Volume-6 Issue-6

S. No

Volume-6 Issue-6, November 2016, ISSN:  2278-3075 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Nishad Nazar, K. Govardhan

Paper Title:

Collision Avoidance based on Blind Spot Detection and Automatic Steering Control

Abstract: There have been constant research and development in the area of automotive electronics focusing on safe driving and driver safety concerns. Thus, automotive industry has developed ADAS (Advanced driver assistance systems) to adapt different technologies for the better HMI to better the driver safety. Blind spots of the automobile have been a major reason for the accidents caused to the vehicle and many sensing technologies have been developed and implemented to prevent the accidents. The automotive industry has always evolved with new development in the sensing and safety technologies for the automobile. Blind spot of an automobile is a critical issue, until now, which cannot be completely eliminated. There have been usage of convex mirror for eliminating blind spots manually by driver assistance, but the current study aims at managing Blind spots automatically.  This study works on the Blind Spot monitoring and automatic steering control to steer away the vehicle from the obstacle at the both rear end corners. Autonomous driving concept has seen a tremendous development from almost all the automotive industries till today hence, the concept of monitoring Blind spot and to automatically steer the vehicle to avoid the accident is a great task. The Blind spot is detected using the Ultra sonic sensing technology and set margins using mirrors and the automatic steering control is achieved using the Hall Effect sensing mechanism in the driving shaft. The project uses the Arduino MEGA as the Central Unit for the system.

Keywords: Blind Spot detection, Ackermann steering, Arduino MEGA etc.

References:
1.       Muhammad Zahir Hassan and Haziq Irfan Zainal Ariffin, “Development of Vehicle Blind Spot System for Passenger Car”, Applied Mechanics and Materials, Vol (393), 2013
2.       Akhil Samnotra, Dr. Mahesh Kolte, “Collision Avoider using Lane Departure Warning”, International Journal of Scientific and Research Publications, Vol (4), February 2014
3.       Shunji Miyahara, “Blind Spot Monitoring by a single camera”, SAE International, January 2009.
4.       Yasuhisa Hayakawa and Osamu Fukatu, “All Round Blind Spot Detection by Lens condition adaptation based on Rearview Camera Images”, SAE International in October 2013
5.       Y. K. Wang and S. H. Chen, “A Robust Vehicle Detection Approach,” IEEE International Conference on Advanced Video and Signal-based Surveillance, 2005, pp. 117-122.
6.       Z. Sun, G. Bebis and R. Miller, “On-Road Vehicle Detection Using Optical Sensor: A Review,” IEEE Transactions on Intelligent Transportation Systems, 2004, pp. 585-590.
7.       Techmer, “Real-time motion analysis for monitoring the rear and lateral road,” in Proceedings of the IEEE Intelligent Vehicles Symposium, pp. 704–709, June 2004.
8.       M. Ruder, W. Enkelmann, and R. Garnitz, “Highway lane change assistant,” in Proceedings of the IEEE Intelligent Vehicles Symposium, vol. 1, pp. 240–244,
9.       J. C. McCall and M. M. Trivedi, “Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation,” IEEE Transactions on Intelligent Transportation Systems, vol. 7, no. 1, pp. 20–37, 2006.
10.    Diarmaid O Cualain and Martin Glavin “Lane Departure and Obstacle Detection Algorithm for Use in an Automotive Environment” Journal of Intelligent and Fuzzy Systems, vol.2, no.3, May 2012.
11.    Bhupendra Pratap Singh1 , Brijesh Kumar Yadav2 , Lal Bahadur Singh3 , Badri Vishal4 , Raj Kumar Yadav5 Mr.Ram Pratap Yadav6 , Mr.Rahul Srivastav7, “Advanced Four Wheel Steering System“ ,IJREAT International Journal of Research in Engineering & Advanced Technology, Volume 3, Issue 2, April-May, 2015
12.    Sharul Agrawal, Mr. Ravi Prakash, Prof. Zunnun Narmawala, “Implementation of WSN which can simultaneously monitor Temperature conditions and control robot for positional accuracy”, Green Computing Communication and Electrical Engineering (ICGCCEE), March 2014
13.    Aidan Lalor , “Vehicle Handling Characteristics and Development of a Formula Student Car”, Swansea Metropolitan University, 2012

1-5

2.

Authors:

Nwokoro C.O., Chinmanma O. 

Paper Title:

The Utilitarian Indexs of ICT in the Effective Administration Secondary School Education in Rivers State

Abstract:  This study investigated the impact of ICT for quality secondary school education delivery in River State. To address the issue raised therein, 36 item questionnaire titled questionnaire for Application of Information and Communication Technology for Quality secondary school Education Delivery (QAICTQSED). The responses were correlated and analysed using the Pearson Product Moment Correlation Co-efficient to establish the reliability co- efficient of 0.90. The study adopted descriptive survey design and Technology acceptance model theory. For the analysis of data mean (x) was used to answer the research questions, while Z-test was used for the testing of hypotheses of no significant difference. The findings from the analysis revealed that ICT application for the delivery of quality secondary school education to a minimal extent has been achieved. This study recommends that teachers in secondary schools should be armed with appropriate and requisite skills in ICT so as to be able to impact these skills in the students and especially help in trouble-shooting ICT related problems. Educational managers should ensure that students are provided with practical and functional knowledge of computers, the internet and associated areas of ICT. Adequate funds should be allocated and disbursed to public secondary schools for proper financing and maintenance of ICT appliances. This study has provided an empirical basis for problem solving on the application of ICT for quality secondary school education delivery in River State among others.

Keywords:  Quality secondary school education, ICT, Application, Educational managers, delivery.

References:
1.    Adeyemi, T. O. &Olaleye, F. O. (2010). Information Communication and Technology (ICT) for the Effective management of Secondary schools for Sustainable Development in Ekiti State, Nigeria. American-Eurasian Journal of Scientific Research, 5(2). Pp. 106-113.  Retrieved September 18, 2012. http://www.idosi.org/aejsr/5(2)10/4.pdf
2.    Federal Republic of Nigeria, (2004). National Policy on Education (4th ed.). Yaba, Lagos: NERDC.
3.    Encarta Premium DVD (2009). English Dictionaries. Retrieved May 21, 2011. http://top.windows9download.net/list/encarta-premium-dictionary-2009.html
4.    Ndioho, O. F. &Ndioho, E. O. (2011).  Practical Experience: A tool for Quality Assurance in Secondary School Science: African Journal of Educational Research and Development, 4 (2a), 176-182.

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3.

Authors:

Poorva Khemaria, Shiv Kumar, Babita Pathik

Paper Title:

A Review of Fog Computing In Cloud Enterprise for Data Security and Privacy Management

Abstract: Fog computing is the extension of cloud computing. The fog computing proceeds the security scenario of extended technology. Now a day’s internet on things is demanded technology for the connection of home network to another network. The interpretability of cloud computing through other network is face some problem of data sociability. The data scalability is major issue in fog computing. The privacy of data and location privacy is also major issue. The location of fog server is also major issue. In this paper present the review of fog computing for the extension of cloud computing for the process of transportation and some other things.

Keywords: cloud computing, Fog computing, scale, Privacy, security

References:
1.       Jürgo S. Preden and KalleTammemäe, Axel Jantsch, MairoLeier and AndriRiid and EmineCalis “The Benefits of Self-Awareness and Attention in Fog and Mist Computing”, IEEE, 2015, Pp 37-45.
2.       Mohammad Abdullah Al Faruque and KoroshVatanparvar “Energy Management-as-a-Service Over Fog Computing Platform”, IEEE, 2016, Pp 161-169.
3.       FatemehJalali, Kerry Hinton, Robert Ayre, TansuAlpcan and Rodney S. Tucker, “Fog Computing May Help to Save Energy in Cloud Computing”, IEEE, 2016, Pp 1728-1739.
4.       Mugen Peng, Shi Yan, Kecheng Zhang and Chonggang Wang “Fog-Computing-Based Radio Access Networks: Issues and Challenges”, IEEE, 2016, Pp 46-53.
5.       Amir VahidDastjerdi and RajkumarBuyya “Fog Computing: Helping the Internet of Things Realize Its Potential”, IEEE, 2016, Pp 112-116.
6.       Flavio Bonomi, Rodolfo Milito, Jiang Zhu and SateeshAddepalli “Fog Computing and Its Role in the Internet of Things”, Mobile cloud computing, 2012, Pp 13-16.
7.       Luis M Vaquero and Luis. Rodero-Merino “Finding your Way in the Fog: Towards a Comprehensive Definition of Fog Computing”, HP Laboratories, 2014, Pp 1-7.
8.       Salvatore J. Stolfo, Malek Ben Salem and Angelos D. Keromytis “Fog Computing: Mitigating Insider Data Theft Attacks in the Cloud”, IEEE, 2012, Pp 125-128.
9.       Tom H. Luan, Longxiang Gao, Zhi Li, Yang Xiang, Guiyi We and Limin Sun “Fog Computing: Focusing on Mobile Users at the Edge”, arXiv, 2016, Pp 1-11.
10.    Mohammad Aazam and Eui-Nam “Fog Computing and Smart Gateway Based Communication for Cloud of Things”, IEEE, 2014, Pp 464-470.
11.    Flavio Bonomi, Rodolfo Milito, Preethi Natarajan and Jiang Zhu “Fog Computing: A Platform for Internet of Things and Analytics”, Springer, Pp 169-184.
12.    Ivan Stojmenovic and Sheng Wen “The Fog Computing Paradigm: Scenarios and Security Issues”, IEEE, 2014, Pp 1-8.
13.    Shanhe Yi, Cheng Li and Qun Li “A Survey of Fog Computing: Concepts, Applications and Issues”, ACM, 2015, Pp 1-6.
14.    Clinton Dsouza, Gail-JoonAhn and MarthonyTaguinod “Policy-Driven Security Management for Fog Computing: Preliminary Framework and A Case Study”, IEEE, 2014, Pp 16-23.
15.    Nguyen B.Truong, GyuMyoung Lee and YacineGhamri-Doudane “Software Defined Networking-based Vehicular Adhoc Network with Fog Computing”, IFIP, 2015, Pp 1202-1207.
16.    John K. Zao SMIEEE, Tchin-TzeGan, Chun-Kai You, Ser-gio José Rodríguez Méndez, Cheng-En Chung, Yu-Te Wang, Tim Mullen and Tzyy-Ping Jung “Augmented Brain Computer Interaction based on Fog Computing and Linked Data”, IEEE, 2014, Pp 374-377.

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4.

Authors:

Rakhee Single, S. G. Vaidya, M. B. Ansari

Paper Title:

Improvised Blowfish under Bouncy Castle Framework

Abstract:  Greater demand for Internet applications require data to be transmitted securely with improved facilities for networking. But the transmission of data in the public communication system is not secure because of the interception and improper handling by indiscreet. It is necessary to secure the information we want to convey. This need to secure information introduces the concept "Cryptography", which is the art and science of writing hidden. Cryptography before the modern age was effectively synonymous with encryption, data conversion means readable in an apparent nonsense. The proposed system uses Bouncy Castle APIs for cryptography, which is founded in 2000 year. Basically bouncy castle is a collection of APIs used in cryptography. Using Bouncy Castle APIs for cryptography provides security information such as data confidentiality, data integrity, authentication and non-repudiation.

Keywords:  Bouncy castle, cryptography, Blowfish algorithm, Homomorphic Encryption

References:
1.       Bruce Schneier, “Applied Cryptography”, John Wiley & Sons, Inc. 1996
2.       T. Morkel, J.H.P. Eloff, M.S. Olivier, “An overview of Cryptography”, (ICSA), 2004.
3.       The homepage of description of a new variable-length key, 64-bit block cipher http://www.counterpane.com/bfsverlag.html.
4.       Ms Neha Khatri – Valmik, Prof. V. K Kshirsagar, “Blowfish Algorithm”, IOSR Journal of Computer Engineering (IOSR-JCE), Volume 16, Issue 2, Ver. X (Mar-Apr. 2014), PP 80-83.
5.       Patterson and Hennessy, “Computer Organization & Design: The Hardware/ Software Interface”, Morgan Kaufmann, Inc. 1994.
6.       P. Karthigai Kumar and K. Baskaran. 2010. An ASIC implementation of low power and high throughput blowfish crypto algorithm Microelectron. J. 41, 6 (June 2010), 347-355.
7.       B. Schneier, "Description of a New Variable-Length Key, 64-bit Block Cipher (Blowfish)," Fast Software Encryption: Second International Workshop, Leuven, Belgium, December 1994, Proceedings, Springer-Verlag, 1994, pp.191-204.
8.       TingyuanNie; Chuanwang Song; XulongZhi, "Performance Evaluation of DES and Blowfish Algorithms," Biomedical Engi-neering and Computer Science (ICBECS), 2010 International Conference on, vol., no., pp.1-4, 23- 25 April 2010.
9.       S. Vaudenay, "On the Weak Keys in Blowsh," Fast Software Encryption, Third International Workshop Proceedings, Springer-Verlag, 1996, pp. 27-32.
10.    B. Schneier, Applied Cryptography: Protocols, Algorithms, and Source Code in C, 2nd ed., John Wiley & Sons, 1995J. Jones. (1991, May 10). Networks (2nd ed.) [Online]. Available: http://www.atm.com
11.    Manikandan Ganesan, Krishnan Ganesan, “A Novel Approach to the Performance and Security Enhancement Using Blowfish Algorithm”, International journal of Advanced Research in Computer Science, 2011
12.    Kishnamurthy G.N, Dr. V. Ramaswamy and Mrs. Leela.G.H ,“Performance Enhancement of Blowfish algorithm by modifying its function” Proceedings of International Conference on Computers, Information, System Sciences and Engineering 2006, University of Bridgeport, Bridgeport, CT, USA. pp. 240-244.
13.    William Stallings, Cryptography and Network Security, 3rd Ed, Wiley, 1995.
14.    B. Schneier, “Description of a New Variable-Length Key, 64-Bit Block Cipher (Blowfish)”, Fast Software Encryption, Cambridge Security Workshop proceedings (December 1993), Springer-Verlag, 1994, pp. 191-204.
15.    Dr.V. Ramaswamy,  Kishnamurthy. G.N, Mrs.  Leela. G.H, Ashalatha M.E, “Performance enhancement of
16.    CAST –128 Algorithm by modifying its function” Proceedings of International Conference on Computers, Information, System Sciences and Engineering 2007, University of Bridgeport, Bridgeport, CT, USA.

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5.

Authors:

Ritesh Pawar, Maiytree Dutta

Paper Title:

PSF Estimation with PSO and SURE LET Deconvolution for Blurred Image

Abstract: In this research we proposed a technique for the Point spread function estimation in the form of the particle swarm optimization, here also use unbiased risk estimation for the MSE in filtered version with blur stein’s unbiased risk estimation in the form of the novel criterion to calculate only PSF from the blurred image which is unknown. This process of minimization of PSF is obtained by the wiener filtering. On the estimation of this blur kernel, non blind deconvolution is done with the SURE LET deconvolution algorithm. The best positions of the particles are calculated by the PSO. Here we use gaussian kernel for parametric form. In this research we found that position calculation from PSO gives the more accurate PSF parameter estimations, this may lead the high accuracy in restoration of degraded images which is as similar to the exact PSF, when whole result is performed with the help of the SURE LET deconvolution algorithm. From the result it is found that non blind deconvolution has highly accurate results in the form of the visually and computationally form.   

Keywords:   PSF estimation, PSO, Exact Wiener filtering, SURE LET, Blur SURE.

References:
1.       Neel Joshi, Richard szeliski and Dayid J. Kriegman, “PSF estimation using Sharp Edge Prediction”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008.
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4.       Tikhonov and V. Arsenin, “Solutions of Ill-Posed Problems”, Published by Winston, 1977.
5.       O. Michailovich and A. Tannenbaum, “Blind Deconvolution of Medical Ultrasound Images: A Parametric Inverse Filtering Approach”, IEEE Transaction on Image Processing, Vol. 16, No. 12, pp. 3005–3019, December 2007.
6.       G. V. Poropat, “Effect of System Point Spread Function, Apparent Size, and Detector Instantaneous Field of View on the Infrared Image Contrast of Small Objects”, Optical Engineering, Vol. 32, No. 10, pp. 2598–2607, 1993.
7.       T. F. Chan and C.-K. Wong, “Total variation blind deconvolution,” IEEE Trans. Image Process., vol. 7, no. 3, pp. 370–375, Mar. 1998.
8.       R. Molina, J. Mateos, and A. K. Katsaggelos, “Blind Deconvolution Using a Variational Approach to Parameter, Image, and Blur Estimation”, IEEE Transaction on Image Processing., Vol. 15, No. 12, pp. 3715–3727, Dec. 2006.
9.       H. Liao and M. K. Ng, “Blind Deconvolution Using Generalized Cross Validation Approach to Regularization Parameter Estimation”, IEEE Transaction on Image Processing, Vol. 20, No. 3, pp. 670–680, Mar. 2011.
10.    J. Markham and J. A. Conchello, “Parametric Blind Deconvolution: A Robust Method for the Simultaneous Estimation of Image and Blur”, Journal of Optical Society of America A, Vol. 16, No. 10, pp. 2377–2391, 1999.
11.    Haiyan CHEN, Minghua CAO, Huiqin WANG,Yan YAN and Lan MA, “Estimation   the Point Spread Function of motion-Blurred Images of the Ochotona Curzoniae”, International Congress in Image and Signal Processing, Vol. 1, pp. 369-373, 2013.
12.    Feng- Qing Qin, Juin Min and Hong-Rong Guo, “A Blind Image Restoration Method Based on PSF Estimation”, IEEE World Congress on Software Engg., Vol. 2, pp. 173-176, 2009.
13.    Chang-Hwan Son and Hyung-Min Park, “A Pair of Nosiy/blurry Patches-based PSF Estimation and Channel- dependent Deblurring”, IEEE Transaction on Consumer Electronics, Vol. 57, No. 4, pp. 1791-1799, November 2011.
14.    R. Molina, J. Mateos, and A. K. Katsaggelos, “Blind Deconvolution Using a Variational Approach to Parameter, Image, and Blur Estimation”, IEEE Transaction on Image Processing., Vol. 15, No. 12, pp. 3715–3727, Dec. 2006.
15.    H. Liao and M. K. Ng, “Blind Deconvolution Using Generalized Cross Validation Approach to Regularization Parameter Estimation”, IEEE Transaction on Image Processing, Vol. 20, No. 3, pp. 670–680, Mar. 2011.
16.    T. F. Chan and C.-K. Wong, “Total variation blind deconvolution,” IEEE Trans. Image Process., vol. 7, no. 3, pp. 370–375, Mar. 1998.
17.    S. D. Babacan, R. Molina, and A. K. Katsaggelos, “Variational Bayesian Blind Deconvolution Using a Total Variation Prior”, IEEE Transaction on Image Processing, Vol. 18, No. 1, pp. 12–26, Jan. 2009.
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19.    Q. Shan, J. Jia, and A. Agarwala, “High-Quality Motion Deblurring from a Single Image,” ACM Transaction on Graphics, Vol. 27, No. 3, August 2008.
20.    J. Markham and J. A. Conchello, “Parametric Blind Deconvolution: A Robust Method for the Simultaneous Estimation of Image and Blur”, Journal of Optical Society of America A, Vol. 16, No. 10, pp. 2377–2391, 1999.
21.    T. Kenig, Z. Kam, and A. Feuer, “Blind Image Deconvolution using Machine Learning for Three-Dimensional Microscopy”, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 32, No. 12, pp. 2191–2204, Dec. 2010.
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24.    J. P. Oliveira, M. A. T. Figueiredo, and J. M. Bioucas-Dias, “Parametric Blur Estimation for Blind Restoration of Natural Images: Linear Motion and Out-of-Focus”, IEEE Transaction on Image Processing, Vol. 23, No. 1, pp. 466–477, January 2014.
25.    P. Pankajakshan, B. Zhang, L. Blanc-Féraud, Z. Kam, J. C. Olivo-Marin and J. Zerubia, “Blind Deconvolution for Thin-Layered Confocal Imaging”, Journal of OSA Applied Optics, vol. 48, no. 22, pp. 4437–4448, 15 June 2009.
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28.    Chaux, L. Duval, A. Benazza-Benyahia, and J. Pesquet, “A Nonlinear Stein-Based Estimator for Multichannel Image Denoising”, IEEE Transaction on Signal Processing, Vol. 56, No. 8, pp. 3855–3870, August 2008.
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32.    F. Luisier, T. Blu, and M. Unser, “A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding”, IEEE Transaction on Image Processing, Vol. 16, No. 3, pp. 593–606, March 2007.
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42.    T. Kenig, Z. Kam, and A. Feuer, “Blind Image Deconvolution using Machine Learning for Three-Dimensional Microscopy”, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 32, No. 12, pp. 2191–2204, Dec. 2010.
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