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Volume-3 Issue-9: Published on February 10, 2014
37
Volume-3 Issue-9: Published on February 10, 2014

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Volume-3 Issue-9, February 2014, ISSN:  2278-3075 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Debasish Bhaskar, Mousumi Gupta, Rabindranath Bera

Paper Title:

Adaptive Mitigation of Jammer & Clutter in an Airborne GMTI scenario using Sample Matrix Inversion Processing

Abstract: In this paper, we propose an adaptive jammer & clutter suppression scheme using digital beam formation (DBF) technology in RADAR with uniform rectangular phased-array antennas. Digital Beam Forming (DBF) algorithm is employed to cover a detection area of long range (2000 m) and angular orientation of [900, -35.260] w.r.t the RADAR platform flying in an Airplane under the airborne scenario. The airplane is actually a carrying a spaceborne radar with its baseband source using linear frequency modulated (LFM) waveform. The RF carrier is used as a single 3 GHz oscillator. The simulation of the flying radar is done with consideration of a ground clutter being generated near to the target zone and also the existence of a wideband Gaussian-distributed barrage-jammer is encountered. The back-end processing uses Sample Matrix Inversion (SMI) of Clutter & Jammer Covariance matrix with subspace-based DBF algorithm [1]. The proposed 3 GHz Adaptive Beamforming and Jammer Suppression (ABJS) in Airborne RADAR can be used for mitigating the Jammers and Clutters in a Ground Moving Target Indicator (GMTI) system prevailing under the war-field condition.

Keywords:
 Digital beam formation, GMTI, Jammer suppression, Airborne RADAR, Gaussian distributed barrage-jammer.


References:

1.       J. R. Guerci, Space-Time Adaptive Processing for Radar, Artech House, 2003.
2.       S.-H. Jeong, H.-Y. Yu, J.-E. Lee, J.-N. Oh and K.-H. Lee: ‘A Multi-Beam and Multi-Range Radar with FMCW and Digital Beam Forming for automotive applications’, Progress In Electromagnetics Research, Vol. 124, 285-299, 2012.

3.       Van Trees, H., 'Optimum Array Processing'. New York: Wiley-Interscience, 2002.

4.       Wulf-Dieter Wirth, Radar techniques using array antennas, The Institution of Electrical Engineers, London, United Kingdom, 2001.

5.       WIRTH, W. D.: 'Omnidirectional low probability of intercept radar'. International conference on Radar, Paris, France, April 1984, pp. 27-30.

6.       C. K. Kim, S. Choi, and Y. S. Cho: ‘Adaptive beamforming for an OFDM system’. In Proc. IEEE Vehicular Technology Conf., pages 484–488, Houston, TX, May 1999.

7.       Jonathan D. Fredrick, Yuanxun Wang and Tatsuo Itoh: 'A Smart Antenna Receiver Array Using a Single RF Channel and Digital Beamforming', IEEE Transactions On Microwave Theory And Techniques, Vol. 50, No. 12, December 2002.

8.       Nickel, U. (2006), 'Fundamentals of Signal Processing for Phased Array Radar', In Advanced Radar Signal and Data Processing (pp. 1-1 – 1-22). Educational Notes RTO-EN-SET-086, Paper 1. Neuilly-sur-Seine, France: RTO. Available from: http://www.rto.nato.int/abstracts.asp   Abbreviations and Acronyms.


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2.

Authors:

Thiruneelakandan, B., Jeyavel Raja Kumar, T., Dushiyanthan, C., Suresh, R., Karthikeyan, K, Davidraju, D

Paper Title:

A Study on Spectral Reflectance with Surface Water Quality and Chlorophyll-A Concentrations in Muthupet Lagoon of Thiruvarur District, Tamilnadu

Abstract: In this paper, processing techniques for field measurements of spectral reflectance on chlorophyll-a in part of Muthupet lagoon, Thiruvarur district, Tamilnadu. This study focused upon improving the accuracy of chlorophyll quantification by applying wavelet analysis to reflectance spectra. Spectral reflectance measurement was carried out 5 different locations using ASD Field spectrometer in month of July 2011. The reflectance factor was computed and analyzed in RS3 software package the compared spectral curve shows peaks between 400 to 850 nm in most of the measuring locations. The chlorophyll-a content in spectral investigated locations 0.046, 2.258, 2.181, 3.569, 2.378 g/l. Our results show that spectral signatures for chlorophyll-a observed in the lagoon and the field had similar characteristics with high reflectance in visible region of the spectrum from 500 to 650 nm, but low in the NIR region from 750 to 850 nm.

Keywords:
  chlorophyll-a, Reflectance, Spectral Signature.


References:

1.       U.S. Panda., P.K. Mohanty. Monitoring and modeling of Chilika Environment using remotesensing data. Proceeding of Taal 2007: the 12th world lake conference.
2.       M. Jeyanthi, P. Ravichandiran, and A. G. Ponniah. Status of Magroves in relation to brackeshwater aquaculture development in Tamilnadu, India. Bulletin no.21, August, 2010.

3.       Davies, K. M. (Ed.). (2004). Plant pigments and their manipulation: Annual plant reviews, Vol.14. Oxford, UK: Blackwell Publishing.

4.       Moran, J. A., Mitchell, A. K., Goodmanson, G., & Stockburger, K. A. (2000). Differentiation among effects of nitrogen fertilization treatments on conifer seedlings by foliar reflectance: A comparison of methods. Tree Physiology, 20, 1113−1120.

5.       George Alan Blackburn., Jelle Garke Ferwerda., Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis. Remote Sensing of Environment 112 (2008) 1614–1632.

6.       Bricaud, A., and Sathyendranath, S., 1981, Spectral signatures of substances responsible for the change in ocean colour. signatures spectrales d’objects in telede´tection, avignon, 8–11 Sept.  1981.

7.       Garci´a, M. J. L., and Caselles, V., 1990, A multi-temporal study of chlorophyll-a concentration in the Albufera lagoon of Valencia, Spain, using Thematic Mapper data. International Journal of Remote Sensing, 11, 301–311.

8.       Ekstrand, S., 1992, Landsat TM based quantification of chlorophyll-a during algal blooms in coastal waters. International Journal of Remote Sensing, 13, 1913–1926.

9.       Harding, L. W., Itsweire, E. C., and Esaias, W. E., 1994, Estimates of phytoplankton biomass in the Chesapeake Bay from aircraft remote sensing of chlorophyll concentrations, 1989–92. Remote Sensing of Environment, 49, 41–56.

10.    Sathyendranath, S., Subba rao, D. V., Chen, Z., Stuart, V., Platt, T., Bugden, G. L., Jones, W., and Vass, P., 1997, Aircraft remote sensing of toxic phytoplankton blooms: a case study from Cardigan River, Prince Edward Island. Canadian Journal of Remote Sensing, 23, 15–23.

11.    Witte, W. G., Whitlock, C. H., Harris, R. C., Usry, J. W., Poole, L. R., Houghton, W. M., Morris,W. D., and Gurganus, E. A., 1982, Influence of dissolved organic materials on turbid water optical properties and remote sensing reflectance. Journal of Geophysical Research, 87, 441–446.

12.    Novo, E. M. L. M., Steffen, C. A., and Braga, C. Z. F., 1991, Results of a laboratory experiment on relating spectral reflectance to total suspended solids. Remote Sensing of Environment, 36, 67–72.

13.    Han, L., and Rundquist, D. C., 1994, The response of both surface reflectance and the underwater light field to various levels of suspended sediments: preliminary results. Photogrammetric Engineering and Remote Sensing, 60, 1463–1471.

14.    Jensen, J. R., Kjerfve, B., Ramsey III, E. W., Magill, K. E., Medeiros, C., and Sneed, J. E., 1989, Remote sensing and numerical modeling of suspended sediment in Laguna de Terminos, Campeche, Mexico. Remote Sensing of Environment, 28, 33–44.

15.    Braga, C. Z. F., Setzer, A. W., and Lacerda, L. D., 1993, Water quality assessment with simultaneous Landsat-5TM data at Guanabara Bay, Rio de Janeiro, Brazil.
Remote Sensing of  Environment, 45, 95–106.

16.    Mayo, M., Karnieli, A., Gitelson, A., and Ben-Avraham, Z., 1993, Determination of suspended sediment concentrations from CZCS data. Photogrammetric Engineering and Remote Sensing, 59, 1265–1269.

17.    Liedtke, J., Roberts, A., and Luternauer, J., 1995, Practical remote sensing of suspended sediment concentration. Photogrammetric Engineering and Remote Sensing, 61, 167–175.

18.    Tassan, S., 1997, A numerical model for the detection of sediment concentration in stratified river plumes using Thematic Mapper data. International Journal of Remote Sensing, 18, 2699–2705.

19.    Selvam, V., Gnanappazham, L., Navamuniyammal, M., Ravichandiran, K.K and Karunagarn, V.M, 2002. Atlas of mangrove wetlands of India, part of Tamilnadu, M.S. Swaminathan Research foundation, India.

20.    G.V.M.Guptha, UshaNatesan, M.V. Ramanamurthy, V.G. Sravan Kumar, S. Viwanathan, M.S. Bhat, AjayKumar Ray and B.R. Subramanian: 2006: Nutrient budget for Muthupet lagoon, south India. Current science, volume 90, no 7,10.

21.    Cole, G.A., 1988. Textbook oflimnology, Waveland Press, Prospect Heights, Illinois, pp. 173-187.

22.    Gitelson, A.A., 1992. The peak near 700 nm on radiance spectra of algae and water: relationships of its magnitude and position with chlorophyll concentration, International Journal of Remote Sensing, 13:3367-3373.

23.    Rundquist, D.C., J.F. Schalles, and J.S. Peake, 1995. The response of volume reflectance to manipulated algal concentrations abovebright and dark bottoms at various depths in an experimental pool, Geocarto International, 105-14.


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3.

Authors:

Viktor Iliev, Darko Babunski, Igor Seso, Saso Andovski

Paper Title:

Direct Digital Control of HVAC System and CO2-Based Demand Controlled Ventilation

Abstract:  In modern world, ‘saving’ or ‘cut down costs’ are commonly used expressions. As an answer to the demands, the idea of integrated facility management and building automation, as part of it, has been proposed and recognized. While overall operating costs of a building may represents as much as 75% of all the expenses incurred on the building, they can be reduced 25% by means of integrated facility management comprising all system functions during the life cycle of the building which is one step closer to energy efficient and environmental aware buildings. That is the point that is worth thinking. This paper presents simulation model and structure of a SCADA application for Direct Digital Control(DDC) of HVAC (Heating Ventilation and Air-Conditioning) system in cooling/heating mode and design a system that provides suitable air quality in school through the existing air conditioning system using CO2-based demand controlled ventilation.  For simulation of this applications, PLC model number Siemens S7-200 is used, extended with an analog module EM235. Program package Micro WIN Step7 is used for control algorithm creation. SCADA application in software package WinCC is used for visualization and monitoring the work of the HVAC system.

Keywords:
HVAC system, PLC, SCADA, DDC, CO2 demand controlled ventilation.

References:

1.       N. Peter, P. Drago: Racunalnisko vodenje in nadzor ogrevanja in prezracevanja v novi osnovni soli Grosuplje Domzale - Slovenia, Johnson Controls, 2003.
2.       Recknagel, Sprenger, Schramek, Čeperković: Heating and Air-Conditioning, Interklima, 2006.

3.       Air-handling unit for commercial and industrial ranges – TRANE. PROD-PRC005-E4, USA, 2003.

4.       Direct Digital Control (DDC) system for control of HVAC system in Government of the Republic of Macedonia, Main project, INVEST A - Skopje, Macedonia, 2004. 

5.       L.A. Bryan, E. A. Bryan: Programmable Controllers-Theory and Implementation, Second Edition, USA, 1997.

6.       PLC Siemens S7-200 user’s manual and installation, Siemens, 2005.

7.       Gebaudeautomation /System-Beschreibungen - Feldgerate, Weishaupt/Neuberger, Germany, 2006.

8.       S. Morris: Measurement and Instrumentation Principles, 3rd Edition, London, 2001.

9.       MicroWin Step 7, User’s manual, Siemens, 2004.

10.    SIMATIC WinCC flexible, Getting Started Options, Siemens Edition 04/2006.

11.    Steven T. Taylor: CO2-Based DCV Using 62.1-2004D, American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc. (www.ashrae.org). ASHRAE Journal, Vol. 48, May, 2006.

12.    Mike B. Schell, Stephen C. Turner, Omar S.,: Application of CO2-Based Demand-Controlled Ventilation using ASHRAE Standard 62: Optimizing energy use and ventilation ASHRAE Transaction Symposia.

13.    FEMP: Demand-Controlled Ventilation Using CO2 sensors, produced for the U.S. Department of Energy Efficiency and Renewable Energy, by Oak Ridge National Laboratory, 2004. 

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4.

Authors:

Tejaswini Dilip Patil, Kaustubh Dilip Patil, Sunil M. Mahajan

Paper Title:

Efficient Use of Renewable Energy in Train and Railway Station

Abstract: the quick social economic development of Vietnam stimulates great demand of quality as well as quantity on transport service by the increasingly growing needs of customer for transportation. The railway passenger transport is currently still an important branch of a country’s transport system because it is safer, more eco-friendly and much more efficient in comparison to another means. However, the increasing of the number of passengers is the main causes of fast increasing waste amount from the rail service. The aim of this paper is to study how the organic waste from rail service is managed and treated today by the Vietnam railways. The paper ends with some proposal solutions for treating and disposing of organic waste by applying renewable energy technologies for climate change mitigation to protect human health and the environment.  We propose an electricity supply system suitable for public transportation. In this system, solar cells are installed on the roof of the platform. Wind turbines and water wheels are built around the station. Electric double layer capacitors (EDLCs) are installed at the station, and EDLCs are always charged by renewable energy. EDLCs are also mounted on the railcar. When the railcar stops at the station, EDLCs of the railcar are rapidly charged from EDLCs of the station. The battery driven light rail vehicle developed by Railway Technical Research Institute consumes the electricity of 2.5kWh per kilometer. Assuming that interval between stations is 500m; railcar needs 1.3kWh to reach the next station. If we assume that railcars arrive and depart every 10 minutes, and railcars are operated for 18 hours a day, the power generation capacity of 99,000kWh is necessary at each station in one year. 

Keywords:
Renewable energy, Solar energy, Wind energy, Biogas system.


References:

1.          Aso, T., Iida, K., Tanaka, T., Sakuyama, T., Tani, K., Horiuchi, K. & Seki, K., Experimental Study on Vertical Axis Wind Turbine Generation System, PROCEEDINGS OF JSES/JWEA JOINT CONFERENCE, pp. 429–430, Japan Solar Energy Society and Japan Wind Energy Association, Koriyama,Japan, November 2010
2.          Fujii, O., Solar Train-Hybrid Truck System, Technical Report 27, Kurume Institute of Technology, Kurume, Japan, 2004

3.          Fujinaka , M., ELECTRIC ENGINE CAR, Tokyo Denki University Press, Tokyo, Japan, first edition,November 2003

4.          Hashiguchi, M., SOLARCAR, Sankaido, Tokyo,Japan, May 1993

5.          Kameya, T., Suzuki, G., Harada, Y. & Katsuma, H.,Basic Experiment Concerning a Rail Transport System Using Natural Energy, Bulletin 24, Tama Art University, Tokyo, Japan, March 2010

6.          Kameya, T., Suzuki, G., Harada, Y. & Katsuma, H.,Proposal of LRT Using Photovoltaic, Wind and Micro Hydro Power Generation, PROCEEDINGS OF JSES/JWEA JOINT CONFERENCE, pp. 441–444, Japan Solar Energy Society and Japan Wind Energy Association, Koriyama, Japan, November 2010

7.          Kameya, T., Suzuki, G., Harada, Y. & Katsuma, H.,Proposal of LRT Using Renewable Energy, SOLAR WORLD CONGRESS 2011, International Solar Energy Society, Kassel, Germany, August 2011

8.          Kameya, T., Suzuki, G. & Katsuma, H., Proposal of Suitable LRT for Okinawa Using Natural Energy, THE 4TH INTERNATIONAL WORKSHOP ON LIGHT RAIL TRANSIT, Organizing Committee on LRT WORKSHOP 2010, Okinawa, Japan,November 2010

9.          Kameya, T., Suzuki, G., Seki, K. & Katsuma, H., Proving Experiment Concerning LRT That Runs by Renewable Energy, PROCEEDINGS OF JSES/JWEA JOINT CONFERENCE, pp. 223–224, Japan Solar Energy Society and Japan Wind Energy Association, Wakkanai, Japan, September 2011

10.       Ministry of Economy, Trade and Industry, FY2008 Energy Report, 2009

11.       Mori, I., Hori, Y. & Asaoka, S., Capacitor Trolley Bus in Shanghai, ECASS FORUM, volume 3, pp. 2–8, 2008

12.       Ochiai, T., Study on the electric double layer capacitors, Master’s thesis, Tokyo Denki University,Tokyo, Japan, 2000

13.       Ogasa, M., LRT Technology Up To Date 1, ROLLING STOCK & TECHNOLOGY, volume 16, 8, pp. 18–23, November 2010

14.       Ogasa, M., LRT Technology Up To Date (Contactwire-less LRV), THE 4TH INTERNATIONAL WORKSHOP ON LIGHT RAIL TRANSIT, Organizing Committee on LRT WORKSHOP 2010, Okinawa, Japan, November 2010

15.       Ogasa, M., LRT Technology Up To Date 2, ROLLING STOCK & TECHNOLOGY, volume 17, 2, pp. 2–5, February 2011

16.       Okamura, M., ELECTRIC DOUBLE LAYER CAPACITOR AND CHARGING SYSTEM, Nikkan Kogyo Shimbun Ltd., Tokyo, Japan, third edition, September 2005 6

17.       Suzuki, H., Hon-nami, K., Yoshimura, Y. & Obara,H., Bio-hydrogen procurement for solar hydrogen car : An attempt of screening microorganism to,degrade cellulosic biomass as molasses substitute, 62TH SBJ ANNUAL MEETING, p. 157, The Society for Biotechnology, Japan, Miyazaki, Japan, October 2010

18.       Holliger,  C.  2008. Microbiologie  et  Biotechnologie Environmentale. Enseignements  au  2iE.  Lausanne: Swiss  Federal  Institute  of  Technologies  Lausanne (EPFL)

19.       IFC - International Finance Corporation, 2007. Environmental,  Health,  and  Safety  Guidelines RAILWAYS

20.       Müller,  C.  2007.  Anaerobic  Digestion  of  Biodegradable  Solid  Waste  in  Low- and  Middle-Income Countries.  Swiss  Federal Institute  of  Aquatic  Science, Department of Water and Sanitation in Developing  Countries  (http://www.eawag.ch/forschung/sandec/publikationen/swm/dl/Anaerobic_Digestion_low_resolution.pdf, retrieved on 2012-9-08)

21.       OWS - Organic  Waste  Systems. The  DRANCOtechnology(http://www.ows.be/pages/index.php?menu=85&choose_lang=EN, retrieved on 2012-9-06)

22.       Vietnam  Railways,  2009.  The  Annual  Statistical Report 2009 (in Vietnamese)

23.       Vietnam  Railways,  2010.  The  Annual Statistical Report 2010 (in Vietnamese)

24.       Vietnam  Railways,  2011.  The  Annual Statistical Report 2011 (in Vietnamese)

25.       Vietnam  Railways,  2012.  Introduction  on  Vietnam Railways (http://www.vr.com.vn/tin-tuc/gioi-thieuve-dsvn.html, retrieved on 2012-8-28)

26.       TRICC_JSC 2009. Vietnam Transport Investment & Construction Consultant Joint Stock Company. The research on the environmental management in rail


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5.

Authors:

Seyed Arsalan Hoseyni, Javad Zaree, Pejman, Masoud Zahedizadeh

Paper Title:

Feature Selection for Application on Predicting Alzheimer’s Disease Progress

Abstract:  In this paper, the Bayes classifier is used to predict Alzheimer’s disease progress. The classifier is trained on a subset of the Alzheimer’s Disease Neuroimaging Initiative database. Subjects are diagnosed by doctors as belonging to healthy, mild-cognitive impaired, and Alzheimer’s disease class. A software tool for features selection and time regression is developed. The tool utilizes a variant of the Sequential Forward Selection (SFS) algorithm for feature selection, where the criterion used for selecting features is the correct classification rate of the Bayes classifier. The tool also employs linear regression to predict future values of selected biomarkers from past measurements, so that future class of the subject can be predicted.

Keywords:
feature selection, alzheimer, prediction


References:

1.     G. Miller, “Alzheimer’s biomarker initiative hits its stride,” Els. Neurosc. meth., vol. 326, pp. 386–389, 2009.
2.     J. Ram´ırez, J. G´orriz, D. Salas-Gonzalez, A. Romero, M. L´opez, I. ´Alvarez, and M. G´omez-R´ıo, “Computeraided diagnosis of alzheimers type dementia combining support vector machines and discriminant set of features,” Els. Inf. Sciences, vol. In Press, Corr. Proof, 2009.

3.     J. L¨otj¨onen, R. Wolz, J. Koikkalainen, L. Thurfjell, G. Waldemar, H. Soininen, D. Rueckert, and The Alzheimer’s Disease Neuroimaging Initiative, “Fast and robust multi-atlas segmentation of brain magnetic resonance images,” Els. Neuroimage, vol. 49, no. 3, pp. 2352–2365, 2009.

4.     S. Vasto, G. Candore, F. List´ı, C. Balistreri, G. Colonna- Romano, M. Malavolta, D. Lio, D. Nuzzo, E. Mocchegiani, D. Di Bona, and C. Caruso, “Inflammation, genes and zinc in alzheimer’s disease,” Els. Brain research reviews, vol. 58, pp. 96–105, 2008.

5.     W. Liang, T. Dunckley, and T. B. et al., “Neuronal gene expression in non-demented individuals with intermediate alzheimers disease neuropathology,” Els. Neurobiology of Aging, vol. In Press, Corr. Proof, 2008.

6.     D. Ververidis and C. Kotropoulos, “Fast and accurate feature subset selection applied to speech emotion recognition,” Els. Signal Processing, vol. 88, no. 12, pp. 2956– 2970, 2008.

7.     D. Ververidis and C. Kotropoulos, “Information loss of the Mahalanobis distance in high dimensions: Application to feature selection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 12, pp. 2275–2281, 2009.

8.     The Alzheimer’s Disease Neuroimaging Initiative, “Database home page,” www.loni.ucla.edu/ADNI.


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6.

Authors:

Mojtaba Mohseni, Abdolhamid Sohrabi, Ali Ghareaghaji

Paper Title:

A Survey to Micro Grids and Its Applications

Abstract:   Application of individual distributed generators can cause as many problems as it may solve.  A better way to realize the emerging potential  of distributed generation is  to take a system approach which views generation and associated loads as a subsystem  or a  “microgrid”. During disturbances, the  generation and corresponding loads can separate from the distribution system to isolate the microgrid’s  load  from the disturbance  (providing  UPS  services)  without  harming  the transmission grid’s integrity. This ability to island generation and  loads together has  a  potential  to provide a  higher local reliability than that provided by the power system as a whole. In this model it is also critical to be able to use the waste heat by placing the sources near the heat load. This implies that a unit can be  placed at any point  on  the  electrical  system  as required by the location of the heat load.

Keywords:
microgrid, distributed generation, CHP, intentional islanding, voltage droop, power vs. frequency droop, inverters.


References:

1.       M. Valenti “Reaching for 60 percent, the General Electric H turbine system taking shape in Wales is making a  bid for  a  new  record  in thermal efficiency.” Mechanical Engineering, April 2002.
2.       Colleen Williams, “CHP Systems,” Distributed Energy, March/April 2004, pp.57-59.

3.       Office of Energy Efficiency and Renewable Energy, U.S. Department of   Energy,  “2001   BTS   Core   Databook,”  Office   of   Building Technology, July 13, 2001

4.       Lasseter, R.,” MicroGrids,” IEEE PES Winter Meeting, January 2002

5.       Zang, H., M.Chandorkar, G. Venkataramanan, “Development of Static Switchgear for Utility Interconnection in  a  Microgrid.” Power  and Energy Systems PES, Feb. 24-26, 2003, Palm Springs, CA.

6.       Illindals, M., G. Venkataramanan, “Battery Energy Storage for Stand- Alone Micro-Source Distributed Generating System,“ 6th  IASTED Intl. Conf. On power and Energy Systems.


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7.

Authors:

H.I Jaafar, S.Y.S Hussien, N.A Selamat, M.S.M Aras, M.Z.A Rashid

Paper Title:

Development of PID Controller for Controlling Desired Level of Coupled Tank System

Abstract:    The industrial application of Coupled Tank System (CTS) are widely used especially in chemical process industries. The overall process need liquids to be pumped, stored in the tank and pumped again to another tank for certain desired level. Nevertheless, the level of liquid in tank need to be controlled and flow between two tanks must be regulated. This paper presents development of Proportional-Integral-Derivative (PID) controller for controlling the desired liquid level of the CTS. Various conventional techniques of PID tuning method will be tested in order to obtain the PID controller parameters. Simulation is conducted within MATLAB environment to verify the performances of the system in terms of Rise Time (Ts), Settling Time (Ts), Steady State Error (SSE) and Overshoot (OS). Four techniques which are trial and error method, auto-tuning method, Ziegler-Nichols (Z-N) method and Cohen-Coon (C-C) method will be implemented and all the performance results will be analyzed. It has been demonstrated that performances of CTS can be improved with appropriate technique of PID tuning methods.

Keywords:
Coupled Tank System (CTS), PID Controller, PID Tuning Method, Water Level Control.


References:

1.       M. F. Rahmat and S.M. Rozali, “Modelling and Controller Design for a Coupled-Tank Liquid Level System: Analysis & Comparison”, Journal of Technology, vol. 48 (D), June. 2008, pp. 113-141.
2.       Kealy, T. and O' Dwyer, A. “Comparison of Open and Closed Loop Process Identification Techniques in the Time Domain”, Proc. of the 3rd Wismarer Automatisierungs Symposium, Wismar, Germany, Sept. 2001, pp. 3-4.

3.       S. N. Basir, H. Selamat, H. Yussof, N. I. Zahari and S. Shamsuddin, “Parameter Estimation of a Closed Loop Couple Tank Time Varying System using Recursive Methods”, IOP Conf. Series: Materials Science and Engineering, vol. 53, Dec. 2013, pp. 1-10.

4.       Gilson, M. and Van Den Hof, P. “Instrumental Variable Methods for Closed Loop System Identification”, Automatica, vol. 48 (2), Feb. 2005, pp. 241-249.

5.       Coupled-Tank Liquid Level Computer-Controlled Laboratory Teaching Package: Experimental and Operation (Service) Manual, Augmented Innovation Sdn. Bhd., Kuala Lumpur, Malaysia

6.       N. Hasim, M. S. M. Aras, M. Z. A. Rashid, A. M. Kassim and S. S. Abdullah, “Development of fuzzy logic water bath temperature controller using MATLAB,” 2012 IEEE International Conference on Control System, Computing and Engineering, 23-25 Nov. 2012, Penang, Malaysia, pp. 11-16.

7.       M. Z. A. Rashid, T. A. Izzuddin, N. Abas, N. Hasim, F. A. Azis and M. S. M.  Aras, “Control of Automatic Food Drive-Through System using Programmable Logic Controller (PLC),” International Journal of U-& E-Service, Science & Technology, vol. 6 (4), 2013.

8.       M. Z. A. Rashid, M. S. M. Aras, H. N. M. Shah, W. T. Lim and Z. Ibrahim, “Design and system parameter's validation of the unicycle mobile robot,” 2012 International Conference on Control, Automation and Information Sciences, 26-29 Nov. 2012, Vietnam, pp. 311-316.

9.       M. Z. A. Rashid and S. N. Sidek, “Dynamic modeling and verification of unicycle mobile robot system,” 2011 4th International Conference on Mechatronics, 17-19 May 2011, Kuala Lumpur, Malaysia, pp 1-5.

10.    M. S. M. Aras, S. N. S. Salim, Eric Chee Sai Hoo, and M. H. Hairi, “Comparison of Fuzzy Control Rules Using MATLAB Toolbox and Simulink for DC Induction Motor-Speed Control”, IEEE International Conference of Soft Computing and Pattern Recognition, 2009. SOCPAR'09, pp 711-715.

11.    M.S.M Aras, M.F. Basar, N. Hasim, M.N. Kamaruddin, and H.I. Jaafar, “Development and Modeling of Water Tank System Using System Identification Method”, International Journal of Engineering and Advanced Technology, Aug. 2013, pp. 278-283.

12.    N. A. Selamat, N. A. Wahab, and S. Sahlan, “Particle Swarm Optimization for Multivariable PID Controller Tuning”, 2013 IEEE 9th International Colloquium on Signal Processing and its Applications, 8 - 10 Mac. 2013, Kuala Lumpur, Malaysia, pp. 170-175.

13.    H. I. Jaafar, Z. Mohamed, J. J. Jamian, A. F. Z. Abidin, A. M. Kassim and Z. A. Ghani, “Dynamic Behaviour of a Nonlinear Gantry Crane System,” Procedia Technology, vol. 11 (C), 2013, pp. 419-425.

14.    H. I. Jaafar, Z. Mohamed, A. F. Z. Abidin and Z. A. Ghani, “PSO-Tuned PID Controller for a Nonlinear Gantry Crane System,” 2012 IEEE International Conference on Control System, Computing and Engineering, 23-25 Nov. 2012, Penang, Malaysia, pp. 515-519.

15.    H. I. Jaafar, M. F. Sulaima, Z. Mohamed and J. J. Jamian, “Optimal PID Controller Parameters for Nonlinear Gantry Crane System via MOPSO Technique,” 2013 IEEE International Conference on Sustainable Utilization and Development in Engineering and Technology, 30 May – 1 June, 2013, pp. 86-91.

16.    J. G. Ziegler, and N. B. Nichols, “Optimum Setting for Automatic Controllers”, Transactions of ASME, vol. 64, Nov. 1942, pp. 759-768.

17.    G.H. Cohen and G.A. Coon, “Theoretical Consideration of Retarded Control”, Transactions of ASME, vol. 75, 1953, pp. 827-834.


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8.

Authors:

Goran Radoičić, Miomir Jovanović, Lepoje Ilić, Bratislav Blagojević

Paper Title:

Expert Shell for On-line Dynamic Control of a Transportation Process

Abstract:   New technologies reach public utility enterprises with difficulty and are slow in finding their everyday application in less developed cities and municipalities in Serbia, particularly when it comes to the utilities of public interest to urban areas. Certain developmental attempts to introduce new technologies have provided initial results, primarily in increasing the effectiveness and optimization of certain work process costs. These attempts are present in a small number of communities and utility companies. This paper provides an example of an advanced system (expert shell) for controlling the process of solid waste collection and transportation within the fleet management system of a public utility company. Characteristic control methods, which are based on tracking the selected parameters in real-time and post-processing of the realized vehicle routes, are shown in the paper. Part of the original software algorithm to support the monitoring of the system and the analysis of the obtained results is also shown. The paper indicates the importance of using modern GPS technology in improving similar systems of city logistics. The original measured and calculated vehicle tracking parameters were used in the paper.

Keywords:
Expert approach, fleet management, GPS application, signal processing, telecommunications.


References:

1.          S.Y. Chang, “Municipal solid waste management and disposal,” Environmentally Conscious Materials Handling (ed M. Kutz), Hoboken, NJ, USA: John Wiley & Sons, Inc., 2009, pp. 137-171.
2.          G. Radoičić, “Life cycle costs of refuse collection vehicles,” Symposium: Machinery and Transportation Equipment Life Cycle Management, Tara, Serbia: Technical System Maintenance Society, March, 2006.

3.          P. Psimoulis, S. Pytharouli, D. Karambalis and S. Stiros, “Potential of global positioning system (GPS) to measure frequencies of oscillations of engineering structures,” Journal of Sound and Vibration, vol. 318, no. 3, pp. 606-623, June, 2008.

4.          H.S. Park, H.G. Sohn, I.S. Kim and J.H. Park, “Application of GPS to monitoring of wind-induced responses of high-rise buildings,” Structural Design of Tall and Special Buildings, vol. 17, no. 1, pp. 117–132, March, 2008.

5.          J.W. Lovse, W.F. Teskey, G. Lachapelle and M.E. Cannon, “Dynamic deformation monitoring of tall structure using GPS technology,” Journal of Surveying Engineering, vol. 121, no. 1, pp. 35–40, February, 1995.

6.          H. Lia, Z. Chenb, L. Yonga and S.C.W. Kong, “Application of integrated GPS and GIS technology for reducing construction waste and improving construction efficiency,” Automation in Construction, vol. 14, no. 3, pp. 323– 331, June, 2005.

7.          G. Tavares, Z. Zsigraiova, V. Semiao and M.G. Carvalho, “Optimization of MSW collection routes for minimum fuel consumption using 3D GIS modeling,” Waste Management, vol. 29, no. 3, pp. 1176-1185, March, 2009.

8.          M.K. Ghose, A.K. Dikshit and S.K. Sharma, “A GIS based transportation model for solid waste disposal – A case study on Asansol municipality,” Waste Management, vol. 26, no. 11, pp. 1287-1293, 2006.

9.          C. Chalkias and K. Lasaridi, “A GIS based model for the optimization of municipal solid waste collection: the case study of Nikea, Athens, Greece,” WSEASTransactions on Environment and Development, vol. 5, no. 10, pp. 640-650, October, 2009.

10.       D. Bajaj and N. Gupta, “GPS based automatic vehicle tracking using RFID,” International Journal of Engineering and Innovative Technology (IJEIT), vol. 1, no. 1, pp. 31-35, January, 2012.

11.       S.L. Ting, L.X. Wang and W.H. Ip, “A study of RFID adoption for vehicle tracking in a container terminal,” Journal of Industrial Engineering and Management (JIEM), vol. 5, no. 1, pp. 22-52, 2012.

12.       G. Radoičić, M. Jovanović and M. Arsić, “Solution to the task of dynamic logistic management of working technology of the refuse collection vehicles,” Proceedings of International Conference: ISWA BEACON 2011 - Waste-to-Energy and Packaging Waste in Developing Countries in the South Eastern European, Middle East and Mediterranean Region, Novi Sad, Serbia: Serbian Solid Waste Association, November, 2011, pp. 245-251.

13.       Software: MobTrack:24 v.2.6.0.7, Mobile Solutions, Belgrade, Serbia, Available: http://www.mobilesolutions.rs.

14.       E. Kaplan and C. Hegarty, Understanding GPS: Principles and Applications, Norwood, MA, USA: Artech House, Inc., 2006.

15.       GPS basics – Introduction to GPS, Leica Geosystems AG, Heerbrugg, Switzerland, Available:
www.leica-geosystems.com.
16.       Ivušić, V. Batoš, and M. Miličević, “GPS – Analysis of measurement errors and applications,” Naše more (Our See), vol. 55, no. 5-6, pp. 177-181, December, 2008.

17.       Regulation on norms and fuel consumption, Public Utility Company Mediana, Niš, Serbia, Available: www.jkpmediana.rs.

18.       Oracle Database 10g Standard Edition, Oracle Corporation, 500 Oracle Parkway, Redwood Shores, CA 94065, USA, Available: http://www.oracle.com.

19.       G. Radoičić, M. Jovanović, L. Ilić and A. Obradović, “Influence of GPS technology on cost control and maintenance of vehicles,” Proceedings of the First Logistics International Conference, Belgrade, Serbia: University of Belgrade, Faculty of Transport and Traffic Engineering, November, 2013, pp. 84-89.

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9.

Authors:

Mukta Ranjan Singha, Bichitra Kalita

Paper Title:

Uninterrupted Traffic Flow at Junctions with Special Reference to Guwahati City

Abstract: In Urban area congestion mostly occurs at the junctions. Junctions are the intersection of roads, where the flow of the vehicles is controlled by traffic police or traffic lights. When the flow of vehicles increases at the junctions, it causes traffic jams and stream of vehicles incur longer waiting time.  When there is a crossing at a junction, a stream of vehicle has to wait for others. Sometimes, the longer stream of waiting vehicles at the junctions causes stalemate situation. Design of an uninterrupted traffic flow system at the traffic junctions without have to wait for others will lead to minimize severe traffic congestion. We have proposed a traffic flow system at the junctions to make the flow of traffic streams an uninterrupted flow system. This will also lead to design of a traffic light and traffic police free system at the junctions of urban traffic roads.   

Keywords:
Urban Traffic Network, Traffic Flow, Traffic Junction, Optimization of Traffic Flow, Traffic path optimization.


References:

1.        As’ad Salkham, Raymond Cunningham, Anurag Garg, and Vinny Cahill,  “A Collaborative Reinforcement Learning Approach to Urban Traffic Control Optimization”, IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, PP 560-566,  978-0-7695-3496-1/08 . DOI 10.1109/WIIAT.2008.88
2.        W. Wen, “A dynamic and automatic traffic light control expert system for solving the road congestion problem”, Expert Systems with Applications 34 (2008) 2370–2381/

3.        Kenedy Aliila Greyson, “ Anticipated Traffic Jam Locations Using Inlet and Outlet Factors Analysis”, Int. J. Emerg. Sci., 2(2), 193-203, June 2012 ISSN: 2222-4254.

4.        Yordphol TANABORIBOON. “BANGKOK TRAFFIC”, pp 14-23 IATSS Research Vol. 17 No. 1, 1993.

5.        Edwin Prem Kumar Gilbert, Baskaran Kaliaperumal, and Elijah Blessing Rajsingh, “Research Issues in Wireless Sensor Network Applications: A Survey”, International Journal of Information and Electronics Engineering, Vol. 2, No. 5, September 2012

6.        Ryota Ayaki, Hideki Shimada, Kenya Sato, “ A Proposal of Sensor Data Collection System Using Mobile Relay Nodes”, Wireless Sensor Network, 2012, 4, 1-7 doi:10.4236/wsn.2012.41001 Published Online January 2012 (http://www.SciRP.org/journal/wsn)

7.        Fernando Losilla, Antonio-Javier Garcia-Sanchez , Felipe Garcia-Sanchez , Joan Garcia-Haro and Zygmunt J. Haas, “A Comprehensive Approach to WSN-Based ITS Applications: A Survey” , Sensors 2011, 11, 10220-10265; doi:10.3390/s111110220.

8.        M. R. Singha, B. Kalita, “ Mapping Mobile Phone Network onto Urban Traffic Network “, Proceeding of  International Multi Conference of Computer Engineers and Scientists 2013“, Vol I, ISBN: 978-988-19251-8-3, 13-15 March 2013, Hongkong.

9.        M. R. Singha, B. Kalita, “Using Mobile Phone Network for Urban Traffic Management” International Journal of Computer  Applications, (0975-8887), Volume 65-No.2, March 2013, Pp 12-17.

10.     Richard A. Becker, Ramon Caceres, Karrie Hanson, Ji Meng Loh, Simon Urbanek, Alexander Varshavsky and Chris Volinsky, “Route Classification Using Cellular Handoff Patterns”, UbiComp’11, September 17–21, 2011, Beijing, China., Copyright 2011 ACM 978-1-4503-0630-0/11/09.

11.     M. R. Singha, B. Kalita, “Estimation of city bus travelers using GSM network” International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-2, Issue-5, April 2013

12.     Florian Knorr, Deniel Baselt, Michael Schreckenberg, and Martin Mauve, “ Reducing Traffic Jam via VANETs”, IEEE Transaction on vehicular Technology, VOL, 61, NO 8, October 2012.


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10.

Authors:

K.M Pandey, Gautam Choubey

Paper Title:

Numerical analysis of Hypersonic Combustion of a Scramjet Combustor with a Central lobed Strut Injector at Flight Mach Number 7

Abstract:  A numerical study of the inlet-combustor interaction and flow structure through a scramjet engine at a flight Mach number M = 7(Hypersonic Combustion) is presented. The scramjet configuration incorporates an inlet with an 8 degree compression ramp, followed by an isolator, and a divergent combustor. Fuel is injected at supersonic speed (M=2) through a central strut injector. The shape of the strut is chosen in a way to produce strong stream wise vorticity and thus to enhance the hydrogen/air mixing. To investigate the influence of the central injector on the flow behavior, reacting cases have been studied. For the reacting cases, the shock wave pattern is modified due to the strong heat release during combustion process. The shock structure and combustion phenomenon are not only affected by the geometry, but also by the flight Mach number and the trajectory. The k-ε realizable computations are capable of predicting mixing and combustion simulations well and good. For all reacting cases, fuel-air stoichiometric conditions are used.

Keywords:
Scramjet, Hypersonic Combustion, k-ε realizable model, Flameholder.


References:

1.       E. Rabadan, B. Weigand. Numerical Investigation of a Hydrogen-fueled Scramjet Combustor at Flight Conditions. In: The 4th European Conference for Aerospace Sciences, St. Petersburg, Russia, 2010.
2.       T. Nguyen. Numerical Investigations of Relaminarization in Supersonic and Hypersonic Flows. Ph.D. Dissertation, Chair for Computational Analysis of Technical Systems, RWTH Aachen University, Aachen, Germany, to be published.

3.       Vadim Yu. Aleksandrov Alexander N. Prokhorov Vyacheslav L. Semenov “Hypersonic Technology Development Concerning High Speed Air-Breathing Engines” Proceedings of ICFD 10:Tenth International Congress of Fluid Dynamics December 16-19, 2010, Stella Di Mare Sea Club Hotel, Ain Soukhna, Red Sea, Egypt

4.       K. A. SKINNER and R. J. STALKER “Species Measurements in a Hypersonic,Hydrogen-Air, Combustion Wake”

5.       J. SWITHENBANK “HYPERSONIC AIR-BREATHING PROPULSION” Department of Fuel Technology and Chemical Engineering, University of Sheffield 2002, AIAA paper 2000-5210.

6.       N. MOMTCHILOFF, E. D. TABACK, AND R. F. BUSWELL “KINETICS IN HYDROGEN-AIR FLOW SYSTEMS. I. CALCULATION OF IGNITION DELAYS FOR HYPERSONIC RAMJETS

7.       Richard C. Oldenborg, David M. Harradine, Gary W. Loge, John L. Lyman, Garry L. Schott. and Kenneth R. Winn “CRITICAL REACTION RATES IN HYPERSONIC COMBUSTION CHEMISTRY”

8.       S. Yungster, K. Radhakrishnan “Simulation of unsteady hypersonic combustion around projectiles in an expansion tube” Shock Waves (2001) 11: 167–177

9.       K. Kumaran and V. Babu, “Investigation of the effect of chemistry models on the numerical predictions of the supersonic combustion of hydrogen”, Combustion and Flame, vol 156, 2009, pp.826–841.

10.    Shigeru Aso, ArifNur Hakim, Shingo Miyamoto, Kei Inoue and Yasuhiro Tani, “Fundamental study of supersonic combustion in pure air flow with use of shock tunnel”, Department of Aeronautics and Astronautics, Kyushu University, Japan , Acta Astronautica, vol 57, 2005, pp.384 – 389.

11.    S. Zakrzewski and Milton “Supersonic liquid fuel jets injected into quiescent air”, International Journal of Heat and Fluid Flow, vol 25, 2004, pp.833–840.

12.    Kyung Moo Kim 1, Seung Wook Baek and Cho Young Han, “Numerical study on supersonic combustion with cavity-based fuel injection”, International Journal of Heat and Mass Transfer, vol 47,2004, pp.271–286

13.    D. Cecere , A. Ingenito , E. Giacomazzi , L. Romagnosi , C. Bruno Hydrogen/air supersonic combustion for future hypersonic vehicles, international journal of hydrogen energy 36( 2011 ) 11969-11984.

14.    Ghislain Tchuen, Yves Burtschell, David E. Zeitoun “Numerical study of the interaction of type IVr around a double-wedge in hypersonic flow Original Research Article Computers & Fluids, Volume 50, Issue 1, November 2011, Pages 147-154

15.    M Deepu “Recent Advances in Experimental and Numerical Analysis of Scramjet Combustor Flow Fields”, Vol. 88, May 2007

16.    P. Gerlinger, P. Stoll, M. Kindler, F. Schneider and M. Aigner. “Numerical investigation of Mixing and Combustion Enhancement in Supersonic Combustors by Strut Induced Streamwise Vorticity.” In: Aerospace Science and Technology ELSEVIER, 2008, 12:159-168.

17.    M.C. Banica, J. Chun, T. Scheuermann, B. Weigand and J. v. Wolfersdorf. “Numerical Investigation of the Performance of a Supersonic Combustion Chamber and Comparison with Experiments”. In: The 6th European Symposium on Aerothermodynamics for Space Vehicles, Versailles, France, 2009.

18.    Ferri. Mixing-controlled Supersonic Combustion. In: Annual Review Fluid Mechanics, 1973, 5:301-338

19.    W. Heiser and D. Pratt. Hypersonic Airbreathing Propulsion. AIAA Education Series, Washington DC, 1994.

20.    Weigand, U. Gaisbauer. An Overview on the Structure and work of the DFG research training group GRK 1095: Aero-thermodynamic Design of a Scramjet Propulsion System". In: 16th AIAA/DLR/DGLR International Space Planes and Hypersonic Systems and Technologies Conference, 2009.

21.    U. Gaisbauer, B. Weigand. Structure and Results of the Research Training Group GRK 1095/2:Aero-thermodynamic Design of of a Scramjet Propulsion System" an overview of the second working phase. In: International Conference on Methods of Aerophysical Research, ICMAR 2010.


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11.

Authors:

D. Sudha, J. Priyadarshini, A. Ranjidha

Paper Title:

Histology Based Image Retrieval in Multifeature Spaces

Abstract:   Content-based histology image retrieval systems have shown great potential in supporting decision making in clinical activities, teaching, and biological research. In content-based im-age retrieval, feature combination plays a key role. It aims at en-hancing the descriptive power of visual features corresponding to semantically meaningful queries. It is particularly valuable in his-tology image analysis where intelligent mechanisms are needed for interpreting varying tissue composition and architecture into histological concepts. This paper presents an approach to auto-matically combine heterogeneous visual features for histology im-age retrieval. The aim is to obtain the most representative fusion model for a particular keyword that is associated with multiple query images. The core of this approach is a multiobjective learn-ing method, which aims to understand an optimal visual-semantic matching function by jointly considering the different preferences of the group of query images. The task is posed as an optimization problem, and a multiobjective optimization strategy is employed in order to handle potential contradictions in the query images associated with the same keyword. Experiments were performed on two different collections of histology images. The results show that it is possible to improve a system for content-based histology image retrieval by using an appropriately defined multifeature fu-sion model, which takes careful consideration of the structure and distribution of visual features.

Keywords:
Content-based image retrieval (CBIR), feature fusion, histology image retrieval, multiobjective optimization.


References:

1.       Muller,¨ N. Michoux, D. Bandon, and A. Geissbuhler, “A review of content-based image retrieval systems in medical applications–Clinical benefits and futuredirections,” Int. J. Med. Inf., vol. 73, no. 1, pp. 1–23, Feb. 2004.
2.       C. Lacoste, J.-H. Lim, J.-P. Chevallet, and D. Le, “Medical-image retrieval based on knowledge-assisted text and image indexing,” IEEE Trans. Cir-cuits Syst. Video Technol., vol. 17, no. 7, pp. 889–900, Jul. 2007.

3.       R. Datta, D. Joshi, J. Li, and J. Z. Wang, “Image retrieval: Ideas, influences, and trends of the new age,” ACM Comput. Surv., vol. 40, no. 2, pp. 1–60, Apr. 2008.

4.       J. Philbin, M. Isard, J. Sivic, and A. Zisserman, “Descriptor learning for efficient retrieval,” in Proc. 11th Eur. Conf. Comput. Vis., 2010, pp. 677– 691.

5.       K. Q. Weinberger and L. K. Saul, “Distance metric learning for large margin nearest neighbor classification,” J. Mach. Learn. Res., vol. 10,

6.       207–244, Jun. 2009.

7.       H. Akakin and M. Gurcan, “Content-based microscopic image retrieval system for multi-image queries,” IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 4, pp. 758–769, Jul. 2012.

8.       B. Andre,´ T. Vercauteren, A. M. Buchner, M. B. Wallace, and N. Ayache, “Learning semantic and visual similarity for endomicroscopy video re-trieval,” IEEE Trans. Med. Imag., vol. 31, no. 6, pp. 1276–1288, Jun. 2012.

9.       El-Naqa, Y. Yang, N. Galatsanos, R. Nishikawa, and M. Wernick, “A similarity learning approach to content-based image retrieval: Applica-tion to digital mammography,” IEEE Trans. Med. Imag., vol. 23, no. 10, 1233–1244, Oct. 2004.

10.    Y. Huang, J. Zhang, Y. Zhao, and D. Ma, “Medical image retrieval with query-dependent feature fusion based on one-class SVM,” in Proc. 13th Int. Conf. Comput. Sci. Eng., Dec. 2010, pp. 176–183.

11.    J. Kim, W. Cai, D. Feng, and H. Wu, “A new way for multidimensional medical data management: Volume of interest (VOI)-based retrieval of medical images with visual and functional features,” IEEE Trans. Inf. Technol. Biomed., vol. 10, no. 3, pp. 598–607, Jul. 2006.

12.    Tabesh, M. Teverovskiy, H.-Y. Pang, V. Kumar, D. Verbel, A. Kotsianti, and O. Saidi, “Multifeature prostate cancer diagnosis and gleason grading of histological images,” IEEE Trans. Med. Imag., vol. 26, no. 10, pp. 1366– 1378, Oct. 2007.

13.    D. Unay, A. Ekin, and R. Jasinschi, “Local structure-based region-of-interest retrieval in brain MR images,” IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 4, pp. 897–903, Jul. 2010.

14.    L. Wei, Y. Yang, and R. M. Nishikawa, “Microcalcification classification assisted by content-based image retrieval for breast cancer diagnosis,” Pattern Recognit., vol. 42, no. 6, pp. 1126–1132, 2009.

15.    W. Cai, D. Feng, and R. Fulton, “Content-based retrieval of dynamic PET functional images,” IEEE Trans. Inf. Technol. Biomed., vol. 4, no. 2, pp. 152–158, Jun. 2000.

16.    K. Chen, J. Lin, Y. Zou, and G. Yin, “Content-based medical ultrasound image retrieval using a hierarchical method,” in Proc. 2nd Int. Congr. Imag. Signal Process., Oct. 2009, pp. 1–4.

17.    L. Zheng, A. W. Wetzel, J. Gilbertson, and M. J. Becich, “Design and anal-ysis of a content-based pathology image retrieval system,” IEEE Trans. Inf. Technol. Biomed., vol. 7, no. 4, pp. 249–255, Dec. 2003.

18.    B. A. Canada, G. K. Thomas, K. C. Cheng, and J. Z. Wang, “SHIRAZ: An automated histology image annotation system for zebrafish phenomics,” Multimedia ToolsAppl., vol. 51, no. 2, pp. 401–440, Jan. 2011.

19.    H. Muller,¨ J. Kalpathy-Cramer, C. Kahn, W. Hatt, S. Bedrick, and W. Hersh, “Overview of the ImageCLEFmed 2008 medical image re-trieval task,” in Proc. 9th Cross-language Eval. Forum Conf. Eval. Syst. Multilingual Multimodal Inf. Access, 2009, pp. 512–522.

20.    R. Lam, H. Ip, K. Cheung, L. Tang, and R. Hanka, “Similarity measures for histological image retrieval,” in Proc. 15th Int. Conf. Pattern Recognit., 2000, vol. 2, pp. 295–298.

21.    H. L. Tang, R. Hanka, and H. H. S. Ip, “Histological image retrieval based on semantic content analysis,” IEEE Trans. Inf. Technol. Biomed., vol. 7, no. 1, pp. 26–36, Mar. 2003.

22.    J. Naik, S. Doyle, A. Basavanhally, S. Ganesan, M. D. Feldman, J. E. Tomaszewski, and A. Madabhushi, “A boosted distance metric: Ap-plication to content based image retrieval and classification of digitized histopathology,” Proc. SPIE, vol. 7260, no. 1, p. 72603F1–12, 2009.

23.    W. Chen, P. Meer, B. Georgescu, W. He, L. A. Goodell, and D. J. Foran, “Image mining for investigative pathology using optimized feature ex-traction and data fusion,” Comput. Methods Programs Biomed., vol. 79, 59–72, 2005.

24.    C. Snoek, M. Worring, and A. Smeulders, “Early versus late fusion in semantic video analysis,” in Proc. 13th Annu. ACM Int. Conf. Multimedia, 2005, pp. 399–402.

25.    P. Duygulu, K. Barnard, J. de Freitas, and D. Forsyth, “Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary,” in Proc. Eur. Conf. Comput. Vis., 2006, vol. 2353, pp. 349–354.

26.    J. Caicedo and E. Izquierdo, “Combining low-level features for improved classification and retrieval of histology images,” Trans. Mass—Data Anal. Imag. Signals, vol. 2, no. 1, pp. 68–82, 2010.

27.    K. Donald and A. Smeaton, “A comparison of score, rank and probability-based fusion methods for video shot retrieval,” Imag. Video Retrieval, vol. 3568, pp. 61
70, Jul. 2005.

28.    W. Cui and H. Shao, “Automatic feature weight assignment based on genetic algorithm for image retrieval,” Comput. Eng. Appl., vol. 44, no. 2, 106–108, 2008.

29.    J. C. Caicedo, F. A. Gonzalez, and E. Romero, “Content-based histopathology image retrieval using a kernel-based semantic annota-tion framework,” J. Biomed. Inf., vol. 44, no. 4, pp. 519–528, Aug. 2011.

30.    Grigorova, F. De Natale, C. Dagli, and T. Huang, “Content-based im-age retrieval by feature adaptation and relevance feedback,” IEEE Trans. Multimedia, vol. 9, no. 6, pp. 1183–1192, Oct. 2007.

31.    R. Steuer and R. Steuer, Multiple Criteria Optimization: Theory, Compu-tation, and Application. New York: Wiley, 1986, vol. 233.

32.    Mojsilovic, “A computational model for color naming and describing color composition of images,” IEEE Trans. Imag. Process., vol. 14, no. 5, 690–699, May 2005.

33.    C. Schmid and R. Mohr, “Local grayvalue invariants for image retrieval,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 5, pp. 530–535, May 1997. 


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12.

Authors:

Narendra Singh Thakur, Ritu Chauhan

Paper Title:

SER Vs SNR Performance Comparison of 3-Time Slot QSTBC for Rician Fading Channel

Abstract: In this paper, we evaluate and compare the SER performance of few quasi-orthogonal space-time block codes (QOSTBCs) with three time slots for two transmit antennas. The decoding used is ML and fading channel is Rician. We observe that codes proposed in [14] performs better than the codes of [6].

Keywords:
 Orthogonal space-time block codes (OSTBCs), Quasi-orthogonal space-time block codes (QOSTBCs), Quasiorthogonal space-time block codes with 3 time slots (3TSQOSTBCs), Maximum-likelihood (ML) decoding, Bit error rate (BER), Long term evolution-Advanced (LTE-A).


References:

1.       S. M. Alamouti, “A simple transmit diversity technique for wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 16, pp. 1451-1458, Oct. 1998.
2.       V. Tarokh, H. Jafarkhani, and A. R. Calderbank, “Space time block codes from orthogonal designs,” IEEE Trans. Inform. Theory, vol. 45, pp. 1456-1467, July 1999.

3.       H. Jafarkhani, “A quasi-orthogonal space-time block code,” IEEE    Trans. Commun., vol. 49, pp. 1-4, Jan. 2001.

4.       3rd Generation Partnership Project, Evolved Universal Terrestrial Radio Access (E-UTRA); Physical Channels and Modulation(Release 8), 3GPP TS 36.211, Nov.
2008.

5.       Alcatel Shanghai Bell, Alcatel-Lucent,,“STBC-II scheme for uplink transmit diversity in LTE-Advanced, R1-082500, 3GPP TSG RAN WG 1Meeting 53 no bis, Jun-Jul. 2008

6.       Alcatel Shanghai Bell, Alcatel-Lucent, “STBC-II scheme with nonpaired symbols for LTE-Advanced uplink transmit diversity, R1- 090058, 3GPP TSG RAN WG 1 Meeting no 55 bis, Jan. 2009

7.       T.P. Ren, C. Yuen, Y.L. Guan, and K.H. Wang, “3-Time-Slot Group-Decodable STBC with Full Rate and Full Diversity, IEEE Commun. letters, vol. 16, issue 1, pp. 8688, Jan 2011.

8.       Z. Lei, C. Yuen, and F. Chin, “Quasi-orthogonal space-time block codes for two transmit antennas and three time slots, IEEE Trans. Wireless Commun., vol. 10, no. 6, pp. 19831991, June 2011

9.       Thakur, N.S.; Thakur, S.S.; Gogoi, A.K.,“Few More Quasi Orthogonal Space-Time Block Codes for Four Transmit Antennas” IEEE Intern. conference on Computational Intelligence and Communication Networks(CICN), vol. 47, pp. 367-374, Oct.2011.

10.    O. Tirkkonen, A. Boariu, and A. Hottinen, “Minimal nonorthogonality rate 1 space-time block code for 3+ Tx antennas,” in Proc. IEEE 6th Int. Symp. Spread-Spectrum Techniques and Applications (ISSSTA 2000), Sept. 2000, pp. 429-432.

11.    C. B. Papadias and G. J. Foschini, “Capacity-approaching space-time codes for systems employing four transmitter antennas,” IEEE Trans. Inform. Theory, vol. 49, pp. 726-732, Mar. 2003.

12.    J. Hou, M. H. Lee, and J. Y. Park, “Matrices Analysis of quasiorthogonal space time block codes,” IEEE Communications Letters, vol. 7, NO. 8, Aug. 2003

13.    P.V. Bien, W. Sheng, X. Ma, H. Wang,“Improved Decoder Schemes forQOSTBCs Based on Single-Symbol Decoding,”IEEE intern. Conference on Advanced Technologies for Communications(ACT), pp. 7-10,Oct. 2010.

14.    Thakur, N.S. ; Bhatia, R. ; Thakur, S.S., “Two New Quasi-Orthogonal Space-Time Block Codes with 3-Time Slots for LTE-Advanced” IEEE Intern. conference on Computers and Devices for Communication (CODEC), pp. 1-4, Dec.2012.


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13.

Authors:

Deepika Sandhu, Ruchi Pandey

Paper Title:

Energy Saving Opportunity in a Waste Water Treatment Plant

Abstract:  About 90 per cent of sewage and 70 per cent of waste water including industrial and domestic domains in developing countries are discharged without treatment, often polluting the usable water supply and also causes massive harm to the marine life as well, for the very fact that the ultimate destination for all the water sources and streams is ultimately the sea. Although the sewage is 99% pure water, still the approximate 1% is harmful to a very large extent. While talking about the economics, a major part is dedicated to the machinery and installation costs, while a considerable portion is also inclined towards the energy costs. In a conventional waste water treatment plant, working on conventional activated sludge process, a portion of energy is spent in operation of the primary clarifiers. If the Extended Aeration process is followed, the energy spent in the operation of primary clarifiers will not be required and thus, without affecting much of the plant operation, for small establishments. A similar waste water treatment plant working on activated sludge process is in operation at an educational institution, namely Educational Institution in  Jabalpur. Originally, the plant is working on Activated Sludge Process. Process modification has been suggested in the research work. Also, an aspect of environmental modeling has been highlighted.

Keywords:
BOD(Biochemical Oxygen Demand),TSS(Total Suspended Solids), Activated Sludge Process, Extended Aeration Process,Process,Modification,Energy.


References:

1.       Primer for Municipal Wastewater Treatment Systems - United States Environmental       Protection Agency - 832-R-04-001 September 2004
2.       Pernille Ingildsen, Realizing Full-Scale Control in Wastewater Treatment Systems Using In Situ Nutrient        Sensors, Doctoral Dissertation in Industrial Automation Department of Industrial Electrical Engineering and Automation

3.       Monika Vyas, Bharat Modhera, Vivek Vyas    and A. K. Sharma., Performance forecasting            of common effluent treatment plant parameters by artificial neural network , ARPN Journal of Engineering and Applied Sciences ©2006-2011 Asian Research Publishing Network (ARPN). All rights reserved.

4.       Hamed Hasanlou, Naser Mehrdadi, Mohammad Taghi Jafarzadeh, Hamidreza Hasanlou , Performance Simulation of H-TDS Unit of Fajr Industrial Wastewater Treatment Plant Using a Combination of Neural  Network and Principal  Component Analysis , Journal of Water Resource and Protection, 2012.

5.       Rabee Rustum, Adebayo Adeloye, Improved Modelling of Wastewater Treatment  Primary Clarifier Using Hybrid Anns ,International Journal of Computer Science and Artificial Intelligence Dec. 2012, Vol. 2 Iss. 4.

6.       Joanne Kirkpatrick Price ,Applied Math for Waste Water plant operator, CRC Press

7.       Small Community Wastewater Issues Explained to the Public, NESC,Pipeline, Spring     2003   Vol. 14, No. 2

8.       Adam Borowa, Mietek A. Brdys, Krzysztof Mazur, Modelling of Wastewater                 Treatment Plant for Monitoring and Control Purposes by State – Space Wavelet         Networks, International Journal of Computers, Communications & Control Vol. II                (2007), No. 2, pp. 121-131.

9.       Shrivastava Kriti and Joshi Smita,Artificial Neural Network Modelling of Shyamala        Water Works, Bhopal MP, India: A Green Approach towards the Optimization of Water Treatment Process, Research Journal of Recent Sciences, ISSN 2277-2502 Vol.   2(ISC-2012), 26-28 (2013)

10.    Tamás Koncsos, The application of neural networks for solving complex optimization problems in modelling, Conference of Junior Researchers in Civil Engineering

11.    Vahid Nourani, Tohid Rezapour Khanghah and Milad Sayyadi,  Application of the Artificial Neural Network to monitor the quality of treated water, International Journal of Management & Information Technology ISSN: 2278-5612             Volume 3, No 1, January, 2013

12.    Ciprian-George Piuleac, Cristina Sáez, Pablo Cañizares, Silvia Curteanu,

13.    Manuel Andrés Rodrigo ,  Hybrid Model of a Wastewater-Treatment Electrolytic   Process,   Int. J. Electrochem. Sci., 7 (2012) 6289 - 6301

14.    Davut Hanbay a, Ibrahim Turkoglu , Yakup Demir , Prediction of wastewater  treatment plant performance based on wavelet packet decomposition and neural         networks, Expert Systems with Applications 34 (2008) 1038–1043

15.    Subhransu Padhee, Nitesh Gupta, Gagandeep Kaur,  Data Driven Multivariate  Technique for Fault Detection of Waste Water Treatment Plant, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-1, Issue-4, April 2012

16.    Parthiban.R, Latha Parthiban, Porselvam.S and Ravindranath.E, Multilayer perceptron modelling for UASB Reactor treating tannery effluent ,International Journal Of Environmental Sciences,Volume 2, No 3, 2012

17.    R.Vijayabhanu, V.Radha,A Survey on Anaerobic Wastewater Treatment Plant Based on Effluent COD, The International Journal of Computer Science & Applications (TIJCSA), Volume 2, No. 02, April 2013 ISSN – 2278-1080


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14.

Authors:

Ram.Subbiah, S.Satheesh, Shoan C.Sunny, G.Kishor, K.Fahad, R.Rajavel

Paper Title:

Assessment of Properties on AISI 316LN Austenitic Stainless Steel Material under Low Temperature Liquid Nitriding Processes

Abstract:   Austenitic stainless steels have been widely used in highly corrosive environments for power generation, chemical, fertilizer, marine, and food and petrochemical reactors. These materials are well known for their good corrosion resistance and mechanical properties like strength etc. However, because of its low hardness and wear resistance their applications are greatly limited. Nevertheless, the performance of these alloys can improved further for both aqueous and high temperature applications and environments by case hardening techniques like carburizing, nitriding and so on. These surface hardening processes offer high corrosion resistance in addition to, improved hardness and wear resistance. In the present study, the effect of gas nitriding on the properties like micro hardness, corrosion resistance and wear resistance of type AISI 316LN grade austenitic stainless steels were investigated. The salt bath nitriding was carried out at a temperature of 5000C for durations of 60, 90 and 120 minutes with a post oxidation process for a period of 30 minutes and named as SBN1, SBN2, SBN3 respectively. The resultant inter metallic phases were analyzed with optical microscope and micro hardness tester for micro hardness, micro structural changes, nature and compositions of the diffused elements. It has been found that the matrix element interacted with alloying elements and formed a ‘ξ ‘ phase or ‘s’ phase consisting of hard complex Fe-Cr nitrides. These phases showed significant influence on the properties. From the experiment results, it was observed that gas nitriding increases the micro hardness to a considerable amount. A maximum of 1410Hv could be obtained on the austenitic grade stainless steel specimens, which were investigated among the various specimens, in order to improve the wear resistance. The untreated specimens were compared with the nitride specimen.. The reason for the increase in the micro hardness could be attributed to the presence of the Mo and the other alloying elements in the solid solution. The value of hardness at the surface level increases with the diffusion time up to a certain level. Beyond this, limit further increase in diffusion duration does not have any impact on the surface hardness. To evaluate the effect of post-oxidation on nitrided specimen’s corrosion and tribological properties were determined. From the results, it was observed that post- oxidation has no significant effect on the hardness but improves the corrosion resistance in comparison with non-oxidized specimen in a larger factor. Also it was observed that the change in the properties was due to the formation iron oxide layer on the specimen and especially during the subsequent treatment in the oxidizing bath. From the micro structural analysis of the nitrided specimens, the case depths were observed to be about 20 -50 microns (μm).

Keywords:
 stainless steels, nitriding, micro hardness, corrosion resistance, microstructure


References:

1.       ASTM Standard G99, 2000, ‘Standard test method for wear testing with a pin on disk apparatus’.
2.       ASTM Standard G40, 1997, ‘Standard terminology relating to wear and erosion’.

3.       ASTM Standard B117, 1997, ‘Standard practice for operating salt spray apparatus’.

4.       C.Dawes, D.F.Tranter (1982), ‘Nitrotech surface treatment, its development and application in design and manufacturing in automobile components’, Heat Treatment Materials, Vol.4, pp.85-90.

5.       C.S.Chandrasekhara Murthy, A.Ramamohana Rao (1983), ‘Performance studies on salt bath treated low carbon steels’, Tribology International, Vol.2, pp.66-70.

6.       C.S.Stawstorm, M.Hillert (1999), ‘An improved depleted zone theory of wear behavior of stainless steel’, Journal of Iron and Steel Institute, Vol.207, pp.77-85.

7.       C.Z.Christiansen Thomas, A.J.Marcel, Somers (2006), ‘Characterization of low  temperature surface hardened stainless steel’, Surface Engineering, Vol.9, pp.122-128.

8.       D.Das (2003), ‘Diffusion coatings – Surface engineering process, fundamentals and applications’, SERC School on Surface Engineering.

9.       D.Diana Lopez, Neusa Alonso Falleiros, Andre Paulo Tschiptschin (2007), ‘Wear behavior of austenitic and martensitic high nitrogen stainless steels’, Wear, Vol.263, pp.347-354.

10.    D.Gawne, U.Ma (1989), ‘Friction and wear of chromium and nickel coatings’, Wear, Vol.129, pp.123-129.


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15.

Authors:

Vijay Jumb, Mandar Sohani, Avinash Shrivas

Paper Title:

Color Image Segmentation Using K-Means Clustering and Otsu’s Adaptive Thresholding

Abstract: In this paper, an approach for color image segmentation is presented. In this method foreground objects are distinguished clearly from the background. As the HSV color space is similar to the way human eyes perceive color, hence in this method, first RGB image is converted to HSV (Hue, Saturation, Value) color model and V (Value) channel is extracted, as Value corresponds directly to the concept of intensity/brightness in the color basics section. Next an Otsu’s multi-thresholding is applied on V channel to get the best thresholds from the image. The result of Otsu’s multi-thresholding may consist of over segmented regions, hence K-means clustering is applied to merge the over segmented regions. Finally background subtraction is done along with morphological processing. This proposed system is applied on Berkley segmentation database. The proposed method is compared with three different types of segmentation algorithms that ensure accuracy and quality of different types of color images. The experimental results are obtained using metrics such as PSNR and MSE, which proves the proposed algorithm, produces better results as compared to other algorithms.

Keywords:
Color image segmentation, HSV color space, Otsu’s multi-thresholding, K-means clustering, morphological processing, PSNR and MSE.


References:

1.       Rajeshwar Dass, Priyanka, Swapna Devi, “Image Segmentation Techniques”, International Journal on Electronics & Communication Technology (IJECT),  Vol. 3, Issue 1, pp. 66-70, Jan. - March 2012.
2.       N. R. Pal, S. K. Pal,“A Review on Image Segmentation Techniques”, Pattern Recognition, Vol. 26, No. 9, pp. 1277-1294, 1993.

3.       W. X. Kang, Q. Q. Yang, R. R. Liang,“The Comparative Research on Image Segmentation Algorithms”, IEEE Conference on ETCS, pp. 703-707, 2009.             

4.       H. G. Kaganami, Z. Beij,“Region Based Detection versus Edge Detection”, IEEE Transactions on Intelligent information hiding and multimedia signal processing, pp.1217-1221, 2009.

5.       C. Zhu, J. Ni, Y. Li, G. Gu,“General Tendencies in Segmentation of Medical Ultrasound Images”, International Conference on ICICSE, pp. 113-117, 2009.

6.       V. K. Dehariya, S. K. Shrivastava, R. C. Jain,“Clustering of Image Data Set Using K-Means and Fuzzy K-Means Algorithms”, International conference on CICN, pp. 386- 391, 2010.

7.       Md. Habibur Rahman, Md. Rafiqul Islam, “Segmentation of Color Image using Adaptive Thresholding and Masking with Watershed Algorithm”, IEEE, 2013.

8.       N.Otsu, “A threshold selection from gray level histograms”, IEEE Trans. Systems, Man and Cybernetics, vol.9, pp. 62-66. Mar. 1979.

9.       T. Saikumar, P. Yugander, P. Murthy and B.Smitha, "Colour Based Image Segmentation Using Fuzzy C-Means Clustering," International Conference on Computer and Software Modeling IPCSIT, IACSIT Press, Vol. 14, 2011.

10.    Kumar and P. Kumar, "A New Framework for Color Image Segmentation Using Watershed Algorithm," Computer Engineering and Intelligent Systems, Vol. 2(3), pp. 41-46, 2008.

11.    R. Vijayanandh, G. Balakrishnan, "Hillclimbing Segmentation with Fuzzy C-Means Based Human Skin Region Detection using Bayes Rule," European Journal of Scientific Research (EJSR), Vol. 76(1), pp.95-107, 2012.

12.    C. Mythili and V. Kavitha, "Color Image Segmentat ion using ERKFCM," International Journal of Computer Applications (IJCA), Vol. 41(20), pp. 21-28, 2012.

13.    Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. IP, Vol. 13, pp. 600-612, 2004.

14.    The Berkley Segmentation Database and Benchmark. [online] http://www.eecs.berkley.edu/-Research/Projects/CS/vision/bsds/.

15.    Md. Habibur Rahman, Md. Rafiqul Islam, “Segmentation of Color Image using Adaptive Thresholding and Masking with Watershed Algorithm”, IEEE, 2013.


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16.

Authors:

Tanvi Dharmarha, Ajay Jain

Paper Title:

Load Testing Strategy Review When Transitioning to Cloud

Abstract:  The core objective of testing is to certify the product to a quality level at which the application is ready for releasing to the end customers. Apart from functional parameters, there are many other key parameters, especially operational parameters, which play a major role in deciding how the testing is performed. This paper focusses on reviewing the strategy for load testing and changes that a testing team undergoes when transitioning their in-house infrastructure to the cloud. Further to this, the paper also talks about the advantages and efficiencies for the testing team, when shifting to cloud.

Keywords:
 Cloud Testing, Infrastructure, Load Testing, Testing Efficiency,


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17.

Authors:

Mohini Reddy, Vidya Sawant

Paper Title:

WSN based Parameter Monitoring and Control System for DC Motor

Abstract:  Wireless based industrial automation is a prime concern in our day-to-day life. The approach to Zigbee Based Wireless Network for Industrial Applications has been standardized nowadays. In this paper, a wireless control and monitoring system for a D.C motor is realized using the Zigbee communication protocol for safe and economic data communication in industrial fields where the wired communication is either more expensive or impossible due to physical conditions. The D.C motor can be started and stopped wireless due to the computer interface developed with Zigbee. It is also possible to protect the motor against some faults such as over current, higher/lower voltage, over temperature in windings, overloading of motor. Moreover, a database is built to execute online measurements and to save the motor parameters received by radio frequency (RF) data acquisition system. Therefore, controlling, monitoring, and protection of the system are realized in real time. Since the wireless communication technology is used in this study, controlling abilities of the system are increased and also hardware and the necessities of other similar equipment for data communication are minimized. The system is fully controlled by the Personal Computer through Visual Basics GUI (Graphical User Interface).The GUI is developed based on application by the user. All the processor and controllers are interconnected to personal computer through Zigbee. The Personal Computer will continuously monitor all the Data from remote processing unit and compare with value preloaded process structure. If any error is found the personal computer takes necessary action. An 8- bit AVR microcontroller has been used to interface the sensor using the IEEE 802.15.4 standard, ZigBee protocol. ZigBee has the characteristics of low power consumption, low cost and self organizing features. The designed embedded system can be used in applications such as food industry, chemical industry, etc.

Keywords:
 DC Motor, Control and monitoring System,Wireless communication, Zigbee Networks.


References:

1.    Ramya, C.M.,Shanmugaraj, M. ; Prabakaran, R.,“Study on ZigBee technology”  ,Electronics Computer chnology (ICECT), 2011 3rd  International Conference on  Volume: 6,April 2011
2.    S. Palanisamy, S. Senthil Kumar J. Lakshmi Narayanan, “Secured WirelessCommunication for Industrial Automation and Control” ,978-1-4244-86793/11/$26.00 ©2011 IEEE

3.    Jeetender Singh Chauhan, Gyan Prabhakar, Sunil Semwal, Atul Kumar Pandey, “Wireless Personal Area Network based Simulation and Design to  Control the Speed of Permanent Magnet DC Motor using Zigbee Transceiver Protocol”, International Journal of Computer Applications  (0975 – 8887) Volume 69– No.23 May 2013

4.    Mohit Kumar, Mohnish Sharma, Rishabh Narayan, Sumit Joshi1, Sanjay Kumar2, “Zigbee based Parameter Monitoring and Controlling System for  Induction Machine”, Conference on Advances in Communication and  Control Systems 2013 (CAC2S 2013)

5.    Ramazan BAYINDIR, Mehmet ŞEN, “A Parameter Monitoring System for Induction Motors based on zigbee protocol”, Gazi University Journal of Science. GU J Sci 24(4):763-771 (2011)

6.    Arun Kumar, “A Zigbee Based Wireless Data logging System”,  International Journal of Scientific & Engineering Research Volume 3,        Issue 9, September-2012 1 ISSN 2229-5518

7.    Muhammad AliMazadi,” A text book of 8051 MICROCONTROLLER EMBEDDED SYSTEMS”

8.    Jin-Shyan Lee, Yu-Wei Su, and Chung-Chou Shen,  “ A Comparative Study of  Wireless Protocols: Bluetooth, UWB, ZigBee, and Wi-Fi ” ,Information & Communications Research Labs, Industrial Technology Research   Institute (ITRI)

9.    Philipp Gorski, Frank Golatowski, Ralf Behnke, Christian Fabian, Kerstin    Thurow,Dirk Timmermann:”Wireless Sensor Networks in Life Science Applications” 3rd International Conference on Human System Interaction (HSI 2010), pp. 594-598, Rzeszow, Poland, 2010


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18.

Authors:

Lalit Dhande, Priya Khune, Vinod Deore, Dnyaneshwar Gawade

Paper Title:

Hide Inside-Separable Reversible Data Hiding in Encrypted Image

Abstract: Recently, more and more attention is paid to reversible data hiding (RDH) in encrypted images, since it maintains the excellent property that the original cover can be losslessly recovered after embedded data is extracted while protecting the image content’s confidentiality. All previous methods embed data by reversibly memory space from the encrypted images, which may be subject to some errors on data extraction and/or image restoration. In this paper, we propose a novel method by reserving memory space before encryption with a traditional RDH technique, and thus it is easy for the data hider to reversibly embed data in the image. The proposed method can achieve real reversibility, that is, data extraction and image recovery are free of any error.

Keywords:
  Data Encryption, Reversible Data Hiding, Image Encryption, Privacy Protection, Data Extraction.


References:

1.       M. Johnson, P. Ishwar, V. M. Prabhakaran, D. Schonberg, and K. Ramchandran, "On compressing encrypted data," IEEE Trans. Signal Process., vol. 52, no.10, pp. 2992-3006, Oct. 2004.
2.       Z. Ni, Y.-Q. Shi, N. Ansari, and W. Su, "Reversible data hiding," IEEE Trans. Circuits Syst. Video Technol., vol. 16, no. 3, pp. 354-362, Mar.2006.

3.       C.-C. Chang, C.-C.Lin, and Y.-H. Chen, "Reversible   data-embedding scheme   using differences between original and predicted pixel values, "IET Inform. Security, vol. 2, no. 2, pp.35-46, 2008.

4.       T. Bianchi, A. Piva, and M. Barni, "On the implementation of the discrete Fourier transform in the encrypted domain," IEEE Trans. Inform. Forensics Security, vol. 4, no. 1, pp. 86-97, Feb. 2009.

5.       W.  Liu, W.  Zeng, L. Dong, and Q.  Yao, "Efficient compression of encrypted gray scale images," IEEE Trans. Image Process., vol. 19, no. 4, pp. 1097-1102, Apr. 2010.

6.       T.  Bianchi, A. Piva, and M.  Barni, "Composite signal representation for fast and storage- efficient processing of encrypted signals," IEEE Trans. Inform. Forensics Security, vol. 5, no. 1, p. 180-187, Feb. 2010.

7.       X.  Zhang, "Lossy compression and iterative reconstruction for encrypted image," IEEE Trans. Inform. Forensics Security, vol. 6, no.1, pp. 53-58, Feb. 2011.

8.       Xinpeng Zhang “Separable Reversible Data Hiding in Encrypted Image” IEEE Trans. VOL. 7, no. 2, Apr 2012.

9.       Kede Ma, Weiming Zhang, “Reversible Data Hiding in Encrypted Images by Reserving Room Before Encryption” IEEE Trans. VOL. 8, no. 3, Mar 2013.


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19.

Authors:

P. Samundiswary, K Dilip

Paper Title:

Performance Analysis of Energy Aware LAR Protocol in IEEE 802.15.4 based Mobile Wireless Sensor Networks

Abstract:  In this paper, performance analysis of energy aware Location Aided Routing (LAR) Protocol is done for IEEE 802.15.4 based Mobile Wireless Sensor Networks considering mobile nodes. Random Waypoint Mobility Model is considered as the mobility model in the scenario. The various scenarios are designed and simulated by increasing the number of mobile nodes and varying the speed of the mobile nodes. The performance parameters such as throughput, average end to end delay, average jitter and residual energy for different type of scenarios are determined. The simulation is done by using Qualnet 6.1 simulator.

Keywords:
  MWSN, Random Waypoint Mobility Model, LAR,  Requested Zone, Expected Zone.


References:

1.       Waltenegus Dargie, Christian Poellabauer,  “Fundamentals of Wireless Sensor Networks: Theory and Practice”, Chapter 1: Motivation for a Network of Wireless Sensor Nodes, pp. 3-15,John Wiley and Sons Ltd., July 2010.
2.       Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A survey on sensor networks” IEEE Communications Magazine, vol. 40, no. 8, pp. 102-114, August 2002.

3.       Sonam Jain and  Sandeep Sahu “ Topology vs Position based Routing Protocols in Mobile Ad hoc Networks: A Survey” International Journal of Engineering Research & Technology, vol. 1 no. 3, pp. 1-11, May  2012 .

4.       Elizabeth M. Royer and Charles E. Perkins. "An Implementation Study of the AODV Routing Protocol." Proceedings of the IEEE Conference on  Wireless Communications and Networking, Chicago,  pp. 1-6 September 2000.

5.       Simardeep Kaur And Anuj K. Gupta “Position Based Routing In Mobile Ad-Hoc Networks: An Overview” International Journal Of Computer Science And Technology, vol. 3, no. 4, pp. 792-796, Oct - Dec 2012.

6.       B. Karp, H. Kung, GPSR: Greedy Perimeter Stateless Routing for Wireless Networks: Proceedings of ACM Mobile Communications, Boston, Massachusetts, USA,  pp. 243–254, Aug 2000.

7.       Young- BaeKo and Nitin H. Vaidya, “Location Aided Routing (LAR) in Mobile Ad Hoc Networks” Wireless Networks, vol. 6 no. 4 pp. 307-321, July 2000.

8.       P.T.V.Bhuvaneswari and V.Vaidehi,” Location Aided Energy Efficient Routing Protocol in Wireless Sensor Network”, International Journal of Simulation Systems, Science &Technology, vol. 11, no. 4, pp. 41-50, 2011.

9.       QualNet Network Simulator; Available: http://www.scalable-networks.com.p#docs

10.    QualNet documentation,“QualNet 4.0 Model Library: Advanced Wireless”;Available:http://www.scalablenetworks.com/products/qualnet/download.php

11.    Broch, J., Maltz, D.A., Johnson, D.B., Hu, Y.-C., and Jetcheva, J.: “A Performance Comparison of Multi-Hop Wireless ad Hoc Network Routing Protocols” Proceedings of  IEEE International Conference on Mobile Computing and Networking , Dallas, Texas, pp. 85–97,Oct 25-30, 1998.

12.    Tracy Camp, Jeff Boleng and Vanessa Davies, “A Survey of Mobility Models for Ad Hoc Network” Special issue on Mobile Ad Hoc Networking: Research, Trends and Applications, vol. 2, no. 5, pp. 483-502, 2002.


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20.

Authors:

H.S. Hota

Paper Title:

Identification of Breast Cancer Using Ensemble of Support Vector Machine and Decision Tree with Reduced Feature Subset

Abstract:   Breast cancer is very common disease found in woman in which breast masses are increases abnormally .A recent survey in united kingdom proved that breast cancer is not only a problem of young woman but it is also a problem of old age woman those who have crossed the age of sixty or even seventy. An early identification and then prevention with proper medication of breast cancer can save life of human being. A robust and efficient breast cancer identification system is necessary for this purpose. Statistical technique like support vector machine and data mining technique like decision tree are widely used by the researcher since last few years. These techniques proved their ability to efficiently diagnose breast cancer problem. In this research work an ensemble model based on above two techniques are explored with special reference to feature selection. A rank based feature selection technique reduces features one by one based on its rank of breast cancer data ,downloaded from UCI repository site. An ensemble of support vector machine and C5.0 decision tree technique with reduced subset of only five features produced high accuracy of 92.59%.

Keywords:
Decision Tree (DT), C5.0, Support Vector Machine (SVM),Feature Selection (FS).


References:

1.       Breimen.R. & Anand.T. The process of knowledge discovery in databases:A human centered approach. In Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R. (Eds),Advances in Knowledge Discovery and Data Mining.Cambridge:MIT press,1996.
2.       Kurt, Imran,Ture, Mevlut, and Kurum, A. Comparing performance of logistic regression,classification and regression tree and neural network for predicting coronary artery disease. Expert Systems with Application34(1),366-374,2008.

3.       Gupta, S., Kumar, D., and Sharma, A.(2011).Performance analysis of various data mining classification techniques on   health care data. International Journal of Computer Science and Information Technology,3(4),155-169,2011.

4.       Jiawei Han, Kamber Micheline. Data mining: Concepts and Techniques, Morgan Kaufmann Publisher,2009.

5.       Vapnik, V.: The nature of statistical learning theory. Springer, New York ,1995.

6.       Quinlan.J.R. (1993).C4.5:Programs for machine learning (1st edition), San Francisco, Morgan Kaufmann Publishers,1993.

7.       UCI (2014).Web source:http://archive.ics.uci.edu/ml/datasets.html,last accessed on Jan 2014.

8.       Dinesh K. Sharma, Hari, S.Hota ,”Development of rule base system using intelligent techniques to diagnose life threatening diseases “,Proceeding published in review of business and technology research ,Vol. 9 ,No. 1 ,Pp 14-19,2013.

9.       Hari S.Hota , “Data mining techniques for effective and intelligent health care predictive model “ Proceeding published in review of business and technology research ,Vol. 8 ,No. 1 ,Pp 143-149,2012.

10.    Ali K.,Ayturk K.,Ugur Y. “Expert system based on neuro-fuzzy rules for diagnosis breast cancer “,Expert system with applications Vol. 38,Pp 5719-5726,2011.

11.    Elsayad, A. M. (2010). Predicting the severity of breast masses with ensemble of Bayesian classifiers. Journal ofComputer Science, 6(5), 576-584,2010.

12.    Bendi V.R., Prasad M. S. Babu and Venkateswarlu N. B, “A Critical Comparative Study of Liver Patients from USA and INDIA: An Exploratory Analysis”, International Journal of Computer Science Issues, Vol.9. Issue 3,No. 2 ,PP 506-516,2012.

13.    Bendi V. R., Prasad M. S. Babu and Venkateswarlu N. B. “A Critical Study of Selected ClassificationAlgorithms for Liver Disease Diagnosis”, International Journal of Database Management Systems (IJDMS), Vol.3,No.2, PP 101-114,2011.

14.    Bendi V. R ,”A Critical Evaluation of Bayesian Classifier for Liver Diagnosis using Bagging and BoostingMethods”, International Journal of Engineering Sciences and Technology (IJEST) ,Vol.3,No. 4 PP 3422-3426,2011.

15.    Wei Z. ,Rick C.,Jieyue H. “Clinical charge profiles prediction for patients with chronic disease using multi-level support vector machine” Expert systems with application ,Vol. 39,Pp. 1474-1483,2012.

16.    Web source www.rulequest.com/see5-info.html, last accessed on Jan 2014.

17.    Wang, J. (2003). Data Mining: opportunities and challenge, Idea Group, USA. 


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21.

Authors:

Mahmoud Gaballah, Mohammed El-Bardini

Paper Title:

Low Cost Transient Free Thyristor Switching Capacitor for Power Factor Correction Panels

Abstract:  The paper discusses the operating principles and control characteristics of a thyristor switching capacitor (TSC) that used to improve the transient response of capacitor switching. Since the capacitor draws too much current from the main supply at the instant of turn-on. In this paper, the TSC is implemented in such a way that need a minimum number of thyristors with low cost logical control circuit which introduces an economical way to replace the contactor based power factor correction panels. The proposed TSC operations verified through experimental results.

Keywords:
Capacitor banks, Power factor, Thyristor switching capacitor.


References:

1.    Vedam, R.S., Sarma, M.S., “Power Quality VAR Compensation in Power Systems", Taylor & Francis Group, Florida, 2009.
2.    Kusko, A., Thompson, M.T., “Power Quality in Electrical Systems", McGrawHill Professional, New York, 2007.

3.    Smith, I.R., Creighton, G.K., “Reactive-Current Compensation by Switched Capacitors", IEEE Transactions on Industrial Electronics and Control Instrumentation, Vol. IECI-22, Issue 1, pp. 75-78, February 1975.

4.    Olwegard, A., Walve, K., Waglund, G., Frank, H., Torseng, S., “Improvement of Transmission Capacity by Thyristor Controlled Reactive Power", IEEE Transactions on Power Apparatus and Systems, Vol. PAS-100, No. 8, pp. 75-78, August 1981.

5.    Ohyama, T., Yamashita, K., Maeda, T., Suzuki, H., Mine, S., “Effective Application of Static Var Compensators to Damp Oscillations", IEEE Transactions on Power Apparatus and Systems, Vol. PAS-104, Issue 6, pp. 1405-1410, June 1985.

6.    Karady, G.G., “Continuous Regulation of Capacitive Reactive Power", IEEE Transactions on Power Delivery, Vol. 7, No. 3, July 1992.

7.    Tabandeh, M., Alavi, M.H., Marami, M., Dehnavi, G.R., “ Design and Implementation of TSC Type SVC Using a New Approach for Electrical Quenctities Measurement", Power Tech Proceedings, 2001 IEEE Porto, Vol.2, p. 6, 10-13 September 2001.

8.    Jianhua, Z., Guanping, D., Gang, X., Jie, Z., Hui, Z., Shuying, W., “Design of the Control System for Thyristor Switched Capacitor Devices", Transmission and Distribution Conference and Exposition, 2003 IEEE PES, Vol. 2, pp. 606-610, 7-12 September 2003.

9.    Ai-jun, H., Fei, S., Wen-jin, C., “Zero-Cross Triggering Technology of Series SCRs with Optical Fiber at Medium Voltage: Application for Thyristor Switched Capacitor", Transmission and Distribution Conference and Exhibition: Asia and Pacific, 2005 IEEE/PES, pp. 1-5, 2005.


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22.

Authors:

V. Prasath R.Buvanesvari, N. Thilartham, K. Nirosha

Paper Title:

Image Super Resolution Reconstruction Using Wavelet Transform Method

Abstract:   Image super-resolution (SR) has been extensively studied to solve the problem of limited resolution in imaging devices for decades. This paper addresses the problem of recovering a super-resolved image from a set of warped blurred and decimated versions thereof. Several algorithms have already been proposed for the solution of this general problem. In this paper, we propose the image super-resolution reconstruction using wavelet transform method. By using multi surface fitting the low resolution pixel image is converted to high resolution image. The super resolution image is then formed using interpolation based method. The noise and the blur in the resulting image are reduced using our wavelet transform method

Keywords: data fusion, multi surface fitting, super resolution, stationary wavelet transform.


References:

1.       S. Lertrattanapanich and N. K. Bose, “High resolution image formation from low resolution frames using Delaunay triangulation,” IEEE Trans. Image Process., vol. 11, no. 12, pp. 1427–1441, Dec. 2002.
2.       Sánchez-Beato and G. Pajares, “Noniterative interpolation-based super-resolution minimizing aliasing in the reconstructed image,” IEEE Trans. Image Process., vol. 17, no. 10, pp. 1817–1826, Oct. 2008.

3.       S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Fast and robust multi-frame super-resolution,” IEEE Trans. Image Process., vol. 13, no. 10, pp. 1327–1344, Oct. 2004.

4.       Fei Zhou, Wenming Yang, and Qingmin Liao, “Interpolation-Based Image Super-Resolution Using Multisurface Fitting”, IEEE Trans. Image Process., vol. 21, no. 7, July 2012.

5.       F. Zhou, W. Yang, and Q. Liao, “A coarse-to-fine sub pixel registration method to recover local perspective deformation in the application of image super-resolution,” IEEE Trans. Image Process., vol. 21, no. 1, pp. 53–66, Jan. 2012.

6.       H. Takeda, S. Farsiu, and P. Milanfar, “Kernel regression for image processing and reconstruction,” IEEE Trans. Image Process., vol. 16, no. 4, pp. 349–366, Feb. 2007.

7.       K. Moorthy and A. C. Bovik, “A two-step framework for constructing blind image quality indices,” IEEE Signal Process. Lett., vol. 17, no. 5, pp. 513–516, May 2010.

8.       K. Moorthy and A. C. Bovik, “A two-step framework for constructing blind image quality indices,” IEEE Signal Process. Lett., vol. 17, no. 5, pp. 513–516, May 2010.

9.       H. Song, L. Zhang, P. Wang, K. Zhang, and X. Li, “An adaptive hybrid error model to super-resolution,” in Proc. Int. Conf. Image Process., Sep. 2010, pp. 26–29.

10.    Alfonso Sánchez-Beato and Gonzalo Pajares ,”Noniterative Interpolation-Based Super-Resolution minimizing aliasing in the reconstructed image”, IEEE Trans. Image Process., vol. 17, no. 10, October 2008.

11.    Russell Hardie,”A Fast Image Super-Resolution Algorithm using an adaptive wiener filter”, IEEE Trans. Image Process., vol. 16, no. 12, December 2007.

12.    Vismi V, Suvi V, “Brightness Preserved Resolution Enhancement Using DWT-SWT Technique”, IJTEEE  (ISSN 2347-4289),vol.1- Issue 4,November 2013.

13.    Mirajkar Pradnya ,P.Sachin ,D.Ruikar,”Image fusion based on stationary wavelet transform”,Int. J. Adv. Engg. Res. Studies, Sept.,2013.


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23.

Authors:

Padmini Sahu, Anurag Singh Tomer

Paper Title:

Dynamic Modelling Of Seven- Link Biped Robot on Matlab/Simulink: Survey

Abstract:    In this paper, we are going to propose an artificial neural network controller design based on radial basis neural network to control level walking of biped robot. The model used for the biped robot simulation consists of 7-links which are connected through revolute joins. The identical legs have hip, knee & ankle of both legs & torso.  A PID controller is used on a linear model in state variable form in order to simulate the dynamic of the system in Matlab.

Keywords:
Gait cycle, Biped robot, dynamic modelling, neural network


References:

1.       H.K. Lum, M. Zribi *, Y.C. Soh, “Planning and control of a biped robot,” International Journal of Engineering Scienc Journal, 37 (1999) 1319±1349, 1998,April 09.
2.       Jih-Gau Juang, “Fuzzy Modeling Control for Robotic Gait Synthesis”, Proceedings of the 36th Conference on Decision & Control, pp. 3670-3675, December 1997.

3.       Yasuhisa Hasegawa, Takemasa Arakawa and Toshio Fukuda, “Trajectory generation for biped locomotion robot”, Mechatronics, Published by Elsevier Ltd., pp. 67-89, 2000.  

4.       Changjiu Zhou and Qingchun Meng, “Reinforcement Learning with Fuzzy Evaluative Feedback for a Biped Robot”, Proceedings of the 2000 IEEE International Conference on Robotics & Automation, pp. 3829-3835, April 2000.

5.       Jun Morimoto, Gordon Cheng, Christopher G. Atkeson, and Garth Zeglin, “A Simple Reinforcement Learning Algorithm For Biped Walking”, Proceedings of the IEEE lnternational Conference on Robotics &Automation, pp. 3030-3035, April 2004.

6.       Shinya Aoi and Kazuo Tsuchiya, “Locomotion Control of a Biped Robot Using Nonlinear Oscillators”, Autonomous Robots, Published by Springer, pp. 219–232, 2005

7.       Fumihiko Asano, Zhi-Wei Luo and Masaki Yamakita, “Biped Gait Generation and Control Based on a Unified Property of Passive Dynamic Walking”, IEEE Transactions On Robotics, Vol. 21, No. 4, pp. 754-762, August 2005

8.       Christophe Sabourin, Olivier Bruneau and Gabriel Buche, “Control Strategy for the Robust Dynamic Walk of a Biped Robot”, The International Journal of Robotics Research, Vol. 25, No. 9, pp. 843-860, SAGE Publications, September 2006

9.       Olli Haavisto & Heikki Hyötyniemi,” Simulation tool of a Biped Robot Model”,   Helsinki University of Technology  Control Engineering Laboratory , 2004

10.    Yutaka Nakamura, Takeshi Mori, Masa-aki Sato and Shin Ishii, “Reinforcement learning for a biped robot based on a CPG-actor-critic method”, Neural Networks, Published by Elsevier Ltd., 2007

11.    Reza Ghorbani, Qiong Wu and G. Gary Wang, “Nearly optimal neural network stabilization of bipedal standing using genetic algorithm”, Engineering Applications of Artificial Intelligence, Published by Elsevier Ltd.,  pp. 473–480, 2007

12.    H. Khalife, N. Malouch, S. Fdida, “Multihop cognitive radio networks: to route or not to route,” IEEE Network, vol. 23, no. 4, pp. 20-25, 2009.

13.    In-sik Lima, Ohung Kwonb,, Jong Hyeon Parka, “Gait optimization of biped robots based on human motion analysis,” in Robotics and Autonomous Systems Elsevier 2013

14.    Ehsan Kouchaki and Mohammad Jafar Sadigh, “Constrained-Optimal Balance Regulation of a Biped with Toe-Joint Using Model Predictive Control,”Iinternational Conference on Robotics and Mechatronics, pp.

15.    R.K. Mittal and I. J. Nagrath, “ Robotics and Control” in pp. 192 to 203,2006

16.    Ashitava Ghosal, “Robotics fundamental concepts and analysis” in pp.223 to236,2006.


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24.

Authors:

V.Prasath  R.Buvanesvari  R.Kalaivani  M.Megala

Paper Title:

Enhancement of Website Visibility using Search Engine Optimization Techniques

Abstract: Search engine optimization is often about making small modifications to parts of your website. When viewed individually, these changes might seem like incremental improvements, but when combined with other optimizations, they could have a noticeable impact on your site's user experience and performance in organic search results. The results generated by search engines can be natural (organic or algorithmic) and/or paid search. Here we have discussed different techniques used for achieving better optimization the search will differ with different users. The search will be done according to the keywords given by the users.The ranking functions are typically learned to rank search results based on features of individual documents i.e., point-wise features. This will increase the website visibility and make the user to get the information what they are actually looking for. We can use this technique also in the standalone systems.

Keywords:
organic search, search engine optimization, ranking methods, websites


References:

1.          http:\\www.seotutorial.com\\”webmaster”.
2.          “Optimization of Ranking Measures “ by Quoc V. Le  Alex   Smolaa, Olivier Chapelle, ChoonHuiTeo in 2000

3.          “Methods for comparing rankings of search engine results” by Judit Bar-Ilan, Mazlita Mat-Hassan, Mark Levene in 2005

4.          “What Users See – Structures in Search Engine Results Pages” by NadineHöchstötter, Dirk Lewandowski in 2008

5.          Search Engine Marketers Professional Organization, SEM Glossary. [Online].Available: http://www.sempo.org/?page=glossary

6.          Google. (2010). Search engine optimization starter guide. Webmaster tools help.

7.          http://www.google.com/webmasters/docs/search-engine-optimization-starter-guide.pdf

8.          B. J. Jansen and P. R. Molina, “The effectiveness of web search engines for retrieving relevant ecommerce links,” Inf. Process. Manage., vol. 42, pp. 1075–1098, 2006.

9.          J. Bar-Ilan, “Comparing rankings of search results on the web,” Inf. Process. Manage., vol. 41, no. 6, pp. 1511–1519, 2005.

10.       J. Bar-Ilan, M. Mat-Hassan, and M. Levene, “Methods for comparing rankings of search engine results,” Comput. Netw., vol. 50, no. 10, pp. 1448–1463, 2006
11.       http://moz.com/beginners-guide-to-seo/how-search-engines-operate
12.       http://en.wikipedia.org/wiki/Search_engine_optimization

13.       http://www.webdeveloper.com/forum/showthread.php?278251-5-Super-Advantages-of-SEO


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25.

Authors:

V.Prasath, R.Buvanesvari, V.Anitha, M.Keerthana

Paper Title:

Improving Web Service Selection using Fuzzy Quality of Protection

Abstract:  We aim to solve the selection of secure web services in a global and flexible manner by introducing a Fuzzy logic method. This paper presents a stride model based evaluation of  web service security using quality of protection parameters like spoofing, tampering, reputation, information disclosure, denial of service  and elevation of privileges. In this paper quality of protection parameterized tasks that are given to fuzzier where the input values for decision making that are converted into the range between 0 and 1 for selection and choice of the most appropriate web service with respect to quality of protection.

Keywords:
fuzzy,qualityofprotection,webservicediscovery, web service security


References:

1.       World Wide Web Consortium. Web Service Activity. www.w3.org/2002/ws/.
2.       Z. Stojanovic, A. Dahanayake and H. Sol, “Modeling and design of service oriented architecture”, Proc. of 2004 IEEE International Conference on Systems, Man and Cybernetics, the Hague, the Netherlands, Vol. 5, pp. 4147- 4152, Oct. 2004.

3.       D.A. Menasce, “QoS issues in Web services”, IEEE Internet Computing, Vol. 6, Iss. 6, pp. 72-75, Nov/Dec.2006.

4.       Jiang Li1chen Hao1 Deng Fei1, 2 Zhong Qiusheng. “A Security Evaluation Method Based on Threat Classification for Web Service”. JOURNAL OF SOFTWARE, VOL. 6, NO. 4, APRIL 2011.
5.       Zhang Liang, Zhu Leiming, Wang Kang. “A Website Security Analyzing Technology Based on Web Vulnerability Threat Model” Microcomputer Applications. 2008.24(5):56-58
6.       Shi Yinsheng, Deng Shiwei, Gu Tianyang. “Research on the Web Services Security Testing Technology”. Computer Engineering and Science. 2007. 29[10]\

7.       Myung-Hee Kang, Kyung-Nam Kim, Hwang-Bin Ryon.“An authorization mechanism for Web Services using an attribute certificate”. Proceedings IEEE 37th Annual 2003 International Carnahan Conference on Security Technology, 14-16 Oct. 2003, 144~150

8.       Artsiom Yautsiukhin. “Quality of Protection Determination for Web Services”. This work was partly supported by the project EU-IST-IP-SERENITY, contract N 27587.

9.       Le-Hung Vu, Manfred Hauswirth and Karl Aberer. “QoS-based Service Selection and Ranking with Trust and Reputation”. Management School of Computer and Communication Sciences Ecole Polytechnique F¶ed¶erale de Lausanne (EPFL) CH-1015 Lausanne, Switzerland .

10.    Davoud Mougouei1, Wan Nurhayati Wan Ab. Rahman. “Fuzzy Description Of Security Requirements For Intrusion Tolerant Web-Services”, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia.

11.    J.Scambray and M.Shema, Hacking Exposed Web Applications, McGrawHill, 2002.

12.    Li Jiang, Hao Chen, Fei Deng, Qiusheng Zhong.“A Security Evaluation Method Based on Threat Classification for Web Service”, Journal of Software, Vol 6, No 4 (2011), 595-603, Apr 2011

13.    Ambler, W. S (2005). Introduction to security threat modeling. Agile Modeling. Available at http://www.agilemodeling.com/ artifacts/securityThreatModel.htm

14.    Swiderski, F. & Snyder, W. (2004). Threat modeling. Microsoft Press Professional Book Series

15.    Casteele, S.V. (2005). Threat modeling for web application using STRIDE model.

16.    Zadeh L.A., “Knowledge Representation in FuzzyLogic,”. IEEE Trans. Knowledge and Data Eng., vol. 1, pg.  89.100, 1989 .

17.    Rachna Satsangi1,Dr. Pankaj Dashore 2 and Dr.Nishith Dubey  “Risk Management in Cloud Computing Through Fuzzy Logic”. International Journal of  Application or Innovation in Engineering & Management  Volume 1, Issue 4  December 2012

18.    Abdallah Missaoui, and Kamel Barkaoui. “A Neuro-Fuzzy Model for QoS Based Selection of Web Service”. J. Software Engineering & Applications, 2010

19.    Simone A. Ludwig, Venkat Pulimi, Andriy Hnativ. “Fuzzy Approach for the Evaluation of Trust and Reputation of Services “. Fuzz-IEEE 2009 Korea August20-24 2009S. Sodiya, S. A. Onashoga, and B. A. Oladunjoye Threat Modeling Using Fuzzy Logic Paradigm Issues in Informing Science and Information Technology Volume 4, 2007.

20.    Colin Wong and Daniel Grz elak, “A Web Services Security Testing Framework”, SIFT SPECIAL PUBLICATION, Information security services, Version 1.00.

21.    Duan Youxiang 1 , Gao Yang. “Evaluating Vulnerabilities Quantitatively Based On  the Rank of Web Services confidentiality ,Journal of Next Generation Information Technology, volume 2, Number 1, February, 2011.


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