti and Mg Alloys”, Periodica Polytechnica Mechanical Engineering. doi: https://doi.org/10.3311/PPme.12117.
19. P. Sundar Singh Sivam, Ganesh Babu Loganathan, K. Saravanan (2019), Impact of Point Angle on Drill Product Quality and Other Responses When Drilling EN- 8: A Case Study of Ranking Algorithm, International Journal of Innovative Technology and Exploring Engineering, ISSN 2278-3075 PP. No. 280-282
20. P. Sundar Singh Sivam, Ganesh Babu Loganathan, K. Saravanan, S. RajendraKumar (2019), Outcome of the Coating Thickness on the Tool Act and Process Parameters When Dry Turning Ti–6Al–4V Alloy: GRA Taguchi & ANOVA, International Journal of Innovative Technology and Exploring Engineering, ISSN 2278-3075 PP. No. 419-423
21. P. Sundar Singh Sivam, Ganesh Babu Loganathan, K. Saravanan, S. Rajendra Kumar (2019), Multi-Response Enhancement of Drilling Process Parameters for AM 60 Magnesium Alloy as per the Quality Characteristics utilizing Taguchi-Ranking Algorithm and ANOVA, International Journal of Innovative Technology and Exploring Engineering, ISSN 2278-3075 PP. No. 437 - 440

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Paper Title:

Abstract: The immovability and permanence of thermal barrier coatings is very significant parameter for turbo machinery used at high atmospheric temperature such as turbine, compressor used in power generation and space industry. In this paper nano-structured ceramic pinnacle coating of 8YSZ (8wt%Y2O3ZrO2) and NiCoCrAlY atomic bond is deposit on Ni based super alloy INCONEL718 by air plasma spray (APS). Thermal shock test of INCONEL718 has been performed as a function of temperature at 1100°C. This thermal shock testing is performed with 45 min heating and 15 min cooling in water and air separately. Delamination is observed due to thermal fatigue damage on 8YSZ coating. Damage of work piece is originated at the corner due to thermal fatigue and is sustained upto 20 cycles, 8 cycles for air and water cooling respectively. Nano-Microstructure characterization was done by high resolution scanning electron microscopy-energy dispersive x-ray (HRSEM-EDX). Result from HRSEM-EDX is co-related to nano and Micro structural development of thermal barrier coatings. After conducting of water and air cooling, lots of non-regular shaped nano size agglomerated clusters were observed in the 8YSZ coating. These non-regular structured leads to the formation of weak in the bond which cause delamination of coatings.

Keywords: Delamination, Nano-sized APS coating, Thermal barrier coating, Thermal shock

References:

1. Sheng , “Situation and prospect of application of rare earth in cast iron”, Chin. Rare Earths (in Chinese),Vol 16, 1995, pp. 36.
2. L. Wang., Q. B. Zhang and M. L. Sun., “Micro structural characteristics of laser clad coatings with rare earth metal elements”, J. Muter. Process. Technol., Vol. 139, 2003, pp. 448.
3. A. Li, Z. H. Yu, C.S Wang and H. Yu, “The effect of CeOz on the microstructure and wear resistance of laser cladding M2 + 4B coating”, J. Chin. Rare Earth SOC. (in Chinese), Vol. 13, 1995, pp. 280.
4. F. Lian, L. G. Yu., and Q. J. Xue, “The effect of ce-rium dioxide on the friction and wear properties of flame spraying nickel-based alloy coating”, Wear, Vol. 181-183, 1995, pp. 436.
5. Iakovou , L. Bourithis and G. Papaditnitriou, “Syn-thesis of boride coatings on steel using plasma trans-ferred arc (ETA) process and its wear performance” W ear, Vol. 252, 2002, pp.1007.
6. L. Deuis, J. M. Yellup and C. Subramanian, “Metal-matrix composite coatings by ETA surfacing”, Compos. Sci. Technol., Vol. 58, 1998, pp. 299.
7. Gilbert, K. Kokini, S. Sankarasubramanian, “Thermal fracture of zirconia–mullite composite thermal barrier coatings under thermal shock: A numerical study”, Surf. Coat. Technol., Vol.203 (1), 2008, pp. 91-98.
8. Gilbert, S. Kokini and Sankarasubramanian, “Thermal fracture of zirconia–mullite composite thermal barrier coatings under thermal shock: An experimental study” Surf. Coat. Technol. Vol. 202(10), 2008, pp. 2152-2161.
9. G. Rabiet and Evans, “Failure Mechanisms Associated with the Thermally Barrier Coatings” Acta Mater. Vol. 48(15), 2000, pp. 3963-3976.

10. Kumar, K.N.Pandey, “Study on Thermal Fatigue Behaviour of Plasma Sprayed Yttria-Zirconia Thermal Barrier Coatings (THERMAL BARRIER COATINGS) Systems on Aluminium Alloy”, Int. J.Mech. Prod. Engng. 2 (2014) 19-22.
11. Kumar, K.N.Pandey, D.K.Das, “Thermal Cyclic Resistance Behavior of Inconel 800 Super Alloy Substrate with Thermal Barrier Coatings by Plasma Spraying” Procedia Mater. Sci., Vol.5, 2014, pp. 1075-1080.
12. Uzun, I. Çevik, M. Akcil, “Effects of thermal barrier coatingmaterial on a turbocharged diesel engine performance”, Surf. Coat. Technol., Vol.505, 1999, pp. 116–119.
13. W.J. Clegg, K. Kendall, “'A simple way to make tough ceramics” N.Mc. Alford, et al., Nature, Vol. 347, 1990, pp. 455-457.
14. A.J. Phillipps, W.J. Clegg, T.W. “Fracture behavior of ceramic laminates in bending—I. Modeling of crack propagation” clyne, acta metall. mater. , Vol 41, 1993, pp.805-817.
15. D. Kovar, M.D. Thouless, J.W. Halloran, “crack deflection and propagation in layer silicon nitride/boron nitride ceramics”J. Am. Ceram. Soc., Vol. 81(4), 1998, pp. 1004-12.

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Authors:

Paper Title:

Abstract: In present world, water distribution network has a considerable role in maintaining the required living standard. It has various components such as pipe, pump, and control valve to deliver water to the consumer withdrawal point from its source point. Among these elements, more than 70% of the project cost depends on the pipe cost, which justifies the need of optimal sizing of pipes. Unfortunately, optimal pipe sizing is a non-linear problem. Importance and complexity of the pipe flow problem makes it popular in research field. The literature unfolds that the stochastic optimization algorithms are successful in examining the combination of least-cost pipe diameters from the commercially available distinct diameter set, but with the liability of considerable computational effort. The hybrid method i.e. Genetic -Fuzzy System, presented in this research work has a main focus to develop a parameter for increasing the reliability of the overall network using fuzzy logic concepts based on the difference in simulated pressure and acceptable pressure limits in demand nodes and to incorporate this parameter in a bi-objective optimization model for water distribution network design using Genetic Algorithms, hence attaining the optimized pipe diameters with minimum computational effort. The method is applied for optimizing three different water distribution systems [1, 13]. The variation of reliability index with the optimal cost is presented in graphical form.

Keywords: Optimization, water distribution system, fuzzy logic, genetic algorithm, reliability index

References:

1. Alperovits, E. and Shamir, U. (1977). Design of Optimal Water Distribution Systems. Water Resources Research, 13(6), pp.885-900.
2. Fujiwara, O. and Khang, D.B. (1990). A Two-Phase Decomposition Method for Optimal Design of Looped Water Distribution Networks. Water Resources Research. 26(4), p.p.539-549.
3. Gupta, I., Gupta, A. and Khanna, P. (1999). Genetic Algorithm for Optimization of Water Distribution Systems. Environmental Modelling & Software, 14(5), pp.437-446.
4. Prasad, T.D., Hong, S.H. and Park, N. (2003). Reliability Based Design of Water Distribution Networks Using Multi-Objective Genetic Algorithms. KSCE Journal of Civil Engineering, 7(3), pp.351-361.
5. Kapelan, Z.S., Savic, D.A. and Walters, G.A. (2005). Multiobjective Design of Water Distribution Systems Under Uncertainty. Water Resources Research, 41(11).
6. Suribabu, C.R. and Neelakantan, T.R. (2006). Particle Swarm Optimization Compared to other Heuristic Search Techniques for Pipe Sizing. Journal of Environmental Informatics, 8(1).
7. Reca, J. and Martínez, J. (2006). Genetic Algorithms for the Design of Looped Irrigation Water Distribution Networks. Water Resources Research, 42(5).
8. Zimmermann, H.J. (2010). Fuzzy Set Theory. Wiley Interdisciplinary Reviews: Computational Statistics, 2(3), pp.317-332.
9. Chandramouli, S. and Malleswararao, P. (2011). Reliability Based Optimal Design of a Water Distribution Network for Municipal Water Supply. International Journal of Engineering and Technology, 3(1), pp.13-19.
10. Babu, K.J. and Vijayalakshmi, D.P. (2012). Self-Adaptive PSO-GA Hybrid Model for Combinatorial Water Distribution Network Design. Journal of Pipeline Systems Engineering and Practice, 4(1), pp.57-67.
11. Minakshi S., Dr. Vishnu Prasad and Dr. Ruchi Khare. (2014). Optimization Techniques for Water Supply Network: A Critical Review, International Journal of Mechanical Engineering and Technology, Volume 5, Issue 9, pp. 417-426.
12. Minakshi S., Dr. Vishnu Prasad and Dr. Ruchi Khare. (2015). Multi-Objective Optimization of Water Distribution System Using Particle Swarm Optimization, IOSR Journal of Mechanical and Civil Engineering, Volume 12, Issue 6, PP 21-28.
13. Geem, Z.W. (2015). Multiobjective Optimization of Water Distribution Networks Using Fuzzy Theory and Harmony Search. Water, 7(7), Pp.3613-3625.
14. Bozorg-Haddad, O., Ghajarnia, N., Solgi, M., Loáiciga, H.A. and Mariño, M.A. (2017). Multi-Objective Design of Water Distribution Systems Based on the Fuzzy Reliability Index. Journal of Water Supply: Research and Technology-Aqua, 66(1), Pp.36-48.
15. Suribabu, C.R. (2017). Resilience-Based Optimal Design of Water Distribution Network. Applied Water Science, 7(7), Pp.4055-4066.

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Authors:

Paper Title:

Abstract: From last few decades computer system becomes an essential part of everyone’s life and day by day its necessity is keep on rising. Computation and processing time is main source & plays a key role to achieve a better performance with very low power & energy consumption. In every electronic devices used in data processing & thus fast data processing requires frequent transfer of data from main memory to CPU. However the performance grid-back of different data-oriented applications is depends on various way of accessing the data cache. Thus, cache memory enhances the performance of processor by tens or hundreds of times much better. Hence the key technique is to diminish overall energy consumption is to vitally shut off the part of a processor’s cache. Reconfigurable cache memory is vital to enhance the store execution and lessens the vitality utilization. In this paper, an audit for past papers related with reconfigurable store memory were given and analyzed it our work .This paper presents our proposed technique that reduces overall power consumption and improved performance in computer system if implemented with the help of some already existing architecture. Paper has introductory section followed by literature review, proposed work, then flow of execution, simulation flow and then results & analysis section which contains observation taken on by doing various simulations. Detailed block diagram describes the working of proposed technique, followed by execution flow diagram that depicts the flow of execution of our proposed technique then simulation flow which depicts the simulation process of technique , then result and analysis sections depicts the partial result of proposed technique ,after that there is a conclusion section followed by future work and limitation of out proposed technique.

Keywords: Cache, computer system, pipeline, memory architecture, data processing.

References:

1. P. Dandamudi, “Fundamentals of Computer Organization and Design”, 2nd ed. New York, USA: Springer, 2012.
2. Albonesi D.H, “Selective cache ways: on demand cache resource allocation,” in Proc. 32nd Inter-national Symposium on Microarchitecture, 1999.
3. Santana Gil, D., Benavides, Hernandez, Herruzo, ”Reconfigurable Cache implemented on an FPGA” International Conference on Reconfigurable Computing and FPGAs, IEEE. 2010.
4. Jungwoo Park, Jongmin Lee and Soontae Kim, “A Way-Filtering-Based Dynamic Logical–Associative Cache Architecture for Low-Energy Consumption”. In IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 25, 2017, pp.793-805.
5. Vincent Heuring, and Harry Jordan,”Computer Systems Design and Architecture”, Massachusetts: Addison-Wesley, 1997.
6. Michael Powell, Se-Hyun Yang, Babak Falsafi, Kaushik Roy and T. N.
7. Vijaykumar. "Gated-Vdd: A Circuit Technique to Reduce Leakage in Deep- Submicron Cache Memories .” Proceedings of the

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Authors:

Paper Title:

Abstract: A detailed review on matrix converter circuits for adjustable speed drive applications has been presented in this paper. The power converter that is made up of a group of nine switches used to link three phase ac source to the load is called AC to AC Matrix Converter. Matrix Converter is capable to transform input with constant amplitude and frequency to three phase output with variable amplitude and variable frequency, as it is able to produce any frequency at the output as integer multiple of input. The attractive characteristics of Direct matrix converter are intrinsic four quadrant operation, high power factor at the input side, no intermediate capacitor, high regenerative capability, increased power density, light weight and reliable. However, some of the striking feature of these converters has been under research for the last few decades. The use of various topologies of matrix converter, and its PWM methods to get desired performance in adjustable speed drives have been discussed in this paper.

Keywords: Direct matrix Converter, Four Quadrant Operation, PWM Methods, Regenerative Capability

References:

1. Alesina .A and Venturini .M, “Analysis and design of optimum amplitude nine switch direct ac–ac converters,” IEEE Tra Power Electron., vol. 4, no. 1, pp. 101–112, Jan. 1989.
2. P. W, Rodriguez. J, Clare. J. C, Empringham. L, and Weinstein. A,“Matrix converters: A technology review,” IEEE Trans. Ind. Electron., vol. 49, no. 2, pp.276–288, Apr. 2002.
3. J. W, Friedli. T, Krismer. F, and Round. S. D, “The essence of three-phase ac/ac converter systems,” in Proc. Eur. Power Electron.-Power Electron. Motion Control, Sep. 2008, pp. 27–42.
4. Bharani Kumar R., Maheswari K.T., ‘Single Stage Power Conversion Using Wind Energy Conversion Systems Using AC-AC Matrix Converter’, Journal of Electrical Engineering, Romania, vol. 17, no. 4, pp.93-99,Dec 2017.
5. Kim. S, S. K, and Lipo,. T. A “AC/AC power conversion based on matrix converter topology with unidirectional switches,” IEEE Trans. Ind. Appl., vol. 36, no. 1, pp. 139–145, Jan./Feb. 2000.
6. J. W, Schafmeister. F, Round. S. D, and Hans. E, “Novel three-phase ac–ac sparse matrix converters,” IEEE Trans. Power Electron., vol. 22, no. 5, pp. 1649–1661, Sep. 2007.
7. Klumpn C, Nielsen. P, Boldea. I, and Blaabjerg. F, “A new matrix converter motor (MCM) for industry applications,” IEEE Trans. Ind. Electron., vol. 49, no.2, pp.325–335, Apr. 2002.
8. Klumpn C and Blaabjerg. F, “An ac/ac direct power conversion topology having multiple power grid connections with adjustable loading,” in Proc. IET, Mar./Apr., 2004, pp. 730–735.
9. Ni P, Blaabjerg. F, and Pedersen. J. K, “New protection issues of a matrix converter: Design considerations for adjustable-speed drives,” IEEE Trans. Ind. Appl., vol. 35, no. 5, pp. 1150–1161, Sep. 1999.
10. P, Blaabjerg. F, and Pedersen. J. K, “Space vector modulated matrix converter with minimized number of switching and feedforward compensation of input voltage unbalanced,” in Proc. Power Electr. Energy Syst., Jan. 1996, pp. 833–839.
11. Matt M, “Control techniques for matrix converter adjustable speed drives,”Ph.D. dissertation, Dept. Electr. Eng. Univ. Bologna, Bologna, Italy 2001.
12. Bradaschia, M. C. Cavalcanti, F. Neves, and H. de Souza, “A modulation technique to reduce switching losses in matrix converters,” IEEE Trans. Ind. Electron., vol. 56, no. 4, pp. 1186–1195, Apr.2009.
13. Yue, P. W. Wheeler, and J. C. Clare, “Relationship of modulation schemes for matrix converters,” in Proc. IET PEMD Conf., Dublin, Ireland, Apr. 4–6, 2006, pp. 266–270.
14. Jose Rodriguez, Marco Rivera, Johan W. Kolar, Patrick W. Wheeler, “A Review of Control and Modulation Methods for Matrix Converters”, IEEE transactions on industrial electronics, vol. 59, NO. 1, January 2012.
15. Keliang Zhou and Danwei Wang, “Relationship between Space-Vector Modulation and Three-Phase Carrier-Based PWM: A Comprehensive Analysis”, IEEE transactions on industrial electronics, vol.49, NO. 1,February 2002.

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Paper Title:

Abstract: In this paper, the Automatic Fake Currency Recognition System (AFCRS) is designed to detect the counterfeit paper currency to check whether it is fake or original. The existing counterfeit problem due to demonetization effects the banking system and also in other fields. A new approach of Convolution Neural Network towards identification of fake currency notes through their images is examined in this paper which is comparatively better than previous image processing techniques. This method is based on Deep Learning, which has seen tremendous success in image classification tasks in recent times. This technique can help both people and machine in identifying a fake currency note in real time through an image of the same. The proposed system, AFCRS can also be deployed as an application in the smartphone which can help the society to distinguish between the fake and original currency notes. The Accuracy in the proposed system can be increased through the original fake notes, where as the proposed system contains the images from children’s bank churan label.

Keywords: Deep Learning, Convolutional Neural Network, Counterfeit paper currency, Automatic recognition, Currency, Image Processing.

References:

1. Akamatsu, M. Fukumi, A method to design a neural pattern recognition system by using a genetic algorithm with partial fitness and a deterministic mutation (1996).
2. Chowdhury, N. Jahangir, Bangladeshi banknote recognition by neural network with axis symmetrical masks in masks, in: 10th international conference on computer and information technology.
3. Da-costa, Multiview banknote recognition with component and shape analysis (2014).
4. Das, CNN Architectures: LeNet, AlexNet, VGG, GoogLeNet, ResNet and more, 2017.
5. P.A. Frosini, A neural network based model for paper currency recognition and verification, IEEE Transactions on Neural Networks 7 (1996). 1482–1490.
6. Gidveer, S.R. Darade, Automatic Recognition of Fake Indian Currency Note, International Conference on Electrical Power and Energy Systems (ICEPES), Bhopal, India, pp. 290–294.
7. A.K.S. Gunaratna, N.D. Kodikara, Premaratne, ANN Based Currency Recognition System using Compressed Gray Scale and Application for Sri Lankan Currency Notes - SLCRec, World Academy of Science, Engineering and Technology International Journal of Computer and Information Engineering 2 (2008) 2957–2962.
8. Mirza, V. Nanda, Design and Implementation of Indian Paper Currency Authentication System Based on Feature Extraction by Edge Based Segmentation Using Sobel, Operator, International Journal of Engineering Research and Development 3 (2012) 41–46.
9. R. Nagpure, T.Ghotkar, S. Shetty, Recognition and Fake Note Detection, International Journal of Innovative Research in Computer and Communication Engineering Vol 4 (March 2016).
10. Nishikage, F. Takeda, Multiple kinds of paper cur- rency recognition using neural network and applica- tion for Euro currency (2000) 27–47.
11. Omatu, F. Takeda, High speed paper currency recognition by neural networks (1995) 73–77.
12. Paisios, Currency recognition using a smartphone: Comparison between color SIFT and gray scale SIFT algorithms (2012).
13. Sarfraz, A. Sargano, Robust Features and Paper Currency Recognition, in: The 6th International Conference on Information Technology Cite this pub- lication.
14. S.Dewan, Extended Local Binary Pattern for Face Recognition, Technology ICAET, 2015.
15. V.Vashishtha, M.Sadim, A Paper Currency Recogni- tion System Using Image Processing To Improve the Reliability with PCA Method, International Journal of Engineering Science & (2015).

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Paper Title:

Abstract: In the present society, propels in invention have made life less demanding by giving us more hoisted measures of information through development of numerousdevices. In any situation, each mechanical development harbors capability of shrouded risks to its customers. One notabledanger is burglary of private specificdata &information. As advanced data turned out to be more predominant, customers endeavor to anchor their data with ID cards &encoded passwords. Nonetheless, the abuse and burglary of these safety efforts are likewise on the ascent. Exploiting security imperfections in ID cards result in cards being copied or forged and being abused. This increasing fight with digital safety has prompted introduction of biometric security systems. Laying out standard contrasts among strategies for biometric invention utilized to confirm client characters will reveal insight into the preferences and drawbacks of individual information security frameworks.

Keywords: Multimodal biometric authentication, Security of information dwelling, data fragmentation

References:

1. Srinivasan MK, Sarukesi K, Rodrigues P, Manoj MS, Revathy P. State-of-the-art cloud computing security taxonomies: a classification of security challenges in the present cloud computing environment. InProceedings of the international conference on advances in computing, communications and informatics 2012 Aug 3 (pp. 470-476). ACM.
2. European Commission, "Exploiting the potential of cloud computing in Europe," 27 September 2012.
3. Sokol AW, Hogan MD. NIST Cloud Computing Standards Roadmap. 2013 Jul 22.
4. Zhang Y, Juels A, Reiter MK, Ristenpart T. Cross-VM side channels and their use to extract private keys. InProceedings of the 2012 ACM conference on Computer and communications security 2012 Oct 16 (pp. 305-316). ACM.
5. Ross AA, Nandakumar K, Jain AK. Handbook of multibiometrics. Springer Science & Business Media; 2006 Aug 11.
6. Vielhauer C. Biometric user authentication for IT security: from fundamentals to handwriting. Springer Science & Business Media; 2005 Dec 28.
7. OpenStack, «OpenStack Cloud Administrator Guide,» [Online]. Available: http://docs.openstack.org/admin-guidecloud/content/.
8. Ke Y, Sukthankar R. PCA-SIFT: A more distinctive representation for local image descriptors. InComputer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on 2004 Jun 27 (Vol. 2, pp. II-II). IEEE.
9. Lowe DG. Object recognition from local scale-invariant features. InComputer vision, 1999. The proceedings of the seventh IEEE international conference on 1999 (Vol. 2, pp. 1150-1157). Ieee.
10. Lowe DG. Local feature view clustering for 3D object recognition. InComputer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on 2001 (Vol. 1, pp. I-I). IEEE.
11. Lowe DG. Distinctive image features from scale-invariant keypoints. International journal of computer vision. 2004 Nov 1;60(2):91-110.
12. Bicego M, Lagorio A, Grosso E, Tistarelli M. On the use of SIFT features for face authentication. InComputer Vision and Pattern Recognition Workshop, 2006. CVPRW'06. Conference on 2006 Jun 17 (pp. 35-35). IEEE.
13. Jain AK, Bolle R, Pankanti S, editors. Biometrics: personal identification in networked society. Springer Science & Business Media; 2006 Apr 18.
14. Heusch G, Rodriguez Y, Marcel S. Local binary patterns as an image preprocessing for face authentication. InAutomatic Face and Gesture Recognition, 2006. FGR 2006. 7th International Conference on 2006 Apr 2 (pp. 6-pp). IEEE.
15. Zhang G, Huang X, Li SZ, Wang Y, Wu X. Boosting local binary pattern (LBP)-based face recognition. InAdvances in biometric person authentication 2004 (pp. 179-186). Springer, Berlin, Heidelberg.
16. Placek M, Buyya R. A taxonomy of distributed storage systems. Reportetécnico, Universidad de Melbourne, Laboratorio de sistemasdistribuidos y cómputo grid. 2006 Jul 3.
17. Assuncao MD, Calheiros RN, Bianchi S, Netto MA, Buyya R. Big Data computing and clouds: challenges, solutions, and future directions. arXiv preprint arXiv:1312.4722. 2013 Dec 17:1-39.

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Paper Title:

Abstract: In the early days recognition of the errors in transmissions may diminish the time postponement of communications. The customary error recognition methods are not exact adequate. A machine learning based methodology is proposed to take care of this issue because of the ongoing momentous advancement. The machine learning technique acquires the transmission state is thought to be a component of the highlights of a channel situation like the impedance and the noise. The preparation dataset is produced by reproductions on the channel condition. The ensemble machine learning algorithms are namely AdaBoostM1, Attribute Selected Classifier, Bagging, Classification via Regression, and Random Committee implemented in this research work and found the best algorithm for giving best accuracy.

Keywords: Bagging, MIMO, AdaBoostM1 , OFDM, and RandomCommittee.

References:

1. Abebe, A.T.; Kang, C.G. Overlaying machine-to-machine (M2M) traffic over human-to-human (H2H) traffic in OFDMA system: Compressive-sensing approach. In Proceedings of the 2016 International Conference on Selected Topics in Mobile & Wireless Networking (MoWNeT), Cairo, Egypt, 11–13 April 2016; pp. 1–6.
2. Shariatmadari, H.; Ratasuk, R.; Iraj, S.; Laya, A.; Taleb, T.; Jantti, R.; Ghosh, A. Machine-tpe communications: Current status and future perspectives toward 5G systems. IEEE Commun. Mag. 2015, 53, 10–17.
3. Bhave, P.; Fines, P. System Behavior and Improvements for M2M Devices Using an Experimental Satellite Network. In Proceedings of the IEEE Region 10 Symposium, Ahmedabad, India, 13–15 May 2015; pp. 13–16.
4. Ksairi, N.; Tomasin, S.; Debbah, M. A multi-service oriented multiple-access scheme for next-generation mobile networks. In Proceedings of the 2016 European Conference on Networks and Communications (EuCNC), Athens, Greece, 27–30 June 2016; pp. 355–359.
5. Monsees, F.; Woltering, M.; Bockelmann, C.; Dekorsy, A. Compressive Sensing Multi-user Detection for Multicarrier Systems in Sporadic Machine Type Communication. In Proceedings of the IEEE 81st Vehicular Technology Conference (VTC Spring), Glasgow, UK, 11–14 May 2015; pp. 1–5.
6. Beyene, Y.; Boyd, C.; Ruttik, K.; Bockelmann, C.; Tirkkonen, O.; Jäntti, R. Compressive Sensing for MTC in new LTE uplink multi-user random access channel. In Proceedings of the IEEE AFRICON 2015, Addis Ababa, Ethiopia, 14–17 September 2015; pp. 1–5.
7. Wang, S.; Li, Y.; Wang, J. Multiuser detection in Massive Spatial Modulation MIMO with Low-Resolution ADCs. IEEE Trans. Wirel. Commun. 2015, 14, 2156–2168.
8. Lu, L.; Li, G.Y.; Swindlehurst, A.L.; Ashikhmin, A.; Zhang, R. An Overview of Massive MIMO: Benefits and Challenges. IEEE J. Sel. Top. Signal Process. 2014, 8, 742–758.
9. https://pdfs.semanticscholar.org/d091/af5c2f1e693b5a66ccb76f93956c3199f152.pdf
10. Johanna Ketonen; Markku Juntti; Joseph R. Cavallaro. Performance—Complexity Comparison of Receivers for a LTE MIMO–OFDM System, IEEE Transactions on Signal Processing, June 2010, 58, 6, pp.3360-3372.
11. Sumitra N. Motade,; Anju V. Kulkarni. Channel Estimation and Data Detection Using Machine Learning for MIMO 5G Communication Systems in Fading Channel, Technologies, 2018, 6, 72.

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Paper Title:

Abstract: Animation can imagine outlines changing certainly and what's more in space. Research on getting from animations reports incredibly conflicting works out as intended concerning the central focuses related to picking up from animations. However, in E-content development field , although some companies have developed their individual production methodology for animation and multimedia content, there is lack of relevant research from the perspective of animation and multimedia integration techniques. In a trial mull over, we utilized three evaluations of learning achievement. In perspective of these evaluations, we dismembered grabbing from static pictures what's all the more, getting from animations. This blended learning system may be a potential trends of animation content in teaching and appropriate method of teaching especially for school children.

Keywords: Animation, Multimedia, Education, IOT, MOOCs

References:

1. Australian Science Technology and Engineering Council. (1997). Foundations for Australia’s future: Science and technology in primary schools. Canberra: AGPS.
2. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215.
3. Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W.H. Freeman.
4. http://www.theanimatorssurvivalkit.com/
5. Batterham, I. (2000). The chance to change: The final report by the Chief Scientist. Canberra: AGPS.
6. Bybee, R. W. (1997). Achieving scientific literacy: From purposes to practices. Portsmouth, NH: Heinemann.
7. animationmentor.com
8. Ciardiello, A. (1998). Did you ask a good question today? Alternative cognitive and metacognitive strategies. Journal of Adolescent & Adult Literacy, 42, 210-219.
9. Diezmann, C. M., & Watters, J. J. (2001a). Learning science in lower primary: Floating and sinking. Brisbane, Australia: Queensland University of Technology.
10. Fenstermacher, G. D. (1986). Philosophy of research on teaching: Three aspects. In M. C. Wittrock (Ed.), Handbook of Research on Teaching (pp. 37-49). New York: Macmillan.
11. Go Animate. [Online]. Available: http://www.goanimate.com/
12. [Online]. Available: http://www.xtranormal.com/
13. Hegarty, “Dynamic visualizations and learning: getting to thedifficult questions,” Learning and Instruction, vol. 14, pp. 343–351,2004.
14. [Online]. Available: http://www.animasher.com/
15. Mennell Media. [Online]. Available: http://www.mennellmedia.co.uk/

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Paper Title:

Abstract: India is home for various treasures of knowledge which are inscribed, stored and passed on from one generation to the next through a unique medium – palm leaves. In the present scenario, the need to preserve our rich heritage and provide an easy interface is the major challenge, using modern technology and techniques, thereby enhancing accessibility, applicability and appreciation for the repository of knowledge. A well-built catalogue is a primary requirement to facilitate effective and efficient information retrieval. The main aim of this research is to provide users with a standard means for intellectual access to digitized materials. Hence the outcome of this research can be useful in two ways firstly to prioritize the least/high damaged manuscript to perform restoration and secondly to obtain accurate search results from two methods proposed using TF-IDF and crowdsourcing approach. These can be widely utilized in various digital libraries across the globe. This metadata schema can be incorporated into an enhanced search engine for obtaining better precision and recall results.

Keywords: Palm Leaf Manuscripts, Digitization, Information Retrieval, TF-IDF, Crowdsourcing, Libraries, Precision, Recall.

References:

1. G A Survey on the Application of Image Processing Techniques on Palm Leaf Manuscripts by Tulasi Krishna, VK Mehta, Prashant in International Journal of Advanced Engineering Research and Science (IJAERS), 2016.
2. Rapeeporn Chamchong, Chun Che Fung, Text Line Extraction Using Adaptive Partial Projection for Palm Leaf Manuscripts from Thailand, International Conference on Frontiers in Handwriting Recognition, DOI 10.1109/ICHFR.2012.280, 2012.
3. R Vasanth Kumar Mehta and Nagendra Panini Challa, “Facilitating Enhanced User Access Through Palm-Leaf Manuscript Digitization – Challenges and Solutions”, Proceedings of IEEE International Conference on Electrical, Computer and Communication Technologies, February 2017.
4. A survey on Script and Language identification for Handwritten document images by Prasanthkumar P V , Midhun T P , Archana Kurian in IOSR Journal of Computer Engineering (IOSR-JCE), Volume 17, Issue 2, Mar – Apr. 2015.
5. Cross-Linking Between Journal Publications and Data Repositories by Sarah Callaghan, Jonathan Tedds, Rebecca Lawrence in International Journal of Digital Curation, 2014.
6. Metadata Development for Palm Leaf Manuscripts in Thailand by Lampang Manmart, Vilas Wuwongse in Proc. Int’l Conf. on Dublin Core and Metadata Applications 2012.
7. Digital Repository Best Practices for USCultural Heritage Organizations by Katherine Kott on February 3, 2012.
8. Nagendra Panini Challa and R Vasanth Kumar Mehta, “Automatic Data Acquisition- A Major Challenge”, Proceedings of IEEE International Conference on Electrical, Computer and Communication Technologies, February 2017.
9. Ontology Based Search Mechanism in Bilingual Database Resource by Norasykin Mohd Zaid, and Sim Kim Lau in The 11th International DSI and the 16th APDSI Joint Meeting, Taipei, Taiwan, July 12 – 16, 2011.
10. Rafael C. Gonzales, Richard E. Woods, “Digital Image Processing”, Second Edition, Tata McGrawHill Education, 2010.
11. Nagendra Panini Challa and R.Vasanth Kumar Mehta, “Applications of Image Processing Techniques on Palm Leaf Manuscripts- A Survey”, Proceedings of International Conference on - “Cognitive Science and Artificial Intelligence, Sree Vidya Niketan Engineering College, Tirupathi, July 2017
12. Image Segmentation of Historical Handwriting from Palm Leaf Manuscripts by Olarik Surinta and Rapeeporn Chamchong from Department of Management Information Systems and Computer Science Faculty of Informatics, Mahasarakham University Mahasarakham, Thailand, 2008.
13. Marcia L.Zeng and Jian Qin, “Metadata”, 1^st Edition, New York, 2008.
14. An Evaluative Study of Some Selected Libraries in India Undergoing the Process of Digitization by Anup Kumar Das, Jadavpur University, 2008.
15. Metadata Creation System for Mobile Images by Rista Sarvas, Errik Herratte in MobiSys’04, June 6-9, Boston, Massachusetts, USA, 2004.
16. Liu, W. Huang, and C. L. Tan. Extraction of vectorized graphical information from scientific chart images. In Document Analysis and Recognition, 2007. ICDAR 2007. Ninth International Conference on, volume 1, pages 521–525. IEEE, 2007
17. Lopez, J. Yu, C. Arighi, H. Huang, H. Shatkay, and C. Wu. An automatic system for extracting figures and captions in biomedical pdf documents. In Bioinformatics and Biomedicine (BIBM), 2011 IEEE International Conference on, pages 578–581. IEEE, 2011.
18. Lu, S. Kataria, W. J. Brouwer, J. Z. Wang, P. Mitra, and C. L. Giles. Automated analysis of images in documents for intelligent document search. IJDAR, 12(2):65–81, 2009.
19. Savva, N. Kong, A. Chhajta, L. Fei-Fei, M. Agrawala, and J. Heer. Revision: Automated classification, analysis and redesign of chart images. In Proceedings of the 24th annual ACM symposium on User interface software and technology, pages 393–402. ACM, 2011.
20. Prasad, B. Siddiquie, J. Golbeck, and L. Davis. Classifying computer generated charts. In Content-Based Multimedia Indexing, 2007. CBMI ’07. International Workshop on, pages 85 –92, June 2007.
21. Han, H., Giles, C.L., Manavoglu, E., Zha, H., and Zhang, Z. “Edward A. Fox: Automatic Document Metadata Extraction Using Support Vector Machines”. JCDL 2003, p. 37-48, 2003.

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Abstract: Accidents or Mishaps are happening calm regularly now days for some reasons. Among those reasons the calmest reason is because of the liquor dependent purchasers. A large number of the general population used to go to work places like production lines, ventures, medical clinics, workplaces and military by expending liquor. These reason hazardous mishaps in numerous spots over out of indiscretion. A definitive purpose behind this proposed work is to lessen mishaps because of liquor utilization by identifying it.. This guarantees appropriate hard working attitudes are pursued. In this way, our proposed framework takes into account liquor checking in addition to announcing framework that screens this and reports it to concerned individual remotely over web. Our framework is made out of an IOT based circuit framework that utilizes Arduino board. The framework has MQ3 liquor sensor and to look at the liquor utilization of driver and to control vehicle start automatically. This data refresh to the cloud server alongside area and liquor content. This guarantees no marvel of mishaps because of liquor affect.

Keywords: Alcohol detection, Arduino board, cloud server. IoT circuit system, MQ3 alcohol sensor.

References:

1. Venkat, Narayana Rao; & Karthik Reddy Yellu 2017. “Preventing Drunken Driving Accidents using IoT”. Available at www.ijcset.net.| Vol.8.
2. Bhuta; Desai; &Keni. 2015. Alcohol Detection and Vehicle Controlling. IJ E T A. Vol.2 Issue 2.
3. Vaishnavi; Umadev; &Vinothini. 2014.Intelligent Alcohol Detection System for Car. International Journal of Scientific & Engineering Research, Vol. 5, I ssue 11.
4. Drunken driving protection system IJSRE, Volume2, Issue 12, December-2011 1 ISSN 2229-5518.
5. Kiyomi Sakakibara, Toshiyuki Taguchi, Atsushi Nakashima and Toshihiro Wakita, “Development of a New Breath Alcohol Detector without Mouthpiece to Prevent Alcohol-Impaired Driving,” Proceedings of the 2008 IEEE .
6. National Police Agency (Japan),” Fatal traffic accidents in 2007,” January 2008, p. 30
7. Thum Chia Chieh; Mustafa, M.M.; Hussain, A.; Zahedi, E.; Majlis, B.Y., “Driver fatigue detection using steering grip force,” Research and Development, 2003.

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Paper Title:

Abstract: This research work discusses the methodology for cloud workloads prediction and simulation for developing a load balancing algorithm. The main goal of the methodology is to develop an efficient load balancing algorithm which would require less processing power. In-order to enhance load balancing, these following parameters should incorporated, namely, determining as well as comparing the load and efficiency of the system, connection between the nodes, transmission nature and obtaining the proper nodes. Therefore. to enhance the performance of the load balancing, a firefly based algorithm was developed. The proposed algorithm main task is to increase the utilization of resource among cloud servers, thereby enhancing performance of the cloud servers by proper load balancing.

Keywords: Firefly algorithm, Cloud computing, Load balancing, Global Optimization

References:

1. Al-Ta’i, Z., & Al-Hameed, O. A. (2013). Comparison between pso and firefly algorithms in fingerprint authentication. International Journal of Eng. and Innovative Technology (IJEIT), 3, 421–425.
2. Apostolopoulos, T., & Vlachos, A. (2010). Application of the firefly algorithm for solving the economic emissions load dispatch problem. International Journal of Combinatorics, 2011.
3. Arora, (2004). Introduction to optimum design. Academic Press.
4. Richard Málek. Seage: Search agents for optimization. http://www.seage.org, July
5. Magnus Erik Hvass Pedersen and Andrew John Chipperfield. Simplifying particle swarm optimization. Applied Soft Computing Journal,
6. Sartaj Sahni and Teofilo P-complete approximation problems. Journal of the Association for Computing Machinery, 1976.
7. Michal Simple Firefly Synchronization. PhD thesis, University of Daleware, 2008.
8. Szymon Łukasik and Sławomir Žak. Firefly algorithm for continuous constrained optimiza- tion tasks, Lecture Notes in Computer Science
9. Udendhran. A hybrid approach to enhance data security in cloud storage.Proceeding ICC '17 Proceedings of the Second International Conference on Internet of things, Data and Cloud Computing, Cambridge University, United Kingdom — March 22 - 23, 2017 ACM ISBN: 978-1-4503-4774-7 doi>10.1145/3018896.3025138
10. Tim Mather, Subra Kumaraswamy, and Shahed Latif. Cloud Security and Privacy: An Enterprise Perspective on Risks and Compliance. O’Reilly Media, Inc., 2009.
11. Matthias, S., Klink, M., Tomforde, S., & Hahner, J. (2016). Predictive Load Balancing in Cloud Computing Environments based on Ensemble Forecasting (4 ed., Vol. Augsburg,: IEEE International Conference on Autonomic Computing.
12. Alexandre, D., Tomasik, J., Cohen, J., & Dufoulon, F. (2017). Load prediction for energy-aware scheduling for Cloud computing platforms. Orsay: IEEE 37th International Conference on Distributed Computing System
13. LaCurts, K. L. (2014, June). Application workload prediction and placement in cloud computing systems (Unpublished doctoral dissertation). Massachusetts Institute of Technology, Cambridge Massachusetts.
14. Lee, R., & Jeng, B. (2011). Load-balancing tactics in cloud. In Proceedings of the International Conference on Cyber-Enabled Distributed Computing and Knowledge CyberC Discovery, pp. 447-454.
15. Mahmood, Z. (2011). Cloud computing: characteristics and deployment approaches. In the 11th IEEE International Conference on Computer and Information Technology, pp. 121-126.
16. Mathur, S., Larji, A. A., & Goyal, A. (2017). Static load balancing using SA Max-Min algorithm. International Journal for Research in Applied Science & Engineering Technology, 5(4), 1886-1893.
17. Xin-She Yang. Nature-Inspired Metaheuristic Algorithms. Luniver Press,

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Paper Title:

Abstract: In a MANET each nodes are deployed in a self configurable, organisable and controlled manner. Rapid node mobility will lead to signal fluctuation and reduces packet delivery. It also leads to path loss in near future. In our proposed failure path recovery algorithm, we analyze medium to measure loss and gain of signal strength. Whenever a signal fluctuation occurs, a path alteration is done by selecting a stable one hop neighbor node with better processing capacity. This is done prior to path failure and ensures the reliable data transmission. This projected concepts is evaluated by NS-2 Simulator, it involves minimizing the computation overhead, packet loss and increase delivery of data transmission reasonably than the AOMDV and CA-AOMDV. The maximum lifetime of the node on the AOMDV and CAAOMDV is less than 3.615% when compared to PPFR-AOMDV and also the path stability of PPFR-AOMDV is 1.925 greater than that of CA-AOMDV AND AOMDV.

Keywords: Medium analysis: signal fluctuations; link quality; processing capacity.

References:

1. G Helen, D., &Arivazhagan, D.”.Applications, advantages and challenges of ad hoc networks”. Journal of Academia and Industrial Research, 2, No.8, pp.453-457, 2014
2. Rajabhushanam, C., &Kathirvel, A.”Survey of wireless MANET application in battlefield operations.”International Journal of Advanced Computer Science and Applications, Vol.2, No.150-58, 2011.
3. Ranjan, P., &Velusamy, R. L. “Optimized localroute repair and congestion control in Mobile Adhoc Network”. In ComputingandCommunicationTechnologies (ICCCT), International Conferenceon (pp. 328-333). 2015.
4. Jagadeesan, D., Narayanan, S., &Asha, G“Efficient load sharing using multipath channel. Awareness routing in mobile ad hoc networks”.Indian Journal of Science andTechnology, 8(15), 2015.
5. Chen, X., Jones, H. M., &Jayalath, D.“Channel- aware routing in MANETs with route Handoff” IEEE Transactions on Mobile computing, 10(1),108-121, 2011
6. Sundar, R., &Kathirvel, A.“Enhanced trust basedDelegation for load balancing in Manet”.ternational Journal of Applied Engineering Research, Vol.10 No.92, 104-109, 2015.
7. Singal, G., Laxmi, V., Gaur, M. S., &Lal, C. “LinkStability based multicast routing protocol in MANETs”. In Contemporary Computing (IC3), Seventh International Conference on (pp254-259).IEEE
8. Oh, S. Y., Park, J. S., &Gerla, “M. E-ODMRP:enhanced ODMRP with motion adaptive refresh”. Journal of Parallel and Distributed Computing, 68(8), 1044-1053, 2008.
9. Cai, J., & Liu, K. “An improved AOMDV routing protocol based on prediction of link stability”. In Fourth InternationalConference on MachineVision(ICMV 11), International Society for Optics and Photonics, Singapore, Singapore (Vol. 9, p.83500V). 2011.
10. Wang, S., Song, Q., Feng, J., & Wang, X.”Predicting the link stability based on link connectivity changes in mobile ad hoc networks”. In Wireless Communications, Networking and Information Security (WCNIS), IEEE International Conference on (pp. 409-414). 2010
11. Sundar and Dr.A.Kathirvel “Enhanced routing algorithm to reduce number of transmission inmanet”Australian journal of basic and applied sciences9(35), Pages: 142-146, 2015.
12. Mallick, B.” Introducing Efficient AODV Routing Protocol for MANET” International Journal of Computer Applications, 124(3),2015.
13. Biradar, S. R., Majumder, K., Sarkar, K., &Puttamadappa, C. “Performance evaluation and comparisonof AODV and AOMDV”.International Journal on Computer Science and Engineering, 2(2), 373-377, 2010).
14. NetworkSimulator, http://www.isi.edu/nsnam/ns.
15. Kathirvel, A., &Srinivasan, R.” Enhanced tripleumpiring system for security and performanceimprovement in wireless MANETS”InternationalJournal of Communication Networks and Information Security, 2(2), 77,2010.
16. Kathirvel, A., &Srinivasan, R.”EnchancedSelfUmpiring System for Security UsinSalvagingRouteReply”.International Journal of ComputerTheory and Engineering, 2(1),129.2010.

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Abstract: In this Paper, It has been proposed a de-speckling method on Indian Synthetic-aperture radar (SAR) image with block matching 3D transformation and has been used for classification method. This block- matching 3D algorithm clearly explained how to generate de-speckling of SAR image for classification. In this technique has been tested on RISAT-1 SAR image data set and practical results exhibit that this technique is the better in terms of de speckling quality image factors. The despeckled image has been fused with LANSAT-8 optical image. The resultant multi spectral and good resolution image has been classified using supervised classification.

Keywords: Synthetic-aperture radar, de-speckling, block matching 3D algorithm.

References:

1. isro.gov.in
2. S. Rao, Shaunak De, Vineet Kumar and Anup Das, "Full and Hybrid Polarimetric SAR Data Analysis for Various Land", International Experts Meet on Microwave Remote Sensing,, Ahmedabad, India Features, 16‐17 Dec 2013.
3. Murali Mohan Babu, M.V. Subramanyam, M.N. Giri Prasad, " Analysis of Back Scattering Coefficient Measurement of RISAT-1 data ", Proceedings of ICC-2014, 253-265, June, 2014.
4. Subramanyam MV, MM Babu Y, & Giriprasad MN, “A Modified BM3D Algorithm For SAR Image De-speckling”, Procedia Computer Science (Elsevier), Volume 70, Issue 1, 69 - 75, December 2015.
5. Qingsong Zhu, Jiaming Mai & Ling Shao, “A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior”, IEEE Transactions on Image Processing, Volume 24, Issue 11, 3522 - 3533, November 2015.
6. Kai Zhang, Wangmeng Zuo & Yunjin Chen, “Beyond a Gaussian De-noiser: Residual Learning of Deep CNN for Image De-noising”, IEEE Transactions on Image Processing, Volume: 26, Issue: 7, 3142-3155, July 2017.
7. Murali Mohan Babu. Y and Radhika K. A new approach for microwave imagery de-noising, “International Journal of Image, Graphics and Signal Processing”, Volume 5, issue 1, 52-60, May 2016.

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Paper Title:

Abstract: This work is aimed to examine the strength and durability properties of Self-Compacting Concrete (SCC) replacing fine aggregate partially by volume with e-waste High impact polystyrene (HIPS) granules. In addition, Cement is replaced with fly ash in the optimized binder content of 497 kg/m3 using 0.36 water-to-binder ratio in all SCC mixtures. Compressive strength is studied at 28 and 90 days curing age. Durability tests of SCC such as water absorption and sorptivity are investigated at the age of 28 and 90 days with varying fine aggregate replacement up to 40% at an interval of 10%. The increase in HIPS up to 30% replacement shows a linear declination in the reported values of water absorption, and sorptivity tests at all curing periods. Reduction of compressive strength is minimal at all ages with an increment in the volume of HIPS replacement up to 30%. For SCC mixtures, all test values are within the permissible limits. Different particle sizes of aggregates in SCC achieved continuous gradation. In addition, fly ash contributed its effort acting as filler and holds good bonding at interfacial transition zone. Hence, the reduced porosity helps in improvement of durability properties. This investigation is aided in identifying the excellent durability properties with the both cement and sand replacement by fly ash and e-waste aggregate respectively. Replacing e-waste HIPS as fine aggregate in SCC serves as an eco-friendly multipurpose solution. It compensates the disposal problem, conserves natural resources, and reduces energy, emissions, dead load and cost of concrete production.

Keywords: Durability, e-waste, HIPS aggregate, Self-Compacting concrete, Sorptivity, water absorption.

References:

1. ASTM C642-13, Standard Test Method for Density, Absorption, and Voids in Hardened Concrete, ASTM International, West Conshohocken, PA, 2013, astm.org
2. ASTM C1585-13, Standard Test Method for Measurement of Rate of Absorption of Water by Hydraulic—Cement Concretes, ASTM International, West Conshohocken, PA, 2013, astm.org
3. Akram A, Sasidhar C, Pasha K.M, “E-Waste Manage by Utilization of E-Plastics in Concrete Mixture as Coarse Aggregate Replacement,” Int. J. Innov. Res. Sci. Eng. Technol. 2015, 4.
4. BIS 2386 (Part I- IV)-1963, Methods of Test for Aggregates for Concrete, Bureau of Indian Standards, New Delhi, India, 1963.
5. CEB-FIP, “Diagnosis and Assessment of Concrete Structures— State of Art Report,” CEB Bull, Vol. 192, 1989, pp. 83–85.
6. Bala Rama Krishna And P. Jagadeesh, “Influence Of Admixtures On Plastic Wastes In An Eco-Friendly Concrete A Review,” International Journal Of Civil Engineering And Technology, 8(6), 2017, 388–397.
7. Bala Rama Krishna and P. Jagadeesh, “Fresh and Hardened Properties of Self-Compacting Concrete Replacing Fine Aggregate with High Impact Polystyrene Plastic Granules, International Journal of Civil Engineering and Technology,” 9(12), 2018, pp. 831–838
8. Bala Rama Krishna and P. Jagadeesh, “Compressive strength evaluation of eco-friendly concrete replacing sand partially with High impact polystyrene,” International Journal of Civil Engineering and Technology, 9(1), 2018, 865–870.
9. Coppola B. Courard L, Michel F, Incarnato L, Scarfato P, Di Maio L, “Hygro-thermal and durability properties of a lightweight mortar made with foamed plastic waste aggregates,” Constr. Build. Mater. 170, 2018, 200–206.
10. EFNARC, 2005. Specification and guidelines for self-compacting concrete. English ed.Norfolk, UK: European Federation for Specialist Construction Chemicals and Concrete Systems.
11. IS12269-1987 Specification for 53 grade ordinary Portland cement. Bureau of Indian Standards, New Delhi, India.
12. IS 456-2000 Plain and reinforced concrete—code of practice. Bureau of Indian Standards, New Delhi, India
13. IS 10262:2009 Concrete mix proportioning—guidelines. Bureau of Indian Standards, New Delhi, India
14. IS 516-2004 Indian standard code of practice—methods of test for strength of concrete. Bureau of Indian Standards, New Delhi, India.
15. Guru Jawahar, C. Sashidhar, I.V. Ramana Reddy, J. Annie Peter, “Micro and macro level properties of fly ash blended self compacting concrete,” Materials and Design, 46, 2013, 696–705.
16. Guru Jawahar, C. Sashidhar, I.V. Ramana Reddy, J. Annie Peter, “Effect of coarse aggregate blending on short-term mechanical properties of self compacting concrete,” Materials and Design, 43, 2013, 185–194.
17. Guru Jawahar, C. Sashidhar, I.V. Ramana Reddy J, Annie Peter, “Design of cost-effective M 25 grade of self compacting concrete, Materials and Design,” 49, 2013, 687–692.
18. Jacob-Vaillancourt, C.; Sorelli, L, “Characterization of concrete composites with recycled plastic aggregates from postconsumer material streams,” Constr. Build. Mater. 182, 2018, 561–572.
19. De Schutter and K. Audenaert, “Evaluation of water absorption of concrete as a measure for resistance against carbonation and chloride migration,” Materials and Structures, vol. 37, no. 273, 2004, pp. 591–596.
20. Senthil Kumar, K. and Baskar, K., “Development of Ecofriendly Concrete Incorporating Recycled High Impact Polystyrene From Hazardous Electronic Waste,” J. Hazard. Toxic Radioact. Waste., Vol. 19, No. 3, 2015, 04014042.
21. Senthil Kumar, P. V. Premalatha, and K. Baskar, “Evaluation of Transport Properties of Concrete Made With E-Waste Plastic,” Journal of Testing and Evaluation, ASTM, 45 (5), 2016, 1849-1853.
22. K. Mehta and P. J. M. Monteiro, Concrete: Microstructure, Properties and Materials, 2006, McGraw-Hill, NewYork, NY, USA.
23. Ramli and A.A. Tabassi, “Effects of polymer modification on the permeability of cement mortars under different curing conditions: a correlational study that includes pore distributions, water absorption and compressive strength,” Construction and Building Materials, vol. 28, no. 1, 2012, pp. 561–570.
24. Shafiq and J. G. Cabrera, “Effects of initial curing condition on the fluid transport properties in OPC and fly ash blended cement concrete,” Cement and Concrete Composites, vol. 26, no.4, 2004, pp. 381–387.
25. Tasdemir, “Combined effects of mineral admixtures and curing conditions on the sorptivity coefficient of concrete,” Cement and Concrete Research, vol. 33, no. 10, 2003, pp. 1637–1642.
26. Sadeghi-Nik, Aref, Lotfi-Omran, Omid, “Estimation of compressive strength of self-compacted concrete with fibers consisting nano-SiO2 using ultrasonic pulse velocity,” Constr. Build. Mater. 44, 2013, 654-662.
27. Ruiz-Herrero J.L, Nieto D.V, Lopez-Gil A, Arranz A, Fernandez A, Lorenzana A, Meriono S, De Saja J.A, Rodriguez M.A, “Mechanical and thermal performance of concrete and mortar cellular materials containing plastic waste,” Constr. Build. Mater. 104, 2016, 298–310.
28. P. S. Dias, “Reduction of concrete sorptivity with age through carbonation,” Cement and Concrete Research, vol. 30, no. 8, 2000, pp.1255–1261.
29. Lakshmi and S. Nagan, “Investigations on durability characteristics of e-plastic waste incorporated concrete.” Asian journal of civil engineering, (Building and Housing), 12(6), 2011, 773-787.
30. Alexander, M. G., Ballim, Y., and Maketchnie, J. R., “Guide to Use of Durability Indexes for Achieving Durability in Concrete Structures,” Research Monograph No. 2, Collaborative Research by Universities of Cape Town and Witwatersrand, Cape Town, South Africa, 1999.
31. Alexander, M. G., Ballim, Y., and Maketchnie, J. R., “Concrete Durability Index Testing Manual,” Research Monograph No. 4, Collaborative Research by Universities of Cape Town and Witwatersrand, Cape Town, South Africa, 1999.

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Paper Title:

Abstract: MANET is a self-configured network of devices in wireless linked network, in an arbitrary topology. Each node is an independent node, which can play a role of host, router & receiver. The connectivity is established by operating system hosted on participating nodes. Routing algorithm establishes routes and forwarding information as packets to and from source to sink station. Many routing techniques attempt to achieve optimal performance; however modifications are still required in existing routing protocols to improve the performance of MANET. An efficient MANET leads to fulfillment of three key performance metrics (PDR,AE2ED, and Overhead). There exist some predominant anomalies in Mobile Ad-hoc Network in terms of above performance metrics. Anomalies in MANET arise due to various environmental factors like variation in number of connections among participating nodes, mobility of nodes, pause time of node, rate of data packet forwarded by nodes and total density of nodes, adversely affecting its performance. In order to overcome some predominant anomalies, in this research a systematic approach has been used to develop an intelligent system model, which controls the performance adaptively.

Keywords: MANET, PDR, AE2ED, Overhead, Fuzzy

References:

1. VenkataRamana et al., ” Bio Inspired Approach to Secure Routing in MANETs”, International Journal of Artificial Intelligence & Applications (IJAIA), Vol.3, No.4, July 2012.
2. Umamaheswari and G. Radhamani,” An Improved ACO Based Algorithm for EnhancingPerformance in Wireless Adhoc Network”, American Journal of Scientific Research ;ISSN 1450-223X Issue 54 (2012), pp. 68-80; © EuroJournals Publishing, Inc. 2012 ;
3. Jun-Zhao Sun, Mobile Ad Hoc Networking: An Essential Technology for pervasive Computing Mediate team, Machine Vision and Media Processing unit ,info Tech Unit, InfoTech Oulu O.Box 4500, FIN-90014 University of Oulu, Finland.
4. Caixia li, SreenathaGopalaraoAnavatti and Tapabrata Ray, “ Analytical Hierarchy Process using Fuzzy Inference Techniques for real – time route Guidance system , IEEE Transaction on Intelligent Transportation Systems , vol 15 No 1 February 2014
5. -K Toh, Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad hoc Networks‖, , 2001 ,IEEE.
6. L. Flood and M.C. Jackson , "Creative problem solving". John Wiley and Sons, Chichester, (1991), ISBN 0-471-93052-0.
7. SiddeshGundagattiKaribasappa , N Muralidhara, “ Neuro Fuzzy Based Routing International Conference on Industrial and Information Systems, ICIIS 2011, Aug. 16 19, 2011 IEEE
8. Dr .C. Suresh GnanaDhass and N. Kumar, Power Aware Routing protocols in in Mobile Ad hoc Networks-Survey, International Journal of advanced research in Computer Science and Software Engineering, 2012,Vol. 2, Issue 9.
9. Battat and H. Kheddouci, “HMAN: Hierarchical Monitoring for Ad Hoc Network,” in IEEE/IFIP EUC, 2011.
10. Kwak, G. Huerta-Canepa, Y. Ko, D. Lee, and S. J. Hyun, “An Overlay-Based Resource Monitoring Scheme for Social Applications in MANET,” in IEEE COMPSAC, 2009.
11. Ramachandran, E. Belding-Royer, and K. Almeroth, “DAMON: A Distributed Architecture for Monitoring Multi-hop Mobile Networks,” in IEEE SECON, 2004.
12. Riggio, M. Gerola, D. Miorandi, A. Zanardi, and F. Jan, “A Distributed Network Monitoring Framework for Wireless Networks,” in IFIP/IEEE IM, 2011.
13. Graffi, D. Stingl, J. Rueckert, A. Kovacevic, and R. Steinmetz, “Monitoring and Management of Structured Peer-to-Peer Systems,” in IEEE P2P, 2009.
14. Jelasity, A. Montresor, and O. Babaoglu, “Gossip-Based Aggregation in Large Dynamic Networks,” ACM Transactions on Computer Systems, vol. 23, no. 3, pp. 219–252, 2005.
15. van de Bovenkamp, F. Kuipers, and P. Van Mieghem, “Gossip-based Counting in Dynamic Networks,” in IFIP NETWORKING, 2012.
16. Yalagandula and M. Dahlin, “A Scalable Distributed Information Management System,” ACM SIGCOMM Computer Communication Review, vol. 34, no. 4, pp. 379–390, 2004.
17. Muhammad Aamir_ and Mustafa A. Zaidi, A Buffer Management Scheme for Packet Queues in MANET, TSINGHUA SCIENCE AND TECHNOLOGY ISSNll1007- 0214ll01/10llpp543-553 Volume 18, Number 6, December 2013
18. Anita Yadav • Y. N. Singh • R. R. Singh, Improving Routing Performance in AODV with Link Prediction in Mobile Adhoc Networks, Wireless PersCommun DOI 10.1007/s11277-015-2411-5, Springer Science+Business Media New York 2015.
19. ShariqMahmood Khan • R. Nilavalan • Abdulhafid F. Sallama, A Novel Approach for Reliable Route Discovery in Mobile Ad-Hoc Network, Wireless PersCommun DOI 10.1007/s11277-015-2461-8, Springer Science+Business Media New York 2015. Networks, ACM MSWIM’04, October 4–6, 2004, Venezia, Italy.

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Abstract: Adaptation of this method of identification of weeds in specific areas on crop lands which help for healthy agriculture. Mostly separations of weeds from the crops are done with the help of image processing. It is the process of separation of weeds specifically from the crops. This method of separation will be helpful in optimization of herbicide usage. There are several methods of image pre-processing used to remove the information which is irrelevant from the image other than weeds. Crops are separated in rows. Different colour models are used and then proposed for grey image, vertical projection is used to identify centre line of crop row. Image segmentation is the first process to segment the image using various methods like greenness identification with the help of RGB and H-components with YIQ, HSV, HIS colour spaces. It leads to 126 colour feature extraction from the image. Cultural algorithm is used for separation of feature from the image and Search algorithm is used in specific identification of crops and weeds for specific spraying. After feature extraction SVM algorithm and BP algorithm is used for experimental result for higher accuracy of about 92.8% in real-time decision.

Keywords: Colour Features, Feature extraction, Image pre-processing, Image segmentation, Image separation, Weed Identification

References:

1. Duan, R.Q., Zhao, W., Huang, S.L., Chen, J.Y., 2010. Fast line detection algorithm based on improved Hough transformation. Chin. J. Sci. Instrum. 31 (12), 2774– 2779.
2. Gebhardt, S., Khbauch, W., 2007. A new algorithm for automatic rumexobtusifolius detection in digital images using colour and texture features and the influence of image resolution. Precision Agric. 8 (1–2), 1–13.
3. Ghazali, K.H.B., Ma, J., Xiao, R., Lubis, S.A., 2012. Yang and Li (2014)). An innovative face detection based on YCGCR color space.
4. Procedia 25. He, D.J., Geng, N., Zhang, Y.K., 2008. Digital Image Processing. Xian University of Electronic Science and Technology Press.
5. Jiang, G., Wang, Z., & Liu, H. (2015). Automatic detection of crop rows based on multi-ROIs. Expert Systems with Applications, 42(5), 2429e2441.
6. Saha, D., Hanson, A., Shin, S.Y., 2016. Development of enhanced weed detection system with adaptive thresholding and support vector machine. Proceedings of the International Conference on Research in Adaptive and Convergent Systems, pp. 85–88.
7. Kataoka, T., Kaneko, T., Okamoto, H., &Hata, S. (2003). Crop growth estimation system using machine vision. In Proceedings of the IEEE International conference on advanced intelligent mechatronics (AIM 2003) (pp. 1079e1083).
8. Kise, M., & Zhang, Q. (2008). Development of a stereovision sensing system for 3D crop row structure mapping and tractor guidance. Biosystems Engineering, 101(2), 191e198.
9. Leemans, V., &Destain, M. F. (2006). Line cluster detection using a variant of the Hough transform for culture row localisation. Image and Vision Computing, 24(5), 541e550.
10. Montalvo, M., Guerrero, J. M., Romeo, J., Emmi, L., Guijarro, M., &Pajares, G. (2013). Automatic expert system for weeds/crops identification in images from maize fields. Expert Systems with Applications, 40(1), 75e82.
11. Siddiqui MH, Mohammad F, Khan MMA, Al-Whaibi MH (2012) Cumulative effect of nitrogen and sulphur on Brassica juncea L. genotypes under NaCl stress. Protoplasma 249:139–153
12. Onyango, C. M., &Marchant, J. A. (2003). Segmentation of row crop plants from weeds using colour and morphology. Computers and Electronics in Agriculture, 39(3), 141e155.
13. Mahalanobis, P.C. (1936) On the Generalized Distance in Statistics. Proceedings of the National Institute of Science of India, 2, 49-55..

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Paper Title:

Abstract: Natural disasters pose a serious threat to national economy, human lives and can disturb the social fabric of the society, although we can not entirely prevent these natural disasters from happening but with the advancements in the satellite imagery, remote sensing and machine learning it has become possible to minimize the damage caused by them. Satellite images are very useful because they can give you a huge amount of information from a single picture. Since it is becoming easy to get these satellite images the climate and environmental detection systems are in high demand. In this paper, we propose a post disaster system which we have named, Automatic Disaster Detection System (ADDS) which is designed to detect the disaster affected areas and help in the relief operations. The existing methods for detection of disaster affected regions are mostly dependent on manpower where people use the drone technology to see which area is affected by flying that drone over a large area which takes a lot of time. A new approach of Convolution Neural Network towards detection of disaster affected areas through their satellite images is examined in this paper which is comparatively better than previous image processing techniques. This method is based on deep learning which has been a widely popular technique for image processing in recent past. This technique can help save lives by reducing the response time and increasing the efficiency of the relief operations.

Keywords: Convolution Neural Network, Deep Learning, Image Processing, Flood Detection, Satellite Imagery, Remote Sensing, Machine Learning.

References:

1. Jahari, S. Khairunniza-Bejo and A.R.M. Shariff (Eds.) ”Change detection using a local similarity measure”, in IEEE Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, 12-13 July, 2008 Multimedia University, Cyberjaya, Malaysia.
2. Fernando, C. Canagarajah and D.R. Bull, “A unified approach to scene change detection in uncompressed and compressed video”,in Digest of Technical Papers, International Conference on Consumer Electronics, Nineteenth in the Series (Cat. No.00CH37102), 13-15 June 2000, Los Angles, CA, USA, USA.
3. R. Joshi, I. Tarte, S. Suresh, S.G. Koolagudi, ”Damage identification and assessment using image processing on post-disaster satellite imagery”, in IEEE Global Humanitarian Technology Conference (GHTC), 19-22 October. 2017, San Jose, CA, USA.
4. Karthik, B.R. Shivakumar, “Change detection using image differencing: A study over area surrounding Kumta, India”, in Second International Conference on Electrical, Computer and Communication Technologies (ICECCT), 22-24 Feb. 2017.
5. Kumar, M. Anouncia, S. Johnson, ”Agriculture change detection model using remote sensing images and GIS”, in International Conference on Radar, Communication and Computing (ICRCC), 21-22 Dec. 2012, Tiruvannamalai, India.
6. Liu, G. Gigli, G.A. Lampropoulos, “Change detection methodology based on region classification fusion”, in 10th International Conference on Information Fusion, 9-12 July 2007, Quebec, Canada.
7. J. Reno, D.B. David,”An application of image change detectionurbanization”, in International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015], 19-20 March 2015, Nager-coil, India.
8. Sun, H. Tang, H. Chen, G. Yu, ”Research of unsupervised image change detection algorithm based on 2-D histogram”, in IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, 24-28 Oct. 2010, Beijing, China.

124-128

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Paper Title:

Abstract: The paper studies the scope, performs the modelling and validation for conversion of any Convetional Vehicle to a Split Parallel Hybrid Electric Vehicle. The introduction of a smart Energy Management System for sucha setup is also evaluated. The EMS enables load sharing between the IC Engine and the Traction motor based on the gradient of the road. The gradient analysis is performed using the GPS based road gradient database. For the accurate modelling and the dynamic analysis of the designed model the performance of the vehicle’s Degrees of Freedom (DoF) for the variation in steering angle is analyzed. 14 DoF parameters are considered and the designed vehicle is subjected to variation in steer angle followed by the analysis on the response of the DoF parameters.

Keywords: DoF, Reva-I, HEV, ICE, SoC.

References:

1. Assocham India, “Electric Mobility in India: Leveraging collaboration and Nascency”, Ernst & Young LLP, January 2018
2. “Karnataka Electric Vehicle & Energy Storage Policy 2017”, Commerce and Industries Department, Government of Karnataka, October 2017.
3. Husain, “Electric and hybrid vehicles: Design Fundamentals.” CRC press, 2011.
4. Muhammad Ikram Mohd Rashid and Hamdan Danial, “Modelling And Simulation Of Split Plug-In Hybrid Electric Vehicle Using Advisor ,” ARPN Journal of Engineering and Applied Sciences, Vol. 10, No. 21, November 2015
5. Saiful A. Zulkifli, Syaifuddin Mohd, Nordin Saad1 and A. Rashid A. Aziz , “Operation And Control Of Split-Parallel, Through-The- Road Hybrid Electric Vehicle With In-Wheel Motors ,” International Journal of Automotive and Mechanical Engineering, Volume 11, pp. 2793-2808, January-June 2015
6. F. M. Sabri, K. A. Danapalasingam, M. F. Rahmat , Md Ridzuan and Md Yusof , “Fuel Economy Analysis of Through-the-Road Hybrid Electric Vehicle,” in the proceedings of 10^th Asia Control Conference, pp. 1-6 May-June 2015.
7. Joga Dharma Setiawan, Mochamad Safarudin and Amrik Singh, “Modeling, Simulation and Validation of 14 DOF Full Vehicle Model,” in the proceeding of International Conference on Instrumentation, Communication, Information Technology, and Biomedical Engineering 2009, Volume 11, pp. 1-6 , 2009.
8. Hans B. Pacejka & Egbert Bakker, “The Magic Formula Tyre Model ,” Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility, 1-18, August 2007.
9. Joga Dharma Setiawan, Mochamad Safarudin and Amrik Singh, “A 14 Degrees of Freedom Mathematical Model to Predict Car Handling Behaviour on Smooth and Bumpy Roads ,” Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility, Taylor & Francis 2013.
10. Joga Dharma Setiawan, Mochamad Safarudin and Amrik Singh, “Eight Degree of Freedom Vehicle Model with Pitch, Yaw, Tire Control and Sensor Inputs,” California Polytechnic State University, 2015.

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Paper Title:

Abstract: Surveillance systems are used for the data acquisitions in order to monitor the control over an environment and get a good overview of the targeted place which can be secured and have a reassurance of safety to the client. But all the previous systems depend on the human who is monitoring the flow i.e. overviewing the output of those surveillance cameras and control the appropriate response by analyzing the data acquired. Now this is all a tedious work which can be changed as we have a dynamic development in the increase of machine work and reducing the human involvement in the analyzing process and make it an intelligence system. We use an image acquisition system using sensor support and based of the movement detection in the proximity of the sensor and it automatically compiles a message to receive to the user through a SMS carrier and also mailing the acquired image through the mail over internet and we can check the live data of the camera using a port address over the same network using IOT. The script we use for all the programming function for this is python as its cross platform support will help in the execution of this project successfully in a single program. In the modern times we have many smart surveillance systems but they are in the affordable range for the small scale business industry and also in the local society such as towns and districts they are eager to acquire a smart security systems for their offices, shops and for protection of their merchandise but cannot go forward due t the pricing range for such system. This is a low cost and efficient system of image acquisition which almost at the half price of whatever smart surveillance we have in the society now-a-days. The paper is comprised of the brief work done in the future usage of the new form of the image surveillance system.

Keywords: SMS, IOT. The script, offices, The paper is comprised of the brief work done

References:

1. Gantt, Charles. "Raspberry Pi Camera Module Review and Tutorial Guide."TweakTown News. Tweak Town, 22, July 2013.
2. "Python Sending Email Using SMTP."Tutorials Point Simply Easy Learning N.P., n.d. Web Oct. 2013.
3. Buenger, Christoph. "Raspberry Pi as Low Cost HD Surveillance Camera" Code Project N.P., n.d. Web. Oct. 2013. http://www.codeproject.com/Articles/6655 18/Raspberry­Pi­as­low­cost­HDsurveillance­camera
4. "Motion Guide for Motion Version 3.2.12."MotionGuide N.P., n.d. Web.Oct2013 http://www.lavrsen.dk/foswiki/bin/view/Motion/MotionGuide
5. Cheng-Hung Tsai, Ying-Wen Bai, Wang Hao-Yuan and Ming-Bo Lin, “Design and Implementation of a Socket with Low Standby Power”, IEEE Transactions on Consumer Electronics, Vol. 55, No. 3, pp. 1558- 1565, August 2009.
6. International Energy Agency, Things That Go Blip in the Night: Standby Power and How to Limit It, Paris, France, International Energy Agency, 2001.
7. International Energy Agency, Standby Power Use and the IEA “1-watt Plan”, International Energy Agency, April 2007.
8. Ying-Wen Bai, Zi-LI Xie and Zong-Han Li, “Design and Implementation of an Embedded Home Surveillance System with Ultra-Low Alert Power”, International conference on consumer electronics, 2011, pp 299-300
9. S. Sivagamasundari, S. Janani, “Home surveillance system based on MCU and GSM”, International journal of communications and engineering, 2012, volume 06– no.6.

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Paper Title:

Abstract: The main objective is to propose a multi-core architecture for micro-controller implementation, considering availability of various microprocessor cores and advancement in fabrication technology. As it is possible to fabricate 2 million gates per square millimeter at advanced 40nm CMOS fabrication technology and availability of embedded FLASH technology on same process, the innovative multi-core micro-controller can be developed for high performance, low power features. ZLPIC (Zero Latency Programmable Interrupt Controller) module is added along with multi-core to enhance performance. Often embedded micro-controllers need variety of interrupt handling. Zero interrupt latency mechanism is very much required in embedded applications. As long as the interrupt actions are limited and predefined, it is possible to give zero latency response for handling interrupt. A simple device with limited programming model is proposed for achieving zero latency. The logic can be easily integrated with multi-core micro-controller architecture.

Keywords: ARM, CMM(Chip Multi-core Micro-controller), CMOS, GPIO, IPDE, ISR, Interrupt Latency, NVM, SRAM and ZLPIC.

References:

1. Mithu and G Stephan, “Low-power Oriented Microcontroller Architecture”, IEEE 2000 International Semiconductor Conference, October 2000, Romania.
2. Vivek and Meenu D Nair, Mukti B, “Synchronized Microcontroller Architecture to eliminate dominant harmonics for quasi square waveform”, 2016 IEEE 7^th International Symposium on Power Electronics for Distributed Generation Systems, June 2016, Canada.
3. A Mayer and F Hellwig, “System Performance Optimization Methodology for Infeneons’s 32-Bit Automotive Microcontroller Architecture”, 2008 Design Automation and Test, March 2008, Germany.
4. Sakata and T. Hirotsu, “Cost Effective Dependable Microcontroller Architecture with Instruction Level Rollback for Soft Errors Recovery”, 37^th Annual IEEE/IFIP International Conference On Dependable Systems and Networks, June 200, UK
5. K Kim and J Park, “Study on Microcontroller Design and Verification Methodology”, IEEE 7^th Korea-Russia International Symposium on Science and Technology”, July 2003, South Korea
6. K D Kramer, T. Stolze and T Banse, “Benchmark to find the optimal microcontroller architecture”, IEEE, 2009 WRI World Congress on Computer Science and Information Engineering”, April 2009, Los Angeles.
7. J Saalmueller and J Wuertz, “Embedded Controllers for solving complex Industry Applications”, IEEE 2006 SoC Conference, Sep 2006, Taiwan.
8. A.Perez-Quinones and J L Cruz-Rivera, “Integrated Development Environment for a microcontroller systems laboratory”, IEEE Conference 29^th Annual Frontiers in Education, Nov 1999, USA.
9. A J Arellano and C M Lopez, “Design of an academic microcontroller and its application to Authenticated Encryption”, IEEE 2014 International Conference on Electronics, Communications and Computers, Feb 2014, Mexico.
10. H Yue-li,C Jia-lin, ran Feng, “Design of High Performance microcontroller”, IEEE CPMT Conference on High Density Microsystem Design and Packaging and Component Failure Analysis, July 2004.
11. Robert Ashby, My First Five PSoc 3 Designs, Cypress Semiconductors.(Book)
12. Kai Hwang, Advanced Computer Architecture, 2, McGraw-Hill. (Book)
13. David Patterson and John Hennessy, Computer Architectures: A Quantitative Approach, 5, Morgan Koufmann, 2007.(Book)
14. Richard Kain, Advanced Computer Architecture a system design approach, 1,Prentice Hall, 1996.(Book)
15. Max Dameika, Software Development for Embedded Multi-core Systems, 1, Elsevier, 2008.(Book)
16. Michael Barr, Programming embedded systems, ed 2,O’Rielly,(Book)
17. V.Altaf, Microprocessors and Micro-controllers, ed. 1, Imperial, 2011.(Book)
18. K.Ray and Bhurchundi, Advanced Microprocessors and Interface, ed. 3, McGrawHill, 2011.(Book)
19. P. Godse and A.O. Mulani, Embedded Systems, ed. 1. Technical Publications, 2009,(Book)
20. Douglas Hall, Microprocessor and Interfacing, 2, McGraw Hill, 1986.(Book)
21. Raj Kamal, Embedded Systems, 2, Tata McGraw Hill, 2011.(Book)
22. Byron Gottfried, Programming with C, ed 3, Tata McGraw Hill,2010(Book)
23. umc.com
24. microchip.com
25. arm.com
26. http://en.wikipedia.org/wiki/Intrrupt-latency
27. intel.in "Reduce interrupt latency in embedded systems-Intel”
28. smxrtos.com/articles/Isr_art/Isr_art.htm, “Minimizing Interrupt Latency - Multitasking Kernel”
29. tsmc.com

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Paper Title:

Abstract: The four years rainfall accumulationsobservedwith respective rain rate has been presented for the location Lat 16.24 Lon 80.45. From the observations the maximum number of occurrences are below 60 mm/hr with an annual accumulation time less than 1.5% in a year. The stratiform and convective rainfall pattern and its effects on the link has been analyzed with respective rain event. The fine resolution rain rate obtained from Micro Rain Radar with 10 seconds integration time has been presented for a rain event. The observed rain attenuation on the20.2 GHz link has been presented for link design analysis.

Keywords: propagation studies, Rain Rate, GSAT-14, 20.2 GHz, Ka band.

References:

1. Kilaru, S. K. Kotamraju, N. Avlonitis, and K. C. S. Kavya, “Rain rate intensity model for communication link design across the Indian region ,” J. Atmos. Solar-Terrestrial Phys. , vol. 145, pp. 136–142, 2016.
2. Kilaru, N. Avlonitis, S. K. Kotamraju, and I. Otung, “Rain integration time and percentage probability of rain in Indian subcontinent for satellite communications,” in 2014 International Conference on Electronics and Communication Systems (ICECS), 2014, pp. 1–7.
3. ITU-R Recomendation P.837-6, “Characteristics of precipitation for propagation modelling P Series Radiowave propagation,” Radiowave Propag., vol. 6, 2012.
4. K. R. Calla, O. P. N., R. Singh, S. Barathy, “Classification of rain rate regions for propagation applications,” Indian J. Radio Sp. Phys., no. 18, pp. 108–112, 1989.
5. Sulochana, P. Chandrika, and S. V. B. Rao, “Rainrate and rain attenuation statistics for different homogeneous regions of India,” Indian J. Radio Sp. Phys., vol. 43, no. October, pp. 303–314, 2014.
6. A. P. Aldo Paraboni, “Cost Action 255 Radiowave Propagation Modelling for SatCom Services at Ku-Band and Above,” Baveno, Lago Maggiore, Italy, 2002.
7. R. Sujimol, R. Acharya, G. Singh, and R. K. Gupta, “Rain attenuation using Ka and Ku band frequency beacons at Delhi Earth Station,” Indian J. Radio Sp. Phys., vol. 44, no. March, pp. 45–50, 2015.
8. K. Kotamraju and C. S. K. Korada, “Precipitation and other propagation impairments effects at microwave and millimeter wave bands: a mini survey,” Acta Geophys., Feb. 2019.
9. [9] C. Retrieved, “Prediction of convective events using multi- frequency radiometric observations at Kolkata,” no. October, 2015.
10. Maitra, S. Jana, R. Chakraborty, and S. Majumder, “Multi-technique observations of convective rain events at a tropical location,” 2014 31th URSI Gen. Assem. Sci. Symp. URSI GASS 2014, pp. 3–6, 2014.
11. [11] De, R. Chakraborty, and A. Maitra, “Studies on rain induced scintillation during convective events over Kolkata,” vol. 6, no. c, pp. 1–2.
12. [12] Das, A. Maitra, and S. D. and A.Maitra, “Ka-band Radar Observations of Tropical Rain Features 1 . 2 Micro Rain Radar,” in 2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS), 2014, pp. 3–6.
13. Das and A. Maitra, “Some melting layer characteristics at two tropical locations in Indian region,” 2011 30th URSI Gen. Assem. Sci. Symp. URSIGASS 2011, pp. 1–4, 2011.
14. Das, A. Maitra, and A. K. Shukla, “Diurnal variation of slant path ka-band rain attenuation at four tropical locations in India,” Indian J. Radio Sp. Phys., vol. 42, no. February, pp. 34–41, 2013.

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Paper Title:

Abstract: Stock trends are generated in huge volume and it changes every second. Stock market is a complex and volatile system where people will either gain money or lose their entire life savings. This project is about taking quantifiable data from finance API about the top 500 companies in S&P stock exchange and predicting its future stock trend with ensemble learning. To achieve it we have considered mainly two prediction methods, Heat Map and Ensemble Learning, which based on the percentage change in the stock price data will classify the stock into buy, sell or hold categories. Heat map is generated based on the correlation coefficient of the quantifiable data to further classify the stock as one of the three above mentioned categories. On the other hand, we used the ensemble learning model to classify the stock into a majority vote-based system that considers 3 main classification models. Observations shows that Random Forest, SVM and K-neighbors classifiers show the most prominent results of all other possible combinations. The accuracy of the prediction model is more than 51% whereas in comparison with prediction models with a single classifier labelling with 30% accuracy the model has increased the accuracy by 23%.

Keywords: Stock trends, Machine Learning, Ensemble Learning, Heat map, K-Neighbors, Random Forest, SVM.

References:

1. Stock Market Prediction Using Hidden Markov Model, PoonamSomani, ShreyasTalele and SurajSawant.
2. Survey of Stock Market Prediction Using Machine Learning Approach, ASHISH SHARMA, Dinesh Bhuriya and Upendra Singh.
3. Stock Market Prediction Using Machine Learning Techniques, MehakUsmani, Syed Hasan Adil, KamranRaza and Syed Saad Azhar Ali.
4. Stocks Market Prediction Using Support Vector Machine, Zhen Hu, Jie Zhu, and Ken Tse
5. Forward Forecast of Stock Price Using Sliding-window Metaheuristic-optimized Machine Learning Regression,Jui-Sheng Chou and Thi-Kha Nguyen.
6. Improved Stock Market Prediction by Combining Support Vector Machine and Empirical Mode Decomposition, HonghaiYu and Haifei Liu-2012.
7. Stock Volatility Prediction using Multi-Kernel Learning based Extreme Learning Machine, Zhiyong Zhao, Xiaodong Li, FeiYu and Hao Zhang
8. Prediction of Stock Market by Principal Component Analysis, Muhammad Waqar, Hassan Dawood, Muhammad Bilal Shahnawaz, Mustansar Ali Ghazanfar and Ping Guo
9. Stock Market Prediction using Optimum Threshold based Relevance Vector Machines, Karthik HS, Nishanth VA and Manikandan
10. Price Trend Prediction of Stock Market Using Outlier Data Mining Algorithm, Lei Zhao ; Lin Wang

150-155

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Paper Title:

Abstract: Due to the unbelievable increment in the assess of data on the World Wide Web, there is a solid requirement to optimize a web page cataloging to reclaim constructive information rapidly. Proposed clonal selection based artificial immune system algorithm to select the most excellent weights for every feature in the training dataset and implement the KNN (k-Nearest Neighbour) classifier to categorized the new web pages from testing dataset. In addition, the weight determination process is depended on both term and tag weighting method. Structure features are gathered and appointed weights in this scheme. Results obtained show that projected classifier effectively classified to demonstrate the efficacy of the algorithm with respect to single and multi-class.

Keywords: Artificial immune system, k-Nearest Neighbour, Tag weighting, Term weighting, Web page classification.

References:

1. A .Matthew.,Web 2.0, An argument against convergence. In Media Convergence and De-convergence, Palgrave Macmillan, Cham, (2017),pp. 177-196.
2. Rogers, Richard, and Noortje M.,Landscaping climate change: A mapping technique for understanding science and technology debates on the World Wide Web. Public Understanding of Science (2016).
3. Bhalla, K .Vinod, and K. Neeraj, An Efficient Multiclass Classifier Using On-Page Positive Personality Features for Web Page Classification for the Next Generation Wireless Communication Networks. Wireless Personal Communications 93, no. 2, (2017),pp. 503-522.
4. Gani, Abdullah, A.Siddiqa , S. Shahaboddin, and H. Fariza ,A survey on indexing techniques for big data: taxonomy and performance evaluation. Knowledge and information systems 46, no. 2, (2016), pp. 241-284.
5. Pérez, Serge, A. Sarkar, R.Alain, D.Sophie, B. Christelle, and A. Imberty., Glyco3D, A Suite of Interlinked Databases of 3D Structures of Complex Carbohydrates, Lectins, Antibodies, and Glycosyltransferases. In A Practical Guide to Using Glycomics Databases, Springer, Tokyo, (2017),pp. 133-161.
6. Malhotra, Ruchika, and A. Sharma, Quantitative evaluation of web metrics for automatic genre classification of web pages. International Journal of System Assurance Engineering and Management 8, no. 2, (2017), pp. 1567-1579.
7. Khalil, Salim, and F . Mohamed, RCrawler, An R package for parallel web crawling and scraping. SoftwareX 6 , (2017), pp. 98-106.
8. Li, Huakang, Zheng X., Tao L., Guozi S., and R. C Kim-Kwang.,An optimized approach for massive web page classification using entity similarity based on semantic network. Future Generation Computer Systems 76, (2017), pp.510-518.
9. Khatami, Reza, G. Mountrakis, and V. S. Stephen,A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research. Remote Sensing of Environment 177, (2016), pp. 89-100.
10. Bader, Sebastian, and O. Jan, Semantic Annotation of Heterogeneous Data Sources, Towards an Integrated Information Framework for Service Technicians. In Proceedings of the 13th International Conference on Semantic Systems, ACM , (2017), pp. 73-80.
11. P.Sinka and D.W.Corne BankSearch dataset. Retrieved from http://www.pedal.reading.ac.uk/bansearchdataset Accessed January 15, 2005.
12. Verma, P. Srivastava, P. Chakrabarti, Exploring structure oriented feature tag weighting algorithm for web documents identification, Soft computing system. kollam, India : Springer, 2018; pp. 169-180.
13. Fielding, T. Roy, R. N. Taylor, J. R. Erenkrantz, M. G. Michael, W. Jim, R. Khare, and O. Peyman, Reflections on the REST architectural style and principled design of the modern web
14. architecture (impact paper award). In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, ACM, (2017),pp. 4-14.
15. Arya, Chandrakala, and S.K. Dwivedi, News web page classification using URL content and structure attributes. In Next Generation Computing Technologies (NGCT), 2nd International Conference ,IEEE, 2016,pp. 317-322.
16. Castro D.,and Timmis L.N..: Artificial Immune Systems: A New Computational Intelligent Approach. Springer, Berlin (2002).
17. Hossin and M.N. Sulaiman, A Review On Evaluation Metrics For Data Classification Evaluations. International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.5, No.2,(2015).
18. B. I Sadegh., and B.Mohammad, Application of K-Nearest Neighbor (KNN) Approach for Predicting Economic Events, Theoretical Background. International Journal of Engineering Research and Applications Vol. 3, Issue 5, (2013), pp. 605-610.

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Paper Title:

Abstract: Traditional methods of drinking water quality parameters like turbidity, pH, conductivity and temperature etc., may consume time as samples are tested manually in the laboratory. To overcome this, in the current article an attempt has been made for developing the smart and low-cost IoT system. The parameters considered to test the quality of water are Temperature, Turbidity, pH, Conductivity. Sensors immersed in sampled water are used to measure the above said parameters. The sensed data from the sensors was sent to the Raspberry Pi Unit. The sensed data parameters compared with the standard values which already exist in Raspberry Pi Unit. The data stored in Raspberry Pi accessed from the IOT (cloud). If any change in the standard values was observed, a message or a mail will be sent to the Smartphone through Wi-Fi. In the current work, samples of water were collected to test the purity of water. Also to check the variety of particles those were present in the water. In this work, we use sensors for testing purity of water. The current developed model will detect the particles that are present in the water and also the level of purity in the water. The results were displayed in the form of the numerical values at the display unit that was fixed on the IoT unit.

Keywords: IoT, Water Quality, PH Value, Water control.

References:

1. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, M. Ayyash, "Internet of Things: A survey on enabling technologies protocols and applications", IEEE Commun. Surveys Tuts., vol. 17, no. 4, pp. 2347-2376, 4th Quart. 2015.
2. Gubbi, R. Buyya, S. Marusic, M. Palaniswami, "Internet of Things (IoT): A vision architectural elements and future directions", Future Gener. Comput. Syst., vol. 29, no. 7, pp. 1645-1660, Sep. 2013.
3. Atzori, A. Iera, G. Morabito, "The Internet of Things: A survey", Comput. Netw., vol. 54, no. 15, pp. 2787-2805, Oct. 2010.
4. Overview of the Internet of Things, Geneva, Switzerland:, pp. 1-22, Jun. 2012.
5. Evans, The Internet of Things: How the next evolution of the Internet is changing everything, San Jose, CA, USA: CISCO, pp. 1-11, Apr. 2011.
6. The Communications Market Report: U.K., Ofcom, pp. 1-431, Aug. 2015.
   2016. H. Ngu, M. Gutierrez, V. Metsis, S. Nepal, M. Z. Sheng, "IoT middleware: A survey on issues and enabling technologies", IEEE Internet Things J., vol. 4, no. 1, pp. 1-20, Feb. 2016.
7. A. Razzaque, M. Milojevic-Jevric, A. Palade, S. Clarke, "Middleware for Internet of Things: A survey", IEEE Internet Things J., vol. 3, no. 1, pp. 70-95, Feb. 2016.
8. Yasser Gadallah, Mostafael Tager and EhabElalamy, “A Framework for Cooperative Intranet of Things Wireless Sensor Network Applications,” Eight International Workshop on Selected Topics in Mobile and Wireless Computing, the American University in Cairo, vol. pp. 147154, 2012.
9. Hao Chen, XueqinJia, Heng Li, “A Brief Introduction to IOT Gateway,” Proceedings of ICCTA, Network Technology Research Center, China Unicom research Institute, Beijing 100032, China. 2011.
10. WANG Jing-yang, CAO Yu, YU Guange-ping, YUAN Ming-zhe, “Research on Application of IOT in Domestic Waste Treatment and Disposal,” Proceeding of the 11th World Congress on Intelligent Control and Automation, Shenyang, China, June 29 - July 4 2014, pp. 4742-4745.
11. Zaigham Mahmood, “Cloud Computing: Characteristics and Deployment Approaches,” 11th IEEE International Conference on Computer and Information Technology, UK, 2011, pp. 121- 126.

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Paper Title:

Abstract: Introduction of the memristor has paved the way to many inventions in VLSI domain. The properties of memristor such as the nanometer scale measurements and its non-volatile memory qualities have yielded more attention towards research people. The nanometer scale highlight of the memristor makes another door open for the realization of innovative circuits for logic blocks from the more standard designs. Non-volatile memory property empowers us to acknowledge new outline strategies for an assortment of computational components that prompt novel models. By this, there comes the idea of the combination of the Nano-scale memristor and CMOS, which ends up conceivable to diminish usage of silicon territory accordingly giving a promising alternative in the plan of memristor and CMOS based circuits. In this paper, we are presenting a combinational circuit design using memristor and CMOS logic as well as the implementation of a built-in self-test circuit to test the core functionalities of the logic. It is composed of a test pattern generator and output response analyzer which will compare the output response of the unit under test circuit with the pre-stored expected patterns of the unit under test. Designing the circuit utilizing this mix advantage of memristor and CMOS spares a great deal of chip space and power utilization and it is reliable as well.

Keywords: CMOS, memristor,BIST, LFSR.

References:

1. Chua L. Memristor-the missing circuit element. IEEE Trans Circuit Theory 1971;18:507–19.
2. Ravi V, Prabaharan SRS. Memristor Based Memories : Defects , Testing , And Testability Techniques 2017;17:105–25.
3. Ravi V, Prabaharan SRS. Weak Cell Detection Techniques for Memristor-Based Memories 2018:101–10.
4. Ravi V, Prabaharan SRS. Fault tolerant adaptive write schemes for improving endurance and reliability of memristor memories. AEU-International J Electron Commun 2018;94:392–406.
5. Reddy MGSP, Ravi V. Nondestructive Read Circuit for Memristor-Based Memories. Nanoelectron. Mater. Devices, Springer; 2018, p. 123–31.
6. Strukov DB, Robinett W, Snider G, Strachan JP, Wu W, Xia Q, et al. Hybrid CMOS / Memristor Circuits. New York 2010:1967–70. doi:10.1109/ISCAS.2010.5537020.
7. Chua LO, Kang SM. Memristive devices and systems. Proc IEEE 1976;64:209–23.
8. Pandharpurkar NG, Ravi V. Design of BIST using self-checking circuits for multipliers. Indian J Sci Technol 2015;8:1–7.
9. Chaitanya MK, Ravi V. Design and development of BIST architecture for characterization of S-RAM stability. Indian J Sci Technol 2016;9.
10. Chandni MD, Ravi V. Built in self test architecture using concurrent approach. Indian J Sci Technol 2016;9.
11. Sharma A, Ravi V. Built in self-test scheme for SRAM memories. Adv. Comput. Commun. Informatics (ICACCI), 2016 Int. Conf., IEEE; 2016, p. 1266–70.
12. Patnaik S, Ravi V. A Built-in Self-Repair Architecture for Random Access Memories. Nanoelectron. Mater. Devices, Springer; 2018, p. 133–46.
13. Kim H, Sah MP, Adhikari SP. Pinched hysteresis loops is the fingerprint of memristive devices. ArXiv Prepr ArXiv12022437 2012.
14. Vourkas I, Sirakoulis GC. Memristor-based combinational circuits: A design methodology for encoders/decoders. Microelectronics J 2014;45:59–70. doi:10.1016/j.mejo.2013.10.001.
15. Eshraghian K. course notes on" Memristive Circuits and Systems,". Tech June 2011.
16. Singh T. Hybrid memristor-cmos (memos) based logic gates and adder circuits. ArXiv Prepr ArXiv150606735 2015.
17. Cho K, Lee S-J, Eshraghian K. Memristor-CMOS logic and digital computational components. Microelectronics J 2015;46:214–20.
18. [18] Kvatinsky S, Ramadan M, Friedman EG, Kolodny A. VTEAM: A general model for voltage-controlled memristors. IEEE Trans Circuits Syst II Express Briefs 2015;62:786–90.
19. Mane PS, Paul N, Behera N, Sampath M, Ramesha CK. Hybrid CMOS - Memristor based configurable logic block design. 2014 Int Conf Electron Commun Syst ICECS 2014 2014. doi:10.1109/ECS.2014.6892532.
20. [20] Shin S, Kim K, Kang S-M. Memristor applications for programmable analog ICs. IEEE Trans Nanotechnol 2011;10:266–74.
21. Kvatinsky S, Satat G, Wald N, Friedman EG, Kolodny A, Weiser UC. Memristor-based material implication (IMPLY) logic: Design principles and methodologies. IEEE Trans Very Large Scale Integr Syst 2014;22:2054–66. doi:10.1109/TVLSI.2013.2282132.
22. Kvatinsky S, Belousov D, Liman S, Satat G, Wald N, Friedman EG, et al. MAGIC—Memristor-aided logic. IEEE Trans Circuits Syst II Express Briefs 2014;61:895–9.
23. Talati N, Gupta S, Mane P, Kvatinsky S. Logic design within memristive memories using memristor-aided loGIC (MAGIC). IEEE Trans Nanotechnol 2016;15:635–50.
24. Papandroulidakis G, Vourkas I, Vasileiadis N, Sirakoulis GC. Boolean logic operations and computing circuits based on memristors. IEEE Trans Circuits Syst II Express Briefs 2014;61:972–6. doi:10.1109/TCSII.2014.2357351.

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Paper Title:

Abstract: Everything in real word is monitored through surveillance cameras. Video surveillance is a critical tool for a variety of tasks such as law enforcement, personal safety, traffic control, resource planning, and security of assets. The widespread use of surveillance cameras in offices and other business establishments produces huge amount of data every second. The advent of large data is introducing important innovations like availability of additional external data sources, dimensions previously unknown and questionable consistency, poses new challenges to the worldwide spread of data sources (web, e-commerce, sensors). These collections of data sets having video frames which become difficult to process using traditional image processing applications. So, in this paper we propose a new foreground extraction technology using segmentation based on Skew Gaussian Mixture Model. The proposed model is more accurate than traditional approaches.

Keywords: Video Surveillance, Foreground Extraction, Background Subtraction, Illumination Changes, segmentation, Skew Gaussian Mixture Model.

References:

1. P St charles, G A Bilodeau “Improving Background Subtraction using binary similarity patterns”, August, 2014.
2. Ojha,S.Sakhare, “Image processing Techniques for object tracking in video surveillance”, January 2015.
3. Chen, “Spatial and temporal independent component analysis” 06, and “health applications” 2015.
4. S Jeeva, M.Sivabalakrishnan, “Background and foreground detection models for Real time Video Surveillance”, May 2015.
5. M A Alavianmehr,A Tashik,A Sodagaran, “Video foreground detection based on adaptive mixture Gaussian Model for video surveillance systems. June 2015.
6. G chen,Zhezhou Yu,Qing Wen,Yangquan Yu, “Improved Gaussian Mixture Model for Moving Object Detection” July 2015.
7. Yong Xu,J Dong,B Zhang,Daoyun Xu, “Background modelling methods in video analysis – comparative evaluation”, June 2016.
8. H Fradi, J L Dugelay, “Spatial and Temporal variations of Feature Tracks for Crowd Behaviour analysis” (EURECOM,france), July 2016.
9. Rashid M E, Vinu Thomas], “A background Foreground Competitive Model for Background Subtraction in Dynamic Background”, in (RAEREST 2016), Sep 2016.
10. H Fradi, L Bracco, F Canino, J L Dugelay, “Autonomous Person Detection and Tracking Framework using unnamed Aerial Vehicles”, (proceedings of European Signal Processing EUSIPCO), 2018.
11. Nagesh Vadaparthi et. al., “Unsupervised Medical Image Segmentation on Brain MRI Images using Skew Gaussian Distribution”, at IEEE-International conference on Recent Trends in Information Technology, pp: 1293-1297, 2011 at MIT Chennai.
12. Nagesh Vadaparthi et. al., “An Improved Brain MR Image Segmentation Using Truncated Skew Gaussian Mixture”, International Journal of Advanced Computer Science and Applications, 6(7), August 2015.
13. Nagesh Vadaparthi et. al., “Segmentation of Brain MR Images based on Finite Skew Gaussian Mixture Model with Fuzzy C-Means Clustering and EM Al-gorithm”, International Journal of Computer Applications, 28(10), pp: 18-26, 2011.

174-177

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Authors:

Paper Title:

Abstract: The flat slab has gained popularity for many years in the construction industry because of its advantages over the conventional slab. Punching shear failure is the major issue in the flat slab system. The most affected portion in the flat slab system is the slab column joint. The test specimen was modelled by using a finite element software known as ABAQUS. This software has good performance quality and ability to solve the complex problems in different fields of engineering. In order to conduct the study, nine slab-column specimens of the flat slab were casted and subjected to test with four, three and two sides rigidly supported. Three flat slabs with control mix and six flat slabs with hooked end steel fibres were used. The tests were conducted to study the capacity of the test specimen to withstand the punching shear. The crack patterns developed on the test specimens was studied. It was observed that the four side supported slab-column connection of the flat slab enhance more punching shear strength capacity than three and two sides supported. Also, it was found that the usage of the steel fibre can improve the stiffness and the load bearing capacity of the test specimens.

Keywords: Crack pattern, Flat slab, Punching shear, Steel fiber reinforced concrete

References:

1. G. Inacis, Micael, F.O. Almeida, Andre and M.V. Faria, “Punching of high strength concrete flat slabs without shear reinforcement”, Engineering Structures, vol. 103, 2015, pp. 275-284.
2. Min – Yaun Cheng, Gustavo J Parra-Montesinos, “Evaluation of Steel Fiber Reinforcement for Punching Shear Resistance in Slab- Column Connections”, ACI Structural Journal, February 2010.
3. Pilakoutas, Li, X, “Alternative Shear Reinforcement for Reinforced Concrete Flat Slabs”, Journal of Structural Engineering, vol. 129 - issue 9, 2003, pp. 1164-1172.
4. Aurelio Muttoni, “Punching Shear Strength Of Reinforced Concrete Slabs Without Transverse Reinforcement”, ACI Structural Journal, 2008.
5. Nguyen-Minh, M. Rovnak, T. Tran-Quoc and K. Nguyen-Kim, “Punching Shear Resistance of Steel Fiber Reinforced Concrete Flat Slabs”, 2011.
6. Tamara Adnan Qasim Al-Shaikhli, “Effect of Steel Fiber on Punching Shear Strength of Non-Rectangular Reactive Powder Concrete Slabs”, International Journal of Structural and Civil Engineering Research, vol. 5, No. 2, May 2016.
7. S. Vishwanathan, G. Mohan Ganesh, A. S. Santhi,”Shear Stress Distribution Of Flat Plate Using Finite Element Analysis”, International Journal Of Civil And Structural Engineering, Volume 2, 2015.
8. S. Vishwanathan, G. Mohan Ganesh, A. S. Santhi,” Investigation of shear stud performance in flat using finite element analysis” J.Eng. Technol. Sci., vol. 46, no 3, 2014, pp 328-341.
9. IS 10262:2009. Indian standard code of practice for concrete mix proportioning. Bureau of Indian Standard, New Delhi, India.
10. IS 456:2000. Indian standard code of practice for guidelines for plain and reinforced concrete. Bureau of Indian Standard, New Delhi, India

178-184

35.

Authors:

Paper Title:

Abstract: A Graphics Processing Unit (GPU) is a specific electronic circuit proposed to rapidly control and alter memory to quicken the formation of images. GPUs are used as a part of cell phones, PCs, workstations. With upgraded efficiency, the GPU plays crucial part in visual and sound preparing applications, for example, GPU quickened video encoding and image handling. In the meantime, GPU installed cloud suppliers started to give GPU-quickened distributed computing administrations. Hence, as the GPU gadgets bring high cost and vitality utilization, conveying GPU quickened visual and sound handling administrations is a versatile and adaptable arrangement. Due to high upkeep cost and diverse speedups for different applications, GPU-quickened services still need an alternate pricing strategy. Accordingly, here, we propose an ideal GPU-quickened mixed media preparing administration pricing system for increasing the benefits of both cloud suppliers and users. As the distributed computing is an essential business display, the evaluating technique is an imperative issue for both foundations and organizations. The evaluating technique of business cloud administrations is typically considered as delicate insight. With various rebate and differing costs, the last cost isn't totally predictable with the underlying open costs. So here we talk about the mixed media preparing pricing technique in GPU-accelerated cloud computing and investigate the outcomes from the examinations.

Keywords: GPU-quickened, Pricing system, Mixed media, Cloud administrations.

References:

1. He Li, Kaoru Ota, Mianxiong Dong, Athanasios Vasilakos, Koji Nagano, “Multimedia Processing Pricing Strategy in GPU-accelerated Cloud Computing”, IEEE Transactions on Cloud Computing(Early Access),2017.
2. Lin Shi, Hao Chen, Jianhua Sun, and Kenli Li, “Vcuda: GPU- Accelerated High Performance Computing in Virtual Machines”, IEEE Transactions on Computers, vol. 61, no. 6, pp. 804-816, June 2012.
3. S. Yeo, S. Venugopal, X. Chu, and R. Buyya, “Autonomic metered pricing for a utility computing service”, Future Generation Computer Systems, vol. 26, no. 8, pp. 1368-1380, 2010.
4. Hadji, W. Louati, and D. Zeghlache, “Constrained pricing for cloud resource allocation”, in Proceedings of the 10^th IEEE International Symposium on Network Computing and Applications (NCA 2011), Aug 2011, pp. 359-365.
5. Sharma, R. K. Thulasiram, P. Thulasiram, S. K. Grag, and R. Buyya, “Pricing cloud compute commodities: A novel financial economic model”, in Proceedings of the 2012 12^th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (Ccgrid 2012), ser. CCGRID’12. Washington, DC, USA: IEEE Computer Society, 2012, pp. 451-457.
6. D. Patel and A. J. Shah, “Cost model for planning, development and operation of a data center”, hp technical report- hpl- 2005-107(r.l), 2005.
7. Wenwu Zhu, Chong Luo, Jianfeng Wang and Shipeng Li, “Multimedia Cloud Computing”, IEEE Signal Processing Magazine Volume: 28, Issue: 3, May 2011.
8. Huang, S. Xiao and W. Feng, “On the Energy Efficient of Graphics Processing Units for Scientific Computing”, IEEE International Symposium on Parallel & Distributed Processing, 2009.
9. José Duato, Antonio J. Peña, Federico Silla, Rafael Mayo and Enrique S. Quintana-Orti, “Rcuda Reducing the number of GPU-based accelerators in high performance clusters”, IEEE International Conference on High Performance Computing and Simulation, 2010, pp. 224-231.
10. H. Li, M. Dong, K. Ota, and M. Guo, ”Pricing and Repurchasing for Big Data processing in Multi-Clouds”, IEEE Transactions on Emerging Topics in Computing, vol.4 pp. 266-277, 2016.

185-188

36.

Authors:

Paper Title:

Abstract: The radiation from mobile towers affect tissues in the human body. The electrons are stripped away from atoms by splitting some chemical links. In our paper the shielding performance of various materials like aluminum sheet, mumetal, and super-alloy with different thickness is evaluated in terms of reflection loss (RL), re-reflection factor (RRF) and absorption loss (AL). Aluminum acts as a good corrosive in marine, and atmospheric environment. This is very appropriate for the decorative applications, due to its high reflective property. The composition of the particular material contains 16% iron, 5% copper, 2% chromium, and 77% nickel. The soft ferromagnetic alloy of mumetal contains the nickel iron with high permeability, and it is suitable for shielding against low-frequency magnetic fields. It contains 32%-67% iron, 15%-22% chromium, and 9%-38% nickel. The power density values obtained from the shielding effectiveness are converted into specific absorption rate and it is compared with the ICNIRP standard value to show the effectiveness of shielding material. While comparing with the other materials, it is observed that aluminum sheet is the most radiation absorption and protective shielding material against the radiation from cell towers.

Keywords: Shielding algorithm, reflection loss, absorption loss, re-reflection factor, thickness of the material.

References:

1. Monfrecola G, Moffa G, and Procaccini EM, “Non-ionizing electromagnetic radiations, emitted by a cellular phone, modify cutaneous blood flow”, Dermatology, vol. 207, 2003, pp.10-14, 2003.
2. Röösli M, Frei P, Mohler E, and Hug K.,” Systematic review on the health effects of exposure to radiofrequency electromagnetic fields from mobile phone base stations”. Bulletin of the World Health Organization, vol. 88, 2010, pp. 887-896.
3. Abd-Alhameed R A, Excell P S, andMangoud M A, “Computation of specific absorption rate in the human body due to base-station antennas using a hybrid formulation,” IEEE Transactions on Electromagnetic Compatibility, vol. 47, 2005, pp. 374-381.
4. Carew JS, Zhou Y, Albitar M, Carew JD, Keating MJ, and Huang P, “Mitochondrial DNA mutations in primary leukemia cells after chemotherapy: clinical significance and therapeutic implications.” Leukemia, vol. 17, 2003, pp. 1437.
5. Karthick, S, “TDP: A Novel Secure and Energy Aware Routing Protocol for Wireless Sensor Networks”, International Journal of Intelligent Engineering and Systems, vol. 11, 2018, pp. 76-84.
6. Ozgur E, Güler G, and Seyhan N, “Mobile phone radiation-induced free radical damage in the liver is inhibited by the antioxidants N-acetyl cysteine and epigallocatechin-gallate,” International Journal of Radiation Biology, vol. 86, 2010, pp. 935-945.
7. Gutteridge J M, “Lipid peroxidation and antioxidants as biomarkers of tissue damage,” Clinical chemistry, vol. 41, 1995, pp. 1819-1828.
8. Spencer J P, Wong J, Jenner A, Aruoma O I, Cross CE, and Halliwell B, “Base modification and strand breakage in isolated calf thymus DNA and in DNA from human skin epidermal keratinocytes exposed to peroxynitrite or 3-morpholinosydnonimine,” Chemical research in toxicology, vol. 9, 1996, pp. 1152-1158.
9. Gupta S C, Hevia D, Patchva S, Park B, Koh W, and Aggarwal B B, “Upsides and downsides of reactive oxygen species for cancer: the roles of reactive oxygen species in tumorigenesis, prevention, and therapy.” Antioxidants & redox signaling, vol. 16, 2012, pp. 1295-322.
10. Grasso L, “Cellular Telephones and the Potential hazards of RF Radiation: Responses to the Fear and Controversy,” Va. JL & Tech, vol. 3, 1998.
11. Makrigiorgos G M, Alderstein S J and Kassis A I, “Cellular radiation dosimetry and its implications for estimation of radiation risks,” Jama, vol. 264, 1990, pp. 592-595.
12. Chen HY and Wang HH, “Current and SAR induced in a human head model by the electromagnetic fields irradiated from a cellular phone,” IEEE Transactions on Microwave Theory and Techniques, vol. 42, 1994, pp. 2249-2254.
13. Petersen R C and Testagrossa P A, “Radio‐frequency electromagnetic fields associated with cellular‐radio cell‐site antennas,” Bioelectromagnetics, vol. 13, 1992, pp.527-542.
14. Grover S B, Kumar J, Gupta A and Khanna L, “Protection against radiation hazards: Regulatory bodies, safety norms, does limits and protection devices,” Indian journal of radiology and imaging, vol. 12, 2002, pp.157.
15. National Research Council, “Health risks from exposure to low levels of ionizing radiation: BEIR VII phase 2,” National Academies Press, 2006 Apr 23.
16. Goodhead D T, “Understanding and characterisation of the risks to human health from exposure to low levels of radiation,” Radiation protection dosimetry, vol. 137, 2009, pp. 109-117.
17. Brachman D, Fram E K, Nakaji P, inventors; Gt Medical Technologies Inc, assignee, “Radiation shielding,” United States patent application US, vol. 10, 2018, pp. 699.
18. Wong AY, Moreno R, Shields K R, Wang R, inventors; ANTENNA79, INC., assignee, “RF radiation redirection away from portable communication device user,” United States patent US, vol. 9, 2016, pp. 841.
19. Xia C, Yu J, Shi SQ, Qiu Y, Cai L, Wu HF, Ren H, Nie X and Zhang H, “Natural fiber and aluminum sheet hybrid composites for high electromagnetic interference shielding performance,” Composites Part B: Engineering, vol. 114, 2017, pp. 121-127.
20. Belli M, Sapora O, Tabocchini MA. “Molecular targets in cellular response to ionizing radiation and implications in space radiation protection.” Journal of radiation research, vol. 43, 2002, pp. S13-S19.
21. Ali F, Ray S. “SAR analysis for handheld mobile phone using DICOM based voxel model.” Journal of Microwaves, Optoelectronics and Electromagnetic Applications. vol. 12, 2013, pp. 363-375.
22. Fathi E, Farahzadi R. “Interaction of mobile telephone radiation with biological systems in veterinary and medicine.” J Biomed Eng Technol., vol. 2, 2014, pp. 1-4.
23. Elwasife KY. “Numerical analysis of specific absorption rate in breast fat tissue subjected to mobile phone radiation.” Journal of Emerging Trends in Computing and Information Sciences, vol. 7, 2016, pp. 328-331.
24. Needleman S, Powell M. “Radiation hazards in pregnancy and methods of prevention.” Best practice & research Clinical obstetrics & gynaecology, vol. 33, 2016, pp. 108-116.
25. Malathi AC. “Electromagnetic Radiation Hazards on Humans Due to Mobile Phones.” Indian Journal of Science and Technology, vol. 9, 2016, pp. 1-7.
26. Al-Sarray E, Akkurt İ, Günoğlu K, Evcin A, Bezir NÇ. “Radiation Shielding Properties of Some Composite Panel.” Acta Physica Polonica, vol. 132, 2017, pp. 490-492.
27. Nakashima H, Takahashi J, Fujii N, and Okuno T. “Blue-Light Hazard From Gas Metal Arc Welding of Aluminum Alloys.” Annals of work exposures and health, vol. 61, 2017, pp. 965-974.
28. Buckus R, Strukčinskienė B, Raistenskis J, Stukas R, Šidlauskienė A, Čerkauskienė R, Isopescu DN, Stabryla J, Cretescu I. “A technical approach to the evaluation of radiofrequency radiation emissions from mobile telephony base stations.” International journal of environmental research and public health, vol. 14, 2017, pp. 244.
29. Kim KH, Kabir E, and Jahan SA. “The use of cell phone and insight into its potential human health impacts.” Environmental monitoring and assessment, vol. 4, 2016, pp. 221.
30. Kim KH, Kabir E, and Jahan SA. “The use of cell phone and insight into its potential human health impacts.” Environmental monitoring and assessment, vol. 188, 2016, pp. 221.
31. Cheung CS. “Shielding Effectiveness of Superalloy, Aluminum, and Mumetal Shielding Tapes.” Master's Theses and Project Reports, 2009 Apr 1:126.
32. Deepa Vijayakumar Thulasi, and S. Amala Shanthi, “An Optimized Approach for Evaluation of Radiation Hazards from Mobile Tower,” vol. 10, 2018, pp.1278-1288, 2018.

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Paper Title:

Abstract: Detection and estimation of object in motion is crucial issue in tracking. In underwater object tracking, object parameters like course, range and speed of the object are estimated using passive mode operation of the sonar. In this paper particle filter combined with modified gain bearings-only extended Kalman filter (PFMGBEKF) and residual sampling are used. One of the main assumptions is that the object is moving with constant velocity. Bearing measurements are nonlinearly related to the state of the object and sub-optimal filter for a nonlinear approach is unscented Kalman Filter (UKF). But UKF is unreliable under non-Gaussian noise environment. Particle filter is an advanced filter that processes nonlinear data in non-Gaussian noise environment but hassample degeneracy problem. So, PFMGBEKF is applied and the operation is analysed based on the solution convergence time. Simulation of algorithms on numerous scenarios which are close to reality is done using MATLAB.

Keywords: Bearings-only tracking, Modified gain bearings-only extended Kalman filter, Particle filter, Residual sampling, Statistical signal processing.

References:

1 G. Lindgren, KAI F. Gong, “Position and velocity Estimation via Bearing Observations”, IEEE Trans. Aerosp. Electron Syst., Vol. AES-14, No.4, pp 564-577.

2. C. Nardone, A. G. Lindgren and K. F. Gong, “Fundamental properties and performance of conventional bearings-only target motion analysis”, IEEE Trans. Autom. Control, Vol. AC-29, No. 9, pp 775-787, September 1984.
3. Aidala, “Kalman filter behavior in bearings-only tracking applications”, IEEE Trans. Aerosp. Electron. Syst., Vol.AES-15, No. 1, pp. 29-39, 1979
4. C. Nardone, V. J. Aidala, “Observability criteria for Bearing-Only Target Motion Analysis”, IEEE Trans. Aerosp. Electron. Syst., Vol. AES-17, No.2, pp. 162-166, 1981
5. Koteswara Rao, “Comments on Discrete-time observability and estimability analysis for bearings-only target motion analysis,” IEEE Trans. Aerospace Electronic Systems, Vol. 32, No.4 (1998) pp. 1361-1367.
6. Weiliang Zhu; Zhaopeng Xu; Bo Li; Zhidong Wu, “Research on the Observability of Bearings-only Target Tracking Based on Multiple Sonar Sensors”, Second International Conference on Instrumentation, Measurement, Computer, Communication and Control; pp: 631-634, DOI: 10.1109/IMCCC.2012.154; IEEE Conference Publications, 2012.
7. Northardt, S. C. Nardone, “Track-Before - Detect Bearings - Only Localization Performance Complex passive Sonar Scenarios”, IEEE Journal of Ocean Engineering, 2018.
8. Salmond and H. Birch, “A particle filter for track-before-detect”, in Proc. Amer. Control Conf., 2001, vol. 5, pp. 3755-3760.
9. Ristic, S. Arulampalan, and N. Gordon, Beyond the Kalman Filter: Particle Filters for Tracking Applications. Norwood, MA, USA: Artech House, 2004.
10. Dan Simon, “Optimal State Estimation: Kalman, H¥ and Nonlinear Approximations”, Wiley, 2006.
11. Tiancheng Li, P. M. Djuric, M. Bolic, “Resampling Methods for Particle Filtering: Classification, implementation, and strategies”, IEEE Signal Processing Magazine, pp. 70-86, 2015.
12. Kitagawa, “Monte Carlo filter and smoother and non-Gaussian nonlinear state space models,” J.Comput. Graph. Stat., vol. 5, no. 1, pp. 1–25, 1996.
13. Carpenter, P. Clifford, and P. Fearnhead, “An improved particle filter for nonlinear problems,” IEEE Proc., Radar Sonar Navigat., vol. 146, no. 1, pp. 2–7, 1999.
14. R. Beadle and P. M. Djuric ´, “A fast-weighted Bayesian bootstrap filter for nonlinear model state estimation,” IEEE Trans. Aerosp. Electron. Syst., vol. 33, no. 1, pp. 338–343, 1997.
15. Liu and R. Chen, “Sequential Monte-Carlo methods for dynamic systems,” J. Amer. Statist. Assoc., vol. 93, no. 443, pp. 1032–1044, 1998

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Paper Title:

Abstract: Developing a precise segmentation algorithm to delineate ischemic lesion from brain MRI is a challenging research issue in the field of medical image analysis and neuro-radiology. These lesions are generally complex in nature and exhibit heterogeneity in their intensity profile and morphological properties. To address these challenges, a novel segmentation algorithm using Symmetric Bit Plane Pattern analysis is presented in this work. Unlike the classical segmentation algorithms which fail to extract the region of interest in the presence of scattered structures with intensity in-homogeneities, the proposed segmentation algorithm considers the left-right symmetricity property of the brain for better estimation of segmentation parameters. An adaptive filter function is designed based on the gray level profile of the brain tissues to segment the intended region of interest. Once the region of interest is delineated, multi scale co-occurrence matrix based features in Curvelet space are extracted and its significance in detection of ischemic lesion is highlighted. Finally, Support Vector Machine is used to train the learning model for classification. Experimental results of the proposed work have obtained better classification accuracy of 98.8%.

Keywords: About four key words or phrases in alphabetical order, separated by commas.

References:

1. Leiva-Salinas C, Wintermark M. Imaging of Ischemic Stroke. Neuroimaging clinics of North America. 2010; 20(4):455-468.
2. Wardlaw JM, Murray V, Berge E, et al. Recombinant tissue plasminogen activator for acute ischaemic stroke: an updated systematic review and meta-analysis. Lancet 2012; 379:2364-2372.
3. Overgaard, Karsten. The Effects of Citicoline on Acute Ischemic Stroke: A Review. Journal of Stroke and Cerebrovascular Diseases. 2014; 23(7):1764 – 1769.
4. Caligiuri, M.E., Perrotta, P., Augimeri, A. et al. Automatic Detection of WhiteMatterHyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging:A Review. Neuroinform. 2015; 13: 261.
5. Karthik & R. Menaka: Computer-aided detection and characterization of stroke lesion – a short review on the current state-of-the art methods. The Imaging Science Journal, 2018; 66(1):1-22.
6. Artzi, Moran et al. FLAIR lesion segmentation: Application in patients with brain tumors and acute ischemic stroke. European Journal of Radiology. 2013; 82(9):1512-1518.
7. Ghosh, N., Recker, R., Shah, A., Bhanu, B., Ashwal, S. and Obenaus, A.: Automated ischemic lesion detection in a neonatal model of hypoxic ischemic injury. J. Magn. Reson. Imaging, 2011; 33:772–781.
8. Fuk-hay Tang, Douglas K.S. Ng, Daniel H.K. Chow, An image feature approach for computer-aided detection of ischemic stroke, Comput. Biol. Med. 2011;41:529–536.
9. Nabizadeh N, John N, Wright C. Histogram-based gravitational optimization algorithm on single MR modality for automatic brain lesion detection and segmentation. Expert Syst Appl. 2014; 41:7820–7836.
10. Storelli L., Pagani E., Rocca M.A., Horsfield M.A., Filippi M.: A Semi-automatic Method for Segmentation of Multiple Sclerosis Lesions on Dual-Echo Magnetic Resonance Images. In: Crimi A., Menze B., Maier O., Reyes M., Handels H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2015. Lecture Notes in Computer Science, 2016; 9556.
11. De Haan B, Clas P, Juenger H, et al. Fast semi-automated lesion demarcation in stroke. NeuroImage. 2014; 9:69–74.
12. Ghosh N, Sun Y, Bhanu B, et al. Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images. Med Image Anal. 2014; 18:1059–1069.
13. Hema Rajini N, Bhavani R. Computer aided detection of ischemic stroke using segmentation and texture features. Measurement. 2013: 46:1865–1874.
14. AsitSubudhi, Subhranshu Jena, SukantaSabut. Delineation of the ischemic stroke lesion based on watershed and relative fuzzy connectedness in brain MRI. Med BiolEngComput, 2017: 1-13.
15. Menaka, and R. Karthik.: A novel feature extraction scheme for visualisation of 3D anatomical structures. Int. J. Biomedical Engineering and Technology. 2016; 21(1):49-66.
16. Javeed Hussain, A. Satya Savithri and P. V. Sree Devi.: Segmentation of brain MRI with statistical and 2D wavelet features by using neural networks, 3rd International Conference on Trendz in Information Sciences & Computing (TISC2011), 154-159, 2011.
17. Karthik and R. Menaka.: A critical appraisal on wavelet based features from brain MR images for efficient characterization of ischemic stroke injuries. Electronic Letters on Computer Vision and Image Analysis, 2016; 15(3):1-16.
18. Hackmack, F. Paul, M. Weygandt, C. Allefeld, J.D. Haynes, Multi-scale classification of disease using structural MRI and wavelet transform, Neuroimage. 2012;62:48–58.

• Karthik, R. and Menaka, R. “Statistical characterization of ischemic stroke lesions from MRI using discrete wavelet transformation”, Transactions on Electrical Engineering, Electronics, and Communications, Vol. 14, No. 2, pp. 57-64, 2016.
• Karthik, R. Menaka, R. A Novel Brain MRI Analysis System for Detection of Stroke Lesions using Discrete Wavelets. Journal of Telecommunication, Electronic and Computer Engineering. Vol. 8 No. 5, pp. 49-53, 2016.

21. Candes, L. Demanet, D. Donoho, L. Ying, Fast discrete Curvelet transforms, SIAM J. Multiscale Model. Simul. 2006; 5(3):861–899.
22. R, Chellamuthu. C and R. Karthik.: Efficient Feature point detection of CT images using Discrete Curvelet Transform, Journal of Scientific and Industrial Research. 2013; 72:312-315.
23. Sivakumar P, Ganeshkumar P. An efficient automated methodology for detecting and segmenting the ischemic stroke in brain MRI images. Int. J. Imaging Syst. Technol. 2017; 27:265–272.
24. Bhanu Prakash KN, Gupta V, Bilello M, et al. Identification, segmentation, and image property study of acute infarcts in diffusion-weighted images by using a probabilistic neural network and adaptive Gaussian mixture model. AcadRadiol. 2006; 13:1474–1484.
25. Gupta S, Mishra A, Menaka R. Ischemic stroke detection using image processing and ANN. International Conference on Advanced Communication Control and Computing Technologies (ICACCCT); 1416–1420, 2014.
26. Bentley P, Ganesalingam J, Lalani A, et al. Prediction of stroke thrombolysis outcome using CT brain machine learning. NeuroImage. 2014; 4:635–640.
27. R and Menaka. R.: A multi-scale approach for detection of ischemic stroke from brain MR images using discrete Curvelet transformation. Measurement. 2017; 100: 223-232.
28. Maier, O., Menze, B.H., von der Gablentz, J., Häni, L., Heinrich, M.P., Liebrand, M., et al. ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI Medical Image Analysis, 2017; 35:250-269.
29. Sheena XinLiu, Symmetry and asymmetry analysis and its implications to computer-aided diagnosis: A review of the literature. Journal of Biomedical Informatics. 2009; 42(6):1056-64.

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Paper Title:

Abstract: Skew laminated composite plates with cut-outs are used in many engineering applications like fins, wings and tails of aero planes, hulls of ships and parallelogram slabs in buildings, complex alignment problems in bridge design etc. The structure used in these applications, are often subjected to dynamic force which creates vibration. Based on the First-order Shear Deformation Theory (FSDT), this study deals with harmonic analysis of moderately thick laminated composite skew plates with cut-outs using the finite element package ANSYS. The effect of various geometries of the cut-out, on the resonance point has been investigated. An 8 node 281 shell element is adopted to mesh the plate geometry. The accuracy of the present analysis is shown in some typical cases. The results are compared with existing results based on other numerical methods and observed to be in close proximity.

Keywords: Composite plate; Harmonic Response; Frequency Response Function; Skew angle; Cut-out; FEM

References:

1. Dey and M. K. Singha, “Dynamic stability analysis of composite skew plates subjected to periodic in-plane load”, Thin-Walled Structures, 44, 2006, pp. 937-942.
2. K. Singha and R. Daripa, Nonlinear vibration of symmetrically laminated composite skew plates by finite element method, International Journal of Non-Linear Mechanics, 42, 2007, pp. 1144-1152.
3. Das, P. Sahoo and K. Saha, “A variational analysis for large deflection of skew plates under uniformly distributed load through domain mapping technique”, Int. J. of Engg., Sc. and Tech., 1(1), 2009, pp. 16-32.
4. K. Sharma, N. D. Mittal and A. Sharma, “Free vibration analysis of moderately thick antisymmetric cross-ply laminated rectangular plates with elastic edge constraints”, Int. J. of Mechanical Sc., 53, 2011, pp. 688-695.
5. S. Rao and. B. S. Reddy, “Harmonic analysis of composite propeller for marine applications”, Int. J. of Research in Engg. and Tech., 01(03), 2012, pp. 257-260.
6. Useche, E. L. Albuquerque and P. Sollero, “Harmonic analysis of shear deformable orthotropic cracked plates using the Boundary Element Method”, Engg. Analysis with Boundary Elements, 36(11), 2012, pp. 1528–1535.
7. V. Srinivasa, Y. J. Suresh and W. P. Prema Kumar (2014), “Experimental and finite element studies on free vibration of skew plates”, Int. J. of Applied Mechanics and Engineering, 19(2), 2014, pp. 365-377.
8. Gulshan Taj, S. Chakrabarti and V. Praka, “Vibration Characteristics of Functionally Graded Material Skew Plate in Thermal Environment”, Int. J. of Mech. and Mechatronics Engg, 8(1), 2014, pp. 142-153.
9. Vimal, R. K. Srivastavaa, A. D. Bhatta and A. K. Sharma, “Free vibration analysis of moderately thick functionally graded skew plates”, Engineering Solid Mechanics, 2, 2014, pp. 229-238.
10. Vivek K Sai, “Free Vibration of Skew Laminated Composite Plates with Circular Cutout by Finite Element Method”, J. Of Modern Engg. Research, 6(6), 2016, pp. 15-23.
11. Mandal, C. Ray and S. Haldar, “Free vibration analysis of laminated composite skew plates with cut-out”, Arch Appl Mech.2017.
12. Katariya, S. K. Panda and T. R. Mahapatra, “Effect of Skew Angle on Free Vibration Responses of Sandwich Composite Plate”, Int. J. of research in Mech. Engg. & Tech., 7(1), 2017, pp. 21-24.
13. V. Joseph and S. C. Mohanty, “Buckling and free vibration analysis of skew sandwich plates with viscoelastic core and functionally graded material constraining layer”, J. of Aerospace Engg., 2017
14. Narwariya, A. Choudhury and A. K. Sharma, “Parametric study on Harmonic Analysis of anti symmetric laminated composite Plate”, Materials Today: Proceedings, India. vol. 5, 2018, pp. 20232–20238

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Paper Title:

Abstract: It may be more expensive on some system, as manufacturers frequently obtain and spread over new producing materials that are brighter and stouter—and thus a lot of fuel-effectual—it follows that cutting tool manufacturers should mature tools which will machine the new specimens and Geometry at the best attainable levels of productivity. Feasibly the mutual thread through all producing is the determination for exaggerated productivity and dependableness. As metal cutting operations become increasingly fine-tuned, the relationship between cutlery micro (cutting edge preparation) and macro (rake face topography) pure mathematics is changing into a lot of and a lot of necessary. This study intelligences the outcomes of a Turning experiment showed on the Ti–6Al–4V alloy of L9 orthogonal array on CNC Turning center with Taughi gray relative analysis. Emphases on the optimization of Turning method parameters victimization the technique to get minimum Resultant Cutting Force, Tool Wear, Tool Life, and Energy Consumption. The experimentations were performed on Ti–6Al–4V alloy block of the cutting tool of changed pure mathematics of CNMP120408-SM TN8025 of twelve metric linear unit diameter with cutting purpose one hundred forty degrees, used throughout the experimental work beneath totally different cutting conditions. Grey relative Analysis & ANOVA was castoff to total the primary necessary Cutting speed as constant of 3000Rpm, feed rate, Depth of Cut and Different Tool Geometries conditions that moving the response. The main and interaction effect of the input variables on the expected responses are investigated. The expected values and measured values are fairly Near to the Outcome one.

Keywords: Grey Relation Taguchi method, Geometry Parameters.

References:

1. Rodin PR (1972) The Basics of Shape Formation by Cutting (in Russian). Visha Skola, Kyev (Ukraine)
2. Granovsky GE, Granovsky VG (1985) Metal Cutting (in Russian),Vishaya Shkola, Moscow
3. Oxley PLB (1989) Mechanics of Machining: An Analytical Approach to Assessing Machinability. Wiley, New York, NY
4. Astakhov VP (1998) Metal Cutting Mechanics. CRC, Boca Raton, USA.
5. Dahlman, F. Gunnberg and M. Jacobson: J. Mater. Process. Technol., Vol.147 (2004), p.181
6. C. Kong, W.B. Lee C.F. Cheung and S. To: J. Mater. Process. Technol., Vol.180 (2006), p.210.
7. X. (Jack) Feng, X. Wang (2002) , Development of Empirical Models for Surface Roughness Prediction in Finish Turning, Int. J. Adv. Manuf. Technol. 20 ,pp. 348–356.
8. Sahin, A. R. Motorcu (2005), Surface roughness model for machining mild steel with coated carbide tool, Mater. Des. 26, pp. 321– 326. DOI:10.1016/j.matdes.2004.06.015.
9. Ozel , Y. Karpat , L. Figueira, J. P. Davim (2007), Modeling of surface finish and tool flank wear in turning of AISI D2 steel with ceramic wiper inserts, J. Mat. Process. Technol. 189, pp. 192–198.
10. Grzesik, T. Wanat (2006), Surface finish generated in hard turning of quenched alloy steel parts using conventional and wiper ceramic inserts, Int. J. Machine Tools & Manuf. 46 , pp. 1988–1995.
11. I. Galanis, D. E. Manolakos (2010) , Surface roughness prediction in turning of femoral head, Int. J. Adv. Manuf. Technol. 51,pp. 79– 86. DOI 10.1007/s00170-010-2616-4.
12. Asiltürk, M. Cunkas (2011), Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method, Expert Syst. Appl. 38, pp. 5826–5832.
13. Correia, J. P. Davim (2011), Surface roughness measurement in turning carbon steel AISI 1045 using wiper inserts, Measurement 44, pp. 1000–1005.
14. Neseli, S. Yaldız, E. Turkes (2011), Optimization of tool geometry parameters for turning operations based on the response surface methodology.
15. SIVAM, S. P. Sundar Singh et al.”Multi Response Optimization of Setting Input Variables for Getting Better Product Quality in Machining of Magnesium AM60 by Grey Relation Analysis and ANOVA." Periodica Polytechnica Mechanical Engineering, [S.l.], 2017. ISSN 1587-379X. https://doi.org/10.3311/PPme.11034
16. Sivam, S.P.S.S.,, Gopal , S.Venkatasamy, Siddhartha Singh, An Experimental Investigation And Optimisation Of Ecological Machining Parameters On Aluminium 6063 In Its Annealed And Unannealed Form, Journal Of Chemical And Pharmaceutical Sciences. Page No Page (46 – 53), 2015.
17. Sivam, S.P.S.S.,, M.Gopal, S.Venkatasamy, Siddhartha Singh 2015, “Application of Forming Limit Diagram and Yield Surface Diagram to Study Anisotropic Mechanical Properties of Annealed and Unannealed SPRC 440E Steels”. Journal of Chemical and Pharmaceutical Sciences. ISSN: 0974-2115, Page No (15 – 22).
18. P. Sundar Singh Sivam, Abburi Lakshman Kumar, K. Sathiya Moorthy and Rajendrakumar, “Investigation Exploration Outcome of Heat Treatment on Corrosion Resistance of AA 5083 in Marine Application”, Journal of Science and Technology.14 (S2), 2016, 453-460. ISSN 0972-768X.
19. Sivam, S.P.S.S., Umasekar, V.G., Mishra, A., Mishra, S. and Mondal, A. (2016) ‘Orbital cold forming technology – combining high quality forming with cost effectiveness – a review’, Indian Journal of Science and Technology, October, Vol. 9, No. 38, DOI: 10.17485/ijst/2016/ v9i38/91426.
20. Sivam, S.P.S.S., UmaSekar, V.G., Saravanan, K., RajendraKumar, S., Karthikeyan, P. and SathiyaMoorthy, K. (2016b) ‘Frequently used anisotropic yield criteria for sheet metal applications: a review’, Indian Journal of Science and Technology, December, Vol. 9, No. 47, DOI: 10.17485/ijst/2015/v8i1/92107.
21. P. Sundar Singh Sivam, Mrinal Deepak Ji Bhat, Shashank Natarajan, Nishant Chauhan.” Analysis of residual stresses, thermal stresses, cutting forces and other output responses of face milling operation on ze41 magnesium alloy." International Journal of Modern Manufacturing Technologies, Pp. No 92-100. ISSN 2067–3604, Vol. X, No. 1 / 2018.
22. Sivam, S. P. S. S., Saravanan, K., Pradeep, N., Moorthy, K. and Rajendrakumar, S. “The Grey Relational Analysis and Anova to Determine the Optimum Process Parameters for Friction Stir Welding of Ti and Mg Alloys”, Periodica Polytechnica Mechanical Engineering. doi: https://doi.org/10.3311/PPme.12117.
23. P. S. S. Sivam, S. RajendraKumar, S. Karuppiah and A. Rajasekaran, "Competitive study of engineering change process management in manufacturing industry using product life cycle management — A case study," 2017 International Conference on Inventive Computing and Informatics (ICICI), Coimbatore, 2017, pp. 76-81. doi: 10.1109/ICICI.2017.8365247.
24. Sundar Singh Sivam, S., Saravanan, K., Pradeep, N., Rajendra Kumar, S., Mathur, S., Dingankar, U., & Arora, A. (2018). Development of Vibrator Feeding Mechanism Using Two Sets of Rollers for the Separation of Ball Grading For Industry Benefits. International Journal of Engineering & Technology, 7(4.5), 202-206. doi:http://dx.doi.org/10.14419/ijet.v7i4.5.20045
25. P. Sundar Singh Sivam, A. Rajasekaran, S. RajendraKumar, K. SathiyaMoorthy & M. Gopal (2019) A study of cooling time, copper reduction and effects of alloying elements on the microstructure and mechanical properties of SG iron casting during machining, Australian Journal of Mechanical Engineering, DOI: 10.1080/14484846.2018.1560679
26. P. Sundar Singh Sivam, Ganesh Babu Loganathan, K. Saravanan (2019), Impact of Point Angle on Drill Product Quality and Other Responses When Drilling EN- 8: A Case Study of Ranking Algorithm, International Journal of Innovative Technology and Exploring Engineering, ISSN 2278-3075 PP. No. 280-282
27. P. Sundar Singh Sivam, Ganesh Babu Loganathan, K. Saravanan, S. RajendraKumar (2019), Outcome of the Coating Thickness on the Tool Act and Process Parameters When Dry Turning Ti–6Al–4V Alloy: GRA Taguchi & ANOVA, International Journal of Innovative Technology and Exploring Engineering, ISSN 2278-3075 PP. No. 419-423
28. P. Sundar Singh Sivam, Ganesh Babu Loganathan, K. Saravanan, S. Rajendra Kumar (2019), Multi-Response Enhancement of Drilling Process Parameters for AM 60 Magnesium Alloy as per the Quality Characteristics utilizing Taguchi-Ranking Algorithm and ANOVA, International Journal of Innovative Technology and Exploring Engineering, ISSN 2278-3075 PP. No. 437 - 440
29. P. Sundar Singh Sivam, Durai Kumaran, Krishnaswamy Saravanan, Venugopal Guruswamy Umasekar, Sankarapandian Rajendrakumar, Karuppiah Sathiya Moorthy (2018) "THICKNESS DISTRIBUTION AND NUMERICAL MODELLING OF CONVENTIONAL SUPERPLASTIC FORMING IN AA2024 ALLOY", International Journal of Modern Manufacturing Technologies, ISSN 2067–3604,76,85, Vol. X, No. 2 / 2018.

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Paper Title:

Abstract: The Antenna array is an essential part in the wireless communication systems. Design of a low sidelobe level antenna arrays crucial in design of the efficient antenna array system. In this paper, the synthesis of an aperiodic antenna array synthesis for minimal sidelobe levels has been discussed. A novel modified differential evolution (MDE) is proposed for controlling the sidelobe energy by optimizing the antenna element positions. Different mutation schemes have been adopted in developing the MDE algorithm. The steps involved in the development of MDE and problem formulation for the minimizing sidelobe levels is discussed clearly. Various popular synthesis examples have been considered and synthesized. Both small and larger arrays have considered in this paper. The obtained proposed MDE array designs are compared with the traditional differential evolutions (DE) and particle swarm optimization methods (PSO). Numerical results demonstrated that the proposed MDE method outperforms the traditional PSO and DE in terms of producing low PSLL and convergence rate.

Keywords: Antenna array, Sidelobe level, Differential Evolution, Particle swarm optimization, PSLL, Convergence rate.

References:

1. A. Balanis, Antenna theory: Analysis and design, Wiley, Newyork, 1997.
2. Tennat, M. M. Dawoud, and A. P. Anderson, “ Array Pattern Nulling by Element Position Perturbations using a Genetic Algorithm”, Lett., vol. 30, no. 3, pp. 174-176, Feb. 1994.
3. L. Haupt, “Phase-only Adaptive Nulling with a Genetic Algorithm”, IEEE Trans. Antennas Propag., vol. 45, Aug. 1997, pp. 1009-1015.
4. P. Liao and F. L. Chu, “Antenna Pattern Nulling by Phase and Position Perturbations with the use of Genetic Algorithm”, Microwave Opt. Technological Letters, vol. 15, 1997, pp. 251-256.
5. J. ares-pena, J. A. Gonzalez, E. Lopez, and S. R. Rengarajan, “Genetic algorithms in the design and optimization of antenna array patterns” , IEEE Trans. Antennas and Propagation., vol. 47, no. 3, pp. 506-510, Mar. 1999.

6. Kesonchen, Xiaohuayun, Zishu He and Chunlin Hun, “ Synthesis of sparse planar arrays using modified real genetic algorithm,” IEEE Transactions on Antennas and Propagation, Vol. 55, No. 4, 1067-1073, April 2007.
7. Cen, Z. L. Yu,W. Ser, andW. Cen, “Linear aperiodic array synthesis using an improved genetic algorithm,” IEEE Trans. Antennas Propag., vol. 60, no. 2, pp. 895–902, Feb. 2012.
8. W. Boeringer and D. H. Werner, “Particle swarm optimization versus genetic algorithms for phased array synthesis”, IEEE Trans. Antennas Propag., vol. 52, no. 3, pp. 771-779, Mar. 2004.
9. Khodier and C. Christodoulou, “Linear Array Geometry Synthesis with Minimum Sidelobe Level and Null Control Using Particle Swarm Optimization,” IEEE Trans. Antennas Propag., vol. 53, no. 8, Aug. 2005, pp. 2674-2679.
10. ghosh, Pappula L, “ Linear antenna array synthesis for wireless communications using particle swarm optimization”, Proceedings of IEEE Internatioionsnal conference on advanced communications technology, pp. 780-783, January 2013.
11. Perez Lopez, J. R. and J. Basterrechea, “Hybrid particle swarm- based algorithms and their application to linear array synthesis,” Progress In Electromagnetics Research, Vol. 90, 63-74, 2009.
12. Jin and Y. Rahmat-Samii, “Advances in particle swarm optimization for antenna designs: real-number, binary, single-objective and multiobjective implementations,” IEEE Trans. Antennas Propag., vol. 55, no. 3, pp. 556-567, Mar. 2007.
13. Lin, C., A.-Y. Qing, and Q.-Y. Feng, “Synthesis of unequally spaced antenna arrays by using differential evolution,” IEEE Transactions on Antennas and Propagation, Vol. 58, 2553-2561, 2010.
14. Fenggan Zhang, WeiminJia, and Minli Yao, “Linear Aperiodic Array Synthesis Using DifferentialEvolution Algorithm,” IEEE Ant. And Wirele. Ltt., Vol. 12, pp.797-800, 2013.
15. G. Goudos, K. Siakavara, T. Samaras, E. E. Vafiadis, and J. N. Sahalos, “Self-adaptive differential evolution applied to real-valued antenna and microwave design problems,” IEEE Trans. Antennas Propag., vol. 59, no. 4, pp. 1286–1298, Apr. 2011.
16. K. Goudos, K. Siakavara, T. Samaras, E. E. Vafiadis, and N. Sahalos, :Sparse linear array synthesis with multiple constraints using differential evolution with strategy adaptation,” IEEE Antennas Wireless Propag. Lett., vol. 10, pp. 670–673, 2011.
17. D Ghosh, Pappula L “Linear antenna array synthesis using cat swarm optimization,” AEU-International Journal of Electronics and Communications, vol. 68, pp. 540-549, Jun 2014.
18. Eva Rajo-lglesias and Oscar Quevedo-Teruel., “Linear array synthesis using an Ant colony optimization based algorithm,” IEEE Transactions on Antennas and Propagation, vol. 49, No. 2, 70-79, April 2007.
19. Shaya Karimkashi and Ahemd A. Kishk, “Invasive weed optimization and its features in Electromagnetics,” IEEE Transactions on Antennas and Propagation, Vol. 58, No. 4, April 2010.
20. Gourab Ghosh Roy, Swagatam Das, Prithwish Chakraborty, and Ponnuthurai N. Suganthan, “Design of non uniform circular antenna arraysusing a modified invasive weed optimization algorithm,” IEEE Transactions on Antennas and Propagation, Vol. 59, No. 1, 110-118, Jan. 2011.
21. Ali, M., Pant, M., & Abraham, A. (2009). Simplex differential evolution. ActaPolytechnicaHungarica, 6(5), 95–115.
22. Gong, W., &Cai, Z. (2013). Differential evolution with ranking based mutation operators. IEEE Transactions on Systems Man and Cybernetics Part B- Cybernetics,43(6), 2066–2081.

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Abstract: As solar inverters have the ability to inject reactive power along with the active power, a reactive power control methodology to inject and control the reactive power flow into the grid is presented in this paper. A detailed modelling about the components used in this technique is mentioned in the below sections. This paper also discusses about the reactive power capability curve in relation with the inverter current carrying capability and constant reactive power injection method. The system is simulated using the MATLAB/SIMULINK software for constant reactive power injection method and results are presented.

Keywords: reactive power, power capability curve, Solar PV array, inverter.

References:

1. Nibedita Swain, Dr. C.K. Panigrahi, Dr. S.M. Ali “Application of PI and MPPT controller to DC-DC converter for constant voltage and power application”, 2016.
2. Ana Cabrera-Tobar, Eduard Bullich-Massague, M_onica Aragues-Penalba “Capability curve analysis of photovoltaic generation systems”, 2016.
3. Albarracín, R.; Alonso, M., “Photovoltaic reactive power limits” Conference Environment and Electrical Engineering (EEEIC) Conference, 2013.
4. “Park, Inverse Park and Clarke, Inverse Clarke Transformations MSS Software Implementation” by Micro Semi
5. Rajiv K. Varma, Ehsan M. Siavashi “PV-STATCOM: A New Smart Inverter for Voltage Control in Distribution Systems” IEEE Transactions on Sustainable Energy, 2018.
6. Paul brucke June/July 2014, “reactive power control in utility scale system.”
7. Available:https://solarprofessional.com/articles/design-installation/reactive-power-control-in-utility-scale-pv?v=disable_pagination&nopaging=1#.XELeGVwzbIV
8. Scott Partlin 3. Jan. 2018, “Bad power factor? – A reason to over size your inverter.”
9. Available:https://www.sma-sunny.com/en/bad-power-factor-a-reason-to-oversize-your-inverter/
10. Suvom Roy, Arpan Malkhandi, T.ghose “Modelling of 5kw single phase grid tied photo voltaic inverter,” 2016.

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Paper Title:

Abstract: The supplementary cementitious materials (SCM) can be used as a replacement of cement in the construction industry to minimize the drawbacks of normal concrete such as the emission of carbon dioxide so as to be eco-friendly. This paper presents the effect on the properties of concrete with ultrafine slag dosage (i.e. Alccofine of 25%) as a replacement of cement for different water to binder ratios (i.e. 0.38, 0.4 and 0.45). The effect of addition of alccofine on the strength properties (compressive strength, splitting tensile strength and flexural strength) of concrete were studied at 7 and 28 days wherein considerable strength enhancement was observed compared to normal concrete. Water absorption and the effects of acid attack on weight and strength deterioration factor (SDF) were also carried out. It was identified that the concrete with alccofine was more durable as compared to normal concrete. Therefore, it was concluded from this study that alccofine can be used as a viable substitute to cement in normal concrete considering its positive effects on property enhancement and eco-friendless.

Keywords: Alccofine, compressive strength, water absorption, acid attack

References:

1. Kumar SR, Amiya K. Samanta DKSR. An experimental study on the compressive strength of alccofine with silica fume based concrete. Applied Mechanics and Materials. 2017;857:36-40. doi:10.4028/www.scientific.net/AMM.857.36
2. Jindal BB, Jindal BB, Singhal D, Sharma SK, Parveen. Suitability of Ambient-Cured Alccofine added Low-Calcium Fly Ash-based Geopolymer Concrete. Indian Journal of Science and Technology. 2017;10(12):1-10. doi:10.17485/ijst/2017/v10i12/110428
3. Saxena SK, Kumar M, Singh NB. Effect of Alccofine powder on the properties of Pond fly ash based Geopolymer mortar under different conditions. Environmental Technology and Innovation. 2018;9:232-242. doi:10.1016/j.eti.2017.12.010
4. Shadi VK, Scholar PG. Experimental Study on Effect of Alccofine on Properties of Concrete-A Review.
5. Suthar S, Shah SBK, Patel PPJ. Study on effect of Alccofine & Fly ash addition on the Mechanical properties of High performance Concrete. 2013;1(3):464-467.
6. RESEARCH ARTICLE EVALUATING THE STRENGTH BEHAVIOUR OF CONCRETE BY USING COIR FIBRE AND ALCCOFINE AS PARTIAL REPLACEMENT OF CEMENT * Mahesh Mahesh , S . M . and Ravi Chandra , S . 2017.
7. A. NarenderReddy TM. Available Online through ISSN : 0975-766X CODEN : IJPTFI Review Article A COMPREHENSIVE OVERVIEW ON PERFORMANCE OF ALCCOFINE CONCRETE. International Journal of Pharmacy & Technology. 2017;9(1):5500-5506.
8. Kavitha S, Felix Kala T. Evaluation of strength behavior of self-compacting concrete using alccofine and GGBS as partial replacement of cement. Indian Journal of Science and Technology. 2016;9(22):1-5. doi:10.17485/ijst/2016/v9i22/93276
9. Gayathri K, Ravichandran K, Saravanan J. Durability and Cementing Efficiency of Alccofine in Concretes. 2016;5(05):460-468.
10. Upadhyay, Siddharth P MAJ. Effect on compressive strength of high performance concrete incorporating alcofine and fly ash. Journal of international academic research for multidisciplinary. 2014;2(2):125-130.
11. Vipul V. Nahar, Jaya L. Nikam PKD. International Journal of Modern Trends in Engineering and Research. International Journal of Modern Trends in Engineering and Research. 2015;(2349):645-652. doi:10.21884/IJMTER.2017.4014.BVJBN
12. Gautam M, Sood H. Effect of Alccofine on strength characteristics of Concrete of different grades-A Review. 2017:2854-2857.
13. Gupta S, Sharma S, Sharma ED. A Review on Alccofine : A supplementary cementitous material. 2015:114-119.
14. Bureau of Indian Standard. IS 8112: 2013, Ordinary Portland Cement, 43 Grade — Specification, Bureau of Indian Standards, New Delhi. 2013;(March).
15. Aggregates F. 1970.
16. Kisan M, Sangathan S, Nehru J, Pitroda SG. 1959.
17. Kisan M, Sangathan S, Nehru J, Pitroda SG. 1999.
18. ASTM C 642-06. Standard Test Method for Density , Absorption , and Voids in Hardened Concrete. United States: American Society for Testing and Material. 2008:11-13. doi:10.1520/C0642-13.5.
19. Kisan M, Sangathan S, Nehru J, Pitroda SG. . 2000.
20. Narender Reddy, A., Meena, T. Behaviour of ternary blended concrete compression . International Journal of Civil Engineering and Technology.2017;8 (4), pp. 2089-2097.
21. Reddy, A.N., Meena, T. An Experimental Investigation on Mechanical Behaviour of eco-friendly concrete. IOP Conference Series: Materials Science and Engineering; 263 (3) art. no. 032010.
22. Reddy, A.N., Mounika, P., Moulika, R., Study on Effect of Alccofine and Nano-silica on Properites of Concrete- A Review; International Journal of Civil Engineering and Technology, 9(13), pp 559-585.

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Authors:

Paper Title:

Abstract: Drowsiness is turning into a serious issue if there should be an occurrence of auto collision. Typically, Sleeping can be distinguished from a few elements like eye squint dimension, yawning, grasping power on haggle on. Be that as it may, all these estimating procedures will check just the physical exercises of the human. At times, individuals will rationally lay down with eyes open for a couple of moments. This will make huge mishaps in driving. Along these lines, in our proposed undertaking work we are breaking down the psychological exercises of cerebrum utilizing EEG signals dependent on Brain-Computer Interface (BCI) innovation. The key work of the task is breaking down the cerebrum signals. Human mind comprises of a great many interconnected neurons. This neuron example will change as indicated by the human considerations. At each example arrangement remarkable electric mind flag will frame. On the off chance that an individual is rationally laying down with eyes open, the consideration level mind flag will get changed than the typical condition. This undertaking work utilizes a brain wave sensor which can gather EEG based cerebrum signs of various recurrence and plentifulness and it will change over these signs into bundles and transmit through Bluetooth medium in to the dimension splitter segment to check the consideration level. Level splitter section (LSS) examines the dimension and gives the sleepy driving alarm and keeps the vehicle to be in self-controlled capacity until stir state. Notwithstanding this we likewise found a way to stay away from crashes dependent on this signal and LED shines. This can spare a great deal of lives in street transportation.

Keywords: EEG, LED, (LSS), (BCI), Drowsiness, Typically,

References:

1. Hartman, K. and J. Stressor, Saving Lives through Advanced Vehicle Safety Technology: Intelligent Vehicle Initiative Final Report. 2005, Department of Transportation: Washington, DC.
2. Akin; M.; et al., "Estimating vigilance level by using EEG and EMG signals” published in Neural Computing and Applications 2008. 17(3): p. 227-236
3. Li, E. Seignez and P. Loonis, "Vision-based estimation of driver drowsiness with ORD model using evidence theory," 2013 IEEE Intelligent Vehicles Symposium (IV), Gold Coast, QLD, 2013, pp. 666-671
4. Tadesse, W. Sheng and M. Liu, "Driver drowsiness detection through HMM based dynamic modeling," 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, 2014, pp. 4003-4008.
5. Tsuchida, M. S. Bhuiyan and K. Oguri, "Estimation of drivers' drowsiness level using a Neural Network Based ‘Error Correcting Output Coding’ method," 13th International IEEE Conference on Intelligent Transportation Systems, Funchal, 2010, pp. 1887-1892
6. A. Peláez C., F. García, A. de la Escalera and J. M. Armingol, "Driver Monitoring Based on Low-Cost 3-D Sensors," in IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 4, pp. 1855-1860, Aug. 2014.
7. M. Sabet, R. A. Zoroofi, K. Sadeghniiat-Haghighi and M. Sabbaghian, "A new system for driver drowsiness and distraction detection," 20th Iranian Conference on Electrical Engineering (ICEE2012), Tehran, 2012, pp. 1247-1251.

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Abstract: Goat meat is highly competitive with other livestock meat on nutritional and sensory qualities. The preparation method of a delicacy pressed goat meat product and it’s proximate, mineral and vitamin compositions are presented. Three variants of curing pickle with variation of salt, sugar, papain and water are prepared and injected to goat meat product. Using of papain enzyme in formulation of curing pickle tenderize the goat meat and cut meat massaging time up to 3 h. The level of minerals and vitamins in goat meat before and after cooking is significantly different.

Keywords: goat meat, curing pickle, papain, vitamin, nutritional value.

References:

1. Serikova, A., Duyssembayev, S., Nurgazezova, A., Nurymkhan, G., Tugambayeva, S., Ikimbayeva, N., Akhmetzhanova, A., Okuskhanova, E., Yessimbekov, Z., 2018. Nutritive value of goat and cow milk sampled from the region of East Kazakhstan. Journal of Pharmaceutical Research International, 22(5), pp. 1-6.
2. Wattanachant, C., 2018. Goat meat: some factors affecting fat deposition and fatty acid composition. Songklanakarin Journal of Science and Technology, 40 (5), pp. 1152-1157.
3. Webb, E.C., 2014. Goat meat production, composition, and quality. Animal Frontiers, 4 (4), pp. 33-37.
4. Babiker, S.A., El Khider, I.A., Shafie, S.A., 1990. Chemical composition and quality attributes of goat meat and lamb. Meat Science, 28 (4), pp. 273-277.
5. Ospanova, A.E., Serikova, A.T., Iminova, D.Y., Mukhamedjanova. M.Y., 2017. Meat quality of Saanen goats, farming near the radiation zone of SNTS. Young Scientist, 6.1, 42-44.
6. Serikova, A.T., 2017. Nutritive value of small cattle meat sampled from near the SNTS. Young Scientist, 6.1, 44-47.
7. Rogov, I.A., Zharinov, A.I., Tekut’yeva, L.A., Shepel, T.A., 2009. Biotechnology of meat and meat products. DeLi Print, Moscow.
8. Bazhenova, B. A., Vtorushina, I. A., Meleshkina, N. V., Kaurova, S. V., 2015. Influence of the brining process on khainag meat properties. Bulletin of science and education of the North-West Russia, 1 (2), 1-5.
9. Melikhova, T.A., Danilov, M.B.,Kolesnikova, N.V., 2010. Structure-forming components in the technology of restructured lamb products. Meat industry, 11, pp. 76-78.
10. Amirkhanov, K., Igenbayev, A., Nurgazezova, A., Okuskhanova, E., Kassymov, S., Muslimova, M., Yessimbekov, Z., 2017. Comparative analysis of red and white turkey meat quality. Pakistan Journal of Nutrition,16(6), pp. 412-416.
11. Rudenko, A.O., Kartsova, L.A., 2010. Determination of water-soluble vitamin B and vitamin C in combined feed, premixes, and biologically active supplements by reversed-phase HPLC. Journal of Analytical Chemistry, 65(1), 71-76.
12. Okuskhanova, E., Assenova, B., Rebezov, M., Yessimbekov, Z., Kulushtayeva, B., Zinina, O., Stuart, M., 2016. Mineral composition of deer meat pate. Pakistan Journal of Nutrition, 15(3), 217-222.
13. Okuskhanova, E., Rebezov, M., Yessimbekov, Z., Suychinov, A., Semenova, N., Rebezov, Y., Gorelik, O., Zinina, O., 2017. Study of water binding capacity, pH, chemical composition and microstructure of livestock meat and poultry. Annual Research and Review in Biology, 14 (3), pp. 1-7.
14. Nurgazezova, A., Nurymkhan, G., Kassymov, S., Issaeva, K., Kazhybayeva, G., Kulushtayeva, B., Okuskhanova, E., Igenbayev, A., 2016. Meat loaf processing technology. Research Journal of Pharmaceutical, Biological and Chemical Sciences, 7 (6), pp. 984-988.
15. Lushnikov, V.P., Yusova, O.V., 2013. Nutritive value of young goat meat of different breed. Sheep, goat and wool, 4, 7-8.
16. Filatov, A.S., Zabelina, M.V., Belova, M.V., Kochtygov, V.N., 2011.
17. Chemical composition of young sheep and goat meat. Sheep, goat and wool, 3, 67-69.
18. Spirichev, V.B., 2005. Theoretical and practical issues of modern Problems of Nutrition, 5, pp. 33-48.
19. Jakobsen, J., Knuthsen, P., 2014. Stability of vitamin D in foodstuffs during cooking. Food Chemistry, 148, pp. 170-175.

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Paper Title:

Abstract: Real-Time Monotonic algorithm (RMA) is a widely used static priority scheduling algorithm. For application of RMA at various systems, it is essential to determine the system’s feasibility first. The various existing algorithms perform the analysis by reducing the scheduling points in a given task set. In this paper we develop a algorithm to compute the RMA schedulability in a distributed real-time system. In today’s world all the high performance computation are done in some form of distributed systems. We look at the limitations of parallel system and show how a distributed system can overcome it and also propose a distributed algorithm for the Schedulability analysis.

Keywords: distributed systems, real-time systems, RMA, Schedulability.

References:

1. L. Liu and J.W. Layland ”Scheduling Algorithms for Multi-Programming in a Hard Real-Time Environment, Journal of the Assn of Computing Machinery (ACM) 20, 1, 40-61 January, (1973)
2. Lehoczky, John, Lui Sha, and Ye Ding. ”The rate monotonic scheduling algorithm: Exact characterization and average case behavior.” Real Time Systems Symposium (1989)
3. Seto, D., J. P. Lehoczky, L. Sha, and K. G. Shin, On Task Schedulability in Real-Time Control System, Proceedings of 17th Real-Time Systems Symposium, pp. 13-21, December, (1996)
4. W. Kuo, Y. H. Liu, and K. J. Lin, Efficient On-Line Schedulability Tests for Priority Driven Real-Time Systems, Proceedings of the IEEE Symposium on Real- Time Technology and Applications, Washington D.C., USA, June (2000).
5. Chapman, Barbara, Gabriele Jost, and Ruud Van Der Pas. Using OpenMP: portable shared memory parallel programming. MIT press, Vol. 10, (2008).
6. Audsley, Neil, et al. ”Applying new scheduling theory to static priority pre-emptive scheduling.” Software Engineering Journal 8.5 pp. 284-292. (1993)
7. Birman, Kenneth P., and Robbert van Renesse, eds. Reliable distributed computing with the Isis toolkit. IEEE Computer society press, Vol 10, (1994).
8. Gropp, E. Lusk, and A. Skjellum, Using MPI: Portable Parallel Programming with the Message-Passing Interface, MIT Press, (1999).
9. L. Trff, W. Gropp, and R. Thakur, Self-Consistent MPI Performance Requirements, in Proc. of the 14th European PVM/MPI Users Group Meeting (Euro PVM/MPI 2007), pp. 36-45, September (2007),
10. Thakur and W. Gropp, Open Issues in MPI Implementation, in Proc. of the 12th Asia-Pacific Computer Systems Architecture Conference, pp. 327-338. August (2007)
11. Gropp and R. Thakur, Issues in Developing a Thread-Safe MPI Implementation, in Proc. of the 13th European PVM/MPI Users Group Meeting (Euro PVM/MPI 2006), pp. 12-21, September (2006)
12. Buntinas, G. Mercier and W. Gropp, Implementation and Evaluation of Shared-Memory Communication and Synchronization Operations in MPICH2 using the Nemesis Communication Subsystem, Parallel Com-puting, Volume 33, Issue 9, pp. 634-644. September (2007)
13. Thakur, W. Gropp, and B. Toonen, Minimizing Synchronization Overhead in the Implementation of MPI One-Sided Communication, in Proc. of the 11th European PVM/MPI Users Group Meeting (Euro PVM/MPI 2004), Recent Advances in Parallel Virtual Machine and Message Passing Interface,
14. Lecture Notes in Computer Science, LNCS 3241, Springer, September (2004)
15. Garibay-Martnez, Ricardo, et al. ”On the scheduling of fork-join parallel/distributed real-time tasks.” Industrial Embedded Systems (SIES), 2014 9th IEEE International Symposium on. IEEE, (2014)
16. Coulouris, George; Jean Dollimore; Tim Kindberg; Gordon Blair. Distributed Systems: Concepts and Design (5th Edition), (2011)
17. Bini, Enrico, and Giorgio Buttazzo. ”A hyperbolic bound for the rate monotonic algorithm.” Real-Time Systems, 13th Euromicro Conference on, 2001.. IEEE, (2001).
18. Allah, Nasro Min, and S. Islam and Wang YongJi. ”Enhanced Time Demand Analysis.” World Applied Sciences Journal 9.1 (2007)
19. Manabe, Yoshifumi, and Shigemi Aoyagi. ”A feasibility decision algo-rithm for rate monotonic and deadline monotonic scheduling.” Real-Time Systems 14.2 pp. 171-181 (1998)
20. Bini, Enrico, and Giorgio C. Buttazzo, ”Schedulability analysis of periodic fixed priority systems.” IEEE Transactions on Computers 53.11 pp. 1462-1473, (2004).
21. Bini, Enrico, and Giorgio C. Buttazzo. ”The space of rate monotonic schedulability.” In Real-Time Systems Symposium, 2002. RTSS 2002. 23rd IEEE, pp. 169-178. IEEE, (2002).
22. Thomas H.. Cormen, Charles Eric Leiserson, Ronald L. Rivest, and Clifford Stein. ”Introduction to algorithms”. Vol. 6. Cambridge: MIT press, (2001).
23. Enrico Bini and Giorgio C. Buttazzo. ”Measuring the Performance of Schedulability Tests”, Springer, Real-Time Systems, Volume 30, Issue 1, 122 154, May (2005)
24. Rajib Mall. ”Real Time System: Theory and Practice” 3rd Edition, Pearson Education pp 390-395, (1986)
25. Lalatendu Behera and Durga Prasad Mohapatra, ”Schedulability Analy-sis of Task Scheduling in Multiprocessor Real-Time Systems Using EDF Algorithm”, International Conference on Computer Communication and Informatics, Jan. (2012)
26. Zhang and A. Burns. ”Schedulability Analysis for Real-Time Systems with EDF scheduling” , IEEE Transaction on computers, vol. 58, no. 9, pp. 1250-1258, September (2009)
27. Sjodin, Mikael, and Hans Hansson. ”Improved response-time analysis calculations.” Real-Time Systems Symposium, 1998. , The 19th IEEE, (1998).
28. Joseph, Mathai, and Paritosh Pandya. ”Finding response times in a real-time system.” The Computer Journal 29.5, pp. 390-395, (1986)

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Authors:

Paper Title:

Abstract: Natural resources are the part of human life. Water is also one of the natural resources. It is the main source of living things without presence of water there will be no living things on earth. Iot will be most prestigious technology in upcoming days. This paper presents an IoT device that monitors efficient usage of natural resources in agriculture. Now a days it so hard to farmer to give water, because. If he turned on the motor for soil, hedon’t know whether the water is filled or not. It leads to wastage of water and also don’t know water quality. So, in this system two types of sensors used in this project. pH sensor, moisture sensor. These sensors will inform the water quality and pH level. According to those values by using Iot technology the motor functioning is automatically controlled. The given data will be stored in cloud. After getting values it will be send to the farmers phone by using GSM module.This process is done without any interaction of human.It can help the farmer without wastage of water and loss of time. Iot is very friendly to the environment and it can be used every sector.This technology will be best for the farmers.

Keywords: IOT, environment, pH level, agriculture, moisture.

References:

1. Atzori, A. Iera, and G. Morabito, “The Internet of Things: A survey,” Computer. Networks, vol. 54, no. 15, pp. 2787–2805, 2010.
2. Jia, Q. Feng, T. Fan, and Q. Lei, “RFID technology and its applications in Internet of Things (IoT),” Consumer Electronics,Communications and Networks (CECNet), 2012 2nd International Conference on. pp. 1282–1285, 2012.
3. M. Lee, N. Crespi, J. K. Choi, and M. Boussard, “Internet of things,” in Evolution of Telecommunication Services, Springer, 2013,pp. 257–282.
4. Vakacola, M., “ Biannual Sea Water Monitoring Program”, Retrieved from Mamanuca Environment Society: http://mesfiji.org/biannualsea-water-monitoring-program, Accessed on: November 4, 2015.
5. Postscapes, “Retrieved from History of the Internet of Things”, Retrived from Online: http://postscapes.com/internet-of-thingshistory, Accessed on: November 4, 2015.
6. A. Mamun, Sharma, A., A. S. M. Hoque, T. Szecsi, “Remote Patient Physical Condition Monitoring Service Module for iWARD Hospital Robots”, Asia-Pacific World Congress on Computer Science and Engineering, 2014.
7. Enabling Agricultural Automation to Optimize Utilization of Water, Fertilizer and Insecticides by implementing Internet of Things (IoT)
8. Yao, C. Feng, Y. He, and S. Zhu. “Application of IOT in agriculture,” Journal of Agricultural Mechanization Research, vol. 07, pp. 190-192, 2011.
9. Lan. “Greenhouse precise management system based on production rules,” Journal of Agricultural Mechanization Research, no. 2, pp. 80-83, 2012.
10. Li, and L. Huang. “Application research of RFID in agriculture,” Journal of Anhui Agricultural Sciences, ,vol. 35, no. 20, pp. 6333~6334, 2007.
11. Asaad Ahmed Mohammed ahmed Eltaieb1, Zhang Jian Min2 . “Automatic Water Level Control System”, International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064.
12. George Suciu, Adela Vintea, Stefan CiprianArseni, Cristina Burca, Victor “Challenges and Solutions for Advanced Sensing of Water Infrastructures in Urban Environments”, 2015 IEEE SIITME , 22-25 Oct 2015, pp 349-352.
13. Amir Ali Khan, Shaden Abdel-Gawad, Haseen Khan, “A real time Water Quality Monitoring Network and Water Quality Indices for River Nile” , abs894_article, IWRA Congress.
14. A.C. Khetre, S.G. Hate, “Automatic monitoring & Reporting of water quality by using WSN Technology and different routing methods”, IJARCET Vol 2, Issue 12, Dec 2013, pp 3255- 3260.

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Authors:

Paper Title:

Abstract: Logo based document analysis plays an important role in many organizations for collecting information from massive number of administrative documents so that it can be summarized easily. Many researches are going on for improving the excellence of system, but the issues increase as the logos are similar to each other with minor differences. Conventional methods would not suitable for such complex process of identifying exact match so optimized models plays a vital role in this process. Cuckoo search algorithm is used in many cluster-based applications and it provides better convergence results than other optimization models. This proposed research model uses hybrid cuckoo search algorithm using global search procedure for enhancing its performance in analysing the logo-based document retrieval from the data set and proves its effectiveness in terms of fitness function and classification accuracy.

Keywords: Hybrid Cuckoo Search Algorithm (HCSA), Global Search, Deep Learning Neural Networks (DLNN), Ant Colony Metaheuristic, Ant Colony Optimization (ACO).

References:

1. Matteo Cristani, Andrea Bertolaso, Simone Scannapieco, Claudio Tomazzoli, “Future paradigms of automated processing of business documents” International Journal of Information Management, Vol.40, Pp.67-75, 2018
2. Alireza Alaei, ParthaPratim Roy, Umapada Pal, “Logo and seal based administrative document image retrieval: A survey” Computer Science Review, Vol.22, Pp. 47-63, 2016
3. Simone Bianco, Marco Buzzelli, Davide Mazzini, Raimondo Schettini, “Deep learning for logo recognition” Neurocomputing, Vol.245, Pp.23-30, 2017
4. Umesh D. Dixit ,M. S. Shirdhonkar, “Logo based document image retrieval using singular value decomposition features” 2016 International Conference on Signal and Information Processing (IConSIP), Pp. 1 – 4, 2016
5. NabinSharma ,Ranju Mandal ,Rabi Sharma ,Umapada Pal ,Michael Blumenstein, “Signature and Logo Detection using Deep CNN for Document Image Retrieval” 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), Pp. 416 – 422, 2018
6. SinaHassanzadeh and Hossein Pourghassem, “A Novel Logo Detection and Recognition Framework for Separated Part Logos in Document Images”, Australian Journal of Basic and Applied Sciences, Vol.5, No.9, Pp.936-946, 2011.

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Authors:

Paper Title:

Abstract: In this paper a comparative analysis among Proactive, Reactive and Hybrid routing protocol is presented using simulation. As we well aware that a MANET is self-configuring network and most of the real world scenario involving MANET requires individual nodes to route data. Keeping in view MANET is infrastructure less and at times nodes are free to move in different direction, making routing protocol a vital component for network operational effectiveness and efficiency.

Keywords: MANET; Routing Protocol Analysis; Routing Protocol Simulation

References:

1. Thomas Heide Clausen, Phillippejacquet And Laurent Viennot, “Comparative Study Of Routingprotocols For Mobile Ad-Hoc Networks”.
2. Sivarammurthy, B.S.Manoj, Adhoc Wireless Networks:Architectures, And Protocols, Pearson Education, 2004.
3. Mehranabolhasan, Tadeuszwysoci, Erykdutkiewicz, “A Review Of Routing Protocols Formobile Ad Hoc Networks”, Elsevier, 2003.
4. Tracy Camp, Jeff Boleng And Vanessa Davies, “A Survey Of Mobility Models For Ad Hocnetworks Research”,Wireless ommunication& Mobile Computing (Wcmc) 2002.
5. Zuraidabinti Abdullah Hani And M. Dani Bin Baba, “Designing Routing Protocols For Mobile Ad-Hoc Networks”. Ieee 2003.
6. Nadia Qasim, Fatin Said And Hamid Aghvami, “Mobile Ad Hoc Networks Simulations Usingrouting Protocols For Performance Comparisons”, Proceedings Of The World Congress Onengineering, Wce, Vol I, 2008.
7. Mohammed Bouhorma, H.Bentaouit and A.Boudhir, “Performance comparison of Ad hoc Routing protocols AODV and DSR”,IEEE 2009.
8. Sagar R. Deshmukh, P. N. Chatur, "Secure routing to avoid black hole affected routes in MANET", Colossal Data Analysis and Networking (CDAN) Symposium on, pp. 1-4, 2016.
9. Gopatoti, A., Naik, M.C., Gopathoti, K.K.” Convolutional Neural Network based image denoising for better quality of images”, International Journal of Engineering and Technology(UAE), Vol.7, No.3.27, (2018), pp. 356-361.
10. Abrar Omar Alkhamisi, Seyed M. Buhari, "Trusted Secure Adhoc On-demand Multipath Distance Vector Routing in MANET", Advanced Information Networking and Applications (AINA) 2016 IEEE 30th International Conference on, pp. 212-219, 2016.

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Authors:

Paper Title:

Abstract: The objective of the paper is, to propose one of the best approaches of quality assessment of undergraduate project. In introduced methodology, some criteria’s and sub-criteria’s have been proposed. The Examiners and Supervisor may use this approach for grading the level of students of a particular group (Project).The recommended approaches are mathematical, which are evaluating the weight-age, performance, and contribution of individual member in their respective group. To sort out the existing issues, Result has been collected in the form of passive observation. The supervisor has to observe an individual one based on different observation techniques. i.e. the process of questionnaire and task assigned to individual one for self-evaluation and maintain a log-book for future reference.

Keywords: Project Assessment; Self-Assessment; Group-Evaluation; individual– weightage; passive-Observation; log-table.

References:

1. Simon Williams, “Investigating the allocation and corroboration of individual grades for project-based learning” Science Direct, Elsevier, Volume 53, 1-9, June 2017.
2. Mohammad Rashid Hussain, International Journal: “Project Quality Analysis and Measurement of Undergraduate Level: Project Quality Assessment Methodology (PQAM) Model” International Journal of Research in Engineering and Technology, eISSN: 2319-1163 | pISSN: 2321-7308, Volume: 06 Issue: 12 | Dec-2017, Available @ http://www.ijret.org.
3. Dr Kate Exley, “Managing and Assessing Students Working in Groups” Queen’s University, Belfast, May 2010.
4. Bryan W. Griffin, “Perceived autonomy support, intrinsic motivation, and student ratings of instruction” Science Direct, Elsevier, Volume 51, 116-125, December 2016.
5. SALLY BROWN, “Assessment for Learning” Learning and Teaching in Higher Education, Issue 1, 2004-05.
6. Ian Jones and Chris Wheadon, “Peer assessment using comparative and absolute judgement” Science Direct, Elsevier, Volume 47, 93-101, December 2015.
7. Stavroula Valiandes, “Evaluating the impact of differentiated instruction on literacy and reading in mixed ability classrooms: Quality and equity dimensions of education effectiveness” Science Direct, Elsevier, Volume 45, pp. 17-26, June 2015.
8. Heidi Hyytinen , Kari Nissinen , Jani Ursin , Auli Toom , and Sari Lindblom-Yla¨nne, “Problema tising the equivalence of the test results of performance-based critical thinking tests for undergraduate students” Science Direct, Elsevier, Volume 44, 1-8, March 2015.
9. Emily R. Lai, “Collaboration: A Literature Review” Always Learning, Pearson, June 2011
10. Antonella Certa, Mario Enea and Fabrizio Hopps, “A multi-criteria approach for the group assessment of an academic course: A case study” Science Direct, Elsevier, Volume 44, pp. 16-22, March 2015.
11. Robbert Smit and Thomas Birri, “Assuring the quality of standards-oriented classroom assessment with rubrics for complex competencies” Science Direct, Elsevier, Volume 43, 5-13, December 2014.
12. Phil Race, “A Briefing on Self, Peer and Group Assessment” LTSN Generic CentreA Briefing on Self, Peer and Group Assessment, November 2001
13. Hien M. VO, Chang Zhu and Nguyet A. Diep, “The effect of blended learning on student performance at course-level in higher education: A meta-analysis” Science Direct, Elsevier, Volume 53, pp. 17-28, February 2017.

265-269

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Authors:

Paper Title:

Abstract: Machine learning is the sub branch of artificial intelligence and it is making computers to learn from data without being explicitly programmed Heart disease prediction is used to determine the root cause of getting heart attack and the probability of getting a heart attack, group the people into different clusters based on getting heart attack or not There are five levels in heart attack from level 0 to level 4. There are 14 important attributes to be considered in analysis of heart attack namely age, BP, CHOL, gender, CP, CA, THAL.

Keywords: Naïve Bayes, Decision Tree, Clustering, Linear Regression, Correlation.

References:

1. Kirubha and S. M. Priya, “Survey on Data Mining Algorithms in Disease Prediction,” vol. 38, no. 3, pp. 124–128, 2016.
2. Mai Shouman, Tim Turner, and Rob Stocker, “Applying k-Nearest Neighbour in Diagnosing Heart Disease Patients”, International Journal of Information and Education Technology, Vol. 2, No. 3, June 2012.
3. Sudhakar, and Dr. M. Manimekalai, January 2014, “Study of Heart Disease Prediction using Data Mining”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 4, Issue 1,pp. 1157-1160.
4. S. Dangare and S. S. Apte, “Improved study of heart disease predictionsystem using data mining classification techniques,” InternationalJournal of Computer Applications, vol. 47, no. 10, pp. 44–48, 2012.
5. Sairabi H. Mujawar, and P. R. Devale, October 2015,“Prediction of Heart Disease using Modified k-means and by using Naive Bayes”, International Journal of Innovative Research in Computer and Communication Engineering(An ISO 3297: 2007 Certified Organization) Vol. 3, Issue 10, pp. 10265-10273.
6. Ashwini Shetty A, and Chandra Naik, May 2016,“Different Data Mining Approaches for Predicting Heart Disease”, International Journal of Innovative Research in Science,Engineering and Technology(An ISO 3297: 2007 Certified Organization), Vol. 5, Special Issue 9, pp. 277-281.
7. Serdar AYDIN, Meysam Ahanpanjeh,and Sogol Mohabbatiyan,February 2016, “Comparison And Evaluation of Data Mining Techniques in the Diagnosis of Heart Disease”,International Journal on Computational Science & Applications (IJCSA), Vol. 6,No.1, pp. 1-15.
8. Shadab Adam Pattekari,and Asma Parveen, 2012,“Prediction System for Heart Disease using Naive Bayes”, International Journal of Advanced Computer and Mathematical Sciences, ISSN: 2230-9624,Vol. 3, Issue 3, pp. 290-294.

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Authors:

Paper Title:

Abstract: This article is devoted to an interesting educational direction i.e. Radio Frequency (RF) concepts and the use of basic digital signal processing tools for the development of real-time applications. A system with digital audio broadcasting (DAB) based generalised frequency division multiplexing (GFDM) is considered, estimates of its transmission modes are utilized. Software Defined Radio (SDR) is an advanced radio which can tune to any recurrence band and goes about as handset by utilizing universal software radio peripheral device (USRP) Programmable with LabVIEW programming. In this paper GFDM system proposal for digital audio broadcasting is investigated. The kernel is GFDM system, Emphasis is on the use of channel coding with low-density parity-check and convolutional codes at the transceiver. Furthermore, by using LabVIEW RF communications module software, a transceiver platform is built-up to analyze the performance of the DAB channel based on USRP hardware set up. BER is calculated under rural area (RA) consideration for entire area with quality of audio signal received is mentioned with different colours along with goggle map. This experiment was successfully conducted in GITAM University, India. The paper concludes with better audio quality of GFDM and slightly outperforms OFDM in terms of BER by a margin of 13% in the RA only.

Keywords: BER, DAB, GFDM, SDR.

References:

1. Kozamernik, Franc. “ Digital Audio Broadcasting”, EBU Technical Review,1999, pp.13,nical,Available:
2. Shelswell, Peter. “The COFDM Modulation System: The Heart of Digital Audio Broadcasting.” Electronics & Communication Engineering Journal, Vol 7, No.3, 1995, pp.127-136.
3. Park, Jeong-Hoon , Method and Apparatus of Providing and Receiving Video Services in Digital Audio Broadcasting (DAB) System. 2010, U.S. Patent 7,707,607.
4. Laki, Bradley Dean, Cornelis Jan Kikkert., “Adaptive Digital Predistortion for Wideband High Crest Factor Applications based on the WACP Optimization Objective: An Extended Analysis”. IEEE Transactions on Broadcasting, 2013, Vol 59, No. 1, pp- 136-145.
5. Wright, A.R., Naylor, P.A., “I/Q Mismatch Compensation in Zero-If OFDM Receivers with Application to DAB”. In Acoustics, Speech, And Signal Processing, 2003. Proceedings.(Icassp'03). 2003 IEEE International Conference On 2, Pp. 329,
6. Gaetzi, Lukas M., Malcolm Oj Hawksford, “Performance Prediction Of Dab Modulation And Transmission Using Matlab Modeling”. In IEEE International Symposium on Consumer Electronics–Proceedings, 2004, pp. 272-277.
7. Drakshayini, M. N., Arun Vikas Singh. “An Efficient Orthogonal Frequency Division Multiplexing system and Performance Analysis of Digital Audio Broadcasting System”. International Journal of Computer Applications, 2016, Vol 148, No. 8.pp.12-16.
8. Srivastava, Monika, Bhupendra Singh., “Detailed Performance Analysis of Digital Audio Broadcasting Under Different Modulation Schemes And Channels”. In Control, Computing, Communication And Materials (ICCCCM), 2016 International Conference on IEEE,2016, pp. 1-6.
9. Fischer, Joerg, Dirk Johannes Van Ginkel. Advanced Digital Audio Broadcasting Forward Error Correction Processing In Packet Mode Utilizing Tokens.2017, U.S. Patent 9,606,859.
10. Chang, Yu-Pin, Kai-Sheng Yang, Chao-Tang Yu., “Improved Channel Codec Implementation And Performance Analysis Of OFDM Based DAB Systems”. In Proceedings of the 2006 International Conference on Wireless Communications and Mobile Computing, 2006, pp. 997-1002.
11. Marwanto, Arief, Mohd Adib Sarijari, Norsheila Fisal, Sharifah Kamilah Syed Yusof, And Rozeha A. Rashid., “Experimental Study of OFDM implementation Utilizing GNU Radio and USRP-SDR. In Communications (MICC), 2009 IEEE 9th Malaysia International Conference on IEEE, 2009, pp. 132-135.
12. Hoeher, P., J. Hagenauer, E. Offer, Ch Rapp, H. Schulze, “Performance of an Rcpc-Coded Ofdm-Based Digital Audio Broadcasting System”. In Global Telecommunications Conference. Globecom'91.'Countdown to the New Millennium. Featuring A Mini-Theme On: Personal Communications Services, 1991, pp. 40-46.
13. Sarijari, Mohd Adib, Arief Marwanto, Norsheila Fisal, Sharifah Kamilah Syed Yusof, Rozeha A. Rashid, And Muhammad Haikal Satria., “Energy Detection Sensing Based On Gnu Radio And USRP: An Analysis Study”. In Communications (MICC), 2009 IEEE 9th Malaysia International Conference on IEEE, 2000, 338-342.,
14. Welch, Thad B., Sam Shearman. “Teaching Software Defined Radio Using The USRP and LABVIEW”. In Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference On IEEE, 2012, pp. 2789-2792.
15. Subramaniam, Sriram, Hector Reyes, Naima Kaabouch, “Spectrum Occupancy Measurement: An Autocorrelation Based Scanning Technique Using USRP”. In Wireless And Microwave Technology Conference (WAMICON), IEEE 16th Annual, 2015, 1-5.
16. Shahin, Nikookhoy, Nickolas J. Lasorte, Samer A. Rajab, Hazem H.Refai., “802.11 G Channel Characterization Utilizing Labview and NI-USRP”. In Instrumentation and Measurement Technology Conference (I2mtc),2013 IEEE International, 2013, pp. 753-756.
17. El-Hajjar, Mohammed, Quoc A. Nguyen, Robert G. Maunder, Soon Xin Ng., “Demonstrating The Practical Challenges Of Wireless Communications Using USRP”. IEEE Communications Magazine, 2014, Vol 52, No. 5, pp- 194-201
18. Costanzo, Sandra, Francesco Spadafora, Giuseppe Di Massa, Antonio Borgia,Antonio Costanzo, Ginaluca Aloi, Pasquale Pace, Valeria Loscri, And O.Hugomoreno. “Potentialities of USRP-Based Software Defined Radar Systems”. Progress in Electromagnetics Research B.2013,
19. Danneberg, Martin, Nicola Michailow, Ivan Gaspar, Dan Zhang, Gerhard Fettweis., “Flexible GFDM Implementation In FPGA With Support to Run-Time Reconfiguration”. In Vehicular Technology Conference (VTC FALL),IEEE, 2015, 82nd, pp. 1-2.
20. Danneberg, Martin, Nicola Michailow, Ivan Gaspar, Maximilian Matthé, Dan Zhang, Luciano Leonel Mendes, Gerhard Fettweis., “Implementation of a 2 By 2 MIMO-GFDM Transceiver for Robust 5G Networks. In Wireless Communication Systems (ISWCS), 2015 International Symposium on IEEE, 2015, pp. 236-240.
21. Danneberg, Martin, Rohit Datta, Gerhard Fettweis., “Experimental Testbed for Dynamic Spectrum Access and Sensing of 5g GFDM Waveforms”. In Vehicular Technology Conference (VTC FALL), 2014 IEEE 80th, pp. 1-5.
22. Demel, Johannes, Carsten Bockelmann, Armin Dekorsy., “Evaluation Of A Software Defined GFDM Implementation for Industry 4.0 Applications”. In Industrial Technology (ICIT), 2017 IEEE International Conference On IEEE, 2017,pp. 1283-1288.
23. Chaos, Dictino, Jesús Chacón, Jose Antonio Lopez-Orozco, Sebastián Dormido. “Virtual and Remote Robotic Laboratory Using Ejs, Matlab And Labview”. Sensors, 2013, Vol 13, No. 2, pp.2595-2612.
24. Telagam, Nagarjuna, Nehru Kandasamy, Menakadevi Nanjundan. “Smart Sensor Network Based High Quality Air Pollution Monitoring System Using Labview”. International Journal of Online Engineering 2017, Vol 13, No. 08, pp. 79-87.
25. Telagam, Nagarjuna, Nehru Kandasamy, Menakadevi Nanjundan, T. S. Arulanandth. “Smart Sensor Network Based Industrial Parameters Monitoring In Iot Environment Using Virtual Instrumentation Server.” International Journal of Online Engineering, 2017, Vol 13, No. 11, pp. 111-119.
26. Somanaidu, Utlapalli, Nagarjuna Telagam, Nehru Kandasamy, Menakadevi Nanjundan. USRP 2901 Based FM Transceiver With Large File Capabilities In Virtual And Remote Laboratory. International Journal of Online Engineering, 2018, Vol 14, No. 10, pp. 193-200.
27. Kandasamy, Nehru, Nagarjuna Telagam, Seshagiri Rao Vr, and T. S. Arulananth. “Simulation of Analog Modulation and Demodulation Techniques in Virtual Instrumentation and Remote Lab”. International Journal Of Online Engineerin, 2017, Vol. 13, No. 10. Pp. 140-147.

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Abstract: The growth of any country depends on the availability, accessibility and affordability of electricity to all connected consumers; hence the electricity is the key for any developed country. To make electricity affordable and available to all consumers, many countries started implementing time based pricing to interested consumers. In India, energy tariff is slab wise for the residential load. The philosophy behind this slab wise tariff is to facilitate or reward low energy users and charge extra for high energy users. But in that case, the middle class group of society has no encouragement for optimal utilization of electricity. Therefore, in such circumstances the real time tariff is very much needed for residential consumers also. Some of the developed countries like Australia, United States (US), United Kingdom (UK), Europe, Japan, etc. are already implementing this concept. They have found that real time tariff is superior than slab wise tariff. In India, it has been a concern for economically poor class. A policy can be made for consumers below the poverty line or low income group; for them slab wise tariff will be continuing and for rest others, a provision of real time tariff can be made. Hence in this work proposing real time tariff for residential consumers in India. The existing tariff structure for India and other countries is discussed in this work and proposing the solution for the Indian residential sector.

Keywords: Demand side management, Energy tariff, Mixed tariff, Real time tariff, Smart metering.

References:

1. Dong, B. Zou, “A Research of Real Time Pricing Mechanism and Its Characteristics”, Journal of Power and Energy Engineering, Vol. 3, (2015), pp. 240 - 249, available online: http://www.scirp.org/journal/jpee, last visit:13.12.2018.
2. Doostizadeh, M. and Ghasemi, H. (2012) A Day-Ahead Electricity Pricing Model Based on Smart Metering and Demand- Side Management. Energy, 46, 221-230. http://dx.doi.org/10.1016/j.energy.2012.08.029
3. Ameren Services (2018) Real-Time Pricing for Residential Customers, available online: http://www.ameren.com/sites/aiu/ElectricChoice/Pages/ResRealTimePricing.aspx. last visit:13.11.2018.
4. Edison Electric Institute (2018) Retail Electricity Pricing and Rate Design in Evolving Markets. http://www.eei.org/ourissues/electricitydistribution/Documents/Retail-Electricity-Pricing.pdf; Ontario Energy Board(OEB), Ontario, Canada. http://www.ontarioenergyboard.ca/OEB/Consumers/Electricity/ElectricityþPrices.
5. Samadi, Mohsenian-Rad, A., Schober, R., Wong, V. and Jatskevich, J. (2012), “Optimal Real-Time Pricing Algorithm Based on Utility Maximization for Smart Grid”, First IEEE International Conference on Smart Grid Communications (Smart GridComm).
6. Alexander, B., “Smart Meters, Real Time Pricing, and Demand Response Programs: Implications for Low Income Electric Customers”, Oak Ridge Natl. Lab., Tech. Rep. (2007). http://www.smartgridinformation.info/pdf/2438-doc-1.pdf.
7. Zhong F (2012), “A Distributed Demand Response Algorithm and Its Application to PHEV Charging in Smart Grids”, IEEE Transactions on Smart Grid,
8. Li, H. Huang, C. Zang and H. Yu (2013), “Day-Ahead Real-Time Pricing Strategy Based on the Price-Time-Type Elasticity of Demand”, IEEE conference, Proceedings of ICCT, pp. 449 – 455.
9. Xing Yan, Dustin Wright, Sunil Kumar, Gordon Lee, Yusuf Ozturk (2015), “Real-Time Residential Time-of-Use Pricing: A Closed-Loop Consumers Feedback Approach”, Seventh Annual IEEE Green Technologies Conference, 132 – 138.
10. Lan Mu, Nuo Yu, Hejiao Huang, Hongwei Du, Xiaohua Jia (2016), “Distributed Real-time Pricing Scheme for Local Power Supplier in Smart Community”, IEEE 22^nd International Conference on Parallel and Distributed Systems, pp. 40 – 47.
11. S. M. Nazar, M. P. Abdullah, M. Y. Hassan and F. Hussin (2012), “Time-based Electricity Pricing for Demand Response Implementation in Monopolized Electricity Market”, IEEE Students Conference on Research and Development, pp. 178-181.
12. Azman, M. Abdullah, M. Hassan, D. Said and F. Hussin (2017), “Enhanced Time of Use Electricity Pricing for Industrial Customers in Malaysia”, Indonesian Journal of Electrical Engineering and Computer Science, Vol. 6, No. 1, pp. 155 – 160.
13. Shaikh, A. Dharme (2009), “Time of Use Pricing – India, a Case Study”, IEEE Conference on Power System, Kharagpur.
14. Circular of State of Oklahama Gas and Electricity Company, Date Issued June 19, 2018.
15. Website: https://www.ameren.com/account/retail-energy
16. Website: https://hourlypricing.comed.com/
17. Website:https://www.oge.com/wps/portal/oge/my-account/billing-payments/oklahoma-rate-tariffs.
18. Sacramento Municipal Utility and Arizona Public Service, Website: https://www.utilitydive.com/news/how-sacramentos-public-utility-is-getting-in-the-residential-solar-busines/301840/. last visit:23.12.2018
19. Kadam, S. Rokade and R. Holmukhe (2013), “Overview of Demand Response Options and Regulatory Initiatives from Maharashtra Perspective,” IEEE ISGT Asia, pp. 1-6.
20. MSEDCL Circular no. 302, Date of Issue March 31, 2018.

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Abstract: The aquaponic systems are the combination of fish and plant culture. These are recirculating systems with two components: hydroponics and aquaculture. In these systems, the food given to fish is metabolized. This metabolized food fulfills the nutrient requirement of plants. This is achieved by the recirculation of water from the aquaculture component to the hydroponic component and back to the aquaculture component. This experiment is conducted to test the effect of recycling of water in a longer period on the growth of iridescent shark and balsam plant in the aquaponic system. The system developed is 1.08 m3 area of water in aquaponic component and 1 m2 for plant growth. In the hydroponic component course aggregate of 0.1m diameter was selected to support the plants. Coconut husk and sand particle layers of 0.03m and 0.06m are used for the growth and development of nitrifying bacteria. An average quantity of 1 kg of balsam plant leaves was produced in 60 days of plant growth. It has been found that balsam plant and iridescent shark species in this system has a faster and better growth compared to the conventional growth. The water used in the system is completely replaced once in three months after 45 days. Also, it was recirculated once in 3 days during experimentation. The DO level of fish tank water was dropped below 2 ppm during the span of two successive recirculations. No direct sunlight was available for the plants and no other artificial light source was used in cloudy and humid atmospheric conditions. The experiment also tested the success of the aquaponic system in adverse conditions like unavailability of fresh water for replacement, poor sunlight and minimum DO condition. The combination of balsam and iridescent shark proved to be suitable for the aquaponic system under such adverse conditions.

Keywords: Aquaponic, biofilter, TAN, DO, Nitrates.

References:

1. C. Love, M. S. Uhl, and L. Genello,”Energy and water use of a small-scale raft aquaponics system in Baltimore, Maryland, United States”. Aquacultural Engineering., 2015, vol. 68, pp. 19–27.
2. I. M. Martin, ”New developments in recirculating aquaculture systems in Europe : A perspective on environmental sustainability”, Aquacultural Engineering, vol. 43, no. 3, 2010, pp. 83–93.
3. Zou, Z. Hu, J. Zhang, C. Guimbaud, and Q. Wang, “Effect of seasonal variation on nitrogen transformations in aquaponics of northern China,” Ecol. Eng., vol. 94, 2016, pp. 30–36.
4. Ratnoji and N.Singh, “A study of coconut shell - activated carbon for filtration and its comparison with sand filtration,” International Journal of Renewable Energy and Environmental Engineering, ISSN 2348-0157, Vol. 02, No. 03, July 2014, pp. 201-204.
5. Ranjeet Shanbag, “Aquaponics – Vigyan ashram experience,” 2012, https://vigyanashram.files.wordpress.com/2012/07/vigyan-ashram-experi ment1.pdf
6. Okemwa (2015),”Challenges and Opportunities to Sustainability in Aquaponic and Hydroponics Systems”, International Journal of Scientific Research and Innovative Technology, vol. 2, no. 11, pp. 54–76.
7. E. Wilson, N. C. Duncan, and D. A. Crain , “Comparison of Aquaponics and Hydroponics on Basil (Ocimum basilicum) Morphometrics and Essential Oil Composition”, RURALS: Review of Undergraduate Research in Agricultural and Life Sciences, vol. 11: Iss. 1, Article 3,2017, pp. 1-16.
8. Meenu, E. D. Neeraja, G. Rejimon, and A. Varghese ,”Review Article Impatiens balsamina : An overview",Journal of Chemical and Pharmaceutical Research, vol. 7, no. 9, 2015, pp. 16–21.
9. E. Rakocy, M. P. Masser, and T. M. Losordo, “Recirculating Aquaculture Tank Production Systems : Aquaponics — Integrating Fish and Plant Culture”, southern regional aquaculture center publication, no. 454, 2006, pp 1-16.
10. Somerville, C., Cohen, M., Pantanella, E., Stankus, A. & Lovatelli, A. “Small-scale aquaponic food production. Integrated fish and plant farming”, FAO Fisheries and Aquaculture Technical Paper589. Rome, FAO, 2014, pp. 262.
11. Elia and C. Nicolae, ”Startup stages of a low-tech aquaponic system”, Scientific Papers, Series D. Animal Science. Vol..VII, 2014, pp 263-269.
12. Junge, B. Konig, M. Villarroel, T. Komives, and M. H. Jijakli, “Strategic Points in Aquaponics”,Water, 9, 182, doi:10.3390/w9030182, 2017, pp. 1–9.
13. FAO Fisheries and Aquaculture Department. Technical Paper. No. 500/1. Rome, FAO. 2011. 105 pp.
14. 2012. The State of World Fisheries and Aquaculture 2012. Rome. 209 pp.
15. Durgananda Singh Chaudhary, Saravanamuthu Vigneswaran†, Huu-Hao Ngo, Wang Geun Shim* and Hee Moon,“Biofilter in water and wastewater treatment”, Korean J. Chem. Eng.,20(6),2003, pp. 1054-1065

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Paper Title:

Abstract: The number of cipher-texts in distributed cloud storage increasing rapidly. An objective function based homomorphic approach is proposed that is reasonable for dynamic cloud storage. The design solves cipher-text class requirements and encryption secret key leakage issue. The goal of this research is to construct and execute an encryption design for open cloud access, based on the metaheuristic objective functional homomorphic viewpoint. Ciphertext classes are taken from 100 to 500. Delegation ratio is 0 to 100 and file uploading range is 0 to 45. The measurements inspected set-up time, encryption time, extract time, verification time, decryption time, and compression ratio over delegation ratio with compression ratio over numbers of uploaded files.

Keywords: Cloud, Cryptography, Objective function, Homomorphic. FFHE

References:

1. Agrawal, R., Kiernan, J., Srikant, R., & Xu, Y. (2004, June). Order preserving encryption for numeric data. In Proceedings of the 2004 ACM SIGMOD international conference on Management of data(pp. 563-574). ACM.
2. Arbaugh, W. A. (2003). Real 802.11 security: Wi-Fi protected access and 802.11 i. Addison-Wesley Longman Publishing Co., Inc.
3. Chu, C. K., Chow, S. S., Tzeng, W. G., Zhou, J., & Deng, R. H. (2014). Key-aggregate cryptosystem for scalable data sharing in cloud storage. IEEE transactions on parallel and distributed systems, 25(2), 468-477.
4. Do, J. M., Song, Y. J., & Park, N. (2011, May). Attribute based proxy re-encryption for data confidentiality in cloud computing environments. In Computers, Networks, Systems and Industrial Engineering (CNSI), 2011 First ACIS/JNU International Conference on(pp. 248-251). IEEE.
5. Furukawa, J. (2013, September). Request-based comparable encryption. In European Symposium on Research in Computer Security(pp. 129-146). Springer, Berlin, Heidelberg.
6. Guo, C., Luo, N., Bhuiyan, M. Z. A., Jie, Y., Chen, Y., Feng, B., & Alam, M. (2018). Key-aggregate authentication cryptosystem for data sharing in dynamic cloud storage. Future Generation Computer Systems, 84, 190-199.
7. Hardjono, T., & Dondeti, L. R. (2005). Security in Wireless LANS and MANS (Artech House Computer Security). Artech House Inc.
8. Kessler, G. C. (1998). An overview of cryptography. Published by Auerbach, 22.
9. Liu, Q., Wang, G., & Wu, J. (2012, September). Clock-based proxy re-encryption scheme in unreliable clouds. In Parallel Processing Workshops (ICPPW), 2012 41st International Conference on(pp. 304-305). IEEE.
10. Lu, R., Liang, X., & Lin, X. (2011). Ciphertext policy attribute based encryption with efficient revocation. Waterloo, ON, Canada: BBCR, University of Waterloo.
11. Natarajan, S., & Wolf, T. (2014, December). Network-level privacy for hosted cloud services. In Network of the Future (NOF), 2014 International Conference and Workshop on (pp. 1-8). IEEE.
12. Patel, S. C., Singh, R. S., & Jaiswal, S. (2015, February). Secure and privacy enhanced authentication framework for cloud computing. In Electronics and Communication Systems (ICECS), 2015 2nd International Conference on(pp. 1631-1634). IEEE.
13. Saravanakumar, C., & Arun, C. (2014, November). Survey on interoperability, security, trust, privacy standardization of cloud computing. In Contemporary Computing and Informatics (IC3I), 2014 International Conference on(pp. 977-982). IEEE.
14. Thakur, J., & Kumar, N. (2011). DES, AES and Blowfish: Symmetric key cryptography algorithms simulation based performance analysis. International journal of emerging technology and advanced engineering, 1(2), 6-12.
15. Wang, C., Wang, Q., Ren, K., & Lou, W. (2010, March). Privacy-preserving public auditing for data storage security in cloud computing. In Infocom, 2010 proceedings ieee(pp. 1-9). Ieee.
16. Yu, S., Wang, C., Ren, K., & Lou, W. (2010, April). Attribute based data sharing with attribute revocation. In Proceedings of the 5th ACM Symposium on Information, Computer and Communications Security(pp. 261-270).
17. Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: state-of-the-art and research challenges. Journal of internet services and applications, 1(1), 7-18

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Abstract: Most of the data science contemporary fields like Artificial Intelligence, Machine Learning and Deep Learning are taking advantage of a common model- The Neural Network. Neural network which can learn from experience are becoming popular in solving many of real worlds NP-hard problems. Today any prediction application is taking the support of Neural Network. The accuracy of the neural network models depends on major of the design components like the hidden layers and the activation functions. As we know human brain receives both relevant and irrelevant information at a time and has the capability of segregating both, where the irrelevant can be referred as noise. Just like the human brain the neurons uses activation function to separate the noise from the input and reduce the error. This paper presents the role of hidden layers and activation functions in measuring the accuracy of the Neural Network.

Keywords: Artificial Intelligence, Machine Learning, Neural Networks, Activation function, Neurons.

References:

1. M E Petersen,D de Ridder,H Handels,“Imageprocessing with NN: A review”,A Book on Pattern recognition, Elsevier, Vol 35(10),2002.
2. N Plaziac, “Image Interpolation using Neural Networks”, IEEE Transactions on Image Processing, Vol 8(11), 1999.
3. H A Rowley, S Baluja, T Kanade“Neural Network based face detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 20(1), 1998.
4. M Y Rafiq, G Buggman,“Neural Network design for engineering applications”, Computers and structures, Vol 79 (17), 2001.
5. B D Replay “Neural Networks and related methods for classification”, Journal of the royal statistical society, Vol 56(3),1994.
6. R P Lippmann“Patten clasiification using Neural Networks”, IEEE communications Magazine, Vol 27(11), 1989.
7. J de Villeirs, E Barnard, “Back propagation Neural Networks with one and two hidden layers”, IEEE Transactions on Neural Networks, Vol 4(1), 1993.
8. K O Stanley, R Mikkulnain“Efficient evolution of Neural Network Topologies”, Proceedings of the 2002 congress on evolutionary computation, 2002.
9. D F Specht, “A general regression neural Network”, IEEE Transactions on Neural Networks,1991.
10. B Warner, M Misra,“Understanding neuralnetworks as statistical tools”, The American Statistician, 1996.
11. V P Plagianakos, M N Vrahatis“Training Neural networks with threshold activation functions and constrained integer weights”, Proceedings of the IEEE INNS IJCNN 2000.
12. E Trentin,“Networks with trainable amplitude of activation functions”, Neural Networks, Elsevier, Vol 14 (4), 2001.

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Paper Title:

Abstract: Wireless Sensor Networks transmit data from source to destination and inform base station using various sensors. However, the routing path through multiple hops of WSNs usually becomes the target of severe attacks. Outmoded cryptographic security techniques have proven to be inefficient against several insider attacks such as wormhole attack, sinkhole attack, Sybil attack, selective forwarding attack, etc. These attacks further impacts on restating of routing information and also exacerbate identity deception. To handle such instances, trust aware routing can be the alternative to provide a trustworthiness and energy efficient route irrespective of known geographic information or tight time synchronization. Significantly, it is strong against the attacks caused due to identity theft. Besides that WSNs are also vulnerable to the attacks illegally acquiring network resources which is caused by many popular attacks. These attacks are complex as it affect not only the victim but its legitimate members also. Existing routing techniques could not solve these severe issues. We are proposing an effective solution, M-TARF to significantly defend network from annoying resource acquisition imposed by adversaries on compromised nodes and its neighbours through additional component hop_count_filter. The proposed scheme demonstrated an average throughput of 6.4% which is more that existing scheme when implemented on NS2 for varying number of nodes.

Keywords: Hop count filter, Supervisor, Trust_advisor, Energy_recorder, hop_count.

References:

1. Rijin, I. K., N. K. Sakthivel, and S. Subasree. "Development of an enhanced efficient secured multi- hop routing technique for wireless sensor networks." Development 1.3 (2013): 2320-9801.
2. Sahu, SonaliSwetapadma, and Manjusha Pandey. "Distributed Denial of Service Attacks: A Review." International Journal of Modern Education and Computer Science (IJMECS) 6.1 (2014): 65.
3. Sen, "Routing security issues in wireless sensor networks: attacks and defenses." arXiv preprint arXiv: 1101.2759 (2011).
4. Karlof, N. Sastry, and D. Wagner, “Tinysec: A link layer security architecture for wireless sensor networks,” in Proc. of ACM SenSys 2004, Nov. 2004.
5. L. X. Li, M. R. Lyu, “Taodv: A trusted aodv routing protocol for mobile ad hoc networks,” in Proceedings of Aerospace Conference, 2004.
6. Gong, Z. You, D. Chen, X. Zhao, M. Gu, and K. Lam, “Trust based routing for misbehavior detection in ad hoc networks,” Journal of Networks, vol. 5, no. 5, May 2010.
7. Zahariadis, H. Leligou, P. Karkazis, P. Trakadas, I. Papaefstathiou,C. Vangelatos, and L. Besson, “Design and implementation of a trust-aware routing protocol for large wsns,” International Journal of Network Security & Its Applications (IJNSA), vol. 2, no. 3, Jul. 2010.
8. Yan, P. Zhang, and T. Virtanen, “Trust evaluation based security solution in ad hoc networks,” in Proceeding of the 7th Nordic Workshop on Secure IT Systems, 2003.
9. Rezgui and M. Eltoweissy, “Tarp: A trust-aware routing protocol for sensor-actuator networks,” in IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS 2007), 8-11 2007.
10. Ahmed M. Abd El-Haleem1 and Ihab A. Ali, “TRIDNT: The Trust-Based Routing Protocol with Ccontrolled Degree of Node Selfishness for MANET” in International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.3, May 2011.
11. Cao, J. Hu, Z. Chen, M. Xu, and X. Zhou, “Fbsr: feedback- based secure routing protocol for wireless sensor networks” International Journal of Pervasive Computing and Communications, 2008.
12. Safa, H. Artail, and D. Tabet, “A cluster-based trust-aware routing protocol for mobile ad hoc networks,” Wirel. Netw., vol. 16, no. 4, pp. 969–984, 2010.
13. Zhan Guoxing, Weisong Shi, and Julia Deng. "Design and implementation of TARF: a trust-aware routing framework for WSNs." IEEE Transactions on Dependable and Secure Computing, Volume 9, Issue 2, 2012 pp 184-197.
14. Junqi Duan,Dong Yang,Haoqing Zhu,Sidong Zhang,and Jing Zhao, “TSRF: A Trust-Aware Secure Routing Framework in Wireless Sensor Networks”, Hindawi Publishing Corporation International Journal of Distributed Sensor Networks Volume 2014, Article ID 209436, 14 pages.
15. Sakthivel, R. M. Chandrasekaran, “A Dummy Packet-Based Hybrid Security Framework for Mitigating Routing Misbehavior in Multi-Hop Wireless Networks”, WPC, Springer Volume 101, issue 3, 2018 pp 1581-1618.
16. Pu Gong, Thomas M.Chen, and Quan Xu, “ETARP: An Energy Efficient Trust-Aware Routing Protocol for Wireless Sensor Networks”, Hindawi Publishing Corporation Journal of Sensors Volume 2015, Article ID 46993, 10 pages.
17. PadmavathiGanapathi, Mrs. Shanmugapriya. D, "A Survey of Attacks, Security Mechanisms and Challenges in Wireless Sensor Networks." (IJCSIS) International Journal of Computer Science and Information Security, Vol. 4, No. 1 & 2, 2009.
18. Adnan Ahmed, Kamalrulnizam Abu Bakar, Muhammad Ibrahim Channa, Khalid Haseeb, Abdul Waheed Khan, “A trust aware routing protocol for energy constrained wireless sensor network”, Telecommunication Systems, Springer January 2016, Volume 61, Issue 1, pp 123–140.
19. Gayathri Dhananjayan, Janakiraman Subbiah, “T2AR: trust-aware ad-hoc routing protocol for MANET”, SpringerPlus December 2016
20. S. Ganeriwal, L. Balzano, and M. Srivastava, “Reputation-based framework for high integrity sensor networks,” ACM Trans. Sen. Netw., 2008.

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Abstract: The article is devoted to the discussion of the recent trends and reformation of housing and communal services in Uzbekistan introducing ICT to the sphere. The object of the research is the sector of housing and communal services of the Republic of Uzbekistan. The actuality of the research consists of increasing number of scholars of several developing countries who are interested in how to use the ICT and by this to achieve economic effectiveness both in the scope of country and company. For example, regardless of the reformations and development in this sphere, a row of problems is unsolved. Nowadays population is not satisfied with the supply of communal services; hence the number of complaints on the work of service providers is increasing. In addition, the problems of expenses and debts above normal and not correctly calculated are remain open. Besides, the problems in the sphere of housing and communal services also arise from the lack of the mechanism of monthly taking measurements from the counters and absence of central database of consumers. The analysis of the main approaches determining the contribution of ICT to the housing and communal sphere, formulation of recommendations relating to the prior directions to achieve the step-by-step informatization of the sphere of housing and communal services makes up the scientific novelty of the research. The research consists of four parts. Firstly, the role of ICT in the sphere of housing and communal services is explored. Secondly, application of billing system to the sphere of housing and communal services is investigated. Thirdly, reformation of housing and communal services by introducing CRM system is examined. Lastly, introduction of ICT to the sphere of housing and communal services for integration with e-government is discussed.

Keywords: Information and Communication Technologies (ICT), housing and communal services, Customer Relationship Management (CRM), billing, e-government, online payment, utilities, reformation, Uzbekistan, automation, informatization, debts, tariffs, management.

References:

1. Lee S.M., Sevebeck W.R. An aggregative model for municipal economic planning / Policy Sciences 2. 1971. p. 99-115.
2. Lewis B.D. The impact of public infrastructure on municipal economic development: empirical results from Kenya /Review of urban and regional development studies. 1998. 10(2): р.142–155.
3. Pak K.S., Slivinskaya L.V., Voskresenskaya E, Morozova N. The assessment of municipal economic and social system’s economic security / MATEC Web of Conferences 106, 08053. 2017, p.1-6.
4. Kaul M. Introducing New Approaches: Improved Public Service Delivery / Commonwealth Secretariat. 1998. p.49-57.
5. Stolyar I.V. To the question of improving the management system of the housing and communal services of the city / The space of economics. 2007. № 3-3. p. 292.
6. Papaioannou S.K., Dimelis S.P. Information technology as a factor of economic development: evidence from developed and developing countries / Economics of Innovation and New Technology, р. 179-194.
7. Ukwandu E. and Nnamocha P. The effects of Information Technology on global economy / The Social Sciences. 2013.р. 606-609.
8. Chistova M.V., Kontsevich G.E., Demina N.V. Opportunities of implementing information and communication technologies for reformation of the sphere of housing and communal services of RF / Scientific and practical journal “Humanization of education”. 2014. p. 95-101.
9. Podolyakin O.V. Introduction of information system management at enterprise /Problems of territory development. 2012. № 4 (60). p. 20.
10. Saak A.E. and Tyushnyakov V.N. Application of management information technologies in the sphere of housing and communal services/ Edition YuFU. Technical sciences. (138) / 2013, p. 246-252.
11. Yakimenko I.A., Kozin I.Yu., Medvedev E.V. Application of

information technology in housing and communal services /

Вulletin USUES. Science, еducation, еconomy. Series

еconomy. № 1 (11), 2015, p. 172-173.

12. Molchan A.S. Conceptual aspects of the theory and methodology of the formation and use of the aggregate potential of meso-level socio-economic systems / Scientific Herald of the Southern Institute of Management. 2013. № 3. P. 22-28.
13. Gorbachev D. V. and Khakimova E.G. Review of modern information technologies of automatization of the sphere of housing and communal services // Young scientist. №13. p. 33-35.
14. Maksimov A.D. and Makarova I.V. Billing of housing and communal services: problems and solutions / Russian entrepreneurship. 09 (207) 2012. p. 109-112.
15. Boreyko A. Five markets of communal billing / Business-Forum IT. – October 2003, p. 62-66.
16. Karaev A. The billing systems in the sphere of housing and communal services as an instrument of business / Energymarket. – 2007. – # 10 (47).
17. Duque J., Varajão J., Vitor F. and Dominguez C. (2013). Implementation of CRM systems in Portuguese Municipalities. Local Government Studies. 39.
18. Pan S.L., Tan C.W. and Lim E.T. Customer relationship management (CRM) in e-government: a relational perspective // Decision Support Systems – 2006. #42(1). p.237-250
19. Kuznetsova A.I., Dolmatova A.V. Innovative technologies in the housing system // Bulletin of the Moscow University named after S. Yu. Witte. Series 1: Economics and Management. 2016'3 (18). p.42-43.
20. Belova A.A. ICT in the sector of public utilities: informatization in the sphere of housing and communal services // Electronic bulletin of the Rostov social-economic institute. — 2015. — #3 - 4 (July – December).— p. 453-460
21. Contini F. and Lanzara G.F. ICT and Innovation in the Public Sector: European Studies in the Making of E-Government // Palgrave Macmillan. 2009. P211-220.
22. Torres L., Pina V. and Acerete B. E-government developments on delivering public services among EU cities // Government Information Quarterly – 2005. #22(2). p.217-238.
23. Shibaeva I.V. It is necessary to create a common information space for housing and public utilities // Electric communication. 2013. № 4. p. 14.

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Authors:

Paper Title:

Abstract: Multipliers are the primal constituents of a processing unit. The process of multiplication is carried out using different suggested architectures. Among the developments the common factor observed is the use of recursive register interface in multiplication operation. multiple registers were used in count to buffer the temporary data and gives a new result on addition of these register values. It is needed to minimize the resource requirement to enhance the objective of optimal multiplication operation, in this paper a new low resource adaptive counter based multiplier design is proposed, which minimizes the register requirement by deriving a sub counter logic in multiplier design.

Keywords: Multiplier design, adaptive logical count, low resource overhead.

References:

1. Amin Malekpour, Alireza Ejlali, Saeid Gorgin, “A comparative study of energy/power consumption in parallel decimal multipliers, Microelectron. J , Elsevier, 2014.
2. Parhami, Computer Arithmetic, Algorithm and Hardware Design, Oxford University Press, New York, pp. 91-119, 2000.
3. Stephen Brown and Zvonko Vranesic, Fundamentals of Digital Logic with VHDL Design. 2nd Edn. McGraw-Hill Higher Education, 2005.
4. Wakerly, J.F., 2006. Digital Design-Principles and Practices. 4th Edn. Pearson Prentice Hall.
5. Sertbas and R.S. Özbey, “A performance analysis of classified binary adder architectures and the VHDL simulations”, J. Elect. Electron. Eng.,Istanbul, Turkey, vol. 4, pp. 1025-1030, 2004.
6. R. Strader and V. T. Rhyne, “A canonical bit-sequential multiplier,” IEEE Trans. Comput., vol. C-31, no. 8, pp. 791–795, August 1982.
7. Nam Su Changa,, Tae Hyun Kim, Chang Han Kim, Dong-Guk Han, Jongin Lim, “A New Bit-Serial Multiplier Over Gf(P^m) Using Irreducible Trinomials,” Computers and Mathematics with Applications , 60 (2010),pp-355-361.
8. Deepak Bordiya and Lalit Bandil, “Comparative Analysis Of Multipliers Serial And Parallel With Radix Based On Booth Algorithm,” International Journal of Engineering Research & Technology, Vol. 2 Issue 9, September – 2013.
9. Arash Reyhani-Masoleh and M. Anwar Hasan, “On Low Complexity Bit Parallel Polynomial Basis Multipliers,” CHES 2003, LNCS 2779, pp. 189–202, Springer 2003.
10. Nishat Bano, “VLSI Design of Low Power Booth Multiplier,” International Journal of Scientific & Engineering Research, Volume 3, Issue 2, February -2012.
11. Vignesh Kumar. R and Kamala. J, “High accuracy Fixed Width Multipliers Using Modified Booth Algorithm,” International Conference on Modelling Optimisation and Computing, 38 (2012) 2491 – 2498, 2012.
12. Bellaouar and M.Elmasry, “Low Power Digital VLSI Design: Circuits and Systems”, Kluwer Academic Publishers. 1995.
13. Jin-HaoTu and Lan-Da Van, “Power-Efficient Pipelined Reconfigurable Fixed-Width Baugh-Wooley Multipliers” IEEE Transactions on computers, vol. 58, No. 10, October 2009.
14. ManasRanjanMeher, Ching-ChuenJong, Chip-Hong Chang, “High-Speed and Low-Power Serial Accumulator for Serial/Parallel Multiplier” IEEE, 2010.
15. Parhami, Computer Arithmetic: Algorithms and Hardware Designs. New York: Oxford Univ. Press, 2009.
16. Manas Ranjan Meher, Ching Chuen Jong, Chip-Hong Chang, “A High Bit Rate Serial-Serial Multiplier with On-the-Fly Accumulation by Asynchronous Counters,” IEEE transactions on Very Large Scale Integration (VLSI) systems, 2011.
17. http://en.wikipedia.org/wiki/Kogge Stone_adder.
18. Rabaey, “Digital Integrated Circuits: A Design Perspective”, Prentice Hill, Second Edition, 2003.
19. Ragini Parte and Jitendra Jain, “Analysis of Effects of using Exponent Adders in IEEE- 754 Multiplier by VHDL,” International Conference on Circuit, Power and Computing Technologies, ICCPCT, 2015.
20. Sarita Singh and Sachin Mittal, “VHDL Design and Implementation for Optimum Delay & Area for Multiplier & Accumulator Unit by 32-Bit Sequential Multiplier,” International Journal of Engineering Trends and Technology- Volume 3 Issue 5- 2012.
21. Wasil Raseen Ahmed, “FPGA Implementation of Vedic Multiplier Using VHDL,” International Journal of Emerging Technology and Advanced Engineering, Volume 4, Special Issue 2, April 2014

322-327

60.

Authors:

Paper Title:

Abstract: Security concern in wireless network is a prime issue to overcome to offer better service quality for next generation applications. To offer robust security in wireless network, a optimal loop trace monitoring approach for security provisioning in wireless network is proposed. the approach offer a monitoring of the secure data exchange and builds a security measure to declare the reliability of the node. The communication packets are then chosen based on the offered security value and the route selected. A higher offering security level route is chosen in data exchange for reliable coding. The simulation results were carried out for the existing reliability factor of data exchange over the proposed approach of loop trace monitoring to validate the proposed work.

Keywords: Secure coding, wireless network, loop trace monitoring and robust reliable coding

References:

1. Guanyu Tian; Zhenhai Duan; Todd Baumeister; Yingfei Dong, “A Traceback Attack on Freenet”, IEEE Transactions on Dependable and Secure Computing ,Volume: 14, Issue: 3, , pp-294 – 307, May-June 1 2017.
2. Xiaoxin Wu and Bharat Bhargava, “AO2P: Ad Hoc On-Demand Position-Based Private Routing Protocol”, IEEE transactions on mobile computing, vol. 4, No. 4, July/august 2005.
3. Karim El Defrawy, and Gene Tsudik, “Privacy-Preserving Location-Based On-Demand Routing in MANETs”, IEEE Journal On Selected Areas in Communications, Vol. 29, No. 10, December 2011.
4. Toby Xu, Ying Cai, “LSR: A Location Secure Routing Protocol for Ad Hoc Networks”, Proc. IEEE Conf, 2010.
5. Nitesh Saxena, Gene Tsudik, and Jeong Hyun Yi, “Efficient Node Admission and Certificate less Secure Communication in Short-Lived MANETs”, IEEE Transactions on Parallel And Distributed Systems, Vol. 20, No. 2, 2009.
6. Shushan Zhao, Robert D. Kent, Akshai Aggarwal, “An Integrated Key Management and Secure Routing Framework for Mobile Ad-hoc Networks”, International Conference on Privacy, Security and Trust, 2012.
7. Hanan Saleet, Rami Langar, Otman Basir, and Raouf Boutaba, “A Distributed Approach for Location Lookup in Vehicular Ad Hoc Networks”, IEEE ICC 2009.
8. S. Mangrulkar, Dr. Mohammad Atique, “Trust Based Secured Adhoc on Demand Distance Vector Routing Protocol for Mobile Adhoc Network”, Proc. IEEE Conf, 2010.
9. Mohamed M. E. A. Mahmoud, Sanaa Taha, Jelena Misic, and Xuemin Shen, “Lightweight Privacy-Preserving and Secure Communication Protocol for Hybrid Ad Hoc Wireless Networks”, IEEE Transactions on Parallel and Distributed Systems, IEEE, 2013.
10. Liu Yang, Markus Jakobsson, Susanne Wetzel, “Discount Anonymous on Demand Routing for Mobile Ad hoc Networks”, Proc. IEEE, 2006.
11. Nitesh Saxena a, Gene Tsudik b, Jeong Hyun Yi, “Threshold cryptography in P2P and MANETs: The case of access control”, Elsevier,2007.
12. Elena Renda, Giovanni Resta, and Paolo Santi, “Load Balancing Hashing in Geographic Hash Tables”, IEEE Transactions on Parallel and Distributed Systems, Vol. 23, no. 8, August 2012.
13. Hanan Saleet, Rami Langar, Otman Basir, and Raouf Boutaba, “Proposal and Analysis of Region-based Location Service Management Protocol for VANETs”, proceedings of IEEE "GLOBECOM", 2008.
14. Yan Lindsay Sun, Zhu Han, Wei Yuand K. J. Ray Liu, “A Trust Evaluation Framework in Distributed Networks: Vulnerability Analysis and Defense Against Attacks”, in the proceeding of IEEE INFOCOM,
15. Pathak, D. Yao, and L. Iftode, “Securing Location Aware Services over VANET Using Geographical Secure Path Routing,” Proc. IEEE Int’l Conf. Vehicular Electronics and safety (ICVES), 2008
16. Wei Yuan, “An Anonymous Routing Protocol with Authenticated Key Establishment in Wireless Ad Hoc Networks”, International Journal of Distributed Sensor Networks, Volume 2014.
17. Yanchao Zhang, Wei Liu, Wenjing Lou, and Yuguang Fang, “MASK: Anonymous On-Demand Routing in Mobile Ad Hoc Networks”, IEEE Transactions on Wireless Communications, Vol. 5, No. 9, September 2006.
18. Jun Long, Mianxiong Dong, Kaoru Ota, Anfeng Liu, “Achieving Source Location Privacy and Network Life-time Maximization through Tree-based Diversionary Routing in Wireless Sensor Networks”, IEEE ACCESS, 2014.
19. Qiuna Niu, “Formal Analysis of Secure Routing Protocol for Ad Hoc Networks”, IEEE, 2009.
20. M. Kamruzzaman, E. Kim, D.G. Jeong, W.S. Jeon, “Energy-aware routing protocol for cognitive radio ad hoc networks”, IET Communications, 2012.
21. Haina Ye, Zhenhui Tan, Shaoyi Xu, Xiaoyu Qiao, “Load Balancing Routing in Cognitive Radio AdHoc Networks”, IEEE, 2011.
22. Nitul Dutta, Hiren Kumar Dev Sarma, “A Routing Protocol for Cognitive Networks in presence of Co-Operative Primary User”, IEEE, 2013.
23. Muhammad Zeeshan, Muhammad Fahad Manzoor, Junaid Qadir, “Backup Channel and Cooperative Channel Switching On-Demand Routing Protocol for Multi-Hop Cognitive Radio Ad Hoc Networks (BCCCS)”, ICET, 2010.
24. Alexander W. Min, and Kang G. Shin, “Robust Tracking of Small-Scale Mobile Primary User in Cognitive Radio Networks”, IEEE Transactions on Parallel and Distributed Systems, Vol. 24, No. 4, April 2013.
25. Vadivel, V. Murali Bhaskaran, “Energy Efficient With Secured Reliable Routing Protocol (EESRRP) For Mobile Ad-Hoc Networks, Procedia Technology, Elsevier, 2012.
26. Abu TahaZamani, “A Novel Approach to Security in Mobile Ad HocNetworks (MANETs)”, International Journal of Computer Science and Information Technology Research Vol. 2, Issue 1, pp: (8-17), Month: January-March 2014.

328-336

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Authors:

Paper Title:

Abstract: FSW (Friction Stir Welding) and TIGW (Tungsten Inert Gas Welding) are broadly known welding processes, FSW and TIGW both are acknowledged throughout the industries globally in current age. The aluminum alloys are used widely in Aviation, Shipbuilding and also in general engineering because it is having many applications in these sectors. In this paper a comparative study of Aluminum Alloy AA6062 properties on TIGW (Tungsten Inert Gas Welding) and FSW (friction stir welding) are obtained. Mostly in all the welding processes the purpose is to get welded joint by having its desired weld bead parameters, higher mechanical properties with the low distortion. The quality of the weld is determined through mechanical properties, bead geometry, and distortion. In this paper AA6062 (Aluminum Alloy) similar metal is joined, appraised and compared in TIGW and FSW welding processes for studying the welding process parameters and there different configurations.

Keywords: Friction Stir Welding, Tungsten Inert Gas Welding, Mechanical properties, Aluminum Alloy, Microstructure.

References:

1. W. Becker, C. M. Adams, “Investigation of Pulsed GTA Welding Parameters”, Welding Research Supplement, Vol. I, pp. 135-138, May 1978
2. Edels, H. (1951), “A technique for arc initiation,” J. Appl. Phys., Vol.2, No.6, pp.171–174
3. Wang, D.L.Sun, Y.Na, Y.Zhou, X.L.Han, J.Wang, “Effects of TIG Welding Parameters on Morphology and Mechanical Properties of Welded Joint of Ni-base Super alloy”, Procedia Engineering 10, 37–41, 2011
4. Miami: American Welding Society. ISBN ISO 4063: "Welding and allied processes Nomenclature of processes and reference numbers" (1998).
5. Ding, Jeff; Bob Carter;Kirby Lawless;Dr. Arthur Nunes;Carolyn Russell; Michael Suites;Dr. Judy Schneider (20080214). "A Decade of Friction Stir Welding R&D At NASA's
6. Ahmed Khalid Hussain, Abdul Lateef, MohdJaved, Pramesh.T, “Influence of Welding Speed on Tensile Strength of Welded Joint in TIG Welding Process”, International Journal Of Applied Engineering Research, Vol. 1, No 3, 0976-4259, 2010.

337-342

62.

Authors:

Paper Title:

Abstract: Water is the most essential and foremost constituent for all the organisms living in the earth. Sometimes it would be the major reason for the demolishment of lives when the needed level is increased. This resource is available through low, moderate and high level rainfall and it is preserved in various places for future usages. In this paper we discuss about the disaster caused by water stored in dam. When the storage level of the dam is increased, it is expelled or released without any alarm as a result it squashes many of the lives. Our main focus is to issue alarms before releasing the water from dam. We use an unmanned method using wireless sensor technique to monitor the water level continuously, if it reaches the saturation point (extreme storage level), we propose 3 alarming facilities, as 1. Sending message to the people located in the area, 2. Provide audio alarm which is battery enabled 3. Make drone alarming (UAV) to all the people. After the alarming process is done the shutters of the dam is released to expel the water.

Keywords: Dam monitoring, Drones, Shutters, Unmanned

References:

1. Barton, J.D “Fundamentals of small unmanned aircraft flight”. Johns Hopkins APL. Tech. Digest .2014.
2. R.Conway “Autonomous control of unstable model helicopter using carrier phase GPS.2015
3. Eisenbeiss, H., 2002. WITAS UAV (Wallenberg Laboratory for Information Technology and Autonomous Systems Unmanned Area Vehicle) – Positioning of the helicopter with GPS.
4. Ferreira,”Autonomous bathymetry for risk assessment with ROAZ robotic surface vehicle”2009.
5. Henri Eisenbeiss “A mini Unmanned aerial Vehicle: System overview and Image Acquisition” 2004.
6. S "Dam Monitoring System Using Wireless Sensor Networks." 2017.
7. Iyerm M, Pai S.Badri, Kharche.S "Embedded Dam Gate Control System using ‘C’ and Visual Basic.”2013.
8. Kong “Building Underwater Ad-hoc and Sensor Networks for large scale real time Aquatic Application”2005.
9. Rodrigues, “An open-source watertight unmanned aerial vehicle for water quality monitoring” 2015.
10. Rozhdestvensky “Wing in ground effect vehicles”2006.
11. Sebastian pop “Improving communication between UAV and Ground control station using Antenna tracking system”2018.
12. Zeyana Mohammed, Dr.Abdul Khaliq” A System For Remote Monitoring And Controlling Of Dams” 2017.
13. https://www.iec.ch/WP-internetofthings-LR-en.pdf,
14. http://wuwnet.acm.org/.
15. https://www.militaryfactory.com/aircraft/detail.asp.
16. https://www.vegetronix.com/
17. http://www.arch.mcgill.ca/prof/sijpkes/arch374/.

343-347

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Authors:

Paper Title:

Abstract: The quality of fingerprint or fingerprint verification depends on the quality of the fingerprint image. Most of the fingerprint management algorithms depend on the features which are extracted based on the minutiae of the fingerprints. The quality of minutiae is depends on how good the fingerprint images. The background and foreground of the images are also effect the results of the fingerprint images. Fingerprint segmentation algorithms are used to extract the finger print image from background. In this paper we are presenting the two fingerprint segmentation algorithms which are the modifications of existing mean and variance based approach and gradient based approach.

Keywords: Bio-metric. Fingerprint, Image segmentation

References:

1. Mehtre and Chatterjee, “Segmentation of fingerprint images - a composite method”, Pattern Recognition, Vol.22,No 4, pp.381-385, 1989.
2. Mehtre, B.M., Murthy, N.N., Kapoor, S., Chatterjee,”Segmentation of fingerprint images using the directional image”, Pattern Recognition, Vol. 20, No.4, pp.429-435, 1987.
3. K.Ratha, S.Chen and A.K.Jain, “Adaptive flow orientation-based feature extraction in fingerprint images”, Pattern Recognition, Vol.28, No.11, pp.1657-1672,1995
4. Bazen and S.Gerez, ”Segmentation of fingerprint images”, Proc. Workshop on Circuits Systems and Signal Processing (Pro RISC 2001), pp. 276-280, 2001.
5. Zhu, Yin, J.Hu and C.Zhang, “A systematic method for fingerprint ridge orientation estimation and image segmentation”, Pattern Recognition, Vol.39, No.8, pp.1452-1472, 2006.
6. Alonso-Fernandez, J.Fierrez-Aguilar, J.Ortega-Garcia, An enhanced gabor filter-based segmentation algorithm for fingerprint recognition systems”, Proceedings of the 4thInternational Symposium on Image and Signal Processing and Analysis (ISPA 2005). pp. 239 244, 2005.
7. Parziale G. and Niel A., “A Fingerprint Matching Using Minutiae Triangulation”, Springer-Verlag, 2004.
8. Liu N., Yin Y., and Zhang H., “A Fingerprint Matching Algorithm Based on Delaunay Triangulation Net,” in Proceedings of the 5th International Conference on Computer and Information Technology, Shanghai, pp. 591-595, 2005.
9. Deng H. and Huo Q., “Minutiae Matching Based Fingerprint Verification Using Delaunay Triangulation and Aligned-Edge-Guided Triangle Matching,” in Proceeding of Audio and Video Based Biometric Person Authentication, New York, pp. 270-278, 2005.
10. Finger print Verification Contest 2004; FVC2004: Available at http://bias.csr.unibo.it/fvc2004.html
11. Gonzalez, R.C. and Woods, R.E. Digital Image Processing, Prentice Hall, ISBN 0201180758.

348-351

64.

Authors:

Paper Title:

Abstract: Nowadays, all electronic gadgets and devices are in the race of most compact size and least power consumption. Along with it, we want device to do many jobs at a time. For that it needs to be laced with maximum computing power. CMP/Many core architectures are inn with this advancement. In CMP architectures, parallel task execution is done rather than executing task sequentially, as it is done in conventional programming. Network-on-chip (NoC) is the best approach for interconnecting complex CMP architectures. However, NoC consumes significant percentage of total power. Final objective is to design power efficient NoC. In CMP architecture, many data transfers are done simultaneously. It is required to evolve power saver NOC with unique task scheduling schemes. Our approach enhances network latency, network throughput along with energy reduction. It defines performance of the system with high speed transmission. It shows improvement in the system performance when experimented with various applications. Here, we concentrated on proper distribution of tasks and network traffic required for execution. One needs to balance between over and under utilization of resources. Analysis of both the cases is done in this article. Over utilization is injecting excess traffic in the network which leads to heat up problem. Under utilization is, PEs remaining idle for long time resulting in sluggish performance. Here we tried to list various innovative methods for saving network from reaching power wall. Energy modeling and granular traffic analysis gives us accurate estimation of device performance beforehand. Also, analysis is done for various topologies for different tasks.

Keywords: CMP, CWC, CWN, Energy Consumption Reduction, Network Latency, NoC, Network Throughput.

References:

1. Reetuparna Das, Asit K. Mishra, Chrysostomos Nicopoulos, Dongkook Park, “Performance and Power Optimization through Data Compression in Network-on-Chip Architectures,” IEEE Transactions, pp. 215-225, 2008.
2. Daniel Sanchez, George Michelogiannakis, Christos Kozyrakis, “An analysis of on-chip interconnection networks for large-scale chip multiprocessors,” Journal of ACM Transactions on Architecture and Code Optimization, Vol. 7, No. 1, April 2010.
3. Hyungjun Kim, Pritha Ghoshal, Boris Groty Paul V. Gratz Daniel A. Jiménez, “Reducing Network-on-Chip Energy Consumption through Spatial Locality Speculation,” Pittsburgh, USA, pp. 1-8, May 1-4, 2011.
4. Chen, T.M. Pinkston, “Node-Router Decoupling for Effective Power-gating of On-Chip Routers,” in Proceedings of the 45th Annual IEEE/ACM International Symposium on Micro-architecture, Vancouver, BC, Canada, pp. 270–281, December 1–5, 2012.
5. Stavros Volos, Ciprian Seiculescu, Boris Grot, Naser Khosro Pour, Babak Falsafi, and Giovanni De Micheli, “CCNoC: Specializing On-Chip Interconnects for Energy Efficiency in Cache-Coherent Servers,” in Proceedings of the 6th International Symposium on Networks-on-Chip, IEEE Conference, pp. 1-8, 2012.
6. Wang, S. Roy, N. Ranganathan, “Run-time power-gating in caches of GPUs for leakage energy savings,” in Proceedings of the Design, Automation Test in Europe Conference Exhibition, Dresden, Germany, pp. 300-303, March 12–16, 2012.
7. Ahmed Ben Achballah, Slim Ben Saoud, “A Survey of Network-On-Chip Tools,” International Journal of Advanced Computer Science and Applications, Vol. 4, No. 9, pp. 61-67, 2013.
8. K. Khaitan, J.D. McCalley, “A hardware-based approach for saving cache energy in multi-core simulation of power systems,” in Proceedings of the IEEE Power Energy Society General Meeting, Vancouver, BC, Canada, pp. 1-5, July 21–25, 2013.
9. Mittal, “A Survey of Architectural Techniques for Improving Cache Power Efficiency,” Computational Information System, Vol. 4, pp. 43–48, 2013.
10. Lee, K. Choi, “Energy-efficient partitioning of hybrid caches in multi-core architecture,” in Proceedings of the 22^nd International Conference on Very Large Scale Integration, Playa del Carmen, Mexico, pp. 1-6, October 6–8, 2014.
11. Lou, L. Wu, S. Shi, P. Lu, “An energy-efficient two-level cache architecture for chip multiprocessors,” in Proceedings of the 5th International Conference on Computing, Communications and Networking Technologies, Hefei, China, pp. 1–5, July 11–13, 2014.
12. Mittal, Y. Cao, Z. Zhang, “A Multicore Cache Energy-Saving Technique Using Dynamic Cache Reconfiguration,” IEEE Transactions in Very Large Scale Integration System, Vol. 22, pp. 1653–1665, 2014.
13. Tejasi Pimpalkhute, Sudeep Pasricha, “NoC Scheduling for Improved Application-Aware and Memory-Aware Transfers in Multi-Core Systems,” 27^th International Conference on VLSI Design, IEEE Computer Society, pp. 239-240, 2014.
14. R. Kashwan, G. Selvaraj, “Implementation and performance analyses of a novel optimized NoC router,” International Conference on Convergence of Technology, IEEE Xplore, Pune, India, April 6-8, 2015.
15. Nasirian, M. Bayoumi, “Low-latency power-efficient adaptive router design for network-on-chip,” in Proceedings of the 28th IEEE International System-on-Chip Conference, Kohala Coast, HI, USA, pp. 287–291, August 27–29, 2015.
16. Shenbagavalli, S. Karthikeyan, “An efficient low power NoC router architecture design,” in Proceedings of the Online International Conference on Green Engineering and Technologies, Coimbatore, India, pp. 1-8, November 2015.
17. K. Chien, L.Y. Chiou, C.C. Lee, Y.C. Chuang, S.H. Ke, S.S. Sheu, H.Y. Li, P.H. Wang, T.K. Ku, M.J. Tsai, “An energy-efficient non-volatile microprocessor considering software-hardware interaction for energy harvesting applications,” in Proceedings of the International Symposium on VLSI Design, Automation and Test, Hsinchu, Taiwan, pp. 1-4, April 25–27, 2016.
18. Edoardo Fusella, Alessandro Cilardo, “A Hybrid Optical–Electronic NoC based on Hybrid Topology,” IEEE Transactions on Very Large Scale Integration Systems, Vol. 25, No. 1, pp. 330-343, January 2017.
19. Emmanuel Ofori-Attah, Washington Bhebhe and Michael Opoku Agyeman, “Architectural Techniques for Improving the Power Consumption of NoC-Based CMPs: A Case Study of Cache and Network Layer,” Journal of Low Power Electronics and Applications, Vol. 7, No.14, pp. 1-24, 2017.
20. Kiran, Kamma Solanki, “Design of efficient NOC router for chip multiprocessor,” Invention Computation Technologies International Conference on IEEE Xplore, Coimbatore, 26 January 2017.
21. Letian Huang, Xinxin Lin, Junshi Wang, Qiang Li, “A low latency fault tolerant transmission mechanism for Network-on-Chip”, Circuits and Systems, IEEE International Symposium on Baltimore, USA, pp. 28-31 May 2017.
22. Sascha Roloff, Frank Hannig, Jurgen Teich, “High performance network-on-chip simulation by interval-based timing predictions,” in Proceedings of the 15^th IEEE/ACM Symposium on Embedded Systems for Real-Time Multimedia, Seoul, Republic of Korea, pp. 2-11, October 15, 2017.
23. Salma Hesham, Jens Rettkowski, Diana Goehringer, Mh. A. Abd El Ghany, “Survey on Real-Time Networks-on-Chip,” IEEE Transactions on Parallel and Distributed Systems, Vol. 28, No. 5, pp. 1500-1517, May 2017.
24. Sebastian Werner, Javier Navaridas, Mikel Lujan, “A Survey on Optical Network-on-Chip Architectures,” ACM Computing Surveys, Vol. 50, No. 6, January 2018.
25. “Power Dissipation in CMOS”, Digital Electronic-SCRIBD, Lecture 13, 18-322 Fall 2003.
26. Farzan Fallah, Massoud Pedram, “Standby and active leakage current control and minimization in CMOS VLSI circuits”, IEICE Transactions on Electronics, 2005.
27. Roy, S. Mukhopadhay, H.Mahmoodi-Meimand, “Leakage current in sub-micrometer CMOS gates”, IEEE Transaction Vol. No. 91, Issue No. 2, Feb 2003.
28. David Harris, “Introduction to CMOS VLSI Design: Synthesis and Floor Planning”, Harvey Mudd College, Stanford University, Lecture 7.
29. Gaizhen Yan, Ning Wu, Lei Zhou and Fen Ge, “PDDVB: A Priority Division Distributed Vertical Bus for 3D Bus - NoC Hybrid Network.”, IAENG International Journal of Computer Science, 43:2, IJCS_43_2_14.
30. Zhicheng Zhou, Ning Wu and Gaizhen Yan, “Topology Optimization of 3D Hybrid Optical-Electronic Network-On-Chip.”, Proceedings of the World Congress on Engineering and Computer Science 2016, Vol I WCECS 2016, October 19-21, 2016, San Francisco, U.S.A.
31. Zhang Ying, Chen Xin and Ge Fen, “Collaborative Optimization of Testing and Mapping for Network-on-Chip.” Proceedings of the World Congress on Engineering 2018 Vol I WCE 2018, July 4-6, 2018, London, U.K.
32. Xinxin Yue, Fen Ge, Ning Wu, Gaizhen Yan, “HOG-NoC: Hybrid Optical-Electronic Mesh Based Grouped NoC.” Proceedings of the World Congress on Engineering and Computer Science 2017 Vol I WCECS 2017, October 25-27, 2017, San Francisco, U.S.A.
33. Xintian Tong, Fen Ge, Rongrong Zhou, Ning Wu, Fang Zhou and Yingying Kong, “Desing of Low Power Multi-Mode Router for Network-on-Chip in Dark Silicon Era.”, Proceedings of the World Congress on Engineering 2017 Vol I WCE 2017, July 5-7, 2017, London, U.K.
34. Rui Ben, Fen Ge, Xintian Tong, Ning Wu, Ying Zhang, Fang Zhou, “ A Multicast Routing Algorithm for 3D Network-on-Chip in Chip Multi_Processors”, Proceedings of the World Congress on Engineering 2018 Vol I WCE 2018, July4-6, 2018, London, U.K.
35. Guoming Nie, Ning Wu, Fen Ge, Gaizhen Yan, “A QoS-Enabled Optical-Electronic Network-on-Chip”, Proceedings of the World Congress on Engineering and Computer Science 2017 Vol I WCECS 2017, October 25-27, 2017, San Francisco, U.S.A.
36. Gaizhen Yan, Ning Wu, Zhicheng Zhou, “A Novel Non-Cluster Based Architecture of Hybrid Electro-Optical Network-on-Chip.”, This work is supported in part by the National Science Foundation of China (61376025), The Anhui Scientific Research Funds for University (KJ2017A501), The Jiangsu Innovation Program for Graduate Education (KYLX15-0283) and The Natural Science Foundation of Jiangsu Province (BK 20160806).
37. G. Ramprabu, T. Saravanan, G. Saritha, “Wireless Audio Signal Communication using Li-Fi Technology”, International Journal of Engineering and Advanced Technology, Vol.8, Issue 2, 2018, pp.208-210.

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Paper Title:

Abstract: MANET networks often form with mobile devices to enable communication and connectivity in time-critical applications. The routing protocol employ in the network often defines the energy efficiency and network performance in ad hoc networks. In this paper, we propose a directional flooding-based routing approach to improve data reliability and improve packet delivery ratio in MANET network. The proposed routing protocol simulate in the NS2 environment to evaluate network performance of flood-based routing algorithm.

Keywords: MANET, Energy Efficiency, Ad hoc Network, Routing and Data Reliability.

References:

1. Chou and D. Wei, “An efficient anonymous communication protocol for peer-to-peer applications over mobile ad-hoc networks,” … Areas Commun. …, vol. 25, no. 1, pp. 192–203, 2007.
2. Y. Han and D. Lee, "An adaptive hello messaging scheme for neighbour discovery in on-demand MANET routing protocols," IEEE Commun. Lett., vol. 17, no. 5, pp. 1040–1043, 2013.
3. M. E. Ejmaa, S. Subramaniam, and Z. A. Zukarnain, “Neighbor-based Dynamic Connectivity Factor Routing Protocol for Mobile Ad Hoc Network,” IEEE Access, vol. 4, no. 1, pp. 8053–8064, 2016.
4. Taha, R. Alsaqour, M. Uddin, M. Abdelhaq, and T. Saba, “Energy Efficient Multipath Routing Protocol for Mobile Ad-Hoc Network Using the Fitness Function,” IEEE Access, vol. 5, no. 1, pp. 10369–10381, 2017.
5. Bai, N. Sadagopan, B. Krishnamachari, and A. Helmy, “Modeling path duration distributions in MANETs and their impact on reactive routing protocols,” IEEE J. Sel. Areas Commun., vol. 22, no. 7, pp. 1357–1373, 2004.
6. H. Chen, E. H. K. Wu, and G. H. Chen, “Bandwidth-Satisfied Multicast by Multiple Trees and Network Coding in Lossy MANETs,” IEEE Syst. J., vol. 11, no. 2, pp. 1116–1127, 2017.
7. Jinil Persis and T. Paul Robert, “Review of ad-hoc on-demand distance vector protocol and its swarm intelligent variants for Mobile Ad-hoc NETwork,” IET Networks, vol. 6, no. 5, pp. 87–93, 2017.
8. Meghanathan, “A location prediction-based reactive routing protocol to minimize the number of route discoveries and hop count per path in mobile ad hoc networks,” Comput. J., vol. 52, no. 4, pp. 461–482, 2009.
9. Chen, A. Ben Mnaouer, and C. H. Foh, “OPHMR: An optimized polymorphic hybrid multicast routing protocol (OPHMR) for Ad Hoc networks,” IEEE Int. Conf. Commun., vol. 8, no. 5, pp. 3572–3577, 2006.
10. Oh, “A Tree-Based Approach for the Internet Connectivity of Mobile Ad Hoc Networks,” J. Commun. Networks, vol. 11, no. 3, pp. 261–270, 2009.
11. Varaprasad, S. Hosahalli Narayanagowda, and Shivashankar, “Implementing a new power aware routing algorithm based on existing dynamic source routing protocol for mobile ad hoc networks,” IET Networks, vol. 3, no. 2, pp. 137–142, 2014.
12. Surendran and S. Prakash, “An ACO Look- A head Approach to Q O S Enabled Fault- Tolerant Routing in MANETs,” China Commun., vol. 18, no. August, pp. 93–110, 2015.
13. Zhang, X. Wang, P. Memarmoshrefi, and D. Hogrefe, “A Survey of Ant Colony Optimization Based Routing Protocols for Mobile Ad Hoc Networks,” Proc. - 2013 Int. Conf. Comput. Inf. Sci. ICCIS 2013, vol. 5, no. 1, pp. 1595–1598, 2013.
14. U. Rahman, “Simulation-based analysis of MANET routing Protocols using Group Mobility Model,” 2016 Int. Conf. Inven. Comput. Technol., vol. 1, no. 2, pp. 20–32, 2016.
15. S. Samaras, “Using Basic MANET Routing Algorithms for Data Dissemination in Vehicular Ad Hoc Networks (VANETs),” 26th Telecommun. Forum TELFOR 2016, vol. 1, no. 1, pp. 0–3, 2016.
16. Ramprabu, S. Nagarajan, “Design and Analysis of Novel Modified Cross Layer Controller for WMSN”, Indian Journal of Science and Technology, Vol.8, Issue 4, 2015, pp.438-444.

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Paper Title:

Abstract: In this paper, a case of prediction of sample demands for industrial lubricants has been presented. We have observed that the demand for most of the industrial lubricants depends on three main factors i.e. quality, cost, and delivery time. These factors are studied and compared with other competitors dealing in similar nature of products. The quality is mapped with three fuzzy parameters viz. inferior, alike, and superior. The cost is linked with three linguistic variables viz. low, identical & high. Similarly, delivery time is also associated with three sub-parameters viz. short, equal and long. First, the raw data of demand for 12 number of random samples are collected from supply chain executives of an automotive and industrial lubricant manufacturing company. Thereafter, the membership functions for the causal factors and demand are built on the basis of comparative analysis and collected data. At last, a fuzzy- inference demand model with rule base is constructed. Finally, the demands are predicted by the skilled fuzzy model. Predicted data is compared with the raw data and absolute errors are being calculated. The result shows predictions made by the fuzzy-inference demand model are in tune with the actual demands of industrial lubricants. Thus, the built fuzzy model can be utilized and generalized for effective demand forecasting for industrial products

Keywords: Demand, Forecasting, Fuzzy logic, Membership function, Prediction.

References:

1. Al-Anbuky, S. Bataineh, S. Al-Aqtash, Power demand prediction using fuzzy logic, Control Eng. Pract. 3 (1995) 1291–1298. doi:10.1016/0967-0661(95)00128-H.
2. W. Ma, Fuzzy Set Theory in Power Systems, Power. 10 (1995). doi:10.1007/978-0-387-78171-6.
3. H.M. Tah, V. Carr, Towards a framework for project risk knowledge management in the construction supply chain, Adv. Eng. Softw. 32 (2001) 835–846. doi:10.1016/S0965-9978(01)00035-7.
4. P. Rodriguez, G.J. Anders, Energy Price Forecasting in the Ontario Competitive Power System Market, IEEE Trans. Power Syst. 19 (2004) 366–374. doi:10.1109/TPWRS.2003.821470.
5. L. Chen, W.C. Lee, Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices, Comput. Chem. Eng. 28 (2004) 1131–1144. doi:10.1016/j.compchemeng.2003.09.014.
6. Chenthur Pandian, K. Duraiswamy, C.C.A. Rajan, N. Kanagaraj, Fuzzy approach for short term load forecasting, Electr. Power Syst. Res. 76 (2006) 541–548. doi:10.1016/j.epsr.2005.09.018.
7. Arshinder, A. Kanda, S.G. Deshmukh, Coordination in supply chains: An evaluation using fuzzy logic, Prod. Plan. Control. 18 (2007) 420–435. doi:10.1080/09537280701430994.
8. Peidro, J. Mula, R. Poler, J.L. Verdegay, Fuzzy optimization for supply chain planning under supply, demand and process uncertainties, Fuzzy Sets Syst. (2009). doi:10.1016/j.fss.2009.02.021.
9. Ko, A. Tiwari, J. Mehnen, A review of soft computing applications in supply chain management, Appl. Soft Comput. J. 10 (2010) 661–674. doi:10.1016/j.asoc.2009.09.004.
10. Nieto-Morote, F. Ruz-Vila, A fuzzy approach to construction project risk assessment, Int. J. Proj. Manag. 29 (2011) 220–231. doi:10.1016/j.ijproman.2010.02.002.
11. Wang, D. Li, X. Shi, A fuzzy model for aggregative food safety risk assessment in food supply chains, Prod. Plan. Control. 23 (2012) 377–395. doi:10.1080/09537287.2011.561812.
12. Ahmadi, H. Bevrani, H. Jannaty, A fuzzy inference model for short-term load forecasting, 2012 2nd Iran. Conf. Renew. Energy Distrib. Gener. ICREDG 2012. 37 (2012) 39–44. doi:10.1109/ICREDG.2012.6190465.
13. Kumar, J. Singh, O.P. Singh, Seema, A fuzzy logic based decision support system for evaluation of suppliers in supply chain management practices, Math. Comput. Model. 58 (2013) 1679–1695. doi:10.1016/j.mcm.2013.03.002.
14. T. Chen, F.T.S. Chan, S.H. Chung, W.Y. Park, Optimization of product refurbishment in closed-loop supply chain using multi-period model integrated with fuzzy controller under uncertainties, Robot. Comput. Integr. Manuf. 50 (2018) 1339–1351. doi:10.1016/j.rcim.2017.05.005.

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Paper Title:

Abstract: Passive tracking of a target using bearings-only measurement which is carried out in underwater scenario is most widely used. This paper considers the problem of estimating the position and velocity of a target in an underwater scenario. As bearings-only tracking is a non-linear problem, Kalman filter which is linear and traditional filter can’t give correct approximations. The noise in measurement is more as underwater scenario is considered which leads to less accurate estimation. Particle filter (PF) which is a non-linear filter is considered for this problem. In PF, all particles are assigned with weights based on their likelihood computed with respect to obtained measurements. The weights assigned to the particles, after certain time period tend to equal values called sample impoverishment.The main difficulty using PF is sample degeneracy and sample impoverishment. To avoid these problems, different re-sampling techniques can be used, or PF can be combined with other filters like Extended Kalman Filter (EKF), Unscented Kalman filter (UKF), Modified Gain Bearings-only Extended Kalman filter (MGBEKF) etc. In this paper, PF with Systematic re-sampling technique and combined with MGBEKF is considered for analysing the estimation of target parameters. Evaluation of the algorithm is assessed based on the best convergence time of the solution for many scenarios using MATLAB software.

Keywords: Bearings-only tracking, Modified Gain Bearing-only Extended KalmanFilter,Particle Filter, Signal processing, Systematic Re-sampling.

References:

1. Simon, “Optimal State Estimation: Kalman, H∞ and nonlinear Approximations”,Wiley, 2006.
2. C. Nardone, A. G. Lindgren and K. F. Gong, “Fundamental Properties and Performance of Conventional Bearing-only Target Motion Analysis”, IEEE Transactions on Automatic Control, Vol. AC-29, No.9,pp.775-787, Sep.1984.
3. G. Lingren, K. F. Gong, “Position and Velocity Estimation Via Bearing Observations”, IEEE Transactions on Aerospace and Electronic Systems Vol. Aes-14, No. 4 July 1978.
4. Brehard, Jean-Pierre Le Cadre, “Closed-form Posterior Cramér-Rao Bound for a Manoeuvring Target in the Bearings-Only Tracking Context Using Best-Fitting Gaussian Distribution”, 9th International Conference on Information Fusion, DOI: 10.1109/ICIF.2006.301625, February 2007.
5. C. Nardone and V. J. Aidala, “Observability criteria for bearings-only target motion analysis”, IEEE Trans. Aerosp. Electron Syst., Vol. AES-17, No. 2, pp 162-166, 1981.
6. C. Nardone and V. J. Aidala, “Observability Criteria for Bearing-only Target Motion Analysis”, IEEE Transactions on Aerospace and Electronic Systems Vol. Aes-17, No. 2 March 1981.
7. Karlsson and F. Gustafsson, “RecursiveBayesian estimation: Bearings-only applications”, IEE Proc. Radar, Sonar & Navigation, Vol. 152, No. 5, pp 305-313, October 2005.
8. Cappe, S. J. Godsill, and E. Moulines, “An overview of existing methods and recent advances in sequential Monte Carlo,” Proceedings of the IEEE, vol. 95, no. 5, pp. 899–924, 2007.
9. L. Alspach and H. W. Sorenson, “Nonlinear Bayesian estimation using Gaussian sum approximations,” IEEE Transactions on Automatic Control, vol. 17, no. 4, pp. 439–448, 1972.
10. Terejanu, P. Singla, T. Singh, and P. D. Scott, “Adaptive Gaussian sum filter for nonlinear Bayesian estimation,” IEEE Transactions on Automatic Control, vol. 56, no. 9, pp. 2151–2156, 2011.
11. Y. Fu and Y. M. Jia, “An improvement on resampling algorithm of particle filters,” IEEE Transactions on Signal Processing, vol. 58, no. 10, pp. 5414–5420, 2010.
12. Kabaoglu, “Target tracking using particle filters with support vector regression,” IEEE Transactions on Vehicular Technology, vol. 58, no. 5, pp. 2569–2573, 2009.
13. Jun Ye Yu, Mark J. Coates, Michael G. Rabbat, and Stephane Blouin, “A Distributed Particle Filter for Bearings-only Tracking on Spherical Surfaces”, IEEE Signal Processing Letters, Vol.23,NO.3, March 2016.
14. Martin Clark, Simon Maskell, Richard VinterandMoeen Yaqoob, “A Comparison of the Particle and shifted Rayleigh filters in their application to a multi-sensor bearings-only problem”, IEEE Aerospace Conference, pp.2142 – 2147, 2005.
15. Hongwei Zhang, Liangqun Li, And WeixinXie, “Constrained Multiple Model Particle Filtering for Bearings-Only Maneuvering Target Tracking”, IEEE Access, Vol.6, pp. 51721-51734, September 2018, DOI: 10.1109/ACCESS.2018.2869402.
16. Liu and R. Chen, “Sequential Monte-Carlo methods for dynamic systems”, J. Amer. Statist. Assoc., vol. 93, no. 443, pp. 1032-1044, 1998.
17. E. R. Beadle and P. M. Djuric, “A fast-weighted Bayesian bootstrap filter for nonlinear model state estimation”, IEEE Trans. Aerosp. Electron. Syst., vol. 33, no. 1, pp. 338-343, 1997.

374-378

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Paper Title:

Abstract: Net Asset value (NAV) prediction is considered as a financial time series forecasting problem. Different linear and nonlinear time series forecasting models have been used in NAV prediction by various researchers. In this work, an ensemble model is proposed combining AMA as linear model and ANN and FLANN models as nonlinear models for forecasting of NAV data of TATA Dividend Yield Fund-Direct Growth and SBI Magnum Equity mutual funds. The individual models are trained with conventional LMS algorithm. It is a weighted linear ensemble model where the weights are optimized using a modified Whale Optimization Algorithm. The empiricalforecasting performance of the modified Whale Optimization Algorithm based ensemble model along with GA and PSO based ensemble models and the individual models are analyzed. The results demonstrate that the proposed ensemble model outperforms the other models.

Keywords: Functional Link Artificial Neural Network, Least Mean Square, Net Asset Value, Prediction performance,Whale Optimization Algorithm

References:

1. Agnesens, “A statistically robust decomposition of mutual fund performance”, Journal of Banking & Finance, 37(10), 3867-3877, 2013.
2. Cavalcante, R. Brasileiro, V. Souza, J. Nobrega, A. Oliveira, “Computational Intelligence and Financial Markets: A survey and future directions”, Expert System with Applications, 55, 194-221, 2016
3. C. Indro , C.X. Jiang , B.E. Patuwo, G.P. Zhang, “Predicting mutual fund performance using artificial neural Networks”, Omega Int. J. Mgmt. Sci. , Vol. 27 , pp. 373-380, 1999.
4. C. Chiang, T.L. Urban, G.W. Baldridge , “A Neural Network Approach to Mutual Fund Net Asset Value Forecasting”, Omega Int. J. Mgmt Sci. ,Vol. 24, No. 2, pp. 205-215, 1996.
5. Yan, W. Liu, X. Liu, H. Kong, C. Lv,” Predicting Net Asset Value of Investment Fund Based on BP Neural Network”, In: IEEE International Conference on Computer Application and system modeling (ICCASM), 10, pp. 635–637, 2010.
6. M Anish, B. Majhi, "Net asset value prediction using FLANN model." International Journal of Science and Research, 4(2) 2222-2227, 2015.
7. T. Clemen, “Combining forecasts: A review and annotated bibliography”, International Journal of Forecasting 5, 559-583, 1989.
8. H. Zhou,” Ensemble Methods: Foundations and Algorithms”, A Chapman and Hall book, CRC Press, 2012.
9. M.Anish, B. Majhi, R. majhi,” Development and Evaluation of novel forecasting adaptive ensemble model”, The Journal of Finance 2, 188-201, 2016.
10. Majhi, C. Anish, , R. Majhi, “On development of novel hybrid and robust adaptive models for net asset value prediction”, Journal of King Saud University – Computer and Information Sciences, 2018
11. Khashei, M. Bijari, “A novel hybridization of artificial neural network and ARIMA models for time series forecasting”, Applied Soft Computing, 11, 2664-2675, 2011.
12. P. Zhang, “Time series forecasting using hybrid ARIMA and neural network model”, Neurocomputing, 50, 159-175, 2003.
13. Yu, S. Wang, K. Lai, “A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates”, Computers and Operation Research, 32, 2523-2541, 2005.
14. Mirjalili, A. Lewis, “The Whale Optimization Algorithms”, Advances in Engineering Software, 95, 51-67, 2016.
15. Medani, S. Sayah, A. Bekrar, “Whale Optimization algorithm based optimal reactive power dispatch: A case study of the Algerian power system”, Electric Power Systems Research, 163, 696-705, 2018.
16. R. Sahu, P.K.Hota, S. Panda, “Power system stability enhancement by fractional order multi input SSSC based controller employing whale optimization algorithm”,Journal of. Electrical System and Information Technology, 2017, https://dx.doi.org/10.1016/j.jesit.2018.02.008.
17. Sun, X. Wang, Y. Chen, Z. Liu, “A modified whale optimization algorithm for large-scale global optimization problem”, Expert systems with Applications, 114, 563-577, 2018.
18. Sun, C. Zhang, “Analysis and forecasting of the carbon price using multi—resolution singularvalue decomposition and extreme learning machine optimized by adaptivewhale optimization algorithm”, Applied Energy, 231, 1354-1371, 2018.
19. kaur, S. Arora, “Chaotic whale optimization algorithm”, Journal of Computational Design and Engineering, 5, 275–284, 2018.
20. M.Mafarja, S. Mirjalili, “Hybrid Whale Optimization Algorithm with simulated annealing for feature selection”, Neurocomputing,260, 302–312, 2017.
21. H. Wang, L. Yong, C.Li,S. Zeng, “A Hybrid Particle Swarm Algorithm with Cauchy Mutation”. Proceedings of the 2007 IEEE Swarm Intelligence Symposium,SIS2007.pp.356-360, 2007.

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Paper Title:

Abstract: Software Defined Networking (SDN) is emerging network architecture. The principle of separating control plane and data plane has opened up many opportunities for researchers to deploy new innovations. The central and single point of managing the whole network is SDN controller. This gives the system the weaknesses of being a single point of failure. This issue of failure can be solved using a cluster which is comprised of multiple instances of a SDN controller. In case of failure of one instance, other will come into play and manages the system. We have taken OpenDayLight (ODL) SDN controller which is open of the largest Opensource and industry oriented SDN controller as a point of study for making use of clustering. This paper aims to provide the implementation of clusters of ODL SDN controller. Advantages and Issues of clustering in ODL are also discussed.

Keywords: Software Defined Networking, Controller, OpenDayLight, Clustering.

References:

1. https://wiki.opendaylight.org/view/Running_and_testing_an_OpenDaylight_Cluster (Accessed: 27.02.2019).
2. Hernandez Marulanda, Esteban (2016). Implementation and performance of a SDN cluster-controller based on the OpenDayLight framework.
3. Kim, S.-G. Choi, J. Myung, C.-G. Lim, Load balancing on distributed datastore in opendaylight SDN controller cluster, in: Network Softwarization (NetSoft), 2017 IEEE Conference on, IEEE, 2017, pp. 1–3.
4. Agrawal, Raft: A recursive algorithm for fault tolerance., in: ICPP, 1985, pp. 814–821.
5. Controller platform (oscp): Clustering, http://https://wiki. opendaylight.org/view/OpenDaylight_Controller/Clustering, (Accessed: 17.02.2019).
6. Medved, R. Varga, A. Tkacik, K. Gray, Opendaylight: Towards a model-driven SDN controller architecture, in: World of Wireless, Mobile and Multimedia Networks, 2014, pp. 1–6.
7. Badotra, S., & Singh, J. (2017). Open Daylight as a Controller for Software Defined Networking. International Journal of Advanced Research in Computer Science, 8(5).
8. Feamster, N., Rexford, J., & Zegura, E. (2014). The road to SDN: an intellectual history of programmable networks. ACM SIGCOMM Computer Communication Review, 44(2), 87-9.
9. Wickboldt, J. A., De Jesus, W. P., Isolani, P. H., Both, C. B., Rochol, J., & Granville, L. Z. (2015). Software-defined networking: management requirements and challenges. IEEE Communications Magazine, 53(1), 278-285.
10. Badotra, S., & Singh, J. (2017). A Review Paper on Software Defined Networking. International Journal of Advanced Research in Computer Science, 8(3).
11. Jammal, M., Singh, T., Shami, A., Asal, R., & Li, Y. (2014). Software defined networking: State of the art and research challenges. Computer Networks, 72, 7498.
12. Shenker, S., Casado, M., Koponen, T., & McKeown, N. (2011). The future of networking, and the past of protocols. Open Networking Summit, 20, 1-30.
13. Nunes, B. A. A., Mendonca, M., Nguyen, X. N., Obraczka, K., & Turletti, T. (2014). A survey of software-defined networking: Past, present, and future of programmable networks. IEEE Communications Surveys & Tutorials, 16(3), 16171634
14. Feamster, N., Rexford, J., & Zegura, E. (2014). The road to SDN: an intellectual history of programmable networks. ACM SIGCOMM Computer Communication Review, 44(2), 87-98.
15. Lantz, B., Heller, B., & McKeown, N. (2010, October). A network in a laptop: rapid prototyping for software-defined networks. In Proceedings of the 9th ACM SIGCOMM Workshop on Hot Topics in Networks (p. 19). ACM.
16. Xia, W., Wen, Y., Foh, C. H., Niyato, D., & Xie, H. (2015). A survey on software-defined networking. IEEE Communications Surveys & Tutorials, 17(1), 2751.

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Authors:

Paper Title:

Abstract: The emergence of Software Defined Networking(SDN) is a paradigm shift that re-thinks conventional legacy network design/operations/abstractions and makes future net- works openly programmable, controllable, scalable and afford- able. As a game changer in modern internetworking technologies, SDN is widely accepted by enterprises, with use in domains ranging from private home networks to small/medium scale workgroup networks to corporate backbone to large-scale wide- area cloud networks. Employing SDN in modern networks provides the much-needed agility and visibility to orchestrate and deploy network solutions. But from the security perspectives in terms of threat attack prediction and risk mitigation, especially for the advanced persistent attacks such as DDoS and side channel attacks in Clouds, SDN stack control plane saturation attacks, switch flow table exhaustion attacks - there are still open challenges in SDN environments. In this paper, at first, we present the taxonomy of threats, risks and attack vectors that can disrupt the SDN stack and present various approaches to solve these problems, to deploy SDN securely in production environments. We survey existing research on SDN and the results of our thorough analysis, comparative study of key principles, trade-offs and evaluation of the well-known techniques for SDN security are also presented. To address the key shortcomings and limitations of the existing solutions, we propose our future work a novel framework to effectively monitor and tackle the SDN security issues. Our proposed framework includes a dynamic security se- mantic monitoring system that decouples monitoring from packet forwarding, and offers flexible fine-grained monitoring, which also integrate well with the SDN architecture. This system will employ machine-learning techniques for fingerprinting, accurate detection of behavioral patterns; attack flows and anomalies in the SDN based networks.

Keywords: Software Defined Networking, SDN, OpenFlow, network security, threat monitoring, IDS, Firewall

References:

1. Yu, J. Rexford, M. J. Freedman, and J. Wang, “Scalable flow-based networking with difane,” ACM SIGCOMM Computer Communication Review, vol. 40, no. 4, pp. 351–362, 2010.
2. Shin, V. Yegneswaran, P. Porras, and G. Gu, “Avant-guard: scalable and vigilant switch flow management in software-defined networks,” in Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security. ACM, 2013, pp. 413–424.
3. Ambrosin, M. Conti, F. De Gaspari, and R. Poovendran, “Lineswitch: Efficiently managing switch flow in software-defined networking while effectively tackling dos attacks,” in Proceedings of the 10th ACM Symposium on Information, Computer and Communications Security. ACM, 2015, pp. 639–644.
4. Sonchack, A. J. Aviv, E. Keller, and J. M. Smith, “Enabling practical software-defined networking security applications with OFX,” in Proceedings of the 2016 Network and Distributed System Security Symposium (NDSS), 2016.
5. HassasYeganeh and Y. Ganjali, “Kandoo: a framework for efficient and scalable offloading of control applications,” in Proceedings of the firstworkshoponHottopicsinsoftware-defined-networks. ACM,2012, pp. 19–24.
6. Wang, L. Xu, and G. Gu, “Floodguard: a dos attack prevention extension in software-defined networks,” in Dependable Systems and Networks (DSN), 2015 45th Annual IEEE/IFIP International Confer- ence on. IEEE, 2015, pp. 239–250.
7. -y. Chen, A. R. Junuthula, I. K. Siddhrau, Y. Xu, and H. J. Chao, “Sdnshield: Towards more comprehensive defense against ddos attacks on sdn control plane,” in Communications and Network Security (CNS), 2016 IEEE Conference on. IEEE, 2016, pp. 28–36.
8. Bifulco, J. Boite, M. Bouet, and F. Schneider, “Improving sdn with inspired switches,” in Proceedings of the Symposium on SDN Research. ACM, 2016, p. 11.
9. G. B. A. Nair, Mol and Nair, “A mediator based dynamic server load balancing approach using sdn,” in International Journal of Control Theory and Applications, 2016, pp. 6647–6652.
10. Conti, F. De Gaspari, and L. V. Mancini, “Know your enemy: Stealth configuration-information gathering in sdn,” arXiv preprint arXiv:1608.04766, 2016.
11. Sung, P. K. Sharma, E. M. Lopez, and J. H. Park, “Fs-opensecurity: A taxonomic modeling of security threats in sdn for future sustainable computing,” Sustainability, vol. 8, no. 9, p. 919, 2016.
12. Krishnan and J. Najeem, “A multi plane network monitoring and defense framework for sdn operational security,” in International Conference on Operating System Security (ICOSS 2017), 2017.
13. Krishnan, Prabhakar, Jisha S. Najeem, and Krishnashree Achuthan. "SDN Framework for Securing IoT Networks." In International Conference on Ubiquitous Communications and Network Computing, pp. 116-129. Springer, Cham, 2017
14. Karthik Raghunath, Krishnan Prabhakar, "Towards A Secure SDN Architecture”,2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT)
15. Krishnan Prabhakar, Achuthan Krishnashree, "Managing Network Functions in Stateful Application Aware SDN”, 6th International Symposium on Security in Computing and Communications (2018), Springer Communications in Computer and Information Science Series(CCIS), ISSN: 1865:0929

389-393

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Authors:

Paper Title:

Abstract: In recent years, we have seen the rise of application specific attacks that exploit the vulnerabilities in the network protocols (HTTP, DNS, SMTP, other) and try to overwhelm the server application, not just the connectivity pipe. In this paper, we propose an advanced DoS Threat Analytics System (DTAS) to mitigate the full range of DoS network attacks – not just volumetric, based on comprehensive collaborative detection algorithms, implemented in the Elasticsearch Big Data platform. DTAS security solution is driven by powerful threat detection algorithms that: a) dissects all attack probabilities in the network traffic, b) Uses behavioural analytics to correlate multiple parameters and generate multi-vector representations, c) Employs dynamic challenges to verify normal versus attack traffic. The DTAS analytics engine analyses multiple IP attributes within TCP and UDP flows, ICMP, HTTP and DNS traffic, count, frequency, headers, payloads, detecting covert traffic, amplification attacks trying to target the services on the network. By measuring all these attributes, our system creates a multi-vector heuristic representation of the normal or baseline traffic flows. We have used datasets from UCLA, downloaded traces from real world incidents and tested the efficacy of the system with various large-scale simulated DoS attacks in the test network. Our experiments show that the DTAS framework can detect DoS attacks in real time, without impacting the latency to benign traffic in the network and with accuracy up to 95% detection rate for attacks.

Keywords: Botnet,Distributed Denial of Service (DDoS) attack, Network Security, Threat Analytics

References:

1. Shameli-Sendi et al. "Taxonomy of distributed denial of service mitigation approaches for cloud computing." Journal of Network and Computer Applications 58 (2015): 165-179.
2. Corero DDoS Trends Report,2017 http://info.corero.com/DDoS-Trends-Report.html
3. “Source code for IoT botnet Mirai released”, https://krebsonsecurity. com/2016/10/source- code- for- iot- botnet- mirai- released/
4. Vladimir Kuskov et al. “Honeypots and the Internet of Things”, Kaspersky Research - https://securelist.com/honeypots-and-the-internet-of-things/78751/
5. Krishnan, Prabhakar, Jisha S. Najeem, and Krishnashree Achuthan. "SDN Framework for Securing IoT Networks." In International Conference on Ubiquitous Communications and Network Computing, pp. 116-129. Springer, Cham, 2017
6. Kalkan, Kübra et al. "Filtering-Based Defense Mechanisms Against DDoS Attacks: A Survey." IEEE Systems Journal (2016).
7. Pan, Lanlan, Xuebiao Yuchi, and Yong Chen. "Mitigating DDoS attacks towards Top Level Domain name service." Network Operations and Management Symposium (APNOMS)18th Asia-Pacific. IEEE, 2016.
8. Hsieh, Chang-Jung, and Ting-Yuan Chan. "Detection DDoS attacks based on neural-network using Apache Spark." Applied System Innovation (ICASI), 2016 International Conference on. IEEE, 2016.
9. Fachkha, Claude, Elias Bou-Harb, and Mourad Debbabi. "Fingerprinting internet DNS amplification DDoS activities." New Technologies, Mobility and Security (NTMS), 2014 6th IntlIEEE Conference.
10. Acarali, Dilara, et al. "Survey of approaches and features for the identification of HTTP-based botnet traffic." Journal of Network and Computer Applications 76 (2016): 1-15.
11. Holl,Patrick."ExploringDDoSDefenseMechanisms."Network25(2015).
12. Zargar et al. "A survey of defense mechanisms against distributed denial of service (DDoS) flooding attacks." IEEE communications surveys & tutorials 15.4 (2013): 2046-2069.
13. Mekhitarian, Araxi, and Amir Rabiee. "A simulation study of an application layer DDoS detection mechanism." (2016).
14. B. Pa Poornachandran, Premjith and K. P. Soman, “A distributed approach for predicting malicious activities in a network from a streaming data with support vector machine and explicit random feature mapping,” in IIOAB Journal, 2016, pp. 24–29.

394-398

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Authors:

Paper Title:

Abstract: Multi-path routing in Mobile Ad Hoc Networks (MANETs) minimizes latency and ensures on-demand back-up routing to prevail over route errors. Unplanned network load degrades individual node performance, preventing instant path switch-over. This increases overloading of the nodes and henceforth resulting in drops. We propose a two-phase optimization algorithm in a hybrid manner assimilating Ant Colony Optimization (ACO) and Genetic Approach (GA) to improve load handling capacity of the nodes with improved packet delivery at the destination. Both node and path selection are favored by conditional optimization in both the phases; concentrating in minimum switch-over and higher delivery rate. Precise path and neighbor selection by improved load handling capability minimizes packet drop and control overhead.

Keywords: Optimal Cluster Head Selection, Ant Colony Optimization, Genetic Algorithms, Load balancing.

References:

1. Tashtoush, O. Darwish, and M. Hayajneh, “Fibonacci sequence based multipath load balancing approach for mobile ad hoc networks,” Ad Hoc Networks, vol. 16, pp. 237–246, 2014.
2. Cheng, N. Xiong, A. V. Vasilakos, L. T. Yang, G. Chen, and X. Zhuang, “Nodes organization for channel assignment with topology preservation in multi-radio wireless mesh networks,” Ad Hoc Networks, vol. 10, no. 5, pp. 760–773, 2012.
3. A. Alghamdi, “Load balancing ad hoc on-demand multipath distance vector (LBAOMDV) routing protocol,” EURASIP Journal on Wireless Communications and Networking, vol. 2015, no. 1, Jun. 2015.
4. Souihli, M. Frikha, and M. B. Hamouda, “Load-balancing in MANET shortest-path routing protocols,” Ad Hoc Networks, vol. 7, no. 2, pp. 431–442, 2009.
5. Toh, A.-N. Le, and Y.-Z. Cho, “Load balanced routing protocols for ad hoc mobile wireless networks,” IEEE Communications Magazine, vol. 47, no. 8, pp. 78–84, 2009.
6. Ancillotti, R. Bruno, M. Conti, and A. Pinizzotto, “Load-aware routing in mesh networks: Models, algorithms and experimentation,” Computer Communications, vol. 34, no. 8, pp. 948–961, 2011.
7. E. Momani, M. B. Yassein, O. Darwish, S. Manaseer, and W. Mardini, “Intelligent Paging Backoff Algorithm for IEEE 802.11 MAC Protocol,” Network Protocols and Algorithms, vol. 4, no. 2, 2012.
8. -K. Chen and P.-C. Wang, “Design and Implementation of an Anycast Services Discovery in Mobile Ad Hoc Networks,” ACM Transactions on Autonomous and Adaptive Systems, vol. 6, no. 1, pp. 1–9, Jan. 2011.
9. A. K. Fard, S. Karamizadeh, and M. Aflaki, “Enhancing Congestion Control To Address Link Failure Loss Over Mobile Ad-Hoc Network,” International journal of Computer Networks & Communications, vol. 3, no. 5, pp. 177–192, 2011.
10. S. Kumaran and V. Sankaranarayanan, “Early congestion detection and adaptive routing in MANET,” Egyptian Informatics Journal, vol. 12, no. 3, pp. 165–175, 2011.
11. Kumargujral, M. Singh, and S. K. Rana, “Ant based Algorithm for Load Balancing in Mobile Ad Hoc Networks,” International Journal of Computer Applications, vol. 39, no. 5, pp. 35–42, 2012.
12. Radenkovic and A. Grundy, “Efficient and adaptive congestion control for heterogeneous delay-tolerant networks,” Ad Hoc Networks, vol. 10, no. 7, pp. 1322–1345, 2012.
13. Chandradimri, S. K. Chamoli, and D. Pant, “Delay based Traffic Distribution of Heavy Traffic on K-Paths to achieve the Load Balancing and to minimize the Mean System Delay in MANET,” International Journal of Computer Applications, vol. 63, no. 22, pp. 25–30, 2013.
14. Cervera, M. Barbeau, J. Garcia-Alfaro, and E. Kranakis, “A multipath routing strategy to prevent flooding disruption attacks in link state routing protocols for MANETs,” Journal of Network and Computer Applications, vol. 36, no. 2, pp. 744–755, 2013.
15. Sharma, S. Chugh, and V. Jain, “Energy Efficient Load Balancing Approach to Improve AOMDV Routing in MANET,” 2014 Fourth International Conference on Communication Systems and Network Technologies, 2014.
16. Meng, F. Wu, Z. Yang, G. Chen and A. V. Vasilakos, "Spatial Reusability-Aware Routing in Multi-Hop Wireless Networks," in IEEE Transactions on Computers, vol. 65, no. 1, pp. 244-255, Jan. 1 2016.
17. Zhang, K. Xi, and H. J. Chao, “Load Balancing in IP Networks Using Generalized Destination-Based Multipath Routing,” IEEE/ACM Transactions on Networking, vol. 23, no. 6, pp. 1959–1969, 2015.
18. Ahmed and R. Paulus, “Congestion detection technique for multipath routing and load balancing in WSN,” Wireless Networks, vol. 23, no. 3, pp. 881–888, 2016.
19. Pathak and K. Kumar, “Traffic aware load balancing in AOMDV for mobile Ad-hoc networks,” Journal of Communications and Information Networks, vol. 2, no. 3, pp. 123–130, 2017.
20. P. F. A. Selvi and M. M.s.k, “Ant Based Multipath Backbone Routing for Load Balancing in MANET,” IET Communications, 2016.

399-405

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Authors:

Paper Title:

Abstract: Now a days wireless power Transfer Systems are used mainly to transfer the small amount of power within short distances .In recent applications it is mainly used to charge Smart Phones, Electric Toothbrushes Scanning RFID [Radio wave Frequency Identification tags are used to gather data or read the data] Tags. In medical fields WPT systems are used to charge the Bio-devices like pacemakers without any contact and transfer the energy in magnetic medium. These all applications are low power devices. For high power applications like EV charging is also possible with WPT Systems. From the past few years, researches on Electric vehicles are made more concerned and it gained popularity because it won’t emit any greenhouse gases and it uses green energy. Even Electric vehicles are having many advantages there are some problems erupted when it encounter with the users. One of the main disadvantages is charging infrastructure. Because it is vulnerable to some conditions like Weather, Vandalism and Electrocution. So, WPT systems are better counterpart for charging EVs. In this paper, Overview of distinctive kinds of Wireless Power Transfer technologies has given and simple design of closed and open loop WPT systems in mat lab were given. Further, a comparison has been made in between the mat lab simulation of both open and closed loop systems and different cable systems and wireless power transfer systems.

Keywords: Wireless Power Transfer, Design Optimization, Electric Vehicles, shapes of Coils, Quality Factor and Coefficient of Coupling.

References:

1. Nikola Tesla, “Apparatus for transmitting electrical energy.,” US1119732 A, May 4, 1907
2. Moulali and K. Subbarao ”A Review on Recent Developments in Wireless Power Transfer” in IJCTA, 2016, pp. 421-426.

3. Waffenschmidt and T. Staring,”Limitation of inductive power transfer for consumer applications,” in Proc. Of the 13^th European Conference on Power Electronics and Applications (EPE), 2009,PP.1-10.
4. C. Schuder, “Powering an artificial heart: birth of the inductively coupled-radio frequency system in 1960,” Artificial Organs, vol.26,no. 11, pp.909-915, 2002.
5. C. Schuder, J.H. Gold, and H.E, Stephenson, ”An inductively coupled rf system for the transmission of 1kw of power through the skin,” IEEE Trans. On Biomedical Engineering, vol.18,no.4,pp.265-273,1971.
6. Reinhold, P.Scholz, W.John, and U.Hilleringmann,”Efficient antenna design of inductive coupled RFID-systems with high power demand,” Journal of communications, vol.2, no. 6, pp. 14-23, 2007.
7. Grover, Inductance calculations, ser. Dover phoenix editions. Dover Publications, Incorporated, 2004.
8. L Li, A.P Hu, G. Covic, and C.S Tang, ”Optimal coupling condition of IPT system for achieving maximum power transfer,” Electronics letters, vol. 45, no.1,pp. 76-77, January 2009.

406-409

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Authors:

Paper Title:

Abstract: The word “thing” is a device, Internet is nothing but interconnection, so we can define IoT as the set of objects interconnected together with the network to share the information. A route is needed to share this information while sending data from one device to another device the network route or data can be attacked, to avoid this attacks a device trust mechanism is a useful mechanism to reduce data loss. In this paper, it’s discussed that what are the various routing attacks can be occurred in IoT, and how to avoid these attacks by using trust mechanism, later results shown how data loss is reduced with trust mechanism.

Keywords: IoT, routing, trust, routing attack, trust mechanism.

References:

1. Jaroslav Kadlec*, Radek Kuchta, Radovan Novotný and Ondřej Čožík. (2014), “RFID Modular System for the Internet of Things (IoT)”, Industrial Engineering & Management. Vol.3 issue 4.
2. Raju Stephen, Dalvin vinoth Kumar. (2016). Deist: Dynamic Detection of Sinkhole Attack For Internet Of Things. International Journal of Advanced Trends in Computer Science and Engineering. 5 (12), 19358-19362.
3. The Statistics Portal “Internet of Things (IoT) connected devices installed base worldwide from 2015 to 2025 (in billions)” © Statista 2018.
4. Neelam Janak Kumar Patel1 , Dr. Khushboo Tripathi2. (2017). Analysis of Black Hole Attack in MANET Based on Simulation through NS3.26. International Journal on Recent and Innovation Trends in Computing and Communication. 5 (5), 194-205.
5. Kuan Zhang, Xiaohui Liang, Rongxing Lu, Xuemin Shen. (2014). Sybil Attacks and Their Defenses in the Internet of Things Sign In or Purchase. IEEE Internet of Things Journal. 1 (5), 372-383.
6. International Journal of Engineering and Technical Research (IJETR). (2017). Detecting And Monitoring Wormhole in IoT enabled WSNs Using EyeSim. Nilima Nikam, Poorna R. Pimpale, Pranali Pawar, Anita Shirture. 2 (6), 15-17.
7. Virendra Pal Singh1 , Sweta Jain2 and Jyoti Singhai3. (2010). Hello Flood Attack and its Countermeasures in Wireless Sensor Networks. IJCSI International Journal of Computer Science. 3 (11), 23-27.
8. Nasreen Fathima1 Dr. Reshma Banu2 Dr. G. F. Ali Ahammed3. (2017). A Comparative Study Of Routing Approaches For Energy Constrained Devices In Iot . International Journal Of Current Engineering And Scientific Research (Ijcesr). 4 (1), 47-52.
9. Krushang Sonar 1 , Hardik Upadhyay 2 . (2014). A Survey: DDOS Attack on Internet of Things. International Journal of Engineering Research and Development. 10 (11), 58-63.
10. Christian Simko. (2016). Man-in-the-Middle Attacks in the IoT. https://www.globalsign.com/en/blog/man-in-the-middle-attacks-iot/.
11. “Securing consumer trust in the internet of things”, Principles andRecommendations 2017.
12. Internet of Things (IoT) Trust Concerns, Jeffrey Voas, Rick Kuhn, Phillip Laplante, Sophia Applebaum, October , 2018, NIST Cybersecurity White Paper.

410-413

75.

Authors:

Paper Title:

Abstract: Travelling is a part of our daily life. We all travel from one place to another. The ultimate aim of people is to reach the destination as soon as possible. In wide-scale urbanization processes the route planning is the problem, whilst addressed thoroughly for a single traveler in terms of shortest path computation, becomes quickly unwieldy when dealing with a set of travelers. Smart cities and developed countries are now taking efforts to tackle urban pollution and current travel planners concentrate mainly on travel time and distance to be covered. Here, in addition to distance, air quality and road congestion that affect travel time, crowd sourcing is also considered for finding the best and healthier route. Ant colony optimization is used to select among different routes also used to find optimal route considering the air quality, congestion, distance are used as the parameters for pheromone updation. Here we provide a congestion-less and eco-friendlier route with the help of Google map API and ant colony optimization by exploiting feedback-driven on participation and route suggestions from personal interest. In this sense, collective intelligence and swarm based paradigms are adapted to an innovative crowd sourcing pattern towards the data storage is cloud. In particular the stigmergic algorithm for probabilistic route planning, including the distributed crowd sourcing paradigm based on number of participants, has been used to find the optimal path finding using ACO (ant colony optimization).

Keywords: Traffic Congestion, GSM, DTSP, ACO (ant colony optimization)

References:

1. Cherrett, T., Waterson, B. and McDonald, M. “Remote automatic incident detection using inductive loops”.Proceedings of the Institution of Civil Engineers: Transport, 158, (3), 149-155, 2018.
2. Palubinskas, G., Kurz, F., and Reinartz, P., 2009. “Traffic congestion parameter estimation in time series of airborne optical remote sensing images”. In: Proc. of ISPRS Hannover Workshop 2009 – High Resolution Earth Imaging for Geospatial Information, 2-5 June, 2017.
3. CHEN Wenjie, CHEN Lifeng, CHEN Zhanglong, TU Shiliang, “A realtime dynamic traffic control system based on wireless sensor network,” in Proc. IEEE ICPPW '05, Oslo, Norway, pp. 258 – 264, June 2005.
4. Siuli Roy, Somprakash Bandyopadhyay, Munmun Das, Suvadip Batabyal, Sankhadeep Pal, “Real time traffic congestion detection and management using Active RFID and GSM technology”, LAP Lambert Academic Publishing ( 2012-10-09 )
5. Pradip Singh Maharjan, Ajay Kumar Shrestha, “Automatic Vehicle Detection and Road Traffic Congestion Mapping with Image Processing Technique”, International Journal of Computer Applications (0975 – 8887).
6. Dorigo and T. Stützle," Ant Colony Optimization". Scituate, MA, USA: Bradford Company, 2004.
7. Lee, E.-K. Lee, M. Gerla, and S. Y. Oh, “Vehicular cloud networking: Architecture and design principles,” IEEE Commun. Mag., vol. 52, no. 2, pp. 148–155, Feb. 2017.
8. Michalis Mavrovouniotis, Felipe M. Mller, Shengxiang Yang, "Ant Colony Optimization with Local Search for Dynamic Traveling Salesman Problems", IEEE Transaction On Cybernetics, 2016.
9. Mohan, V. N. Padmanabhan, and R. Ramjee, “Nericell: Rich monitoring of road and traffic conditions using mobile smartphones,” in Proc.6th ACM Conf. Embedded Netw. Sensor Syst. (SenSys), New York, NY, USA, 2008, pp. 323–336.
10. Gitae Kim, Yew Soon Ong, Teasu Cheong, and Pauy Siew Tan, "Solving the Dynamic Vehicle Routing Problem Under Traffic Condition, IEEE Transaction on Intelligent Transportation
11. Sumit Kumar, Gautam Srivastava, Md. Shameem, Amber Khan, Vehicle Positioning System using GPS, GSM and GIS.
12. Saurav Ranjit, Masahiko Nagai, Itti Rittaporn, Thanomsak Ajjanapanya, Fredrik Hilding, Apichon Witayangkurn, Ryosuke Shibasaki (2014) GPS enabled taxi probe’s big data processing for traffic evaluation of Bangkok using Apache Hadoop Distributed Syetm.
13. Joydip Dhar, Gaurav Garg (2014) Real time Traffic Congestion Detection and Optimal Path Slection using Smartphones, IEEE
14. Hong-En Lin, Rocco Zito, M Taylor (2005) a review of travel time prediction in transport and logistics, In Proceedings of the Estaren Asia Society for transportation studies, volume5, pages 1433-1448
15. Mehmet yildirimoglu and Nikolas Geroliminis (2013) Experienced Travel time Prediction for Congested Freeways, Transportation Research Part B: Methodological, 53(0):45-63
16. Pavel /kromer, Jan Martinovic, Michal Radecky, Radek Tomis, Vaclav Snasel (2011) Ant Colony Inspired Algorithm for Adaptive Traffic Routing, IEEE
17. Jerry Kponyo, Yujun Kuang, Enzhan Zhang (2014) Dynamic Trveal Path Optimization System Using ant Colony Optimization, UKSim-AMSS 16th International Conference on Computer Modelling and Simulation.

414-419

76.

Authors:

Paper Title:

Abstract: Now a days the rechargeable batteries are used in many applications. There are many types of rectifiers like Diode bridge rectifier, SCR based rectifier, PWM based rectifier, etc. The rectifier is basically used to charge a battery. This paper discusses front end PWM rectifier based battery charging technique with its performances and for generation of gate pulses using modulation technique namely sine pulse width modulation (SPWM). This paper also features the description of 2-level front end PWM rectifier, Diode bridge rectifier, Thyristor based rectifier based battery charging and control strategy. Simulation models of two level SPWM based front end rectifier, Diode bridge rectifier and Thyristor rectifier based Li-Ion battery charging model is developed and simulated in MATLAB / SIMULINK platform and a comparative analysis is presented..

Keywords: Battery, Diode, IGBT, Li-Ion, Rectifier.

References:

1. Sylvain Lechat Sanjuan, “Volatge oriented control of three phase boost pwm converters” CHALMERS University of Technology, Sweden, 2010.
2. Lavanya, V. Rangavalli, “A novel technique for simulation & analysis of svpwm two & three level inverters”, Journal of Engineering Research and Applications, Vol.3, Issue 5, Sept-Oct 2013, pp.455-460.
3. Lijie Jiang ; Zhengyu Lu ; Huiming Chen ; Xinke Wu, “A novel hybrid 3-phase PWM current source rectifier using SCR’s and IGBT’s”, 2009 IEEE Energy conversion Congress and Exposition, September 2009, pp.1235-1239.
4. Park, Inverse Park and Clarke, Inverse Clarke Transformations MSS Software Implementations User Guide by Microsemi.
5. Yan Liu, Yan Xing, Xu Wang, “Research based on SVPWM method of three level inverter”, Proceedings of the 30^th Chinese Control Conference, July 2011, pp.4513-4515.
6. Chun-yu Zhang ; Ya-bin Li ; Yong-long Peng ; Xu-feng Zhen, "A direct phase control scheme for unity power factor three-phase buck type rectifier based on svpwm” 2006 International conference on machine learning and cybernetics, pp 2840-2845.
7. Zhang, K.Zang, Fang Lu, “Modelling and simulation of three-phase rectifier based on SVPWM”, 2010 third international conference on information and computing, Vol.3, June 2010, pp.318-321.
8. Kazmierkowski, M. P., ” Direct power control of three phase PWM rectifier using space vector modulation and simulation study”. Proceedings of the 2002 IEEE International Symposium , Vol. 4, pp. 1114-1118.
9. A, Gaubert.J.P, Krim.F, “Predictive direct power control of three phase pulse width modulation rectifier using space vector modulation”, Power Electronics, IEEE Transcations 25(1), pp. 228-236.
10. Kada Hartani, Yahia Miloud, Control strategy of three phase voltage source PWM rectifier based on the space vector modulation”, Advanced in Electrical and Computer Engineering, Nov.3 2010, Vol.10, pp.61-65.
11. Sandhya Rani, A.Appa Rao, “A Space vector PWM scheme for three level inverters based on two level SVPWM”, International Journal Of Power System Operation and Energy Management (IJPSOEM) Volume-1, Issue-1, 2011, pp.6-10.
12. Ruchita Koshti, Purohit.D, Nirav.D.Mehta, “Design and simulation of three phase voltage source SVPWM rectifier”, IJLTEMAS,May 2014,Vol.3, Issue.5, pp.77-81.

420-424

77.

Authors:

Paper Title:

Abstract: Innovation is a driver of a long-term economic growth and structural change for economic progress. Innovation must increase value for both the company and the consumer. For consumers, innovation means higher quality and better value goods, more efficient services, and a higher standard of living. For the innovative company, innovation is a competitive advantage, adaptation, a meaning of existence and growth. The software industry has a leading role in that process because it possesses the technology and the knowledge, which are key elements in every innovation process. The software is becoming a widespread phenomenon in modern economies, which plays a significant role in the growing number of new products and processes. Creating innovations isconsidered to be the only skill that software companies should possess in order to stay competitive on the market and provide benefits to society.The productivity of the manufacturing processes in different sectors of the economy depends on the extent to which software innovations are developed. The research paper focuses on the innovations created by software companies and aims to underline the key success factors that determine the development of successful innovations. These key factors ensure that both the innovative companies and the customers receive value from the innovations. The presented key factors are based on literature reviews and a broader survey on the commercialization of innovations in the software industry in Bulgaria. The empirical research is conducted in December 2017, among 33 software companies, which have innovation activities in the period under review. The surveyed period ranges from 2015 to 2017. The defined key success factors of innovations could be used by software companies worldwide in their efforts and desire to create innovations of a value.

Keywords: innovation, key factors, product innovation, software industry.

References:

1. Kozludzhova, “Commercialization of Innovations in Software industry”, Ph.D. dissertation, Dept. of Econ. and Soc.Sci, Plovdiv University, Plovdiv, Bulgaria, 2018.
2. Von Hippel, Democratizing innovation, MIT Press, Cambridge, 2005.
3. Christensen, T. Hall, K. Dillon and D. Duncan, Competing against luck: The story of Innovation and Customer choice, HarperCollins, 2016.
4. Ulwick, “The strategic role of customer requirements in innovation”, Strategyn inc, vol.13, pp. 1-12, 2003.
5. Ulwick and L. Bettencourt,“The customer-centered innovation map’, Harvard Business Review, vol.86, pp. 109-114, 2008, viewed on 15 February 2019,
6. https://hbr.org/2008/05/the-customer-centered-innovation-map.
7. Reeves, “Reality in Advertising”, in The Copy Leverage, BookBaby, 1961, pp. 43-45.
8. Hindle, “Guide to management ideas and gurus”, in Unique Selling Proposition, Profile Books Ltd., London, 2008, pp. 197-198.
9. Benedict, “The Method of Selling: Your Key to Successful Sales with Over 70 Creative Selling Techniques”, in Twenty-five ways to close a sale, CCB Publishing, 2007, pp. 41-52.

425-431

78.

Authors:

Paper Title:

Abstract: Background. Recently, web applications have proliferated rapidly, with the world increasingly dependent on financial transactions, purchasing, billing, education, medicine, and many more. But the security of these applications is worrying where any vulnerability might lead to breaches causing huge damages. In order to increase the security of these applications towards injection attacks, developers have followed a series of procedures, one of them is encrypting parameters and data before sending it. Decrypting these parameters gives attackers opportunities to target web application by launching injection attacks. We have developed a methodology to detect injection vulnerabilities by trying to decrypt hidden parameters which encrypted using MD5, SHA1, SHA2, AES. Methods. In order to implement the proposed methodology, a scanner called DEHP has been developed, DEHP employ a black-box approach to analyze targeted web applications. DEHP is using traditional crawlers to crawl and collect all URLs included in that application and for each single URL does the following steps: analyzing HTML syntax, extracting input parameters which its type is hidden, checking if these parameters’ value is encrypted or not, if it’s encrypted DEHP launching one of two types of attacks, dictionary or brute-force attack depending on user selection to try decrypt of hidden parameter value, DEHP is also checking Cross-Site scripting vulnerabilities by analyzing JavaScript syntax looking for commands related to interaction with the database, checking SQL injection blind vulnerabilities by launching attack towards input nodes (Username, Password), checking HTTP header type and date and finally checking digital certificate of HTTPS connections to make sure of its validity. DEHP has been developed under Visual Studio 2017 environment using C# and ASP .NET framework. Results. DEHP has been tested towards many web applications taking into consideration laws governing for such applications. Crawling speed was very good due to use traditional crawlers, detecting vulnerabilities speed was good using a dictionary attack (the database needs to be extended), by using brute-force attack speed was bad due to miss a suitable test bed and resources for such type of attacks. Results of DEHP were compared with similar open-source applications but none of them care about decryption of encrypted hidden parameters.

Keywords: Vulnerability, SQL injection, Encryption, Crawler, Threats, XSS, Assessment tools.

References:

1. Fonseca, M. Vieira and H. Madeira, “Testing and comparing web vulnerability scanning tools for SQL injection and XSS attacks,” in Dependable Computing, 2007. PRDC 2007. 13th Pacific Rim International Symposium on, 2007.
2. Fonseca, N. Seixas, M. Vieira and H. Madeira, “Analysis of field data on web security vulnerabilities,” IEEE transactions on dependable and secure computing, vol. 11, no. 2, pp. 89-100, 2014.
3. Security, “WEB APPLICATIONS Security Statistics Report,” WhiteHat Security, 2016.
4. One, “The Haker-Powered security report,” Hacker One, 2018.
5. Zhu, Experimental study of vulnerabilities in a web application, Aalto University, 2017.
6. [Online]. Available: https://www.acunetix.com/. [Accessed 1 10 2018].
7. [Online]. Available: https://www.tenable.com/products/nessus/nessus-professional. [Accessed 1 10 2018].
8. OWASP, 1 October 2018. [Online]. Available: https://www.owasp.org/index.php/Main_Page.
9. S. T. A. P. A. R. P. G. Deepa, “DetLogic: A black-box approach for detecting logic vulnerabilities in web applications,” Journal of Network and Computer Applications, 2017.
10. Li and Y. Xue, “A survey on server-side approaches to securing web applications,” ACM Computing Surveys (CSUR), vol. 46, no. 4, p. 54, 2014.
11. a. V. Ciampa, C. A. a. D. Penta and Massimiliano, “A heuristic-based approach for detecting SQL-injection vulnerabilities in Web applications,” in Proceedings of the 2010 ICSE Workshop on Software Engineering for Secure Systems, 2010.
12. L. Doup´e, Advanced Automated Web Application, Califronia, 2014.
13. Khoury, P. Zavarsky, D. Lindskog and R. Ruhl, “An analysis of black-box web application security scanners against stored SQL injection,” in Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on, 2011.
14. Sharma and S. Jain, “Analysis and classification of SQL injection vulnerabilities and attacks on web applications,” in Advances in Engineering and Technology Research (ICAETR), 2014 International Conference on, 2014.
15. E. Ruse and S. Basu, “Detecting cross-site scripting vulnerability using concolic testing,” in Information Technology: New Generations (ITNG), 2013 Tenth International Conference on, 2013.
16. Baojiang, L. Baolian and H. Tingting, “Reverse analysis method of static XSS defect detection technique based on database query language,” in P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), 2014 Ninth International Conference on, 2014.
17. B. H. T. Cui Baojiang, “Reverse Analysis Method of Static XSS Defect Detection Technique Based on Database Query Language,” in 2014 Ninth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 2014.
18. Parvez, P. Zavarsky and N. Khoury, “Analysis of effectiveness of black-box web application scanners in detection of stored SQL injection and stored XSS vulnerabilities,” in Internet Technology and Secured Transactions (ICITST), 2015 10th International Conference for, 2015.
19. Guo, S. Jin and Y. Zhang, “XSS vulnerability detection using optimized attack vector repertory,” in Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2015 International Conference on, 2015.
20. B. J. G. V. J. Bhawna Mewara, “Enhanced Browser Defense for Reflected Cross-Site Scripting,” IEEE, 2014.
21. F. Y. Zainab S. Alwan, “Detection and Prevention of SQL Injection Attack: A Survey,” International Journal of Computer Science and Mobile Computing, vol. 6, no. 8, pp. 5-17, 2017.
22. K. K. K. Yusuke Takamatsu, “Automated Detection of Session Fixation Vulnerabilities,” in Automated Detection of Session Fixation Vulnerabilities, 2010, pp. 1191-1192.
23. Yeole and B. Meshram, “Analysis of different technique for detection of SQL injection,” in Proceedings of the International Conference & Workshop on Emerging Trends in Technology, 2011.
24. -K. H. T.-P. L. C.-H. T. Yao-Wen Huang, “Web Application Security Assessment by Fault Injection,” in WWW 2003, Budapest, Hungary, 2003.
25. X. Xiaowei Li, “A survey on server-side approaches to securing web applications,” ACM, p. 29, 2014.
26. V. A. O. William G.J. Halfond, “A Classification of SQL Injection Attacks and Countermeasures,” IEEE, 2005.
27. Wikipedia, “Microsoft SQL Server,” [Online]. Available: https://en.wikipedia.org/wiki/Microsoft_SQL_Server. [Accessed 30 12 2018].
28. Wikipedia, “C Sharp,” Microsoft, [Online]. Available: https://en.wikipedia.org/wiki/C_Sharp_(programming_language). [Accessed 30 12 2018].
29. Wikipedia, “ASP.NET,” Microsoft, [Online]. Available: https://en.wikipedia.org/wiki/ASP.NET. [Accessed 30 12 2018].
30. [Online]. Available: https://en.wikipedia.org/wiki/HTTPS. [Accessed 10 4 2018].
31. [Online]. Available: http://webscantest.com/report/Vulnerabilities_1.html. [Accessed 7 1 2019].
32. Wang, L. Wang, G. Wei, D. Zhang and Y. Yang, “Hidden web crawling for SQL injection detection,” in Broadband Network and Multimedia Technology (IC-BNMT), 2010 3rd IEEE International Conference on, 2010.
33. D. R. C. D. Trupti V.U dapure, “Study of Web Crawler and its Different Types,” IOSR Journal of Computer Engineering (IOSR, vol. 16, no. 1, pp. 1-5, 2014.
34. Symantec, “Internet Security Threats Report,” Symantec, 2017.
35. Sucuri, “Hacked Website Report,” Sucuri, 2017.
36. V. Studio, “Wikipedia,” Microsoft, [Online]. Available: https://en.wikipedia.org/wiki/Microsoft_Visual_Studio. [Accessed 30 12 2018].
37. E. K. C. K. a. N. J. Stefan Kals, “SecuBat: A Web Vulnerability Scanner,” International World Wide Web Conference Committee, pp. 247-256, 2006.

432-438

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Paper Title:

Abstract: The Mobile Ad hoc Networks (MANETs) very popular at present world and also this MANETs are facing several problems which related security. Security means, network security as well as network data security problems. To avoid such type of problems, we have several protocols for secure routing or secure communication in the MANETs. However, all implemented protocols provide either security to the network data or protect networks from the various attacks. But, no one protocol provides the parallel solution to this existing problem. So, we implemented a framework in existing, named as Security Using Pre-Existing Routing for Mobile Ad hoc Networks (SUPERMAN) which can protect the network and network data from the attackers as parallel. But, SUPERMAN framework can only protect the network routes and its communication data. It cannot balance the network levels of the network nodes in MANETs. Hence, in this paper we are extending that the SUPERMAN framework by adding energy balancing scheme. Through this extension work, we can prove that the proposed mechanism is an energy-aware as well secure framework for MANETs.

Keywords: MANETs, Routing, Network Security, Energy balancing.

References:

1. Maity and Ghosh, “Enforcement of access control policy for mobile ad hoc networks,” in Proceedings of the Fifth International Conference on Security of Information and Networks. 2012, pp. 47–52.
2. Sathishkumar , S. Balakrishnan , A. Vivek , “HOP Optimal Algorithm With Greedy Link Scheduler, To Avoiding Link Failure For Multihop Wireless Networks”, International Journal of Innovative Research & Development Vol 2, Issue 4, April 2013.
3. Park VD, Corson MS. “A highly adaptive distributed routing algorithm for mobile wireless networks” in Proceedings of IEEE 1997.
4. Harn, M. Mehta, and W.-J. Hsin, “Integrating diffiehellman key exchange into the digital signature algorithm” Communications Letters, IEEE, vol. 8, no. 3, pp. 198–200, 2004.
5. Krawczyk and P. Eronen, “Hmac-based extractand-expand key derivation function” 2010.
6. Adekunle and S. Woodhead, “An aead cryptographic framework and tinyaead construct for secure wsn communication,” in Wireless Advanced (WiAd), 2012. IEEE, 2012, pp. 1–5
7. R. Tewari, and Upadhyay, “Different types of attacks on integrated manet-internet communication,” International Journal of Computer Science and Security, vol. 4, no. 3, pp. 265–274, 2010.
8. Smith, J. Wetherall, S. Woodhead, and A. Adekunle, “A cluster-based approach to consensus based distributed task allocation,” in Parallel, Distributed and Network-Based Processing (PDP), 2014 22nd Euro micro International Conference on. IEEE, 2014, pp. 428–431.
9. H. Jhaveri, S. J. Patel, and D. C. Jinwala, “Dos attacks in mobile ad hoc networks: A survey,” in Advanced Computing & Communication Technologies (ACCT), 2012 Second International Conference on. IEEE, 2012, pp. 535–541.
10. Darren Hurley-Smith, Jodie Wetherall, Andrew Adekunle, “SUPERMAN: Security Using Pre-Existing Routing for Mobile Ad hoc Networks”, IEEE Transactions on Mobile Computing.
11. Gopatoti, A., Naik, M.C., Gopathoti, K.K.” Convolutional Neural Network based image denoising for better quality of images”, International Journal of Engineering and Technology(UAE), Vol.7, No.3.27, (2018), pp. 356-361.

439-442

80.

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Paper Title:

Abstract: The discovery of automobiles have reduced the travelling time considerably but in recent times the outburst in vehicle usage and unprecedented planning of infrastructure activities regarding the future vehicular occupancy on road has lead to traffic accumulation on roads their by making it difficult for ambulances and other vehicles to reach the destination of time there by costing people their valuable human life. The proposed system helps in detecting the ambulance when it reaches a signal junction, their by clearing the signal in the path of the ambulance once the detection as taken place. It reduces the delay time to large extent and also ensures the safe flow of traffic when the ambulance passes the junction their reducing manual escorting efforts. The system converts the video feed provided to it into frames and takes frames at a specific interval then checks for the presence of the standard template in the given frame by using cross correlation technique and upon match the system detects the ambulance, else repeats it after specific interval of time.

Keywords: Video, template, cross correlation

References:

1. Parthasarathi, M. Surya1,B. Akshay,K. Murali Siva1 and Shriram, K.Vasudevan, “Smart Control Of Traffic Signal System Using Image Processing”, Indian Journal of Science & Technology,Vol.8,Issue 16,2015.
2. Kratika Garg ,Siew-Kei Lam ,Thambipillai Srikanthan,Vedika Agarwal, “Real-Time Road Traffic Density Estimation Using Block Variance”, IEEE Winter Conference on Applications of Computer Vision (WACV), 7-10 March 2016, 978-1-5090-0641-0.
3. Ariel Amato, Mikhail G. Mozerov, Andrew D. Bagdanov, and Jordi Gonzàlez, “Accurate Moving Cast Shadow Suppression Based On Local Colour Constancy Detection”, IEEE Trans Image Process. Oct;20(10), pp.2954-66,2011.
4. Ke Jiang, Ai-hua Li, Zhi-gao Cui, Tao Wang, Yan-zhao Su, “Adaptive Shadow Detection Using Global Texture And Sampling Deduction”, Chinese Journal of Electronics 22(4):757-762 • October 2013.
5. K SuganyaDevi N Malmurugan R Sivakumar, “Efficient Foreground Extraction Based On Optical Flow And SMED For Road Traffic Analysis”, International Journal of Cyber-Security and Digital Forensics (IJCSDF) ,1(3),pp. 177-182,2012.
6. Zhou Liu, Kaiqi Huang, and Tieniu Tan, “Cast Shadow Removal In A Hierarchical Manner Using MRF”, IEEE Transactions on Circuits and Systems for Video Technology ,Volume: 22 , Issue: 1 , Jan. 2012
7. Kapileswar Nellore and Gerhard P. Hancke, “Traffic Management for Emergency Vehicle Priority Based on Visual Sensing”, Sensors,pp.1-22,2016.
8. Janani Saradha ; G. Vijayshri ; T. Subha, “Intelligent traffic signal control system for ambulance using RFID and cloud”, 2017 2nd International Conference on Computing and Communications Technologies (ICCCT), 23-24 Feb. 2017, 978-1-5090-6221-8.
9. Pooja Dagade, Priyanka Salunke, Supriya Salunke ,Seema T. Patil, “Accident Detection & Ambulance Rescue System Using Wireless Technology”, International Research Journal of Engineering and Technology (IRJET), Volume: 04 Issue: 05 ,pp.1324-1326,May -2017.
10. N.Sivaraj , K.Vigneshwaran , S.Vigneshwaran , M.Vishnu Priyan, “Iot Ambulance With Automatic Traffic Light Control”, SSRG International Journal of Industrial Engineering - (ICRTECITA-2017) -Special issue- pp.12-18,March 2017.
11. Roopa Jaya Singh J , Jeba Kumar R.J.S, “Smart Life Saver Ambulance System (SLSAS) furnished with IoT technology to accelerate the process of early patient treatment in hospital”, International Journal of Pure and Applied Mathematics, Volume 119 No. 16 2018, 1677-1696,2017.
12. Deepa, Navya Kumari, Manisha K, Manu Manjunatha, Kshama shetty, “ Smart Detection of Emergency Vehicles in Traffic”, International Journal Of Current Engineering And Scientific Research (Ijcesr), Vol.5,Issue-4,pp.15-18,2018.
13. Varsha Srinivasan, Yazhini Priyadharshini Rajesh, S Yuvaraj and M Manigandan, “Smart traffic control with ambulance detection”, 2nd International conference on Advances in Mechanical Engineering (ICAME 2018), IOP Conf. Series: Materials Science and Engineering ,2018.
14. Deepali Ahir, Saurabh Bharade, Pradnya Botre, Sayali Nagane, Mihir Shah, “Intelligent Traffic Control System for Smart Ambulance”, International Research Journal of Engineering and Technology (IRJET), Volume: 05 Issue: 06 , pp.355-358,2018.

443-446

81.

Authors:

Paper Title:

Abstract: Information exchange between items (things), having detecting or registering capacities or both over the web is known as the Internet of Things (IoT). IoT and Cloud computing coordinated to shape a stage called IoTcloud. Since these advancements are altogether utilized, there is a requirement for the security of information accumulated from the gadgets associated through these advances. To solve the transport issue in the symmetric key algorithm and to acquire the high performance, the proposed method and RSA algorithm are combined together. While actualizing the proposed framework, the symmetric key algorithm utilizes low RAM for preparing, in this way, it gives rapid. RSA is one of the best and secures asymmetric encryption method. Here it is used only to encrypt the symmetric key, for this, it requires a negligible computational cost. The proposed method using DNA cryptography and Huffman coding for encrypting and decrypting data. DNA Cryptography can have a special advantage for secure authentication, data storage, steganography, digital signatures and so on. Huffman code is a sort of ideal prefix code. What's more, it is ordinarily utilized for lossless information compression. This methodology utilizes variable key length so aggressor won't almost certainly surmise the length of the key recognition.

Keywords: IoT, Huffman Coding, DNA Coding, Encryption, Decryption.

References:

1. Bruce D, GR. Milne, Y. G. Andonova, and F M. Hajjat. "Internet of Things: Convenience vs. privacy and secrecy." Business Horizons 58, no.6, Science Direct, pp. 615-624, 2015.
2. PrajapatiAshishkumar B, PrajapatiBarkha, “Implementation Of Dna Cryptography In Cloud Computing And Using Socket Programming”G. IEEE International Conference on Computer Communication and Informatics (ICCCI -2016), 2016.
3. Gurpreet Singh, Supriya,” ModifiedVigenere Encryption Algorithm and Its Hybrid Implementation with Base64 and AES”, 2nd International Conference on Advanced Computing, Networking and Security-2013.
4. Amirhossein Safi, “Improving the Security of Internet of Things Using Encryption Algorithms”, World Academy of Science, Engineering and Technology International Journal of Computer and Information Engineering – 2017.
5. Rakesh Kumar Jangid, Noor Mohmmad, Abhishek Didel, SwapneshTaterh, “HybridApproach of Image Encryption Using DNA Cryptography and TF Hill Cipher Algrithm”, IEEE International Conference on Communication and Signal Processing, 2014, pp. 934.
6. NidhiDhawale, “Implementation of Huffman algorithm and study for optimization”,International Conference on Advances in Communication and Computing Technologies(ICACACT 2014).
7. L. Rivest, A. Shamir and L. Adleman,” A method for obtaining digital signatures and public-key cryptosystem”, Communi. ACM, vol. 21 no. 2, pp. 120-126.
8. Rupali B. Patil,K, D. Kulat, ”Audio compression using dynamic Huffman and RLE co ing”, 2nd International Conference on Communication and Electronics Systems(ICCES-2017).

447-450

82.

Authors:

Paper Title:

Abstract: Underwater target tracking is a pivotal area in the present scenario. In this paper, passive target tracking is accomplished. By using bearings-only measurements, the parameters like range, course and speed of the target with respect to observer are calculated which helps in determining the target motion. This is called Target Motion Analysis (TMA). As bearings-only tracking is non-linear in nature, traditional Kalman filter which is linear filter, cannot be used. So, Particle filter (PF) which is non-linear filter is preferred. Since particles/samples are used, particle degeneracy or sample impoverishment may occur. To avoid sample impoverishment, resampling of the particles is done after every iteration. So, stratified resampling which can give greater precision is used to reduce the sample impoverishment problem. For improved performance of the filter, PF is combined with Modified Gain Extended Kalman filter (MGEKF). The algorithm is simulated using different scenarios in MATLAB to evaluate its sensitivity. Estimating the performance of the algorithm depending on their convergence time is carried out.

Keywords: Modified Gain Extended Kalman Particle Filter (MGEKPF), Particle Filter (PF), Stratified Resampling, Sample Impoverishment, Target Tracking.

References:

1. C. Nardone, A. G. Lindgren and K. F. Gong,“Fundamental Properties and Performance of Conventional Bearing-only Target Motion Analysis”, IEEE Transactions on Automatic Control, Vol. AC-29, No.9,pp775-787, Sep.1984.
2. Lingren, K. F. Gong, “Position and Velocity Estimation Via Bearing Observations”, IEEE Transactions on Aerospace and Electronic Systems Vol. Aes-14, No. 4 July 1978.
3. C. Nardone and V. J. Aidala, “Observability Criteria for Bearing-only Target Motion Analysis”, IEEE Transactions on Aerospace and Electronic Systems Vol. Aes-17, No. 2 March 1981.
4. Karlsson and F. Gustafsson, “RecursiveBayesian estimation: Bearings-only applications”, IEE Proc. Radar, Sonar & Navigation, Vol. 152, No. 5, pp 305-313, October 2005.
5. Cappe, S. J. Godsill, and E. Moulines, “An overview of existing methods and recent advances in sequential Monte Carlo,” Proceedings of the IEEE, vol. 95, no. 5, pp. 899–924, 2007.
6. L. Alspach and H. W. Sorenson, “Nonlinear Bayesian estimation using Gaussian sum approximations,” IEEE Transactions on Automatic Control, vol. 17, no. 4, pp. 439–448, 1972.
7. Terejanu, P. Singla, T. Singh, and P. D. Scott, “Adaptive Gaussian sum filter for nonlinear Bayesian estimation,” IEEE Transactions on Automatic Control, vol. 56, no. 9, pp. 2151–2156, 2011.
8. Y. Fu and Y. M. Jia, “An improvement on resampling algorithm of particle filters,” IEEE Transactions on Signal Processing, vol. 58, no. 10, pp. 5414–5420, 2010.
9. Kabaoglu, “Target tracking using particle filters with support vector regression,” IEEE Transactions on Vehicular Technology, vol. 58, no. 5, pp. 2569–2573, 2009.
10. Jun Ye Yu, Mark J. Coates, Michael G. Rabbat, and Stephane Blouin, “A Distributed Particle Filter for Bearings-only Tracking on Spherical Surfaces”, IEEE SIGNAL PRCESSING LETTERS, VOL 23,NO. 3, MARCH 2016.
11. Martin Clark, Simon Maskell, Richard VinterandMoeen Yaqoob, “A Comparison of the Particle and shifted Rayleigh filters in their application to a multi-sensor bearings-only problem”, IEEE Aerospace Conference, pp.2142 – 2147, 2005.
12. Hongwei Zhang, Liangqun Li, And WeixinXie, “Constrained Multiple Model Particle Filtering for Bearings-Only Maneuvering Target Tracking”, IEEE Access, Vol.6, pp. 51721-51734, September 2018, DOI: 10.1109/ACCESS.2018.2869402.
13. Liu and R. Chen, “Sequntial Monte-Carlo methods for dynamic systems”, J. Amer. Statist. Assoc., vol. 93, no. 443, pp. 1032-1044, 1998.
14. R. Beadle and P. M. Djuric, “A fast-weighted Bayesian bootstrap filter for nonlinear model state estimation”, IEEE Trans. Aerosp. Electron. Syst., vol. 33, no. 1, pp. 338-343, 1997.
15. Dan Simon, “Optimal State Estimation: Kalman, H∞, Nonlinear Approximations”, Wiley 2006.

451-455

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Authors:

Paper Title:

Abstract: Solar PV power generation is the best option compared to the fossil fuel based power plants now-a-days .Definitely the thermal and nuclear power plant will retire in the future due to the development of the above mentioned .It is extensively developing in the world compared to other renewable options in hand ,cost of manufacturing and installation costs are coming down .This paper discusses an enhanced PV solar power system design with a MPPT controller as a function of temperature compared to other techniques where dealt with variable sunirradiance ,unshaded ,partial shading.This will help designers and maintenance directors in designing stage and maintenance process.

Keywords: Partial shading,Solar Array,Power Converter,sun Irradiance,Temperature Effect.

References:

1. Kumar ,I.Hussain,B.Singh,K.Panigrahi, “MPPT in dynamic condition of partially shaded PV systems by using WODE technique,In IEEE Transactions on Sustainable Energy,vol 8,no,3,pp.1204-1214,july 2017.
2. E.E..Telbany .A.Youssef and A.A. Zekry.“Intelligent techniques for MPPT control in photovoltaic systems .A comprehensive review in proc.of 4^th International conference on Artificial Intelligence withapplications in Engineering and Technology,kotakinabalu,2014,pp,17-22.
3. Pakkiraiah ,G.Durga Sukumar , “A New modified controller for solar photo voltaic system ,2015 IEEE International conference on research in computational intelligence and communications networks 20nov,2015.
4. Bhargavi,P.Linga Reddy, “Improvement of solar energy system under partial shading conditions in koneru lakshmaiah education foundation ”International journal of Engineering and technology 7(1.8)(2018)197-200.
5. Fakherdine keyrouz “Enhanced Bayesian based MPPT controller for PV systems ”,IEEE power and energy technology systems journal year 2018.
6. Ajaykumar,T.Saisourav ,K.Prasad Rao,”. Observation of P-V and I-V characteristic before and after partial shadow effect on photo voltaic array using Boost converter ,”International journal of Engineering and Technology,2018.
7. Ruhi Bharti,Joseph Kuitche,Mani G,Tamizhami ,”Nominal operating cell temperature (NOCT):Effect of module size ,loading and solar spectrum “,2009 34^th IEEE Photovoltaic specialists conference(PVSC).
8. Bashar Mohammed ,Salih Rasha A,Mohameed Ahmed Ibrahim,”The Environment coefficicents effect on I-V and P-V charactersistics curves pf photovoltaic cell using Matlab/simulink .”International journal of engineering and technology,7 (4) (2018) 2651-265

456-460

84.

Authors:

Paper Title:

Abstract: Feature extraction involves feature detection, description and matching which is the baseline of many computer vision applications like content based image retrieval, image classification, image recognition, object detection etc. Features detected should have greater repeatability and should be able to derive descriptors out of it that are highly distinctive and robust to changes in scale, orientation, rotation, illumination etc. This paper provides an insight about the performance comparison of the long existing SIFT and SURF descriptors. The evaluation is carried out in an experimental setup of object category detection which uses a SVM classifier to detect the category.

Keywords: Feature detectors, descriptors, SIFT, SURF, ORB, BRISK, Bag-of-features.

References:

1. Tuytelaars and K. Mikolajczyk, Local Invariant Feature Detectors: A Survey, Foundations and Trends in Computer Graphics and Vision, vol. 3, no. 3, pp. 177-280, 2008.
2. Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference. (1988) 147-151
3. Lindeberg, T., 1998. Feature detection with automatic scale selection. Int. J. Comput.Vision 30 (2), 79–116.
4. Harris, C., Stephens, M., 1988. A combined corner and edge detector. In: Proc. Fourth Alvey Vision Conf., Manchester, UK, pp. 147–151.
5. Lowe, DavidG. "Distinctive image features from scale-invariant keypoints."International journal of computer vision 60.2 (2004): 91-110.
6. Bay, Herbert, et al. "Speeded-up robust features (SURF)." Computer vision and image understanding 110.3 (2008): 346-359.
7. Rosten, Edward, and Tom Drummond. "Machine learning for high-speed corner detection." Computer Vision–ECCV 2006. Springer Berlin Heidelberg, 2006. 430-443.
8. Calonder, Michael, et al. "Brief: Binary robust independent elementary features." Computer Vision–ECCV 2010 (2010): 778-792.
9. Rublee, Ethan, et al. "ORB: an efficient alternative to SIFT or SURF."Computer Vision (ICCV), 2011 IEEE International Conference on. IEEE, 2011.
10. Leutenegger, Stefan, Margarita Chli, and Roland Y. Siegwart. "BRISK: Binary robust invariant scalable keypoints." Computer Vision (ICCV), 2011 IEEE International Conference on. IEEE, 2011.
11. Sivic, Josef, and Andrew Zisserman. "Video Google: A text retrieval approach to object matching in videos." Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on. IEEE, 2003.
12. Csurka, Gabriella, et al. “Visual categorization with bags of keypoints.”Workshop on statistical learning in computer vision, ECCV. Vol. 1. 2004.‏
13. http://wang.ist.psu.edu/docs/related/
14. X. Yuan, et al., "A sift-lbp image retrieval model based on bag-of-features, " in International conference on image processing, 2011.

461-464

85.

Authors:

Paper Title:

Abstract: One of the constituent of concrete is natural sand or river sand. The issues of environmental degradation and expensive nature of the river sand are increasing day by day. The Global consumption of natural sand or river sand has become more due to excessive use of concrete so that the demand of river sand is very high and there is shortage of good quality of river sand. These reasons make us to switch on the alternative sources. Many researches has been done yet to replace the river sand. The objective of this research is to an experimental study of concrete using eco sand as a replacement of fine aggregates. So Eco Sand is replaced with 5%, 10%, 15% and 20% by weight of fine aggregates and mechanical properties such as Compressive strength, Spilt Tensile strength and Flexural strength are investigated. Eco sand acts like filler minerals which helps to reduce pores, reduce moisture resistivity. M40 grade of concrete is taken for study. The rheology studies are also made in detail as the fine content of concrete increases, the water demand increases to make it workable. Hence to overcome this problem, 1% of chemical admixture (water reducer) i.e., super plasticizer is used. The compressive strength, flexural strength and split tensile strength increase when fine aggregate was replaced by eco sand at 5, 10, 15, and 20%. The Optimum percentage of replacement is 15%.

Keywords: Eco sand, Replacement of fine aggregates, River sand, Strength comparison, Super plasticizer

References:

1. Durga, and M. Indira (2016), experimental study on various effect of replacement of fine aggregate with silica sand at different proportions in cement concrete and cement mortor, (IJETT), Vol.33(5), pp.243-248
2. Dharshnadevi .D, Aravindsamy .B, Guru Saravanan .c, Sowdharyan .j, and Tamil Selvi .R (2017), experimental investigation of influence of Eco sand in conventional concrete, (ICLIASET), pp.208-215
3. Selina Ruby, M. Vignesh, G. MangalaSankarraj, M. Kishore kumar, and A. Ajith (2018), investigation on Eco sand, (IJSRR), Vol.7(4), pp.243-248
4. Aswani, M. Indira, and T.M. Jeyashree (2016), a study on effect of crystalline Dolomite silica as a partial replacement, (IJETS), Vol.3(2), pp.6-8
5. Mukesh B. Patel, and S.D. Charkha (2012), effect of silica fumes and partial replacement of ingredients of flexural and split tensile strength of concrete, (IJERA), Vol.2(3), pp.1782-1785
6. Indira, and B. Vdaykiran Reddy (2017), study on replacement for cement and fine aggregates using Eco sand, (IJCIET), Vol.8(4), pp.846-854
7. Prabu, S. Logeswaran, and Dr.Sunilaa George (2015), influence of GGBS and Eco Sand in green concrete, (IJIRSET), Vol.4(6), pp.4519-4525
8. Magudeaswaran, P. Eswaramorthi, and D. Pradeep kumar (2015), green high performance concrete using Eco Sand and industrial waste, (IJCS), Vol.13(2), pp.661-671
9. Sri Ranjani .R (2015), an experimental investigation on rich mineral silica in concrete, (IJACEE), Vol.1(1), pp.24-29
10. Susmitha .T, ShwethaPriya .G, Ramakrishna N, and TharshanBalaaji S G (2018), an experimental study on Eco Sand as a partial replacement for fine aggregates in cement concrete, (IJIRE), Vol.5(3), pp.332-338
11. A (2014), performance of normal concrete with Eco Sand as fine aggregates, (IJESI), Vol.3(5), pp.27-35
12. Ananthayya M.B. and Premakumar W. P., Influence of steel fibers and partial replacement of sand by iron ore Tailings on the Compressive and Split Tensile strength of concrete, (IJCIET), Vol.5(3), pp.117-123

465-469

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Paper Title:

Abstract: Adders are the basic logical elements of arithmetic circuits in any microprocessor or digital signal processor. These act as basic blocks and are widely used components in digital integrated circuits. Optimizing such blocks increase the performance of integrated circuits. A small amount of area or delay reduction leads to great improvement in the performance. Carry chain plays a major role in adders on which the speed of an adder depends. Several adders have been proposed earlier to overcome the problems associated with area, power consumption and speed. Carry select adder is one among the adders with better performance. Carry select adder is favored broadly because it limits the issue of carry propagation delay. However, it occupies more area and power because of the repetitive blocks in the design. In this article, various available design methodologies of carry select adder, such as carry select adder using carry lookahead adder, square-root carry select adder using common Boolean logic, altered XOR gate and binary-to-excess-1 converter, have been discussed. The efficacy of all the design methodologies have been investigated by comparing the parameters like area, delay and power consumption. The design with high efficacy can be used in high speed multiplication, arithmetic logic units, advanced microprocessor design and so on. All the architectures are simulated in Cadence Virtuoso Analog Design Environment and gpdk180 library was utilized.

Keywords: Cadence, Carry select adder, Ripple carry adder, Propagation delay

References:

1. Srilatha, Simulation and Analysis of High Speed Conditional Carry Select Adder. Kingsville, TX: Texas A&M University-Kingsville, 2003.
2. W. Lynch, Binary Adders. Austin, TX: The University of Texas at Austin, 1996.
3. Macsorley, “High-speed arithmetic in binary computers,” Proceedings of the IRE, vol. 49, no. 1, pp. 67–91, 1961, DOI: 10.1109/jrproc.1961.287779.
4. M. Rabaey, Digital Integrated Circuits. London: Pearson Education, 2003.
5. S. Premananda, M. K. Chandana, K. P. Shree Lakshmi, and A. M. Keerthi, “Design of low power 8-bit carry select adder using adiabatic logic,” in 2017 International Conference on Communication and Signal Processing (ICCSP), 2017, DOI:10.1109/iccsp.2017.8286696.
6. Parhami, Computer Arithmetic: Algorithms and Hardware Designs. New York: Oxford University Press, 2010.
7. J. Bedrij, “Carry-select adder,” IRE Transactions on Electronic Computers, vol. EC-11, no. 3, pp. 340–346, 1962, DOI: 10.1109/iretelc.1962.5407919.
8. Tyagi, “A reduced-area scheme for carry-select adders,” IEEE Transactions on Computers, vol. 42, no. 10, pp. 1163–1170, 1993, DOI: 10.1109/12.257703.
9. Priya and S. Mamta, “Design of modified area efficient square root carry select adder (SQRT CSLA),” International Journal of Industrial Electronics and Electrical Engineering, special issue 4, pp. 216–219, 2015.
10. Abu-Shama and M. Bayoumi, “A new cell for low power adders,” in 1996 IEEE International Symposium on Circuits and Systems. Circuits and Systems Connecting the World (ISCAS’96), 1996, DOI: 10.1109/iscas.1996.541898.
11. Amelifard, F. Fallah, and M. Pedram, “Closing the gap between carry select adder and ripple carry adder: A new class closing the gap between carry select adder and ripple carry adder: A new class of low-power high-performance adders,” in Sixth International Symposium on Quality of Electronic Design (ISQED'05), 2005, DOI: 10.1109/isqed.2005.131.
12. Lakshmesha and K. U. Rani, “A novel ripple/carry lookahead hybrid carry select adder architecture,” International Journal of Computer Applications, vol. 70, no. 27, pp. 5–9, 2013.
13. C. Wey, C. C. Ho, Y. S. Lin, and C. C. Peng, “An area-efficient carry select adder design by sharing the common Boolean logic term,” in Proceedings of the International Multiconference of Engineers and Computer Scientists, Hong Kong, vol. 2, 2012.
14. Sanooja and B. Aswathi, “A modified carry select adder using common Boolean logic,” International Journal of Engineering and Technical Research, vol. 3, no. 7, pp. 229–232, 2015.
15. Ragunath and R. Sakthivel, “Low-power and area-efficient square-root carry select adders using modified XOR gate,” Indian Journal of Science and Technology, vol. 9, no. 5, pp. 1–8, 2016, DOI: 10.17485/ijst/2016/v9i5/87181.
16. R. Padmasri, P. A. Christina, P. Kavitha, and T. Mullai, “Modified area efficient carry select adder (MA-CSLA),” International Journal of Advanced Electrical and Electronics Engineering, vol. 2, no. 2, pp. 75–79, 2013.
17. Anto Bennet, S. Sankaranarayanan, V. Banu Priya, P. Jaya Pretheena, and S. Yamini, “Performance and analysis of low power, area-efficient and high speed carry fast adder,” International Journal on Smart Sensing and Intelligent Systems, special issue, pp. 522–538, 2017.
18. Prajwal, “Enhanced low power, fast and area efficient carry select adder,” International Journal of Research in Engineering and Technology, vol. 3, no. 5, pp. 441–443, 2014.
19. Ram Kumar and M. K. Harish, “Low-power and area-efficient carry select adder,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 20, no. 2, pp. 371–375, 2011.
20. T. Abhiram, T. Ashwin, B. Sivaprasad, S. Aakash, and J. P. Anita, “Modified carry select adder for power and area reduction,” in 2017 International Conference on Circuits Power and Computing Technologies, 2017, DOI: 10.1109/ICCPCT.2017.8074371.

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Authors:

Paper Title:

Abstract: Quality assessment of water has a lot of attractions during recent years. Diverse kinds of classification and monitoring techniques were used in this field of study. The present examination investigates the quality of ground water in Kudankulam which is situated Tirunelveli district of Tamil Nadu. A total of 19 samples was accumulated in this region typically from the coastal area during 2011-2012.The evaluation was done on the basis chemical parameters of each samples. This paper explores various classifier models such as KNN, NB and SVM to achieve prediction of groundwater quality. The classification is done based on the Water Quality Index (WQI) of each sample. A near investigation of characterization systems was done dependent on the confusion matrix, accuracy, f1 score, precision and recall. The outcomes propose that SVM is a better method having high accuracy rate than other models.

Keywords: Classification Algorithms, Water Quality Index, Support Vector Machine, Naïve Bayes; K-Nearest Neighbors

References:

1. Central Ground Water Board, Ministry of Water Resources,Government of India- BIS Standard
2. Dollar, E. S. J., James, C. S., Rogers, K. H., &Thoms, M. C. (2007). A framework for interdisciplinary understanding of rivers as ecosystems. Geomorphology, 89(1-2), 147-162.
3. Dubey, H. (2013). Efficient and accurate kNN based classification and regression. A Master Thesis Presented to the Center for Data Engineering, International Institute of Information Technology, Hyderabad-500, 32.
4. Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., &Scholkopf, B. (1998). Support vector machines. IEEE Intelligent Systems and their applications, 13(4), 18-28.
5. Horton, R. K. (1965). An index number system for rating water quality. Journal of Water Pollution Control Federation, 37(3), 300-
6. Khalil, A., Almasri, M. N., McKee, M., &Kaluarachchi, J. J. (2005). Applicability of statistical learning algorithms in groundwater quality modeling. Water Resources Research, 41(5).
7. Moore, A. W. (2001). Support vector machines. Tutorial. School of Computer Science of the Carnegie Mellon University. Available at http://www. cs. cmu. edu/~ awm/tutorials
8. Prakash, R., Tharun, V. P., & Devi, S. R. (2018, April). A Comparative Study of Various Classification Techniques to Determine Water Quality. In 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT) (pp. 1501-1506).
9. Rish, I. (2001, August). An empirical study of the naive Bayes classifier. In IJCAI 2001 workshop on empirical methods in artificial intelligence(Vol. 3, No. 22, pp. 41-46).
10. Sakizadeh, M., &Mirzaei, R. (2016). A comparative study of performance of K-nearest neighbors and support vector machines for classification of groundwater. Journal of Mining and Environment, 7(2), 149-164.
11. Sakizadeh, M. (2015). Assessment the performance of classification methods in water quality studies, A case study in Karaj River. Environmental monitoring and assessment, 187(9), 573.
12. Srinivas, Y., Hudson, O. D., Stanley, R. A., &Chandrasekar, N. (2014). Quality assessment and hydrogeochemical characteristics of groundwater in Agastheeswaram taluk, Kanyakumari district, Tamil Nadu, India. Chinese Journal of Geochemistry, 33(3), 221-235.
13. Townsend, J. T. (1971). Theoretical analysis of an alphabetic confusion matrix. Perception & Psychophysics, 9(1), 40-50.
14. Yogendra, K., &Puttaiah, E. T. (2008). Determination of water quality index and suitability of an urban waterbody in Shimoga Town, Karnataka. In Proceedings of Taal2007: The 12th world lake conference (Vol. 342, p. 346).
15. Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., & Zhou, Z. H. (2008). Top 10 algorithms in data mining. Knowledge and information systems, 14(1), 1-37.
16. Aiswarya Vijayakumar, A S Mahesh (2019). Quality Assessment of Groundwater in Pre and Post Monsoon using various classification Techniques (Unpublished).

475-478

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Authors:

Paper Title:

Abstract: Literature review has revealed that operation lifetime of roller bearings is a factor determining reliability of automotive machinery. In order to increase the lifetime of roller bearings, it is proposed to modify their grease. This method can be applied during machinery operation and does not require for additional chemical–thermal and mechanical treatment of bearings involving complicated and expensive equipment. A modified grease was experimentally developed on the basis of Lithol-24, Russian standard GOST 21150-87 with nanosized metal particles obtained by plasma recondensation. Comparative tribological experiments of commercial grease and nanosized grease demonstrated efficiency of the latter. The acquired experimental results demonstrated that gamma-percentile life of the bearings operating with the developed grease was by 2.4–2.8 times higher than that of the bearings operating with the commercial grease.

Keywords: operation lifetime, roller bearings, greasing composition, metal nanoparticles.

References:

1. S.Denisov, V. N. Baskov, S. S Grigor`ev, “Issledovanie izmeneniya tekhnicheskogo sostoyaniya osnovnykh agregatov avtomobilei KamAZ v protsesse ekspluatatsii” [Study of variation of technical state of KamAZ main units during operation]: Research report, Saratov Polytechnic Institute, 1980.
2. N. Garkunov, E. L. Mel'nikov, V. S. Gavrilyuk, “Tribotekhnika” [Triboengineering]: Handbook, Moscow, KnoRus, 2015.
3. F. Sinel'nikov, V. I. Balabanov, “Avtomobil'nye masla” [Automobile oils]: Handbook, Moscow, Za rulyom, 2005.
4. G. Fuks, S. B. Shibryaev, “Sostav, svoistva i proizvodstvo plastichnykh smazok” [Composition, properties, and production of greases], Moscow, 1992.
5. B. Shibryaev, “Litievye smazki na smeshannoi osnove” [Lithium greases on mixed base], Moscow, 2005.
6. I. Gnatchenko, “Avtomobil'nye masla, smazki, prisadki” [Automobile oils, greases, additives] Reference book, Moscow, AST; St. Petersburg, Poligon, 2000.
7. G. Arabyan, A. B. Vipper, I. A. Kholomonov, “Masla i prisadki dlya traktornykh i kombainovykh dvigatelei” [Oils and additives for engines of tractors and harvesters]: Reference book, Moscow, Mashinostroenie, 1984.
8. I. Balabanov, V. Yu. Bolgov, “Avtomobil'nye prisadki i dobavki” [Additives for vehicle engines], Moscow, 2011.
9. I. Pogodaev, “Nekotorye rezul'taty issledovanii nadezhnosti materialov i oborudovaniya pri iznashivanii” [Studies of reliability of materials and equipment upon wearing]: Proceedings, 5th International symposium on transport triboengineering "Transtribo-2013", St. Petersburg, 2013, p. 12–18.
10. I. Balabanov, “Bases applied nanotechnology”, Moscow, MAGISTR-PRESS, 2007.
11. B. Sergeev, “Nanokhimiya” [Nanochemistry]: Handbook, Moscow, 2006.
12. V. Safonov, V. A. Aleksandrov, A. S. Azarov, E. K. Dobrinskii, “Nanorazmernye dobavki k smazochnym sredam tribosopryazhenii v usloviyakh ikh modelirovaniya” [Simulation of nanosized additives to tribocoupling greases], Remont, vosstanovlenie, modernizatsiya, 2, 2008, p. 8–11.
13. S. Lukinskii, E. I. Zaitsev, “Prognozirovanie nadezhnosti avtomobilei” [Forecasting vehicle reliability], Leningrad, Politekhnika, 1991.
14. Ya. Perel', A. A. Filatov, “Podshipniki kacheniya” [Roller bearings]: Guidebook, 2-nd edition, Moscow, Mashinostroenie, 1999.
15. V. Safonov, A. V. Kirilin, E. K. Dobrinskii, V. A. Aleksandrov, S. V. Safonova, V. N. Builov, Russian patent 2258080, Grease for heavy loaded friction units, No. 2004104508/04; Publicated 10.08.2005, Byul., No. 22.
16. V. V. Amalitskii, “Nadyozhnost' mashin i oborudovaniya lesnogo kompleksa” [Reliability of machinery and equipment of forestry industry]: Handbook, specialty 170400, Moscow, MGUL, 2002.

479-483

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Authors:

Paper Title:

Abstract: Heart disease diagnosis has become a difficult task in the field of medicine. This diagnosis depends on a thorough and accurate study of the patient’s clinical tests data on the health history of an individual. The tremendous improvement in the field of machine learning aim at developing intelligent automated systems which helps the medical practitioners in predicting as well as making decisions about the disease. Such an automated system for medical diagnosis would enhance timely medical care followed by proper subsequent treatment thereby resulting in significant life saving. Incorporating the techniques of classification in these intelligent systems achieve at accurate diagnosis. Neural Networks has emerged as an important method of classification. Multi-layer Perceptron Neural Network with Back-propagation has been employed as the training algorithm in this work. This paper proposes a diagnostic system for predicting heart disease. For diagnosis of heart disease 14 significant attributes are used in proposed system as per the medical literature. The results tabulated evidently prove that the designed diagnostic system is capable of predicting the risk level of heart disease effectively when compared to other approaches.

Keywords: Neural Network; Perception; Back-Propagation.

References:

1. Chaitrali Dangare, Sulabha S. Apte, “Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques”, International Journal of Computer Applications (0975 –888)Volume 47–No.10, June 2012.
2. Srinivas, Dr.G.Raghavendra Rao, Dr. A.Govardhan,“Analysis of Coronary Heart Disease and Prediction of Heart Attack in Coal Mining Regions Using Data Mining Techniques”, The 5th International Conference on Computer Science & Education Hefei, China. August 24–27, 2010.
3. Das, I. Turkoglu, and A. Sengur, “Effective diagnosis of heart disease through neural networks ensembles,” Expert Syst. Appl., vol.36, no. 4, pp. 7675–7680, 2009.
4. Yanwei Xing, Jie Wang and Zhihong Zhao, “Combination data mining methods with new medical data to predicting outcome of Coronary Heart Disease”, 2007 International Conference on Convergence Information Technology.
5. UCI Machine Learning Repository [homepage on the Internet]. Arlington: The Association; 2006 [updated 1996 Dec 3; cited 2011 Feb 2]. Available from:http://archive.ics.uci.edu/ml/datasets/Heart+Disease
6. Jiawei Han, Micheline Kamber & Jian Pei-Data Mining: Concepts and Techniques; 3rd ed; 2011.
7. Shadman Nashif, Md. Rakib Raihan, Md. Rasedul Islam, Mohammad Hasan Imam, “Heart Disease Detection by Using Machine Learning Algorithms and a Real-Time Cardiovascular Health Monitoring System”, World Journal of Engineering and Technology, 2018, 6, 854-873.
8. Hazra, A., Mandal, S., Gupta, A. and Mukherjee, A. (2017) Heart Disease Diagnosis and Prediction Using Machine Learning and Data Mining Techniques: A Review. Advances in Computational Sciences and Technology, 10, 2137-2159.
9. Patel, J., Upadhyay, P. and Patel, D. (2016) Heart Disease Prediction Using Machine learning and Data Mining Technique. Journals of Computer Science & Electronics, 7, 129-137.
10. Chavan Patil, A.B. and Sonawane, P. (2017) To Predict Heart Disease Risk and Medications Using Data Mining Techniques with an IoT Based Monitoring System for Post-Operative Heart Disease Patients. International Journal on Emerging Trends in Technology (IJETT), 4, 8274-8281
11. Weng, S.F., Reps, J., Kai, J., Garibaldi, J.M. and Qureshi, N. (2017) Can Machine-Learning Improve Cardiovascular Risk Prediction Using Routine Clinical Data? PLoS ONE, 12, e0174944. https://doi.org/10.1371/journal.pone.0174944.
12. Soni, J., Ansari, U. and Sharma, D. (2011) Intelligent and Effective Heart Disease Prediction System Using Weighted Associative Classifiers. International Journal on Computer Science and Engineering (IJCSE), 3, 2385-2392.
13. Shouman, M., Turner, T. and Stocker, R. (2012) Using Data Mining Techniques in Heart Disease Diagnosis and Treatment. Electronics, Communications, and Computers, Alexandria, 173-177.
14. Masethe, H. and Masethe, M. (2014) Prediction of Heart Disease Using Classification Algorithms. Proceedings of the World Congress on Engineering and Computer Science, San Francisco, 809-812
15. Subhadra, M.Shashi,‘Effectiveness of Ant Colony Optimization with Hybrid distance for document Retrieval’, IJARCS,Volume 8,2017.
16. Subhadra, M.Shashi ‘An Efficient Relevance-Based Document Ranking Clustering System for Conjunctive Queries’, International Journal of Advanced Computing, ISSN: 2051-0845, Vol.47, Issue.1 1446.
17. Subhadra, M.Shashi]‘Hybrid Distance Based Document Clustering with Keyword and Phrase Indexing’, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 2, No 1, March 2012.
18. Vikas B, B.S.Anuhya, K Santosh Bhargav, Sipra Sarangi, Manaswini Chilla. (2017, June). Application of the Apriori Algorithm for Prediction of Polycystic Ovarian Syndrome (PCOS). 4th International Conference on Information System Design And Intelligent Applications, 2017
19. Santosh Bhargav, Dola Sai Siva Bhaskar Thota, T. Divya Kumari, Vikas B. (2018). Application of Machine Learning Classification Algorithms on Hepatitis Dataset.International Journal of Applied Engineering Research,13(16), 12732-12737
20. Vikas B, Yaswanth D.V.S., Vinay W., Sridhar Reddy B., Saranyu A.V.H. (2017). Classification of Hepatitis C Virus Using Case-Based Reasoning (CBR) with Correlation Lift Metric. 4th International Conference on Information System Design And Intelligent Applications, 2017

484-487

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Authors:

Paper Title:

Abstract: Objectives: To identify the various rational factors and behavioral factors affecting decision making of residential property buyers, to know the dependency of buying decisions on rational factors & behavioral factors and to find out the most prominent factor in each category. Method: A total of hundred residential buyers who previously purchased minimum one asset namely Independent house, flat in apartment and vacant land in Guntur, Andhra Pradesh are participated in the survey. The total data analysis for the study was done through SPSS tool. Result: Chi-Square test results for both the hypotheses shows P-Value of 0.001, so the influence of rational factors and behavioral factors on the decisions of residential property buyers is significant. The Weighted Scores also taken for rational and behavioral factors. Out of four factors the most influencing categories of the rational factors are “location and environmental factors “with weighted Scores as 4.4 & 4.448 respectively and the most influencing variable is “Peaceful & protective Environment” with a weighted Score of 4.64). In behavioral factors category the least important factor is “My buying decisions depend upon the decisions of others” with a weighted Score of 2.97 and most significant factor is “I save a part of my income for buying property “with a weighted Score of 4.41. So it is indicating that buyers are saving their part of income for buying property and buyers are giving much significance for their own individual analysis rather than merely depending on others. Conclusion: From the present analysis it is observed that the impact of rational factors is comparatively high on decisions. So the rationality in decision making of property buyers in residential realty market is a fact and we could also understand that the buyers are giving much value for their own individual analysis rather than purely depending on others in decision making. This study enriches the understanding of the role that rational and behavioral factors play in residential property market, with specific reference to emerging markets.

Keywords: Rational factors, Behavioral factors, Residential property buyers, residential realty market.

References:

1. Adair, A., Berry, J. &McGreal, S. (1996). Valuation of Residential Property: Analysis of Participant Behavior. Journal of Property Valuation & Investment, 14(1), 20-35.
2. Al-Nahdi, T. S. & Abu Bakar, A., (2014). Factors Influencing Purchase Intention of Real Estate in Saudi Arabia. J. Appl. Sci. & Agric., 9(17): 27-39, 2014
3. Al-Nahdi, T. S. (2015). Purchasing housing in Saudi Arabia a behavioral study. J. Appl. Sci. & Agric., 10(2): 12-21, 2015
4. Beracha, E. &Skiba, H. (2014). Real Estate Investment Decision-Making in Behavioral Finance in H. K. Bakers & V. Ricciardi. (Eds.) Investor Behavior: The Psychology of Financial Planning & Investing, John Wiley & Sons, Inc. New Jersey
5. Chapman, D. W., & Lombard, J. R. (2006). Determinants of neighborhood satisfaction on fee-based gated and non-gated communities. Urban Affair Review, 41, 769-799.
6. Daly, J., Gronow, S., Jenkins, D. &Plimmer, F. (2003). Consumer behavior in the valuation of residential property: A
7. comparative study in the UK, Ireland and Australia. Journal of Property Management, 21(5), 295-314.
8. anand bajpai, Mr. prakash bhalchandra (2015), rational & irrational factors affecting realestates buying behaviour of different nationalities with special reference of dubai : a survey, international journal of business quantitative economics and applied management research issn: 2349-5677,volume 2, issue 4, september 2015
9. Elder, H. W., & Zumpano, L. V. (1991). Tenure choice, housing demand and residential location. Journal of Real Estate Research, 6(3), 341 – 356
10. Gabriel, S. & Rosenthal, S. (1989). Housing location and race: Estimates of a multinomial logit model. The Review of Economics and Statistics, 71(2), 240-249.
11. Geoffrey Citau Mwangi(2011) ,Behavioural Factors Influencing Investment Decisions in the Kenyan property market, Strathmore University Nairobi, Kenya.
12. Hurtubia, B., Gallay, O., & Bielaire, M. (2010). Attributes of household, locations and real estate for land use modeling. Sustain City Working Paper, 2.7. Lausanne: EPFL
13. Karsten, L. (2007). Housing as a way of life: Towards an understanding of middle-class families’ preference for an urban residential location. Housing Studies, 22(1), 83-98.
14. Kartajaya, 1994. “Nilai Tambahuntuk Sebuah Properti”, SWA, special edition.
15. Katona, G. (1953). Rational behavior and economic behaviour. Psychological Review, 60(5), 307-318. 191
16. Kauko, T. (2007). An analysis of housing location attributes in the inner city of Budapest, Hungary, using expert judgment, International Journal of Strategic Property Management, 11, 209 – 225.
17. Kaynak, E. and Stevenson, 1982. Comparative study of home buying behaviour of Atlantic Canadians. Management Research News, 5(1): 3-11.
18. Koklic, M. K. & Vida, I. (2009). A Strategik household purchase: consumer house buying behavior. Managing Global Transitions, 7(1), 75-96
19. Lang, R.E., & LeFurgy, J. (2007). Boomburbs: The rise of America’s accidental cities. Washington, D.C: Brookings Institution Press.
20. Lindberg, E., Gärling, T. & Montgomery, H. (1989). Belief-value structures as determinants of consumer behavior: A study of housing preferences and choices. Journal
21. of Consumer Policy, 12(2), 119-137.
22. Louviere, J. & Timmermans, H. (1990). Stated preference and choice models applied to recreation research: A review. Leisure Sciences, 2, 112, 9-32.
23. Njo Anastasia, Aileen Liana Suwitro (2015) ,” The Rational and Irrational Factors Underlying Property Buying Behavior, Journal of Economics and Behavioral Studies , 7, No. 2, pp. 183-191, April 2015 (ISSN: 2220-6140)
24. Numraktrakul, P., Ngarmyarn, A., & Panichpathom, S. (2012). Factors affecting green housing purchase. In 17^th International Business Research Conference. Toronto, Canada.
25. Quigley, J. M. (1976). Housing demand in the short run: An analysis of polychromous Explorate Economic Research, 3, 76-102.
26. Ratchatakulpat, T., Miller, P. &Marchant, T. (2009). Residential real estate purchase decisions in Australia: is it more than location ? International Real Estate Review, 12(3),237-294
27. Reed, R., & Mills, A. (2006). Identifying the drivers behind housing preferences of first-time owners. Property Management, 25(3), 225-241.
28. RongZeng(2013) Attributes influencing home buyers' purchase decisions : a quantitative study of the Wuhanresidential housing market, Southern Cross University, Lismore, NSW.
29. Salzman, D. &Zwinkels, R. C. J. (2013). Behavioral Real Estate (Doctoral thesis, Tinbergen Institute, 2013). Retrieved from http://papers.tinbergen.nl/13088.pdf.
30. Tan, T. H. (2011a). Measuring the willingness to pay for houses in a sustainable neighborhood. The International Journal of Environmental, Cultural, Economic & Social Sustainability, 7, 1 – 12.
31. Tonglet, M., Phillips, P. S., & Read, A. D. (2004). Using the Theory of Planned Behavior to investigate the determinants of recycling behavior: A case study from Brixworth, UK. Resources Conservation and Recycling, 41(3),191-214. http://dx.doi.org/10.1016/j.resconrec.2003.11.001
32. Wang, D. & Li, S. M. (2004). Housing preferences in a transitional housing system: the case of Beijing, China. Environment and Planning A, 36(1), 69 – 87.

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Paper Title:

Abstract: The current study deals with the steady flow of a nanofluid under the influence of magnetic field, heat radiation with prescribed external flow of mixed convective boundary layer flow over a vertical circular cylinder. The radiative heat loss is modelled by Rosseland approximation. Similarity variables are used to remodel the partial differential equations into ordinary differential equations. The remodeled equations are solved numerically by the technique of Runge- Kutta –Fehlberg with shooting. During this study nano particle Alumina(Al2O3) with water and kerosene as the base fluids is studied. For Alumina-water and Alumina-kerosene, nanofluid the nanoparticle volume fraction influences on velocity temperature are presented graphically. The impact of pertinent parameters on velocity and temperatures are resolute and details are mentioned through several plots. The coefficient of skin friction and local Nusselt number for different pertinent parameters are discussed and presented graphically.

Keywords: Mixed convection, nanofluids, nano particle volume fraction, vertical circular cylinder, magnetic parameter, heat source parameter, radiation parameter.

References:

1. U.S. Choi, J.A. Eastman, Enhancing thermal conductivity of fluids with nanoparticles, Materials Science 231 (1995) 99-105.
2. V. Wong, O.D. Leon, Applications of nanofluids: current and future, Advances in Mechanical Engineering 2010 (2010) 1-12.
3. S. Hwang, J.-H. Lee, S.P. Jang, Buoyancy-driven heat transfer of water-based Al2O3 nanofluids in a rectangular cavity, Int. J. Heat and Mass Transfer 50 (2007) 40034010.
4. Akbari, A. Behzadmehr, Developing mixed convection of a nanofluid in a horizontal tube with uniform heat flux, Int. J. Numerical Methods for Heat and Fluid Flow 17 (2007) 566 - 586.
5. Akbari, A. Behzadmehr, F. Shahraki, Fully developed mixed convection in horizontal and inclined tubes with uniform heat flux using nanofluid, Int. J. Heat and Fluid Flow 29 (2008) 545-556.
6. M Narahari, Unsteady free convection flow past a semi-infinite vertical plate with constant heat flux in water based nanofluids, DOI: 10-1088/1757-899X/342/1/012085
7. Tasawar hayat, Modern aspects of homogeneous-heterogeneous reactions and variable thickness in nanofluids through carbon nanotubes. https://doi.org/10.1016/j.physe.2017.07.014
8. Ishak, A., et al., The Effects of Transpiration on the Boundary Layer Flow and Heat Transfer over a Vertical Slender Cylinder, Int. J. Non-Linear Mech., 42 (2007), 8, pp. 1010-1017.
9. Dinarvand et al. “Homotopy analysis method for mixed convective boundary layer flow of a nanofliud over a vertical circular cylinder”hermal Science,Vol.19,No.2(2015) pp.549-561.
10. Karri R.R., Jayakumar N.S., Sahu J.N. "Modelling of fluidised-bed reactor by differential evolution optimization for phenol removal using coconut shells based activated carbon", Journal of Molecular Liquids, 231, pp. 249-262 .
11. Madhavi R., Karri R.R.,et al. "Nature inspired techniques to solve complex engineering problems",Journal of Industrial Pollution Control, 33(1), pp. 1304-1311.
12. Vijaya et al.,”Soret and radiation effects on an unsteady flow of a casson fluid through porous vertical channel with expansion and contraction”, Frontiers in Heat and Mass Transfer, 11.19,(2018).
13. Vijaya, M.Radha Madhavi et al”Boundary layer flow of a mixed convective nanofluid over a Vertical circular cylinder under the influence of magnetic Field, heat radiation and external surface temperature”, International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) , Vol. 8, Special Issue 2, Nov 2018, 411-420.
14. R. Karri and Babovic V, “Enhanced predictions of tides and surges through data assimilation”, in International Journal of Engineering - Transactions A: Basics, Vol. 30, No. 1,pp. 23-29, 2017.
15. Bashir B, Brij M, R. R. Karri and Sabet M, “Studies on the Stability of the Foamy Oil in Developing Heavy Oil Reservoirs”, Defect and Diffusion Forum, Vol. 371, pp 111-116, 2017.

497-504

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Paper Title:

Abstract: In present scenario, the importance of Hand Gesture Object Recognition is widely used in many real time applications. HGOR System is basically a combination of The Fuzzy Reasoning System (FRS), Artificial Neural Network (ANN) with the Fuzzy Measure classifiers. The purpose of this work is to investigate and develop more effective and more accurate system than the earlier developed System. The purpose of this system is to speed up the recognition process because these systems take more training and testing time. This work presents a method for HGOR for the Static hand position to get the meaning for machine interaction. Also, this work is used for facial expression recognition based on hand gesture position surrounding the face to get better position for communication through machine in HCI.

Keywords: Artificial Neural Network (ANN), Fuzzy Reasoning System (FRS), Fuzzy Measure classifier, Hand Gesture Object Recognition (HGOR), Human-Computer Interaction (HCI), Mamdani Classification.

References:

1. Jayesh s. sonkusare, Nilkanth B. chopade, Ravindra sor and Sunil L. tade, “A review on Hand Gesture Recognition System”, in International Conference on Computing Communication Control and Automation, 2015.
2. Mokhtar M. Hasan, and Pramod K. Mishra, 2012. Hand Gesture Modeling and Recognition using Geometric Features: A Review, Canadian Journal on Image Processing and Computer Vision Vol. 3, No.1.
3. Shiguo Lian Wei Hu, kai Wang “Automatic User State Recognition for Hand Gesture Based Low-Cost Television Control System” IEEE Transaction paper,2014.
4. Sukhdip Singh, Yogita Bhardwaj, “Hand Gesture Recognition Techniques: A Review”, in National conference on Innovative Trends in Computer Science Engineering (ITCSE), ISSN:2349-7688, April 2015.
5. Mokhtar M. Hasan, Noor A. Ibraheem and Rafiqul Z.Khan, “Comparative study of Skin Color based Segmentation Techniques”, International Journal of Applied Information Systems (IJAIS), Volume 5, No. 10, August 2013.
6. Anupam Agrawal, Siddharth S. Rautaray,” Vision based hand gesture recognition for human computer interaction: a survey”, Springer/Science Business Media Dordrecht, 2012.
7. Gashree R N , Stafford Michahial , Aishwarya G N ,Beebi Hajira Azeez , Jayalakshmi M R ,and R Krupa Rani, “Hand Gesture Recognition using Support Vector Machine”, in International Journal Of Engineering And Science (IJES), Volume 4, Issue 6, Pages PP.42-46, June – 2015, ISSN (e): 2319 – 1813 ISSN (p): 2319 – 1805.
8. Ohn-Bar and M. M. Trivedi, “Hand gesture recognition in real time for automotive interfaces: A multimodal vision-based approach and evaluations,” IEEE Trans.Intelligent Transportation Systems, vol. 15, no. 6, pp.2368–2377, Dec 2014.
9. Heng, A. Kl¨aser, C. Schmid, and C.-L. Liu, “Dense trajectories and motion boundary descriptors for action recognition,” Intl. Journal of Computer Vision, vol. 103, no. 1, pp. 60-79, May. 2013.
10. Afef Salhi and Ameni Yengui Jammoussi, “Object tracking system using Camshift, Meanshift and Kalman filter,” World Academy of Science, Engineering and Technology International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering Vol:6, No:4, 2012, pp. 421-426.
11. Xianggong Hong, Xiying Zheng, Huimei Xiao, Zhiyi Xue, “An Improved Camshift Algorithm Based on Grabcut with a LBP Model of Correction Tracking Centroid,” Chemical Engineering Transactions, VOL. 46, 2015, DOI: 10.3303/CET1546063, pp. 373-378.

505-509

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Paper Title:

Abstract: The network traffic is increasing exponentially. Managing the vulnerabilities and threats become a major issue due to the heavy volume of data involved. To deal with such problems, network administrators use their experience and understanding of different applications running in their network, to monitor the packet traffic. Here identification and classification of different application that dumps data into the network, becomes challenging. The traditional way is by using behavioral signatures such as port number, application header, transmission frequency, destination IP etc. Although this is still the popular method, it can be beaten by malicious apps and users, by random port changes, proxies, protocol tunneling, and many other tricks. To overcome this issue a technique called flow feature-based analysis can be employed. In this paper, we present a deep learning-based data signature analyses which will identify applications by analyzing the information in traffic flow and some results we have observed. Mainly we are using convolutional neural network based classification and autoencoder based feature extraction to improve the efficiency.

Keywords: Auto encoders, Convolutional Neural Networks, Deep Learning, Internet Applications, Web Browser.

References:

1. Ashwini, V. K. Menon, and K. Soman, “Prediction of malicious domains using smith waterman algorithm,” in International Symposium on Security in Computing and Communication. Springer, 2016, pp. 369–376.
2. Tongaonkar, R. Keralapura, and A. Nucci, “Challenges in network application identification.” in LEET, 2012.
3. “Deep learning,” [Accessed : 29-NOVEMBER-2016]. [Online].
4. Available: https://en.wikipedia.org/wiki/Deep learning
5. Nagananthini and B. Yogameena, “Crowd disaster avoidance system (cdas) by deep learning using extended center symmetric local binary pattern (xcs-lbp) texture features,” in Proceedings of International Conference on Computer Vision and Image Processing. Springer, 2017,487–498.
6. “Deep learning tutorials,” [Accessed : 29-NOVEMBER-2016]. [Online].
7. Available: http://deeplearning.net/tutorial/
8. Soman, R. Loganathan, and V. Ajay, Machine learning with SVM and other kernel methods. PHI Learning Pvt. Ltd., 2009.
9. Soman, S. Diwakar, and V. Ajay, Data Mining: Theory and Practice [WITH CD]. PHI Learning Pvt. Ltd., 2006.
10. “Neural networks,” [Accessed : 02-July-2016]. [Online]. Available: http://ufldl.stanford.edu/wiki/index.php/neural networks
11. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting.” Journal of Machine Learning Research, vol. 15, no. 1,1929–1958, 2014.
12. P. S. K. P. Athira S, Rohit Mohan, “Automatic modulation classifi-cation using convolutional neural network,” IJCTA 9(16) pp 7733-7742, 2016.
13. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane,´ R. Monga, S. Moore, D. Murray, C. Olah, M. Sch