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Exploring Innovation| ISSN:2231-2307(Online)| Reg. No.:61903/BPL/CE/2011| Published by BEIESP| Impact Factor: 3.76
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Volume-1 Issue-4: Published on September 05, 2011
34
Volume-1 Issue-4: Published on September 05, 2011
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S. No

Volume-1 Issue-4, September 2011, ISSN:  2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Pramada Valli, Sudhir Mathur

Paper Title:

A knowledge-based database on municipal waste in road construction applications

Abstract:    Municipal Solid Wastes (MSW) are generated /available in huge quantities. Quite a large amount of solid rubbish is contributed by our households in the form of domestic wastes which constitute heaps of municipal refuse poses serious disposal problems. If these wastes are not properly disposed off, this can prove perilous and environmental hazard. Such places often become a home for rats, flies, bacteria, mosquitoes and a large number of vectors, having the potential of causing many human diseases. The damage to the environment by the uncontrolled disposal of solid wastes can be clearly seen. The waste is dumped in the streets awaiting transport to the disposal sites and into the river. The damage to the environment by the uncontrolled disposal of solid wastes can be clearly visualized.  Thus it is imperative for a large-scale utilization of MSW or Municipal Waste (MW) in the construction of roads. MSW information is available in the form of research papers, articles, reports etc.  However such information is scattered and is not available at one place for the prospective users. Keeping the above in view, it is intended to develop MSW and Municipal Waste Combustor Ash (MWCA) (is the solid residue that remains after the combustion of municipal solid waste) information database which contain detail information in program description files. The program description file on MSW and MWCA provides the user with general information, detailed production and use, engineering and laboratory tests, and environmental information. Developed database is useful in road construction, as well as recommendations for monitoring field trials and also to make the same available to the user at one place which will be Window based and can be used very easily and conveniently.

Keywords:
  Application, engineering properties, program description screens specification, and VISUAL BASIC 6.0.


References:

1.        Bhatt, G. H., etal, 1985. “Management of toxic and Hazardous wastes” Lewis publishers Inc.
2.        Srivastava, N.Y.  1989.  “Environmental pollution” Publishing house, New Delhi.

3.        Henstock, M.E. 1974. “The Recycling and disposal of solid waste” Proceedings of a course organized by the Department of Metallurgy & Material Science, University of Nottingham.

4.        Compendium of Indian Standards on Soil Engineering”BIS.SP.36 (part 1): Bureau of Indian Standards,1987.

5.        Hunt, R.E. 1984."Geotechnical Engineering Investigations Manual" McGraw Hill Book Company.

6.        "Urban Solid Waste Management" Workshop on. Background Material, organized by Ministry of Environment & Forests Jan. 1993. New Delhi.

7.        Garg, G.C. “Recycling of solid waste by composting” a report, Conservancy Sanitation & Engineering,    Municipal   Corporation of Delhi.   

8.        Sherwood, P.T. 1975. “The use of waste and low grade materials in road construction.  PFA”. TRRL Laboratory report no. 686, Transport Research Laboratory. U.K.

9.        Yudhbir, Honjo.Y. (1991). “Application of geotechnical Engineering to Environmental Control” Theme   Lecture at 9th Asian Regional Conference on soil mechanics and foundation Engineering, Bangkok.


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2.

Authors:

R. HariKumar, T.Vijaya Kumar

Paper Title:

Performance Analysis of Soft Decision Trees Models for Fuzzy Based Classification of Epilepsy Risk Levels from EEG Signals

Abstract:   The purpose of this research is to investigate the feasibility of Game theory based Max-Min optimization of fuzzy outputs for the classification of epilepsy risk levels from EEG (Electroencephalogram) signals. The fuzzy pre classifier is used to classify the risk levels of epilepsy based on extracted parameters like energy, variance, peaks, sharp and spike waves, duration, events and covariance from the EEG signals of the patient. Max-Min SDT (Soft Decision Tree) as post classifier with four methods is applied on the classified data to identify the optimized risk level (singleton) that characterizes the patient’s epilepsy risk level. The efficacy of the above methods is compared based on the bench mark parameters such as Performance Index (PI) and Quality Value (QV). A group of ten patients with known epilepsy findings are used for this study. High PI such as 94.56 % was obtained at QV’s of 22.42 in the SDT optimization when compared to the value of 40% and 6.25 through fuzzy classifier respectively. We identified that the SDT provides a better performing tool for optimizing the epilepsy risk levels

Keywords:
   EEG Signals, Epilepsy, Fuzzy Logic, Max-Min Soft Decision Trees, Risk Levels


References:

1.       Joel.J etal, “Detection of seizure precursors from depth EEG using a sign periodogram transform,” IEEE Transactions on Bio Medical Engineering, vol, 51 no,4, pp-449-458, April 2004.
2.       D.Zumsteg and H.G.Wieser,“Presurgical evaluation: current role of invavasive EEG,”Epilepsia,vol.41,   No. suppl 3,pp, S55-60,2000.

3.       K P Adlassnig, “Fuzzy Set Theory in Medical diagnosis”, IEEE Transactions on Systems Man Cybernetics,  16 pp 260-265, March 1986

4.       Alison A Dingle et al, “A Multistage system to detect epileptic form activity in the EEG”, IEEE Transactions on Biomedical Engineering, 40(12) pp 1260-1268,
December 1993.

5.       Haoqu and Jean Gotman, “A patient specific algorithm for detection onset in long-term EEG monitoring- possible use as warning device”, IEEE Transactions on Biomedical Engineering, 44(2) pp 115-122,   February 1997.

6.       J. Seunghan Park et al, “TDAT Domain Analysis Tool for EEG Analysis”, IEEE Transactions on Biomedical Engineering, 37(8) pp 803-811, August 1990

7.       Donna L Hudson, “Fuzzy logic in Medical Expert Systems”, IEEE EMBS Magazine, 13(6) pp 693-698,      November/December 1994.

8.       Arthur C Gayton, “Text Book of Medical Physiology”, Prism Books Pvt. Ltd., Bangalore, 9th Edition, 1996.

9.       R.Harikumar, Dr.(Mrs). R.Sukanesh, P.A. Bharathi, “Genetic Algorithm Optimization of Fuzzy outputs for Classification of Epilepsy Risk Levels from EEG signals,” I.E. India Journal of Interdisciplinary panels  Vol.86, no.1, May 2005.

10.     Aexei.A, “Algorithms for estimating information distance with application to Bioinformatics and Linguistics,” Proceedings of IEEE, CCECE-2004.

11.     Hamid R. Mohseni, A. Maghsoudi and Mohammad B. Shamsollahi, “Seizure Detection in EEG signals: A Comparison of Different Approaches,” Proceedings of the 28th IEEE EMBS Annual International Conference New York City, USA. Aug 30- Sep 3, 2006, pp. 6724-6727.

12.     Jan Magdolen and Vladimir Mokran, “Recognition of Epileptiform Patterns in the Human Encephalogram Using Multi –Layer Perceptron,” Radioengineering, Vol. 4, No.2 June 1995, pp. 12-17,

13.     Ye Yuan, “Detection of Epileptic Seizure Based on EEG Signals,” Proc of IEEE EMBS sponsored 3rd International Congress on Image and Signal Processing (CISP 2010), July 2010, pp. 4209-4211.

14.     Ronald.R.Yager, “Hierarichical Aggregation Functions Generated From Belief structures,” I.E.EE Transaction of Fuzzy Systems, vol.8, no.5, pp 481-490, May 2005.

15.     Cezary.Z Janikow, “Fuzzy Decision Tree: Issues and Methods,” I.E.E.E Trans SMC,vol.28, no.1, pp 1-14,February1998.

16.     S.Rasoul Safavian, David Landgrebe, “A Survey of Decision Tree Classifier Methodology,” IEEE Trans SMC,vol.21, no.3, pp-660-674,May 1991.

17.     Philippe Salembier, Luis Garrido, “Binary Partition Tree as an Efficient Representation for Image Processing, Segmentation, and Information Retrieval,” I.E.E.E Trans  Image processing,vol.9, no.4, pp-561-576, April 2000.

18.     Gleb Beliakov and Jim Warren, Appropriate choice of Aggregation Operators in Fuzzy Decision Systems, IEEE Transactions on Fuzzy Systems, vol 9, no 6, pp 773-784, December 2001.

19.     Ronald R.Yager, On Ordered Weighted Averaging Aggregation Operators in Multi Criteria Decision Making, IEEE Trans on SMC,vol.18,no.1,pp 183-190,1998.

20.     P.Filev and R.R.Yager, Context Dependent Information Aggregation, Proceedings of IEEE International Conference on Fuzzy Systems, pp 672-677, 2003.

21.     R.R.Yager, Including Importance’s in OWA Aggregations Using Fuzzy Systems Modeling, IEEE Trans. on Fuzzy Systems, vol.6, no2,pp 286-294,1996.

22.     Gleb Beliakov, Definition of General Aggregation Operators Through Similarity Relations, Fuzzy sets and   Systems, vol 114, pp437-453, 2000.

23.     R.R.Yager, On role of Anxiety in Decisions Under Possibilistic Uncertainty, IEEE Trans on SMC, vol 34, no 2, pp1224-1234, April 2004.

24.     R.R.Yager, Fuzzy Modeling for Intelligent Decision Making under Uncertainty, IEEE Trans on SMC, vol 30; no  1, pp 60-79, February 2000.

25.     Tomasa Calvo, Radko Mesiar, and R.R.Yager, Quantitative Weights and Aggregation, IEEE Transactions on  Fuzzy Systems, vol 12, no 1, pp 62-69, February 2004.

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3.

Authors:

Dipti Sharma, Parteek Bhatia

Paper Title:

Spatial Application of city using Oracle Spatial Database, MapViewer, and Map Builder

Abstract:    It was not possible in case of traditional database to store, analyze, visualize, and integrate spatial data in business and government applications. GIS systems store spatial and non-spatial data separately. This split data model has several drawbacks. Oracle spatial provides a completely open and standards-based architecture for the management of spatial data within a database management system. This research paper describes the process of developing a spatial application of Patiala city using Oracle spatial technologies. A simple graph is first created using the map of Patiala (obtained from Google Maps tool) and the coordinates of main locations of Patiala are figured out using this graph. A spatial database is then designed by using tables for colleges, roads, banks and Thapar University. MapViewer tool is used to integrate and visualize spatial data with maps. Oracle Map Builder tool is used to define the MapViewer mapping metadata that includes styles, themes and base maps. MapBuilder is used to define line styles for road map of Patiala city. Different color styles are used for representing colleges, roads, banks, Thapar University and main locations of Patiala. Themes are defined by combining different styles and it gives visual representation of data layer. Predefined themes are grouped together to form a base map. This provides a convenient way to include multiple themes in a map request. A base map is generated by putting one or more themes at one place. Data source and Tile layer has been created in MapViewer to connect with the database. The spatial application is developed in HTML and JavaScript code with the use of Map Viewer’s JavaScript mapping library. This application shows the map of Patiala city and is featured with various map options like navigation panel and distance tools etc. Our developed system has the provision to view different locations depicted in the map. It has also a feature to find the distance between two locations in the map. 

Keywords:
   Oracle Spatial, MapViewer, MapBuilder, Spatial application.


References:

1.       Ravi Kothuri, Albert Godfrind and Euro Beinat ”Pro Oracle Spatial for Oracle Database 11g”, Apress publishers, 2007.
2.       "Advance Spatial Data Management for Enterprise Applications", September, 2009. [Online].Available:

3.       http://www.oracle.com/technology/products/spatial/pdf/11gr2_collateral/spatial11gr2_wp_0922.pdf

4.       "Oracle Spatial 11g GeoRaster", September, 2009. [Online].Available:

5.       http://www.oracle.com/technology/products/spatial/pdf/11gr2_collateral/spatial11gr2_georaster_twp.pdf

6.       Siva Ravada and Xavier Lopez, "Oracle Spatial10g". [Online].Available:

7.       http://www.nyoug.org/Presentations/2003/10gspatial.pdf

8.       Oracle Spatial Developer's Guide, 10g Release 2 (10.2) B14255-01, June 2005. [Online].Available:

9.       http://www.youngcow.net/doc/oracle10g/appdev.102/b14255.pdf

10.     “Introduction to MapViewer”, Oracle Documentation. [Online].Available:

11.     http://download.oracle.com/docs/cd/B10464_05/web.904/b10559/vis_star.htm

12.     Liujian (LJ) Qian, “Developing spatial applications using Oracle Spatial and MapViewer”, SpatialGroup, Oracle.

13.     http://www.ucgis.org/visualization/whitepapers/qia2.pdf

14.     Shashi Shekar and Sanjay Chawala,  “Spatial Database A Tour” Book, Prentice   Hall, 2003.

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4.

Authors:

P.Subashini, N.Sridevi

Paper Title:

An Optimal Binarization Algorithm Based on Particle Swarm Optimization

Abstract:    Document binarization is an active research area for many years. Binarization algorithms play an important role in the preprocessing phase of any character recognition system. This paper compares several alternative binarization algorithms for handwritten documents, by evaluating their performance. The algorithms evaluated are, global thresholding, Otsu thresholding, Kittler-Illingworth and local thresholding, Niblack algorithm along with the proposed PSO algorithm. From the tests and results, we can wrap up with the assumption that the proposed algorithm shows improved results.

Keywords:
   Evaluation, Global thresholding, Image Binarization, Local thresholding, PSO.


References:

1.       Chun Che Fung and Rapeeporn Chamchong, “A Review of Evaluation of Optimal Binarization Technique for Character Segmentation in Historical Manuscripts”, Third International Conference on Knowledge Discovery and Data Mining, 2010.
2.       Junker.M, R. Hoch, “On the Evaluation of Document Analysis Components by   Recall, Precision, and Accuracy”, Proceedings of 5th ICDAR, India, pp. 713-716, 1999.

3.       Kennedy. J, and R. Eberhart, “Particle Swarm Optimization”, Proceedings of the IEEE conference on neural networks – ICNN95, Perth, Australia, pp.  1942-1948, 1995.

4.       Khurram Khurshid, Imran Siddiqi, Claudie Faure, Nicole Vincent , “Comparison of Niblack inspired Binarization methods for ancient documents”, 16th International conference on Document Recognition and Retrieval, 2009

5.       Kite, T.D., Evans, B.L., Daamera-Venkata, N., and Bovil, A.C.: “Image Quality Assessment Based on a Degradation Model”; in IEEE Trans. Image Processing, vol.9, pp.909-922, 2000.

6.       Kittler.J and J.Illingworth, “Minimum Error Thresholding”, Pattern Recognition, Vol 19, No.1, pp. 41-47,1986

7.       Ntirogiannis .K, B. Gatos and I. Pratikakis, “An Objective Evaluation Methodology for Document Image Binarization Techniques”, the Eighth IAPR Workshop on Document Analysis Systems, IEEE Transactions, 2008.

8.       Oliveira .S.L., S. A. Britto, and R. Sabourin, “Optimizing Class-Related Thresholds with Particle Swarm Optimization”, Proceedings of International Joint Conference on Neural Networks, IEEE, Montreal, Canada, July 31 - August 4,  pp. 1511-1516, 2005.

9.       Otsu, N., “A threshold selection method from gray level histograms,” IEEE Trans. Syst. Man Cybern. 9, pp. 62–66, 1979.

10.     Pavlos Stathis, Ergina Kavallieratou, Nikos Papamarkos, “An Evaluation Technique for Binarization Algorithms”, Journal of Universal Computer Science, vol. 14, no. 18, 2008.

11.     Russell Eberhart, James Kennedy, “A New Optimizer Using Particle Swarm Theory”, Proceedings of Sixth International Symposium on Micro Machine and Human Science, IEEE, 1995.

12.     Ye zhiwei Chen, hongwei Liu, Wei Zhang jinping,” Automatic threshold selection based on Particle Swarm Optimization algorithm”, International Conference on Intelligent Computation Technology and Automation, IEEE, 2008.


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5.

Authors:

R. Samuel Rajesh Babu, Joseph Henry

Paper Title:

A Comparison of Half Bridge & Full Bridge Isolated DC-DC Converters for Electrolysis Application

Abstract:   This paper presents a comparison of half bridge and full bridge isolated, soft-switched, DC-DC converters for Electrolysis application. An electrolyser is a part of renewable energy system which generates hydrogen from water electrolysis that used in fuel cells. A DC-DC converter is required to couple electrolyser to system DC bus. The proposed DC-DC converter is realized in both full-bridge and half-bridge topology in order to achieve zero voltage switching for the power switches and to regulate the output voltage. Switching losses  are reduced  by zero voltage switching. Switching stresses are reduced by using resonant inductor and capacitor. The proposed DC-DC converter has advantages like high power density, low EMI, reduced switching stresses, high circuit efficiency and stable output voltage. The MATLAB simulation results show that the output of converter is free from the ripples and regulated output voltage and this type of converter can be used for electrolyser application . Experimental results are obtained from a MOSFET based DC-DC Converter with LC filter. The simulation results are verified with the experimental results.

Keywords:
   DC-DC converter, electrolyser, renewable energy sources, resonant converter, TDR.


References:

1.       E.J.Miller, “Resonant switching power conversion,”in Power Electronics Specialists Conf.Rec., 1976, pp. 206-211.
2.       V. Volperian and S. Cuk , “A complete DC analysis of the series resonant converter”, in IEEE power electronics specialists conf. Rec. 1982, pp. 85-100.

3.       R.L. Steigerwald, “High-Frequency Resonant Transistor DC-DC Converters”, IEEE Trans. On Industrial Electronics, vol.31, no.2, May1984, pp. 181-191.

4.       D.J. Shortt, W.T. Michael, R.L. Avert, and R.E. Palma, “A 600 W four stage phase-shifted parallel DC-DC converter,”, IEEE Power Electronics Specialists Conf., 1985, pp. 136-143.

5.       V. Nguyen, J. Dhayanchand, and P. Thollot, “A multiphase topology series-resonant DC-DC converter,” in Proceedings of Power Conversion International, 1985, pp. 45-53.

6.       A.K.S. Bhat, “Analysis of resonant transistor DCDC converter with capacitive output filter, “IEEE trans. on Ind. Electronics, vol. IE-32, pp.439-444, Nov. 1985.

7.       R.L. Steigerwald, “A comparison of half-bridge resonant converter topologies,” IEEE Trans. on Power Electronics, vol. 3, no. 2, April 1988, pp. 174-182.

8.       F.S. Tsai, J. Sabate, and F.C. Lee, “Constant-frequency, zero-voltageswitched, clamped-mode parallel-resonant converter,” IEEE International Energy Conference, 1989, paper # 16.4, pp. 1-7.

9.       A.K.S. Bhat, “Fixed frequency PWM series-parallel resonant converter,” IEEE Industry Applications Society Annual Meeting, vol. 1, October 1989, pp. 1115-1121.

10.     F.S. Tsai, J. Sabate, and F.C. Lee, “Constant- Frequency, zero-voltageswitched, clamped-mode Parallel-resonant converter,” IEEE International Energy Conference, 1989, paper # 16.4, pp. 1-7.

11.     J. A. Sabate, V. Vlatkovic, R.B. Ridley, F.C. Lee and B.H. Cho, "Design considerations for high voltage, high power, full-bridge ZVS PWM converters," IEEE Applied Power Electronics Conf., 1990, pp. 275-284.

12.     J.A. Sabate, and F.C. Lee, “Off-line application of the fixed-frequency clamped-mode series resonant converter,” IEEE Trans. on Power Electronics, vol. 1, no. 1, January 1991, pp. 39-47.

13.     R. Streit and D. Tollik, “High efficiency telecom rectifier using a novel soft-switched boost based input current shaper”, IEEE INTELC Conf. Record, 1991, pp.720-726.

14.     A.K.S. Bhat, “Analysis and design of LCL-type resonant converter”, IEEE Tran. On Industrial Electronics, vol.41, no.1, pp.118-124, Feb.1994.

15.     A.K.S. Bhat, "Analysis and design of LCL-type resonant converter", IEEE Trans. on Industrial Electronics, vol. 41, no. 1, pp. 118-124, Feb. 1994.

16.     A.K.S. Bhat, "Analysis and design of a fixed frequency LCL-type series resonant converter," IEEE Trans. on Aerospace and Electronic Systems, vol. 31, no. 1, Jan. 1995,   125-137.

17.     H. Bodur and A. F. Bakan, “A new ZVT-PWM DC-DC converter,” IEEE Trans. On  Power Electr., vol.17, no.1, Jan. 2002, pp.40- 47.

18.     A. K. S. Bhat, and F. Luo, “A new gating scheme controlled softswitching DC-DC bridge converter,” IEEE Power Electronics and Drive Systems Conf. Record, 2003, pp. 8-15.

19.     D. S. Gautam, “Soft-Switched DC-DC Converters for Power Conditioning of  electrolyser in a Renewable Energy System,” M.A.Sc Thesis, Dept. of ECE, University of
Victoria, 2006.

20.     D.S. Gautam and A.K.S. Bhat, “A comparison of soft-switched DC-DC converters for Electrolyser application”, IEEE IICPE Conf. Record CD, Chennai, 2006.

21.     Deepak S.Gautam and Ashoka K.S.Bhat, “A Comparison of Soft-switched DC-DC Converters for ElectrolyserApplications” proceedings of India International Conference on power electronics 2006.

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6.

Authors:

R. Kirubashankar, K. Krishnamurthy, J. Indra, B.Vignesh

Paper Title:

Design and Implementation of Web Based Remote Supervisory Control and Information System

Abstract:    There is a great deal of benefits for process plants in adopting the Internet to control systems. Over the years, there has been constant increase in the development of industrial automation through remote monitoring and diagnosis virtually. By surveying down the existing remote monitoring system used for process plant equipment, this system tends to focus on the recent trends and developments in the control of equipments and devices in the industries by remote monitoring through Internet. The Internet based automation is made possible by the use of Programmable Logic Controller (PLC), Supervisory Control and Data Acquisition (SCADA), Virtual Private Network (VPN) and other network elements. The objectives of remote monitoring and diagnosis are prevention of unplanned downtime, making optimal control operation and maximizing the operational life of plant assets. An online integrated web based remote supervisory control and information system takes real-time data on process control unit’s performance and helps the remote expert for further analysis and thereby supports the plant engineer. The design, Internet security and user interface challenges are focused in this paper.

Keywords:
   Web based remote monitoring system, Remote Terminal Unit, Web based supervisory control, Real-time control about four key words or phrases in alphabetical order, separated by commas.


References:

1.        Zafer Aydogmus, Omur Aydogmus, “A Web-Based Remote Access Laboratory Using SCADA”, IEEE Transactions on education, Vol. 52, No. 1, February 2009, pp. 126-132.
2.        Xin Zhao , Wei Liang , “Research on the Remote Monitoring and Intelligent Fault Diagnosis System “, Sch. of Computer & Electronic Eng., Hunan University of Commerce, Changsha , Wireless Communications, Networking and Mobile Computing(WiCOM’08), 2008 , pp.1-4.

3.        D .Y. Raghavendra Nagesh, Sowjanya A and Dr S.S TulasiRam, “ Real Time Decision Support for Energy Management “, Proceedings of the World Congress on Engineering  2008, Vol.1., pp. 5-9.

4.        Chengen Wang , Lida Xu , Wuliang Peng , ”Conceptual design of remote monitoring and fault diagnosis systems” , Science Direct (Information Systems) ,Vol.32, 2007, pp. 996-1004.

5.        Shin Bong-Cheol, Kim Gun-Hee, Choi Jin-Hwa, Jeon Byung-Cheol, Lee Honghee, Cho Myeong-Woo, Han Jin-Yong, Park Dong-Sam, “A Web-based machining process monitoring system For E-manufacturing implementation”, Journal of Zhejiang University Science, Vol.7 (9), 2006, pp. 1467-1473.

6.        Adnan Salihbegovic, Vlatko Marinkovic, Zoran Cico, Elvedin Karadzic, Nina Delic, “Web based multilayered distributed SCADA/HMI system in refinery application”, Elsevier Science, Vol.31, 2009, pp. 519-612

7.        Engin Ozdemir, Mevlut Karacor,”Mobile phone based SCADA for industrial automation”, ISA Transactions, Vol. 45, No. 1, 2006, pp. 67–75

8.        Henry Wu, David P. Buse, Junqiu Feng, Pu Sun, “e-Automation, an Architecture for Distributed Industrial Automation Systems”, International Journal of Automation and Computing, Vol. 1, 2004, pp. 17-25

9.        Dr. Aditya Goel & Ravi Shankar Mishra,” Remote Data Acquisition Using Wireless – SCADA System“, International Journal of Engineering (IJE), Vol.3 (1), 2008, pp. 58-65

10.     Albert W.L. Yao, C.H. Ku, “Developing a PC-based automated monitoring and control platform for electric power systems”, Electric Power Systems Research, Vol. 64, 2003, pp. 129-136

11.     Toshiaki Kimuraa, Yuichi Kandab, “Development of a remote monitoring system for a manufacturing support system for small and medium-sized enterprises “, Computers in Industry, Vol.56, 2005, pp. 3-12

12.     Wei-Fu Chang , Yu-Chi Wu , Chui-Wen Chiu ,“Development of a web-based remote load supervision and control system”, International Journal of Electrical Power and Energy Systems, Vol.28(6),2006 ,  pp. 401-407

13.     Juan Jose Gonzalez de la Rosa Antonio Moreno Munoz, Aurora Gil de Castro, Victor Pallares Lopez & Jose Antonio Sanchez Castillejo,” A web-based distributed measurement system for electrical power quality Assessment”,  Elsevier Science, Vol. 43, 2010, pp. 771-780

14.     Hongbo Lana, Yucheng Dinga, Jun Honga, Hailiang Huangb, Bingheng Lua,” A web-based manufacturing service system for rapid product development”, Elsevier Science, Vol.54, 2004, pp. 51-67

15.     Cihan Sahin & Emine Dogru Bolat, “Development of remote control and monitoring of web-based distributed OPC system”, Elsevier Science, Vol.31, 2009, pp. 984-993

16.     A.W.L. Yao, “Design and implementation of Web-based diagnosis and management system for an FMS”, International Journal of Advanced Manufacturing Technology, Vol.26, 2005, pp.1379-1387


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7.

Authors:

Rajneesh Karn, Yogendra Kumar, Gayatri Agnihotri

Paper Title:

ACO Based Method for Service Restoration in Distribution System Using Fuzzy M.O.M.C Approach

Abstract:   Service restoration in power distribution system is to restore power in healthy portion of out of service area as much as possible followed by a fault and isolating the faulted zone by operating the line switches connected in the network. In service restoration, not only the final network configuration but also the number of switching operations held for service restoration is important with a number of constraints like node voltage deviation, load balancing and priority customers. Thus the Service restoration problem is a multi objective multi constraint (M.O.M.C) combinatorial optimization problem. This paper proposes an Ant Colony optimization based methodology for minimizing the area where energy is not supplied and no of manually controlled and remotely controlled switching operations during the restoration process. A fuzzy membership function is defined for each term in objective according to relevant conditions and the final solutions are ranked according to the importance of objective under consideration.

Keywords:
   Ant colony optimization, Fuzzy logic, Service restoration.


References:

1.       Akoi, H. Kuwabara T. Satoh M. Kanezashi, "Outage state optimal load allocation by automatic sectionalizing switches operation in distribution systems," IEEE trans. Power delivery. 1987,vol. 2, no.4, pp. 1177-1185
2.       K.Akoi , H. Kuwabara T. Satoh M. Kanezashi, "Voltage  drop constrained restoration of supply by switch operation in distribution system," IEEE trans. Power delivery. 1988 vol. 3, no. 3, pp. 1267-1279.

3.       K. Akoi, H. Kuwabara T. Satoh M. Kanezashi, "A new algorithm for service restoration in distribution systems," IEEE trans. Power delivery. 1989 vol. 4, no. 3, pp.1832-1839.

4.       C. C. liu S. J. Lee, S.S. Venkata "An expert system operational aid for restoration and loss reduction of distribution systems," IEEE trans.Power syst.1988 vol. 3, no. 2, pp. 619-626

5.       C. E. Lin et al., "A distribution system outage dispatch by data base method with real time revision," IEEE trans. Power Delivery, vol. 4. Jan.1989.

6.       Imamura et al., "An application of fuzzy reasoning for service restoration" (in Japanese) Trans. IEE Jpn., vol. 4, July 1989.

7.       H. Mori et al., Parallel simulated annealing for power system decomposition," IEEE trans. Power system vol. 4, May 1994.

8.       S. Toune, H. Fudo, T. Genji, Y. Fukuyama, Y. Nakanishi, "A reactive tabu search for service restoration in electric power distribution system," IEEE International conference on evolutionary computation," Anchorage Alaska, May 4-11 1998.

9.       H.C.Kuo and Y.Y Hsu, “ Distribution system load estimation and service restoration using a fuzzy set approach”, IEEE Transactions on Power delivery, Vol-8, No-4, pp-1950-1957, Oct.1993

10.     S.J.Lee, S.I.Lim, and B.S.Ahn, “ Service restoration of primary distribution system based on fuzzy evaluation  of multi criteria”, IEEE Transactions on Power System, Vol-13,  No-3, pp-1156-1163, Aug.1998

11.     Y. Fukuyama and H. D. chaing, "A parallel genetic algorithm for service restoration in electric power systems," in proceeding IEEE FUZZ/FES Conference, Yokohama, Japan, Mar. 1995.

12.     A. Augugliaro, L.Dusonchet, E.R. Sanseverino, "Service restoration in compensated distribution networks using a hybrid genetic algorithm," Electric power syst. Research, 1998, vol. 46, 59-66.

13.     W.P. Luan, M.R.Irving, J.S. Daniel, "Genetic algorithm for supply restoration and optimal load shedding in power system distribution networks," IEE Proc. Gener.
Transm. Distrib., 2002, vol. 149, no. 2, pp. 145-15 1.

14.     Y. T. Hsiao and C. Y. Chien, "Enhancement of restoration service in distribution systems using a combination of fuzzy-GA method," IEEE

15.     Marco Dorigo, Member, IEEE, Vittorio Maniezzo, and Albert Colorni Ant System: Optimization by a Colony of Cooperating Agents IEEE Transactions on systems, man & cybernetics-part-b cybernetics, vol 26, no. 1, February 1996,29.

16.     Yogendra Kumar, Biswarup Das, Jaydev Sharma, Genetic Algorithm for supply restoration  in distribution system with priority customers 9th international Conference on Probabilistic Methods applied to power systems , Sweden, June 2006.

17.     Y.Kumar, B.Das and J.Sharma, “ Multiobjective, multiconstraint service restoration of electric power distribution system with priority customers”, IEEE Transactions on Power delivery, Vol-23, No-1, pp-261-270, Jan.2008

18.     Jen-Hao Teng,Yi-Hwa Liu, “Application of the Ant Colony System for Optimum switch adjustment”, IEEE Transactions on power Systems,Vol.17,No.1,pp.751-756,2002

19.     Jen-Hao Teng,Yi-Hwa Liu, “ A Novel ACS-Based optimum Switch Relocation Method” IEEE Transactions on power systems,Vol.18,No.1,pp.113-120,2003.

20.     Indira Mohanty,Jugal Kalita,Sanjoy Das,Anil Pahwa,Erik Buehler, “Ant Algorithm for the optimal restoration of Distribution feeders during cold load pickup’,IEEE Transactions on Power Delivery,pp.132-137,2003.

21.     Isamu Watanabe, “ An ACO Algorithm for service Restoration in Power Distribution Systems”, IEEE Transactions on Power delivery, pp.2864-2871,2005

22.     Zhigang Lu, Ying wen, Lijun Yang, “An Improved ACO algorithm in Power Distribution Systems” IEEE transactions on Power Delivery,2009

23.     H. Falaghi,M.RHaghifam, and Chanan Singh, “Ant Colony optimization-Based Method for placement of Sectionalizing Switches in Distribution Network Using a Fuzzy Multiobjective Approach’ IEEE transactions on Power Delivery,Vol.24,No.1,pp.268-276,2009

24.     Rajeev Annaluru,Sanjoy Das,anil Pahwa, “Multi-level Ant Colony Algorithm for Optimal Pacement of Capacitors in Distribution systems”, IEEE Transactions on Power Delivery,pp.1932-1937,2004.

25.     M. Dorigo, L.M.Gambardella, “Ant Colony System:A Cooperative Learning Approach to the Travelling salesman Problem”,IEEE Transactionson Evolutionary Computations, Vol,1,pp.53-66,1997.

26.     T.Stutzle, M.Dorigo, “ a short convergence proof for a class of ant colony optimization algorithm”, IEEE Transactions on Evolutionary Computation, Vol,6,Issue:4, pp.358-365,2002.

27.     M.Dorigo and T.Stutzle, “ Ant Colony Optimization”,Cambridge,MA:MIT Press,2004

28.     M.Dorigo, M. Birattari, T. Stutzle “ Ant colony optimization: artificial ants as a computational intelligence technique” IEEE Transactions on Evolutionary Computation,  pp.358-365,2006.


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8.

Authors:

K.Sreedhar

Paper Title:

Audio Signal Enhancement Using Non-diagonal Estimator

Abstract: Audio signals are often contaminated by background environment noise and buzzing or humming noise from audio equipments. Audio denoising aims at attenuating the noise while retaining the underlying signals. Removing noise from audio signals requires a nondiagonal processing of time-frequency coefficients to avoid producing “musical noise.” A block thresholding estimation procedure is introduced, which adjusts all parameters adaptively to signal property by minimizing a Stein estimation of the risk. Non Diagonal time-frequency audio denoising algorithm attenuates the noise by processing each spectrogram coefficient independently. This Estimator is to minimize the error between clean signal and the enhanced signal. Numerical experiments demonstrate the performance and robustness of this procedure through objective and subjective evaluations.

Keywords:
   Audio Denoising, Block Thresholding, Audio signal processing, STFT Transform, Spectrogram, Time-Frequency Audio Denoising, Adaptive Block Thresholding.


References:

1.       M. Bahoura and J. Rouat, “Wavelet speech enhancement based on timescale adaptation,” Speech Commun., vol. 48, no. 12, pp. 1620–1637, Dec. 2006.
2.       M. Berouti, R. Schwartz, and J. Makhoul, “Enhancement of speech corrupted by acoustic noise,” in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP), 1979, vol. 4, pp. 208–211.

3.       S. Boll, “Suppression of acoustic noise in speech using spectral  subtraction,” IEEE Trans. Acoustics, Speech, Signal Process., vol. ASSP-27, no. 2, pp. 113–120, Apr. 1979.

4.       T. Cai, “Adaptive wavelet estimation: A block thresholding and oracle inequality approach,” Ann. Statist., vol. 27, pp. 898–924, 1999.

5.       T. Cai and B. W. Silverman, “Incorporation information on neighboring coefficients into wavelet estimation,” Sankhya, vol. 63, pp. 127–148, 2001.

6.       O. Cappé, “Elimination of the musical noise henomenon with the Ephraim and Malah noise suppressor,” IEEE Trans. Speech, Audio Process, vol. 2, pp. 345–349, Apr. 1994.

7.       S. Chang, Y. Kwon, S. Yang, and I. Kim, “Speech enhancement for non-stationary noise environment by adaptive wavelet packet,” in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, 2002, vol. 1, pp. 561–564.

8.       I. Cohen, “Speech enhancement using a noncausal a priori SNR estimator,” IEEE Signal Process. Lett., vol. 11, no. 9, pp. 725–728, Sep. 2004.

9.       I. Cohen, “Speech enhancement using supergaussian speech models and noncausal a priori SNR estimation,” Speech Commun., vol. 47, no. 3, pp. 336–350, Nov. 2005.

10.     I.Cohen, “Noise spectrum estimation in adverse environments: Improved minima controlled recursive averaging,” IEEE Trans. Speech Audio Process., vol. 11, no. 5, pp. 466–475, Sep. 2003.

11.     I. Cohen and B. Berdugo, “Speech enhancement  for non-stationary noise environments,” Signal Process., vol. 81, no. 11, pp. 2403–2418, Nov. 2001.

12.     Y. Ephraim and D. Malah, “Speech enhancement using a minimum mean square error short-time spectral amplitude estimator,” IEEE.Trans. Acoust., Speech, Signal Process., vol. 32, no. 6, pp. 1109–1121, Dec. 1984.

13.     Y. Ephraim and D. Malah, “Speech enhancement using a minimum mean square error log-spectral amplitude estimator,” IEEE Trans. Acoust., Speech, Signal
Process., vol. ASSP-33, no. 2, pp. 443–445, Apr. 1985.

14.     R. R. Coifman and D. L. Donoho, “Translation-Invariant De-Noising,” in Lecture Notes in Statistics: Wavelets and Statistics, A. Antoniadisand G. Oppenheim, Eds. Berlin, Germany: Springer-Verlag, 1995.

15.     Y. Ephraim and H. L. V. Trees, “A signal subspace approach for speech enhancement,” IEEE Trans. Speech Signal Process, vol. 3, no. 4, pp. 251–266, Jul. 1995.

16.     G. Matz and F. Hlawatsch, “Minimax robust nonstationary signal estimation based on a p-point uncertainty model,” J. Franklin Inst. (Special Issue on Time-Frequency Signal Analysis and Applications), vol. 337, no. 4, pp. 403–419, Jul. 2000.

17.     G. Matz, F. Hlawatsch, and A. Raidl, “Signal-adaptive robust timevarying Wiener filters: Best subspace selection and statistical analysis,” in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP), Salt Lake City, UT, May 2001, pp. 3945–3948.

18.     R. J. McAulay and M. L. Malpass, “Speech enhancement using soft decision noise suppression filter,” IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-28, no. 2, pp. 137–145, Apr. 1980.

19.     S. R. Quackenbush, T. P. Barnwell, and M. A. Clements, Objective Measures of Speech Quality. New York: Prentice-Hall, 1988.

20.     C. Stein and W. James, “Estimation with quadratic loss,” in Proc. 4th  Berkeley Symp. Mathematical Statistics Probability 1, Berkeley, CA, 1961, pp. 361–379.

21.     J. S. Walker and Y.-J. Chen, Denoising Gabor Transforms[Online].Available: http://www.uwec.edu/walkerjs/media/DGT.pdf, preprint

22.     G. Yu, E. Bacry, and S. Mallat, “Audio signal denoising with complex wavelets and adaptive block attenuation,” in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP), Apr. 2007, vol. 3, pp. III-869–III-872.

23.     H. Sheikhzadeh and H. R. Abutalebi, “An improved wavelet-based speech enhancement system,” EUROSPEECH, pp. 1855–1858, 2001.

24.     Y. Shao and C. H. Chang, “A generalized time-frequency subtraction method for robust speech enhancement based on wavelet filter bank modeling of human auditory system,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 37, no. 4, pp. 877–889, Aug. 2007

25.     I. J. Kim, S. I. Yang, and Y. Kwon, “Speech enhancement using adaptive wavelet shrinkage,” in Proc. IEEE Int. Symp. Industrial Electronics, 2001, vol. 1, pp. 501–504.

26.     Y. Ephraim, H. Lev-Ari, and W. J. J. Roberts, “A brief survey of speech enhancement,” in The Electronic Handbook. Boca Raton, FL: CRC Press, 2005.

27.     J. S. Garofolo, Getting Started with the DARPA TIMIT CD-ROM: An Acoustic Phonetic Continuous Speech Database. National Institute of Standards and Technology (NIST), Gaithersburgh, MD, 1988.

28.     D. Malah, R. V. Cox, and A. J. Accardi, “Tracking speech-presence uncertainty to improve speech enhancement in mon-stationary noise environments,” presented at the IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP), Phoenix, AZ, Mar. 1999.

29.     Database. National Institute of Standards and Technology (NIST), Gaithersburgh, MD, 1988.

30.     D. Malah, R. V. Cox, and A. J. Accardi, “Tracking speech-presence uncertainty to improve speech enhancement in mon-stationary noise environments,” presented at the IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP), Phoenix, AZ, Mar. 1999.

31.     G. Yu, E. Bacry, and S. Mallat, “Audio signal denoising with complex wavelets and adaptive block attenuation,” in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP), Apr. 2007, vol. 3, pp. III-869–III-872.

32.     J. Yang, “Frequency domain noise suppression approaches in mobile telephone systems,” in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, 1993, vol.
2, pp. 363–366.

33.     P. J. Wolfe and S. J. Godsill, “Simple alternatives to the Ephraim and Malah suppression rule for speech enhancement,” in Proc. IEEE Workshop Statistical Signal Processing, Aug. 2001, pp. 496–499.

34.     J. W. Seok and K. S. Bae, “Speech enhancement with reduction of noise components in thewavelet domain,” in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP), 1997, vol. 2, pp. 1323–1326.

35.     Y. Shao and C. H. Chang, “A generalized time-frequency subtraction method for robust speech enhancement based on wavelet filter bank modeling of human auditory system,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 37, no. 4, pp. 877–889, Aug. 2007.

36.     H. Sheikhzadeh and H. R. Abutalebi, “An improved wavelet-based speech enhancement system,” EUROSPEECH, pp. 1855–1858, 2001.

37.     K. V. Sørensen and S. V. Andersen, “Speech enhancement with natural sounding residual noise based on connected time-frequency speech presence regions,” EURASIP J. Appl. Signal Process., vol. 18, no. 18, pp. 2954–2964, 2005.

38.     M. Johnson, X.Yuan, and Y. Ren, “Speech signal enhancement through adaptive wavelet thresholding,” Speech Commun., vol. 49, no. 2, Feb. 2007.

39.     N. S. Kim and J. H. Chang, “Spectral enhancement based on global soft decision,” IEEE Signal Process. Lett., vol. 7, no. 5, pp. 108–110, May 2000.

40.     I. J. Kim, S. I. Yang, and Y. Kwon, “Speech enhancement using adaptive wavelet shrinkage,” in Proc. IEEE Int. Symp. Industrial Electronics, 2001, vol. 1, pp. 501
504.

41.     M. Li, H. G. McAllister, N. D. Black, and D. T. A. Perez, “Perceptual time-frequency subtraction algorithm for noise reduction in hearing aids,” IEEE Trans. Biomed. Eng., vol. 48, no. 9, pp. 979–988, Sep. 2001.

42.     J. S. Lim and A. V. Oppenheim, “Enhancement and bandwidth compression of noisy speech,” Proc. IEEE, vol. 67, Dec. 1979.

43.     S. F. Lei and Y. K. Tung, “Speech enhancement for nonstationary noises by wavelet packet transform and adaptive noise estimation,” in Proc. Int. Symp. Intelligent Signal Processing Communication Systems, Dec. 2005, pp. 41–44.

44.     C. T. Lu and H. C.Wang, “Enhancement of single channel speech based on masking property andwavelet transform,” Speech Commun., vol. 41, no. 2, pp. 409–427(19), Oct. 2003.


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9.

Authors:

Arpan Deyasi, Swapan Bhattacharyya

Paper Title:

Composite Effect of BenDaniel Duke Boundary Condition and Material Composition on Eigenenergy of  Multiple Quantum Well Structure

Abstract:    Numerical simulation is carried out to determine of first three eigenstates of a multiple quantum well structure for both constant and variable effective mass cases where BenDaniel-Duke boundary condition is introduced for computation of effective mass mismatch along with the consideration of potential barrier dependence on material composition of higher band-gap material. Dimensional asymmetry is introduced to observe the change in eigenvalue, and no. of layers is also varied to observe the same in absence of electric field. GaAs/AlxGa1-xAs material composition is considered for simulation purpose to estimate tunneling probability. Variation of mole fraction provides a shift in eigenenergies for resonance transmission.

Keywords:
   Multiple Quantum Well Structure, Eigenenergy, BenDaniel Duke Condition, Material composition


References:

1.       F.Capasso, K.Mohammed and A.Y.Cho, Resonant Tunneling Through Double Barriers, Perpendicular Quantum Transport Phenomena in Superlattices, and Their Device Applications, IEEE Journal of Quantum Electronics, Vol.22, p.1853, 1986.
2.       D.D.Coon, R.P.G.Karunasiri and L.Z.Liu, “Narrow Band Infrared Detection in Multiquantum Well Structures”, Applied Physics Letters, Vol. 47, p.289, 1985.

3.       D.S.Patil and D.K.Gautam, “Analysis of Effect of Temperature on ZnSSe Based Blue Laser Diode Characteristics at 507 nm Wavelength”, Physica B, Vol.344, 140, 2004.

4.       M.Shen and W.Cao, “Electronic Band-Structure Engineering of GaAs/AlxGa1-xAs Quantum Well Superlattices with Substrates”, Vol.B103, p.122, 2003. 

5.       Leaked and L.L.Chang, “New Transport Phenomenon in Semiconductor Superlattice”, Physical Review Letters, Vol.33, p.495, 1974.

6.       L.L.Chang, L.Esaki and R.Tsu, “Resonant Tunneling in Semiconductor Double Barriers”, Applied Physics Letters, Vol.24, p.12, 1974.

7.       S.Vatannia and G.Gildenblat, “Airy’s Function Implementation of the Transfer-Matrix Method for Resonant Tunneling in Variably Spaced Finite Superlattices”, IEEE Journal of Quantum Electronics, Vol.32, p.1093, 1996.

8.       K.Talele and D.S.Patil, “Analysis of Wavefunction, Energy and Transmission Coefficients in GaN/AlGaN Superlattice Nanostructures”, Progress In Electromagnetics Research, Vol.81, p. 237, 2008.

9.       P.W.A.Mcllroy, “Effect of an Electric Field on Electron and Hole Wavefunctions in a Multiquantum Well Structure”, Journal of Applied Physics, Vol.59, 3532, 1986.

10.     E.Anemogiannis, “Bound and Quasibound State Calculations for Biased/Unbiased Semiconductor Quantum Heterostructures”, IEEE Journal of Quantum Electronics, Vol.29, p. 2731, 1993.

11.     C.E.Simion and C.I.Ciucu, “Triple–Barrier Resonant Tunneling: A Transfer Matrix Approach”, Romanian Reports in Physics, Vol.59, p.805, 2007.

12.     A.K.Ghatak, K.Thyagarajan and M.R.Shenoy, “A Novel Numerical Technique for Solving the One-Dimensional Schrödinger Equation using Matrix Approach - Application to Quantum Well Structures”, IEEE Journal of Quantum Electronics, Vol.24, p.1524, 1988.

13.     A.R.Sugg and J.P.C.Leburton, “Modeling of Modulation-Doped Multiple Quantum-Well structures in Applied Electric Fields using the Transfer-Matrix Technique”, IEEE Journal of Quantum Electronics, Vol.27, p. 224, 1991.

14.     G.Bastard, E.E.Mendez, L.L.Chang and L.Esaki, “Variational Calculations on a Quantum Well in an Electric Field”, Physical Review B, Vol.28, p.3241, 1983.

15.     K. F. Brennan and C.J.Summers, “Theory of Resonant Tunneling in a Variably Spaced Multiquantum Well Structure: An Airy Function Approach”, Journal of Applied Physics, Vol.51, p.614, 1987.

16.     K.Hayata, M.Koshiba, K.Nakamura and A.Shimizu, “Eigenstate Calculations of Quantum Well Structures using Finite Elements”, Electronics Letters, Vol.24, p.614, 1988.

17.     E.P.Samuel and D.S.Patil, “Analysis of Wavefunction Distribution in Quantum Well Biased Laser Diode using Transfer Matrix Method”, Progress in Electromagnetics Research Letters, Vol.1, p.119, 2008.

18.     B.Jonsson and S.T.Eng, “Solving the Schrödinger Equation in Arbitrary Quantum-Well Profiles using the Transfer-Matrix Method”, IEEE Journal of Quantum Electronics, Vol.26, p. 2025, 1990.

19.     Y.Tsuji and M.Koshiba, "Analysis of Complex Eigenenergies of an Electron in Two-and Three-Dimensionally Confined Systems using the Weighted Potential Method", Microelectronics Journal, Vol.30, p.1001, 1999.

20.     Y.J.Hong, J.G.Zhi, Z.Yan, L.W.Wu, S.Y.Chun, W.Z.Guo and X.J.Jun, “Resonant Tunneling in Barrier-In-Well and Well-In-Well Structures”, Chinese Physics Letters, Vol.25, p.4391, 2008.

21.     D.J.BenDaniel and C.B.Duke, "Space-Charge Effects on Electron Tunneling", Physical Review, Vol.152, p.683, 1966.

22.     W.Wang, T.M.Hwang, W.W.Lin and J.L.Liu, “Numerical Methods for Semiconductor Heterostructures with Band Nonparabolicity”, Journal of Computational Physics, Vol.190, p.141, 2003.

23.     H.Asnani, R.Mahajan, P.Pathak and V.A.Singh, “Effective Mass Theory of a Two-Dimensional Quantum Dot in the Presence of Magnetic Field”, Pramana- Journal of Physics, Vol.73, p.573, 2009.

24.     Y.Li, O.Voskoboynikov, C.P.Lee, S.M.Sze and O.Tretyak, “Electron Energy State Dependence on the Shape and Size of Semiconductor Quantum Dots", Journal of Applied Physics, Vol.90, p.6416, 2001.


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10.

Authors:

Hamdy A. Morsy, Zaki B. Nossair, Alaa M. Hamdy, Fathy Z. Amer

Paper Title:

Optimum segment length for embedding in the LSB of JPEG images with Minimum MSE

Abstract:    Steganography is the science of hiding communication in an innocuous cover medium such as image, audio and video. In this paper, a new steganographic algorithm with optimum segment length and minimum MSE is presented, an algorithm that utilizes the redundant bits of discrete cosine transform (DCT) of JPEG images for message embedding. This algorithm offers high capacity with minimum statistical changes and minimum MSE compared to existing steganographic systems.

Keywords:
   JPEG images, steganography, steganalysis, information hiding, JPEG hiding.


References:

1.       Hamdy A. Morsy, Zaki B. Nossair, Alaa M. Hamdy, and Fathy Z. Amer, "Utilizing Image Block Properties to Embed Data in the DCT Coefficients with Minimum MSE," International Journal of Computer and Electrical Engineering vol. 3, no. 3, pp. 449-453 , 2011.
2.       Manoj Kumar Meena, Shiv Kumar, Neetesh Gupta  “ Image Steganography tool using Adaptive Encoding Approach to maximize Image hiding capacity” IJSCE
Volume-1, Issue-2, May 2011

3.       R. J. Anderson, and F.A. Petitcolas, “ On the limits of Steganography,” J. Selected Areas in  Comm., vol.16, no. 4, pp. 474–481, 1998.

4.       A. Kerckhoffs, “La Cryptographie Militaire”, Journal des Sciences Militaires, 9th series, IX  pp 5–38;  Feb. pp 161–191, Jan. 1883.

5.       N. Provos, and P. Honeyman, “Detecting Steganographic Content on the Internet,” CITI Technical Report 01-11, 2001.

6.       C. Cachin,” An Information-Theoretic Model for Steganography,” Cryptology ePrint Archive, 2002.

7.       N. Memon, and M. Kharrazi, “Performance study of common image steganography,” Journal of Electronic Imaging 15(4), 041104 (Oct-Dec), 2006.

8.       G. Cancelli, and M. Barni, “New techniques for steganography and steganalysis in the pixel domain, “, Ph.D. dissertation - Ciclo XXI. Report 2000 /028, 2009.   www.zurich.ibm.com/˜cca/papers/stego.pdf.

9.       A. Westfeld, “F5—A Steganographic Algorithm High Capacity Despite Better Steganalysis,” Springer-Verlag Berlin Heidelberg, 2001.

10.     A. Westfeld, “Detecting Low Embedding Rates, “, 5th Information Hiding Workshop. Nooerdwijkerhout, Netherlands,   Oct. 7−9, 2002

11.     A. Westfeld, and A. Pfitzmann, “Attacks on  Steganographic Systems,” in Andreas Pfitzmann (ed) Information  Hiding. Third International Workshop, LNCS 1768, Springer- Verlag Berlin Heidelberg. pp. 61–76. 289,  291,293, 299, 2000.

12.     N. Provos, and P. Honeyman, “Hide and Seek: An introduction to steganography,” IEEE Computer security 15407993/03, 2003

13.     T. Pevn'y, J. and Fridrich, “Benchmarking for Steganography,”  Information Hiding.10th International. Workshop,   Santa Barbara, CA, LNCS vol. 5284, 2008.

14.     C. Hung, “PVRG-JPEG Codec, 1.1,” Stanford University, 1993. http://archiv.leo.org/pub/comp/os/unix/graphics/jpeg/PVRG 291.

15.     D. Upham, “Steganography software for Windows,” 1997, http: //members.tripod.com/steganography/stego/ software.html

16.     J. Fridrich, M. Goljan, and D. Hogea, “new methodology   for breaking steganographic techniques for JPEGs,” in Proc. of SPIE: Security and Watermarking of Multimedia Contents, vol. 5020,  pp 143–155, 2003.

17.     Jan Kodovský, Jessica Fridrich ”Quantitative Steganalysis of LSB Embeddingin JPEG Domain” MM&Sec’10, September 9–10, 2010, Roma, Italy.2010 ACM 978-1-4503-0286-9/10/09


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11.

Authors:

Mahdi Ebrahimzadeh

Paper Title:

Design of an Ultra Low Power Low Phase Noise CMOS LC Oscillator

Abstract:    In this paper we introduce an ultra low power CMOS LC oscillator and analyze a method to design a low power low phase noise complementary CMOS LC oscillator. A 1.8GHz oscillator is designed based on this analysis. The circuit has power supply equal to 1.1 V and dissipates 0.17 mW power. The oscillator is also optimized for low phase noise behavior. The oscillator phase noise is -126.2 dBc/Hz and -144.4 dBc/Hz at 1 MHz and 8 MHz offset respectively.

Keywords:
   LC oscillator, Low Power, Low Phase Noise.


References:

1.       W. Sheng, B. Xia, A. E. Emira,C. Xin,A. Y. V-López, S. T. Moon, and E. S.-Sinencio, "A 3-V, 0.35-μm CMOS Bluetooth Receiver IC," IEEE Journal os Solid State Circuits, vol. 38, no. 1, pp. 30-42, Jan. 2003.
2.       M. Tiebout, "Low-Power Low-Phase-Noise Differentially Tuned Quadrature VCO Design in Standard CMOS," IEEE Journal os Solid State Circuits, vol. 36, no. 7, pp. 1018-1024, 2001.

3.       B. De Muer, M. Borremans, M. Steyaert, G. Li Puma, “A 2-GHz low-phase- noise integrated LC VCO set with flicker-noise upconversion minimization," IEEE Journal of Solid-State Circuits, vol. 35, no. 7, pp. 1034 - 1038, July 2000.

4.       T. H. Lee, The Design of CMOS Radio Frequency Integrated Circuits, 2nd ed.. Cambridge University Press, 2004.

5.       Ali Hajimiri; Thomas H. Lee, "Design Issues in CMOS Differential LC Oscillators," IEEE Journal of Solid State Circuits, vol. 34, no. 5, pp. 717-724, May 1999.

6.       Roberto Aparicio, Ali Hajimiri, "A Noise-Shifting Differential Colpitts VCO," IEEE Journal of Solid State Circuits, vol. 37, no. 12, pp. 1728-1736, Dec. 2002.

7.       C. C. Boon, M. A. Do, K. S. Yeo, J. G. Ma, X. L. Zhang, "RF CMOS Low-Phase-Noise LC Oscillator Through Memory Reduction Tail Transistor," IEEE Transactions on Circuits and Systems—II: Express Briefs, vol. 51, no. 2, pp. 85-90, Feb. 2004.

8.       Jannesari, A.; Kamarei, M, "Design of a Low Voltage Low-Phase-Noise Complementary CMOS VCO," Integrated Circuits, 2007. ISIC '07. International Symposium on, pp. 426-429, Sep. 2007.

9.       A. M. Niknejad, R. G. Meyer, "Analysis, design, and optimization of spiral inductors and transformers for Si RF ICs," IEEE Journal of Solid State Circuits, vol. 33, no. 10, pp. 1470-1481, Oct. 1998.

10.     Ali Hajimiri, Thomas H. Lee, "A General Theory of Phase Noise in Electrical Oscillators," IEEE Journal of Solid State Circuits, vol. 33, no. 2, pp. 179-194, Feb. 1998.

11.     Sheng-Lyang Jang, Cheng-Chen Liu, Chun-Yi Wu, Miin-Horng Juang, "A 5.6 GHz Low Power Balanced VCO in 0.18 um CMOS," IEEE Microwave and Wireless Components Letters, vol. 19, no. 4, pp. 233-235, Apr. 2009.

12.     Hanil Lee, Saeed Mohammadi, "A Subthreshold Low Phase Noise CMOS LC VCO for Ultra Low Power Applications," IEEE Microwave and Wireless Components
Letters, vol. 17, no. 11, pp. 796-798, Nov. 2007.

13.     Christina F. Jou, Kuo-Hua Cheng, Hsien-Cheng Hsieh, "An Ultra Low Power 2.4 GHz CMOS VCO," International Conference on Electronics, Circuits and Systems ICECS, vol. 3, pp. 1098-1100, Dec. 2003.

14.     Shao-Hua Lee; Jang, S.-L.; Yun-Hsueh Chuang; Chao, J.-J.; Jian-Feng Lee; Juang, M.-H, "A Low Power Injection Locked LC-Tank Oscillator With Current Reused Topology," IEEE Microwave and Wireless Components Letters, vol. 17, no. 3, pp. 220-222, Mar. 2007.


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12.

Authors:

Pedram Hajipour, Ali Forotanpour, Leila Mohammadi

Paper Title:

Performance Evaluation of Noisy Nonlinear QAM and QPSK Systems in the Presence of the Signal Predistortion Linearizer

Abstract:    In this paper, the design and simulation process of a TWTA amplifier which is linearized with a signal predistortion method is presented. The aim of the linearizer circuit which is based on the schottky diodes is to compensation-linearity behavior of the amplifier in a noisy channel. The linearizer circuit is optimized to give the best AM-to-AM and AM-to-PM characteristics. In addition, the stability of the TWTA with combination of the proposed linearizer is investigated through computer simulations. The data is modulated by a 4-QAM and QPSK modulator, separately and is applied to the linearized TWTA.The received data after passing through the linearized TWTA is analyzed by using advanced design system (ADS) and the constellation and eye diagrams are obtained. Finally the BER performance of the system is evaluated using Monte Carlo estimation for three different values of input-back-off (IBO). It is also shown that decreasing the IBO degrades the performance. The results of the modulations are compared with together.

Keywords:
   TWTA, Predistortion, QAM, QPSK, BER


References:

1.       Yong Chae Jeong, “A design of Predistortion Linearizer byIndividual Order Control of Intermodulation DistortionSignals,” Doctorial Dissertation, Sogang Univ., 1996.
2.       J.V. Evans, "Proposed U.S. Global Satellite Systems OperatingAt Ka-Band", Aerospace Conference, 1998 Proceedings IEEE, vol.4, pp. 525-537, Mar. 1998.

3.       M. Ibnkahla, Q.M. Rahman, A.I. Sulyman, H.A. Al-Asady, JunYuan, and A. Safwat, "High-Speed Satellite MobileCommunications: Technologies and Challenges", Proceedings ofthe IEEE, vol. 92, pp. 312-339, Feb. 2004.

4.       Jamalipour, A., Mobile Satellite Communications, Norwood,MA: Arctech House, 1998.M. Wittig, "Satellite Onboard Processing for MultimediaApplications", Communications Magazine, IEEE, vol. 38, no. 6,pp. 134-140, Jun. 2000.

5.       Abbas Ali Lotfi Neyestanak, Mohammad Jahanbakht, "MODELING THE INFLUENCE OF ONBOARD PROCESSING AND LINEARIZATION UNITS ON THE PERFORMANCE ENHANCEMENT OF A HIGH DATA RATE SATELLITE",Iranian Research Institute for ElectricalEngineering, Tehran, Iran, IEEE, 978-1-4244-1643-1

6.       H. Jeong,Y. Jeong"The Research of Satellite Transponder Channel Linearization Technique",Dept. of Information & Communications Engineering, Chonbuk Nat’l Univ

7.       P. Sojoodi Sardrood , G.R. solat , P. Parvand"Pre-distortion Linearization for 64-QAM Modulation in Ka-BandSatellite Link", IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.8, August 2008

8.       H.Young, J.Sang, Park,N.Ryu,Y.Bok,Y.Kim “A Design of K-band Predistortion Linearizer using Reflective Schottky Diode for Satellite TWTAs”,Dept.of Information&Communication Engineering,Chonbuk National University,Korea, 2008

9.       F. Filali, and W. Dabbous, "Issues on The IP Multicast ServiceBehavior Over The Next-Generation Satellite-Terrestrial Hybrid", Computers and Communications
2001 Proceedings, Sixth IEEESymposium, pp. 417-424, 2001.

10.     E.R. Wiswell, Zoltan Stroll, Akram Baluch, Joseph Freitag, andH. J. Morgan, "Gen*Star Results Applicable to Ka-Band", FifthKa-Band Utilization Conference Taromina, Sicily Island, Italy,Oct. 1999.

11.     L. Chang, J. Krogmeier,"Power Optimization of Nonlinear QAM Systems with Data Predistortion",National Taiwan University of Science and Technology ADS Tutorial Stability and Gain CirclesEEE 194RF
12.     Chung-Er Huang,  Chih-Hao Liao,"Performance of the 16QAM Modem in the Satellite Communication Environment",ELLICOTT CITY, MD US


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13.

Authors:

Pedram Hajipour, Leila Mohammadi

Paper Title:

Measurements and Comparative of Resource Management in Satellite Systems

Abstract:    In this paper we describe design and simulation of a queuing delay model base M/M/1 and M/D/1 in Next Generation Network of communication over Internet Protocol which satellite in different orbits are a test bed used to test call setup quality and some of the key performance benchmarks such as mean response time to process the Media Gateway Control protocol calls and mean number of jobs was reviewed. The two different call flows simulation process based on registration information situation that can be used as a test bed is described (Single phase or two phases models (. The test bed simulation will use for deploying Next Generation Network services in order to verify protocols and features implementation. The call flows of the test bed also allow testing and evaluating over different delays in various signaling way. In our scenarios, satellite is a Media Gateway Controller node in call flows and ground stations are Media Gateway nodes.

Keywords:
   MEGACO, COPS, single phase, two phases


References:

1.       Abdi R. Modarressi and Seshadri Mohan "Control and Management in Next–Generation Networks: Challenges and Opportunities", IEEE Comm., pp.94-102, Oct. 2000.
2.       Broadband Satellite Internet VoIP Solution ,http://www.highspeedsat.com/satellite-voip.htm

3.       N. Greene, M. Ramalho, and B. Rosen, “Media GatewayControl Protocol architecture and requirements,” RFC 2805,April 1999: http://www.ietf.org/rfc/rfc2805.txt (accessed inFebruary 2003).

4.       T. Taylor, “Megaco/H.248: a new standard for mediagateway control,” IEEE Communications Magazine, pp.124-132, October 2000.

5.       F. Cuervo, N. Greene, A. Rayhan, C. Huitema, B. Rosen,and J. Segers, “Megaco protocol version 1.0,” RFC 3015,November 2000: http://www.ietf.org/rfc/rfc3015.txt(accessed in February 2003).

6.       V.K.Gurbani, L. Jagadeesan, V.B. Mendiritta, “Charecterizing the Session Initiation Protocol (SIP) Network Performance and Reliability”, ISAS 2005: LNCS 3694, pp. 196-211, April 2005

7.       J-S. Wu and P-Y Wang, "The performance analysis of SIP-T signaling system in carrierclass VoIP network", Proceedings of the 17th IEEE International Conference on AdvancedInformation Networking and Applications (AINA), 2003.

8.       S. M. Ross. Introduction to Probability Models, 9th edition. Elsevier Science Publisher B. V., 1991.

9.       D. Towsley and S. K. Tripathi. A single server priorityqueue with server failures and queue _ushing.Operations Research Letters, 10:353_362, 1991.

10.     F. Lipson, “Verification of Service Level Agreements with Markov Reward Models,”South African Telecommunications Networks and Applications Conference, September2003. Characterizing

11.     P.Hajipour,K.Abbasi Shahkooh “MEGACO Security in the presence Diameter Server” , Volume 4, Number 2, April 2010,JDCTA2010

12.     S. V. Subramanian, R. Dutta, “Comparative Study of M/M/1 andM/D/1 Models of a SIP Proxy Server”, Australasian TelecommunicationsNetworking and Application Conf. (ATNAC08), Adelaide, Australia,December 2008

13.     X. Xiao and L.M. Ni “Internet QoS: A Big Picture,” IEEE Net., Mar. 1999.

14.     J. Boyle et al., ”COPS Usage for RSVP,” IETF RFC 2749, Jan. 2000.

15.     K. Chan et al., “COPS Usage for Policy Provisioning,” IETF RFC 3084, Mar.2001.

16.     I. Adan, J. Resing, “Queuing Theory”, Class notes, Department of ComputerScience and Mathematics, Eindhoven University of Technology, The Netherlands, February 2001.

17.     P.Hajipour,N.Amani,F.Seyed Mostafaei, “Analysis of M/M/1 Queuing model ofReservation Management for MediaGateway Controller”,IEEE,pp.1371-1376,ISBN 978-89-5519-146-2, Feb.7-10, 2010 ICACT 2010

18.     A. Dehestani, P. Hajipour, “Comparative Study of M/Er/1 and M/M/1 Queuing Delay Models of the two IP-PBXs”,  doi:10.4156/jcit.vol5.issue2.4 , April 2010

19.     P. Hajipour, K. Abbasi Shahkooh,”  Characterizing MEGACO Security in the presence Diameter Server”, doi:10.4156/jdcta.vol4.issue2.7, April 2010

20.     Erlang A. K, “The theory of probabilities and telephone conversations inthe life and work of A.K. Erlang”, Trans Danish Academy Tech Science,vol.2, pp 131-137, 1948.

21.     J.Janssen,R.Windy,D.De valeeschauwer,G.Petit,J.Leroy, “Maximum Delay Bounds for Voice Transport over Satellite Internet Access Networks”, pp. 48-55,Rio de janerio,Brazil, 8, December 1999

22.     E. V. Koba, “An M/D/1 Queuing System with Partial Synchronization of Its Incoming Flow and Demands Repeating at IP Networking over Next-Generation Satellite Systems

23.     “IP Networking over Next-Generation Satellite SystemsConstant Intervals”, Cybernetics and System Analysis, Vol. 36, No. 6, November 2000,Springer Publishers.

24.     Sureshkumar V. Subramanian, Rudra Dutta , “Comparative Study of M/M/1 and M/D/1 Models ofa SIP Proxy Server” 2008, IEEE

25.     D. Abendroth, U. Killat, “Numerical Instability of the M/D/1 System OccupancyDistribution”, http://www.comnets.uni-bremen.de/itg/itgfg521/,Janaury 2004.

26.     E. V. Koba, “An M/D/1 Queuing System with Partial Synchronizationof Its Incoming Flow and Demands Repeating at Constant Intervals”,Cybernetics and System Analysis, Vol. 36, No. 6, November 2000,Springer Publishers


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14.

Authors:

Narendra Giradkar, G.M.Asutkar, Abhijit Maidamwar

Paper Title:

OFDM based PHY Performance of IEEE 802.11a Using Various practical channel models

Abstract:   Today with the advent of wireless communication and need for greater bandwidth and speed requirement with noise free reception, research has opened up a whole new market for wireless solutions. The IEEE 802.11a standards using orthogonal frequency division multiplexing (OFDM) can provide data rates up to 54 Mbps which makes good for high speed communications in wireless local area networks. In this paper, We evaluated the OFDM based PHY performance of IEEE 802.11a using various practical channel models such as Rician Fading, Rayleigh multipath Fading & AWGN. The effects of different transmission modes define in PHY on IEEE 802.11a system performance are studied using MATLAB SIMULINK. The performance is characterized in terms of 802.11a receivers bit error rates and signal to noise ratio for various modulation schemes such as 16 QAM, 64 QAM, BPSK and QPSK for different code rates as defined by the IEEE Standards 802.11a. All the Simulink models were studied using convolutional coder and Viterbi Decoder and standard OFDM format with 48 carriers, 4 pilots and a zero insertion in the middle.

Keywords:
   OFDM, wireless network; 802.11


References:

1.       “Wireless LAN Medium Access Control (MAC) and Physical Layer  (PHY) Specification: High speed Physical Layer in the 5 GHz Band “‘1999.
2.       R. D. Van Nee and R. Prasad, “OFDM for Wireless Multimedia Communications”, Artech House, 2000.

3.       A. D. S. Jayalath and C. Tellambura “Peak-to-Average Power ratio of IEEE 802.11a PHY layer Signals”.

4.       Ramjee Prasad, “OFDM for Wireless communication system”, Universal Personal communication, 1998.

5.       J. Heiskala and J. Terry, OFDM Wireless LANs: A Theoretical and Practical Guide,

6.       Sams Publishing, 2002.

7.       J.G. Proakis, Digital Communications, Third Edition, McGraw-Hill, New York, 1995, McGraw- Hill Series in Electrical and Computer Engineering.

8.       Enis Akay and Ender Ayanoglu, “High Performance Viterbi Decoder for OFDM Systems”

9.       Gurprakash Singh and Arokiaswami Alphones, “OFDM Modulation Study for a Radio-over-Fiber System for Wireless LAN (IEEE 802.11a) “ IEEE transaction,2003.

10.     Hassan Zareian and Vahid Tabataba Vakili, “Analysis of Nonlinear Distortion Using Orthogonal Polynomials HPA Model” , IJCSNS, VOL.8 No.1, January 2008.

11.     IEEE 802.11a, “High Speed Physical Layer in the 5GHz Band,” Jan. 1999.

12.     Adel A. M. Saleh, ”Frequency-Independent and Frequency-Dependent Nonlinear Models of TWT Amplifiers”, IEEE Trans. Comm., Vol.29, pp 1715-1720, November, 1981.

13.     D. Dardari, V. Tralli, and A. Vaccari, “A theoretical characterization of nonlinear distortion effects in OFDM systems,” IEEE Trans. Commun., vol. 48, no. 10, pp. 1755– 1764, Oct. 2000.

14.     T. S. Rappaport, "Wireless Communications: Principles and Practice," Prentice Hall, New Jersey, 1995.

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15.

Authors:

Deepak Kumar Mehto, Rajesh Srivastava

Paper Title:

An Enhanced Authentication Mechanism for IEEE 802.16(e) Mobile Wimax

Abstract:    Security is amongst one of the major issues in Broadband Wireless Access (BWA) Networks. After the launch of the IEEE 802.16 standard (WiMAX), a number of security issues were reported in several articles. Ever since the beginning, work has been in progress for the neutralization of these identified threats. In this paper, the analysis of the authentication protocols implemented in WiMAX has been presented along with the description of the threats posed to them. An approach has also been presented for the prevention of these threats like the avoidance of replay; suppress replay and man-in-the-middle attacks. The proposed approach enhances the network security. 

Keywords:
   Mobile Wimax,Authentication , Privacy & Key Management.


References:

1.       Griffin “Creating a Secure Network for Your Business”, White-Paper, 2005. accessed on 24 June 2006
2.       Panagiotis, T. and George, G. 2010. WiFi and WiMAX Secure Deployments," Journal of Computer Systems, Networks, and Communications, vol. 2010.

3.       Perumalraja Rengaraju, Chung-Horng Lung, Yi Qu, Anand Srinivasan,” Analysis on Mobile WiMAX Security”, IEEE TIC-STH 2009.

4.       Jeffrey G. Andrews, Arunabha Ghosh,   Rias Muhamed, “Fundamentals of WiMAX: Understanding Broadband Wireless Networking”, Chapter 9: MAC Layer of WiMAX, Pearson Education Prentice Hall, 2007. ISBN (PDF) 0-13-222552-2

5.       Yang, Y., and Li, R. 2009. Toward Wimax Security. In Proceedings of Computational Intelligence and Software Engineering, Wuhan, China, pp. 1-5.

6.       Dong, H., and Yan, W. 2008. Secure Authentication on WiMAX with Neural Cryptography. In International Conference on Information Security and Assurance, 2008. ISA 2008, pp. 366-369.

7.       Datta A., He C., Mitchell J.C., Roy A., Sundararajan M. “802.16e Notes, Electrical Engineering and Computer Science Departments, Stanford University, CA, USA, 2005,

8.       Yuksel E. “Analysis of the PKMv2 Protocol in IEEE 802.16e-2005 Using Static Analysis Informatics and Mathematical Modeling”, Technical University, Denmark, DTU, 2007.

9.       Ju-Yi Kuo, “Analysis of 802.16e Multicast/Broadcast group privacy rekeying protocol”, Stanford University, CA, USA, 2006,

10.     Krawczyk H., Ballare M., Canetti R. “HMAC: Key- Hashing for Message Authentication”, RFC 2104, http://www.ietf.org/rfc/rfc2104.txt, IETF, 1997.

11.     Dworkin M.: Recommendation for Block Cipher Modes of Operation: The CMAC mode for authentication, NIST special publication 800-38B, National Institute of Standards and Technology (NIST), MD, USA, 2005.

12.     Jamshed Hasan, “Security Issues of IEEE 802.16 (WiMAX)”, School of computer and Information Science, Edith Cowan University, Australia, 2006. Shon, T., and Choi, W. 2007. An analysis of mobile WiMAX security: vulnerabilities and solutions. In Proc. Of the 1st International Conference on Network-based information systems, Regensburg, Germany, pp. 88-97.

13.     IEEE Computer Society and the IEEE Microwave Theory and Techniques Society, 802.16TM IEEE Standard for local and

14.     metropolitan area networks," Part 16: Air Interface for Fixed Broadband Wireless Access Systems", June 2004.

15.     IEEE Std. 802.16e/D12, “IEEE Standard for Local and Metropolitan Area Networks, part 16: Air Interface for Fixed and Mobile Broadband Wireless Access Systems”, IEEE Press, 2005.

16.     D. Johnston and J. Walker, “Overview of IEEE 802.16 security”, IEEE Security and Privacy, pp. 40–48, May/June 2004.


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16.

Authors:

Rajesh Srivastava, Deepak Kumar Mehto

Paper Title:

Prevention of Security Threats in IEEE 802.16 Standards

Abstract:   The WiMAX IEEE 802.16 (e) is defined as the Worldwide Interoperability for Microwave Access by the WiMAX Forum, formed in April 2001 to promote conformance and interoperability of the IEEE 802.16 standard, officially known as WirelessMAN. An authentication and authorization model provides protection for a network or technology and protects its resources from unauthorized use. This article examines the threats which are associated with MAC layer and physical layer of WiMax and also proposes some enhancements to the existing model for improving the performance of the encryption algorithm.

Keywords:
   WiMAX802.16, Authentication, PKMv1& PKMv2, Security mechanisms etc.


References:

1.       IEEE Standard for Local and metropolitan area networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems, IEEE Std 802.16-2004, http://www.ieee802.org/16/, 2004.
2.       M. Barbeau, “WiMax/802.16 threat analysis,” ACM International Workshop on Quality of Service & Security in Wireless and Mobile Networks, 2005, pp. 8–15.

3.       IEEE Std 802.16e-2005, http://ieee802.org/16/published.html, 2005.

4.       IEEE Standard for Local and metropolitan area networks Part 16:Air Interface for Fixed and Mobile Broadband Wireless Access Systems Amendment 2: Physical and Medium Access Control Layers for Combined Fixed and Mobile Operation in Licensed Bands , 2006.

5.       Jukka Ylitalo, Tony Jokikyyny, Tero Kauppinen, Antti J. Touminen, Jaako Laine. “Dynamic Network Interface Selection in Multihomed Mobile Hosts” IEEE 2002.

6.       IEEE Std. 802.16-2004: IEEE Standard for Local and Metropolitan Area Networks Part16: Air Interface for Fixed Broadband Wireless Access Systems, IEEE, 2004.

7.       Whitfield Diffie and Martin E. Hellman: New Directions in Cryptography, Invented Paper.

8.       Shinsaku Kiyomoto, Jun Kurihara, Toshiaki Tanaka, Andreas Deininger :Security Vulnerabilities and Solutions in Mobile WiMAX, KDDI R&D Laboratories, 2-1-15, Ohara, Fujiminoshi, Saitama 356-8502, Japan.

9.       Sanida Omerovic, “WiMAX overview”, Faculty of Electrical Engineering, University of Ljubljana, Slovania.

10.     D. Johnston and J. Walker, “Overview of IEEE 802.16 Security”, IEEE Security and Privacy Magazine, vol. 2, no. 3, pp. 40-48, May-June 2004.

11.     E. Kaasenbrood, “WiMAX Security - A Formal and Informal Analysis,” Master’s thesis, Eindhoven University of Technology, Department of Mathematics and Computer Science, Groningen, Netherlands, August 2006.

12.     R. Housley, W. Polk, W. Ford, and D. Solo, “Internet X.509 Public Key Infrastructure Certificate and Certificate Revocation List (CRL) Profile,” RFC 3280 (Standards Track), April 2002.

13.     G. Lowe, “A Family of Attacks upon Authentication Protocols,” Technical Report 1997/5, University of Leicester, UK, 1997.

14.     M. Burrows, M. Abadi, and R. Needham, “A Logic of Authentication”,Proceedings of the Royal

15.     S. Xu and C. T. Huang, “Attacks on PKM protocols of IEEE 802.16 and its later versions,” In Proceedings of 3rd International Symposium on Wireless Communication Systems (ISWCS 2006), Valencia, Spain, September 2006.

16.     White Paper “Mobile WiMax Security” by Airspan Networks Inc. 2007.

17.     Jamshed Hasan “Security Issues of IEEE 802.16 (WiMax)”,2006 Society of London, vol. 426, pp. 233-271, 1989.


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17.

Authors:

R.N. Yadav, G.P. Chhalotra, R.K. Tiwari, Rajesh Khattri

Paper Title:

Development of Electrical Power System Reliability Based on System Parameters and Fuzzy Control

Abstract:   Static reliability and dynamic reliability are based on electrical power system parameters. The dual network can work as deadbeat controller for the objective network the mechanical parameters are always coupled, with electrical parameters and they cannot be separated. One can use the Fuzzy logic theory to deal with electrical parameters coupled with mechanical parameters in calculation the reliability and costly depends on electrical built in reliability or material reliability and costly depends on electrical resistively P, Permittivity. ( r) and magnetic permeability (µr). These specific (D) and represent R,C,L, parameters in macroscopic models, of  electrical power system. System parameters are too many and one can take help of thermal conductivity, enthalpy, melting point specific heat capacity, adhesivity, compatibility, hardness, tensile strength, Fatigue Creep, Cracks, brittles and fracture.'). 1hese are all Fuzzy Parameters. The parameters represent MTBF and MTTF of the systems.

Keywords:
   Magnetic Perability, MTBF, MTTF


References:

1.       David K. Cheng.: Analysis of linear System~ Addition Wesley Publishing company Inc.   Tokyo Japan (book), 1971 PP 97 Analogous system.
2.       F .A. Firestone: "The mobility method of computing the vibration of linear mechanical  and accosting systems: Mechanical- Electrical analogies" Journal of applied Physics - 9, PP 373 - 387

3.       M.L. Soni and J.C. Gupta:  "A course in electrical circuit analysis" Dhanpat Rai and Sons Delhi 1999 (Book)

4.       Pushpa Devnani, R.S. Parihar Gild G.P. Chhalotra: "Simulation of Reliability of Permalloy and Iron alloys uSing mass law and logarithmic law of mix.ing" The Journal of the Institution of Engineers (India, Vol. 73, April, 1992 PP          71 - 75)

5.       G.P. Chhaiotra: "Reliability Engineering and its applications Adequacy and Security.    Random Variables (Book) Khanna Publishers, 1986

6.       V.R. Ekbote and G.P. Chhalotra: Deadbeat control of AVR and .ELFC loops of syncl) ronous generator      TECHSMM - 5 Seminar February 23, 1995 Proceedings of the seminar          Highlech Publishers' and switch gears of Elect. Enss. Bombay.

7.       A.A. Bondi: "Reliability as a material property" ASME, Journal of Engineering materials and Technology Transactions, Vol. 101, January, 1979 PP 27-32

8.       Abhijt Dasgupta and Michael Pecht Material failure mechanism and Damage models IEEE transactions on Reliability Vol. 40, NO.5, December, 1991, PP 531-36

9.       R.X. Ronald, D.P. Filer and tom Sadegi: "Analysis of Flexible structured Fuzzy logic controller" Transactions  system man and cybernetics,Vol. 24, NO.7 July, 1994, PP 1035-43

10.     'Pushpa Devnani and G.P. Chhalotra: "Reliability assessment and material problems with special reference to pollution hazards" The Journal of the Institution of Engineers (India) Vol. 74, November, 1993, PP 122 -' 125

11.     Neena Chhalotra Etal: "Study of Reliability attributes of general system model of firm under  CBIS and IMS using Fuzzy logic" Proceedings of international
conference on Energy automation and Information technology, liT Kharagpur December 9 to 11, 2001, PP 884 - 888

12.     Pushpa Devnani, R.S. Parihar and G.P. Chhalotra: Simulation of Reliability of power systems using R. LC parameters" AMSE France, Vol. 10, NO.2, 1996 PP 200 to 210

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18.

Authors:

R.N. Yadav, G.P. Chhalotra, R.K. Tiwari, Rajesh Khattri

Paper Title:

A View Mathematical Analysis of Reliability of Power System Considering RLS Parameters in Fuzzy Logic Space

Abstract:    Beside all Engineering Systems Elect. Power System are merely subjected to uncertain failures. The electrical parameters of power system are also responsible for the losses, thermal breakdown and failure rates. The effect of 'R' 'L' and 'C' in failure rates of power system components can be simulated using fuzzy linguistic variables. 'RLC' power system may be lower low medium, higher and high, these. Fuzzy linguistic variables may be used to evaluate the failure rates of lower, low medium, higher and high values. The reciprocal of failure rates of different components will give the MTBF's of those components, along with its membership function or fuzzy grade of truths. The cardinality and relative cardinality of components may be evaluated. Assuming relative cardinality as an average reliability of the system its reliability may be investigate. Fuzzy relations may be used for analyzing the reliability attributes of power system. The optimized reliable condition may be evaluated.

Keywords:
   Magnetic Perability, MTBF, MTTF


References:

1.       Wadhwa C. L “Electrical Power Systems" Book New age in ternational (P) Ltd. publisher 1996.
2.       Nagrath 1.J. & D.P. Kothan : Modern Power System Analysis" Book Tata McGraw Hill publishing Company Ltd. New Delhi 1991.

3.       Timothy J. Ross “Fuzzy Logic with Engineering Applications'" University of New Maxico McGraw Hill Inc.1995.

4.       George. Klir and Bo Yuan "Fuzzy sets and Fuzzy Logic Theory and Applications" PHI Pv1. Ltd. New Delhi 2001 .

5.       Mishra M.K. , G.P. Chhalotra, R.S. Mahajan, MF Qureshi “A study of Reliability attributes of underground Electrical power planning and Distribution" proceeding of all India seminar on power systems recent advances and prospects in 21 century.

6.       Mishra M.K., G.P. Chhalotra, R.S. Mahajan, M.F. Qureshi "A certaining Safety and Reliability Attributes of Electricity uses in underground coal mines - A fuzzy logic approach" International. Sysposium on mine planning and Equipment Selection" New Delhi India 2001.

7.       Endenyl. J. "Reliability Modelling in Electrical Power System" John Wiley & Sons, New York, 1973

8.       Bondi A.A. “Rdiability as a material property "Joumel of Engineering materials and Technology AMSE, Vol-l 0 I PP 24-32,

9.       Bit A.K., MP Biswal and S.S. Alam “Fuzzy programming approach to multicriteria decision making transposition problem" Fuzzy Sets and Systems, North Holland, PP 135-141. 1992.

10.     Chhalotra G P., M. Balakrishnan & A.C. Rao "Simulation of reliability indices of power systems by transient model using transformation method" Modelling Simulation & control, AMSE France, Vol-26, NO.1, 1990.

11.     Zadeh L A. --Fuzzy Sets "Information control Vol-18, 1965.

12.     Qureshi M. F . 'Reliability Evaluation of Generating System" ME. Thesis. Ram Durgavati University Jabalpur (India), 1998.
13.     Mlshra R. B. and A.K. Azad “Generating System reliability evolution - Joint PDF approach IE (I) Journal of IE Nov; •1996.


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19.

Authors:

A.R. Eskandari, L. Mohammadi

Paper Title:

Group Delay Variations in Wideband Transmission Lines: Analysis and Improvement

Abstract:   Although poorly studied in the literature, Group Delay Variations (GDV) versus frequency is an essential factor which causes distortion and degradation in wideband satellite signals specially when using phase modulation and high data rates. In this paper, transmission line is analyzed as a dispersive medium and some kinds of coaxial cables such as RG58U, RG59U, RG213 and ECOFLEX15 are compared as GDV parameter point of view. Then the effect of reflections from discontinuities and impedance mismatches at transmission lines, on GDV quantity, is investigated by suggesting a novel network model of transmission line with discontinuity or impedance mismatch, and extracting a new formula for GDV. Graphical data are presented based upon the formula developed, and the simulation results are also given by AWR software which confirms the theory and formula. At last, based on the developed formula, some calculations will be carried out both to predict the values of GDV parameter and to compensate it. In this paper the frequency range of 100-1000 MHz is selected. The main reason of this selection is due to the practical application of coaxial cables for transmitting wideband satellite signals in remote sensing ground stations from down-converter to modem at IF frequencies such as: 140, 375, 720 MHz, etc. In addition, the introduced model and formula are generalizable to upper frequency bands.

Keywords:
   Group delay variations, transmission lines, coaxial cables, dispersion, discontinuity, mismatch.


References:

1.       Do-Hoon Kwon, “Effect of Antenna Gain and Group Delay Variations on Pulse-Preserving Capabilities of Ultra-wideband Antennas”, IEEE Transaction on Antenna and Propagation, Vol. 54, pp.2208-2215, August 2006
2.       Steven Back, Mark Weigel, “Degradation of Digital Satellite Signals by Group Delay”, Appeared in Word Broadcast News, November 1999

3.       Woods G., Maskell D., “Improving Group Delay Measurement Accuracy Using the FM Envelope Delay Technique” , TENCON 2005 2005 IEEE Region 10, Nov. 2005

4.       G.P. Vergelli and D. Chakraborty, “Computer-Aided Group Delay Equalization for Broad-Band Satellite Transponder Applications”, IEEE Transactions on Communications, October 1973

5.       Stephen A Mass, Nonlinear Microwave and RF Circuits, Artech House ,2003

6.       S.K. Park, H. Choi, Y.C. Jeong, “Microwave Group Delay Time Adjuster Using Parallel Resonator”, IEEE Microwave and Wireless Components Letters, Vol. 17, pp.109-111, Februty 2007

7.       S.K. Park, J.K. Lee, Y.C. Jeong, J.H. Yun, C.D. Kim, “Group Delay Adjuster Resonance Circuit with Varactor Diode”, Microwave Conference Proceedings, APMC2005, Vol.4, Dec. 2005

8.       Beatty, Robert W. and Otoshi, Tom Y:  “Effect of Discontinuities on the Group Delay of a Microwave Transmission Line”, IEEE Transactions on Microwave Theory and Techniques, Volume 23: 919-923 , Nov.1975.

9.       Lanzinger, Donald J., “Group Delay Caused by Impedance Mismatch”, IEEE CNF, ARFTG Conference Digest-Spring, Volum 11: 247-264, June 1987

10.     Canright, Jr. Robert E., “Predicting Effects of Transmission Line Impedance Mismatches”, 36th Electronic Components Conference Proceeding 1986 Sponsor IEEE.

11.     A.R. Eskandari, L. Mohammady, B. Eliasi, “A Novel Method for Analysing & Predicting of Group Delay Variations in Wideband Transmission Lines”, RFIT2007-IEEE International Workshop on RFIT, Dec. 9-11, 2007

12.     O.F. Siddiqui, S.J. Erickson, G.V. Eleftheriades, “Time-Domain Measurement of Negative Group Delay in Negative-Refractive-Index Transmission-Line Metamaterials”, IEEE Transaction on MTT, Vol. 52, No. 5, pp. 1449-1454, May 2004

13.     Shapir, “Suggestion for a New Formula to Calculate Group-Delay from Frequency Domain Measurement”, Microwave Conference, 2006, 36th European, Sept. 2006

14.     Morgan Mitchell and Raymond Y. Chiao, “Causality and Negative Group Delays in a Simple Band-Pass Amplifier”, American Journal of Physics, Vol. 66 No. 1,
January 1998

15.     Cellai L., “A Practical Method to Calculate Group Delay Flatness by Scalar Instrumentation”, Microwave Journal, Feb. 1997

16.     B. Raahemi and A. Opal, “Group Delay and Group Delay Sensitivity of Periodically Switched Linear Networks”, IEEE Transactions on Circuits and Systems, Part 1,
Vol. 47, No. 1, pp. 96-104, January 2000

17.     M. Norgren, “On The Problem with Intermodal Dispersion when Using Multiconductor Transmission Lines as Distributed Sensors”, Progress In Electromagnetics Research, PIER 56, 129-150, 2006

18.     J. Lundstedt and M. Norgren, “Comparison between Frequency Domain and Time Domain Methods for Parameter Reconstruction on Nonuniform Dispersive
Transmission Lines”, Progress In Electromagnetics Research, PIER 43, 1-37, 2003

19.     P.T. Trakadas, P.J. Papakanellos, and C.N. Capsalis, “Probabilistic Response of a Transmission Line in a Dissipative Medium Excited by an Oblique Plan Wave”, Progress In Electromagnetics Research, PIER 33, 45-68, 2001

20.     David M. Pozar, Microwave Engineering, Third Edition, John Wiley & Sons, 2005.

21.     Brian C. Wadell, Transmission Line Design Handbook, Artech House, Boston London, 1991   

22.     Paolo Delmastro , TRANSLIN: Transmission Line Analysis and Design Software and User’s Manual, Artech House, 1999

23.     S. Ramo, J.R. Winnery, and T. Van Duzer, Field and Waves in Communication Electronics, Third Edition, John Wiley & Sons, N.Y., 1994

24.     Ziska P., Vrbata J., “Method for Design of Analog Group Delay Equalizer”, IEEE International symposium on Circuit and System, ISCAS 2006, pp. 445-448, May 2006

25.     Jia-Sheng Hong, McErlean E.P. and Karyamapudi B., “High-Order Superconducting Filter with Group Delay Equalization”, Microwave Symposium Digest, 2005 IEEE MTT-S International, pp. 1467-1470, June 2005

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20.

Authors:

Rakesh Verma, Anuj Goel

Paper Title:

Wavelet Application in Fingerprint Recognition

Abstract:    Fingerprint verification is one of the most reliable personal identification methods and it plays a very important role in forensic applications like criminal investigations, terrorist identification and National security issues. Some fingerprint identification algorithm (such as using Fast Fourier Transform (FFT), Minutiae Extraction) may require so much computation as to be impractical. Wavelet based algorithm may be the key to making a low cost fingerprint identification system. Wavelet analysis and its applications to fingerprint verification is one of the fast growing areas for research in recent year. Wavelet theory has been employed in many fields and applications, such as signal and image processing, communication systems, biomedical imaging, radar, air acoustics, theoretical mathematics, control system, and endless other areas. However, the research on applying the wavelets to pattern recognition is still too weak. As the ridge structure in a fingerprint can be viewed as an oriented texture pattern. The paper proposes a fingerprint recognition technique based on wavelet based texture pattern recognition method. In view to older fingerprint recognition method; based on Fast Fourier Transform (FFT) and Minutiae Extraction, the proposed wavelet based technique results in high recognition rates.

Keywords:
   Fingerprint Recognition, Pattern recognition, Wavelet, Texture.


References:

1.       A.K. Jain, R. Bolle and S. Pankanti, Biometrics: Personal Identification in a Networked Society, Kluwer Academic Publishers, 1999.
2.       D. Polemi, "Biometric Techniques: Review and Evaluation of Biometric Techniques for Identification and Authentication, Including an Appraisal of the Areas Where They are Most Applicable," Final Report, April 1997.

3.       A. K. Jain and A. Ross, “Learning User Specific Parameters In A Multibiometric System”, Proc. Int. Conf. Image Processing (ICIP), New York , pp. 57-60, 2002.

4.       K. Nandakumar, Y. Chen and A. K. Jain, “Quality Based Score Level Fusion In Multibiometric Systems”, Proc. 18th Int. Conf. Pattern Recognition (ICPR), pp. 473-476, 2006.

5.       J.Berry and D.A.Stoney, “ The history and development of finger printing in advances in fingerprint technology”, CRC Press, Florida, 2nd Edition, pp. 1-40, 2001.

6.       Emma Newham, “The Biometric report”, SJB services, 1995.

7.       Federal Bureau of Investigation, “The Science of Fingerprints: Classification and Uses”, US Government Printing office, Washington D.C., 1984.

8.       A.K.Jain, S.Prabhakar, L.Hong and S.Pankanti, “Filter based Fingerprint Matching”, IEEE Transactions on Image Processing, Vol. 9, No. 5, pp. 846-859, May 2000.

9.       W. Yongxu, A. Xinyu, D. Yuanfeng and Y. Li, “A Fingerprint Recognition Algorithm Based on Principal Component Analysis” , Proceedings of IEEE Region 10 Conference TENCON, pp.14-17, 2006.

10.     ASME B46.1, Surface texture: Surface roughness, waviness and lay,1995.

11.     Yuanyan  Tang, “Status  of  Pattern  Recognition with Wavelet Analysis” Front. Comput. Sci. China, pp.268-294, 2008.

12.     T.   Merryman,   K.   Williams,   G.   Srinivasa,   A. Chebira, and J. Kovacevic, “A multiresolution enhancement to generic classifiers of subcellular  protein  location
images,” Proc. in IEEE  Int.  Symp. Biomed. Imaging, Arlington, VA, pp.570–573, Apr. 2006.

13.     A. Chebira, T. Merryman, G. Srinivasa, Y. Barbotin, C. Jackson,R. F. Murphy,   and   J.   Kovacevic, “A   multiresolution   approachto automated  classification  of
subcellular  protein  location  images”, BMC Bioinformatics, 2007.

14.     Jian-De Zheng, Yuan Gao and Ming-Zhi Zhang, “Fingerprint Matching Algorithm Based on Similar Vector Triangle,”  Second International Congress on Image and Signal Processing, pp.1-6, 2009.

15.     Shubhangi  Vaikole,  S.D.Sawarkar,  Shila  Hivrale,Taruna  Sharma,  “Minutiae Eetracion  from  Fingerprint  Images”,  IEEE  International  Advance  Computing Conference, pp. 691-696, 2009.

16.     Ishmael  S. Msiza, Brain  Leke-Betechuoh,  Fulufhelo V. Nelwamondo  and Ntsika Msimang, “A Fingerprint Pattern Classification Approach Based on the Coordinate Geometry  of  Singularities”,  Proceedings of the 29 IEEE  International Conference on Systems, Man, and Cybernetics, pp. 516-523, 2009.

17.     Zhang Qinghui and Zhang Xiangfei, “Research of Key Algorithm in the Technology of Fingerprint Identification,” Second IEEE International Conference on Computer Modelling and Simulation,  pp. 282-284, 2010.

18.     K.Thaiyalnayaki, S. Syed Abdul Karim and P. Varsha Parmar, “Finger Print Recognition using Discrete Wavelet Transform,” International Journal of Computer Applications, Vol. 1, No. 24, 2010.

19.     Avinash Pokhriyal and Sushma Lehri, “A new method of fingerprint authentication using 2d wavelets,” Journal of Theoretical and Applied Information Technology, 2010.

20.     Y.Y.Tan “Wavelet Theory and its Application to Pattern Recognition World” scientific, 2000.

21.     A. Majumdar, R. K. Ward “Fingerprint Recognition with Curvelet Features and Fuzzy KNN Clasiifier” Signal and Image Processing 2008.

22.     S.S.Gornale, V.Humbe, R.Manza and K.V.Kale, “Fingerprint image de-noising using multi-resolution analysis (MRA) through stationary wavelet  transform  (SWT)  method,”    International  Journal  of Knowledge Engineering, vol. 1 (1), pp. 5-14, 2010.

23.     Hongsong Li, Da Li, “A New Image Coding Scheme Based Upon Image Pattern Recognition,” International Conference on Information Systems (ICIS), USA, Dec. 12-15, 2010.

24.     Qichuan Tian, Ziliang Li, Xuhui Sun, Ruishan Zong, Min Wang, “Palmprint Classification Algorithm Based on Wavelet Multi-scale Analysis,” International Conference on Computational Aspects of Social Networks (CASoN 2010), china, Sept. 26-28, 2010.

25.     Yun xiao Bai, Shiru Qu,  “Linear Edges Detections Based on Ridgelet Analysis,” IEEE International conference on Multimedia Technology (ICMT), china,Oct. 29-31, 2010.

26.     Wan  Azizun  Wan  Adnan,  Lim  Tze  Siang,  and  Salasiah  Hitam, “Fingerprint  Recognition  in  Wavelet  Domain,”  Jurnal  teknologi, , pp 25-42, Dis. 2004.

27.     Musa Mohd Mokji and Syed Abd. Rahman Syed Abu Bakar. “Directional Image Construction Based on Wavelet Transform for Fingerprint Classification and Matching”, Proceedings of National Conference on Computer Graphic & Multimedia (CoGRAMM 2002), Melaka (Malaysia), , pp. 331-335, Oct. 2002.

28.     A. K. Jain, S. Prabhakar and S. Chen, “Combining Multiple Matchers for a High Security Fingerprint Verification System. Pattern Recognition”,  Letters. 20(11-13): pp. 1371-1379, 1999.

29.     Guan-Chen Pan “A Tutorial of Wavelet for Pattern Recognition” National Taiwan University, Taipei, Taiwan, ROC.

30.     Van de Wouwer G, P Schenuders, D Van Dyck, “Statistical texture characterization from discrete wavelet representation”,  IEEE Transactions on Image Processing, pp. 592–598, 1999.

31.     S. Mallat, “A theory of multiresolution signal decomposition: the wavelet representation”,  IEEE Transactions on Pattern  Analysis and Machine Intelligence, Vol. 11, pp. 674–693, 1989.

32.     Dipankar Hazra, “Texture Recognition with combined GLCM,Wavelet and Rotated Wavelet Features” International Journal of Computer and Electrical Engineering, Vol.3(1), pp.  1793-8163, February 2011.


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21.

Authors:

G.Lavanya, A. Ebenezer Jeyakumar

Paper Title:

An Enhanced Secured Dynamic Source Routing Protocol for MANETS          

Abstract:    Mobile Ad hoc Networks are established for extemporaneous services customized to application. These networks exist for limited period of time based on demands. This infrastructure less networks support data networking services using routing protocols. Reactive routing protocols serve the issue over proactive routing protocols [7]. As the communication is through multiple intermediate nodes, circumstances lead for the attacks lacking security [12]. Existing proactive routing protocols does not endow with security aspects within [1]. In this paper, we introduce an enhanced secured routing protocol and its performance is compared with the existing protocols namely, Ad hoc On demand Distance Vector Routing (AODV), Dynamic Source Routing (DSR) & Zone Routing Protocol (ZRP) in terms of delay, jitter and throughput using Qualnet simulation software.

Keywords:
   Ad hoc networks, jitter, routing protocols, secured routing


References:

1.       L. Abusalah, A. Khokhar, M. Guizani, "A survey of secure mobile Ad Hoc routing protocols," IEEE Communications Surveys & Tutorials, vol. 10, no. 4, pp. 78-93, 2008
2.       G. Acs, L. Buttyan, I. Vajda, "Provably Secure On-Demand Source Routing in Mobile Ad Hoc Networks," IEEE Trans. Mobile Computing, vol. 5, no. 11, pp. 1533-1546, Nov. 2006.

3.       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,” ,ACM/IEEE International Conference on Mobile Computing and Networking (MOBICOM’98),  October 1998, pp. 85–97.

4.       Chakrabarthi S., Mishra A., "QoS issues in ad hoc wireless networks", communications magzine, IEEE, volume 39, issue 2, Feb 2001, pp.142-148

5.       Das S. R., Perkins C. E., Royer E. M. and Marina M. K., “Performance comparison of two on demand routing protocols for ad hoc networks,”  IEEE Personal Communications Magazine, special issue on Mobile Ad Hoc Networks, vol. 8, no. 1, pp. 16–29, February 2001.

6.       S. R. Das, R. Castaneda, and J. Yan. “Simulation-based Performance Evaluation of Routing Protocols for Mobile Ad hoc Networks”, ACM/Baltzer Mobile Networks and Applications (MONET), 5(3): 179–189, 2000.

7.       Elizabath.M.Royer and Chai-Keong Toh,  ”A review of current routing protocols for AdHoc mobile networks”, IEEE personal communications, Volume 6, April 1999,pp-46 –55.

8.       Z.J. Hass, R. Pearlman, “Zone routing protocol for ad-hoc networks”, Internet Draft, draft-ietf-manet-zrp-02.ttxt, work in progress, 1999.

9.       Z. Haas and M. Pearlman, “The Performance of Query Control Schemes for the Zone Routing Protocol”, IEEE/ACM Transactions on Networking,9(4):427–38, 2001.

10.     Huang R., Zhuang Y., Cao Q., “Simulation and Analysis of Protocols in Ad Hoc Network”, 2009 International Conference on Electronic Computer Technology © 2009 IEEE.

11.     D. Johnson, D. Maltz, J. Jetcheva, “The dynamic source routing protocol for mobile ad hoc networks”, InternetDraft, draft-ietf-manet-dsr-07.txt, 2002.

12.     D. B. Johnson, D. A. Maltz, Y. Hu, and J. G. Jetcheva.” The Dynamic Source Routing Protocol for Mobile Ad Hoc Networks (DSR)”. http://www.ietf.org/internet-drafts/draft-ietf-manet-dsr-07.txt, Feb 2002. IETF Internet Draft

13.     G. Lin, G. Noubir, and R. Rajaraman, “Mobility models for ad hoc network simulation,”    Proc. IEEE INFOCOM, 2004, pp. 454–463.

14.     D.A.Maltz, J.Broch, J.Jetcheva and D.B.Johnson, “The effects of on-demand behavior in routing protocols for multihop wireless AdHoc networks”, IEEE journal on Selected areas of communication, Volume 17, 1999, pp-1439–1453.

15.     Mehran Abolhasan , Tadeusz Wysocki, Eryk Dutkiewicz , “A review of routing protocols for mobile ad hoc networks”, 2004 Elsevier, Adhoc networks,  pp. 1-22.

16.     Perkins C. E. and Royer E. M., “Ad-Hoc On-Demand Distance Vector Routing, Mobile Computing Systems and Applications,” Proc. IEEE Workshop Mobile Computing Systems & Applications (WMCSA ’99), pp. 90-100, 1999.

17.     C. E. Perkins, E. Belding-Royer, and S. R. Das, “Ad hoc On-Demand Distance Vector Routing” http://www.ietf.org/rfc/rfc3561.txt,July 2003. RFC 3561.

18.     M. Weeks and G. Altun, "Efficient, Secure, Dynamic Source Routing for Ad-hoc Networks," Journal of Network and Systems Management, Vol.14, No. 4, pp. 559- 581, Dec. 2006.


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22.

Authors:

SarathChand P.V., VenuMadhav K., Arya Bhanu M., Nagamani K., Balaram A.

Paper Title:

Sets Sequential Emission By Transmitting Streams

Abstract:    The increasing generation and collection of data have been increased rapidly in the last several decades. The contribution in the widespread of commercial products like bar coding, computerization of many business corporations, bank transactions and advancement of data collections ranges from scanned text to image platforms. The popular usage of internet as a global search engine for information system has flooded with a tremendous amount of data. The explosion of data is stored and an urgent need for new technologies and techniques should evolve day by day. The paper is the concepts and techniques for data retrieval method and promising the flourishing mechanism in database systems and new database applications. The SETS is a knowledge discovery mechanism and automated for extraction data sets which are stored in the database. The paper SETS can be viewed as a result of natural evolution of information technology. The SETS makes the user can gain the convenient and flexible data access through the queries on set of data[1]. This technology provides a great boost to the database and information industry and makes a huge amount number of databases and information repositories available for transaction management, data analyzing methods and information managements. It can be a powerful tool for the fast growing and tremendous access of data which are collected and stored in large and numerous data bases. The SETS provides the data analysis and covers the important patterns which are contributing to the business requirements..

Keywords:
   Transaction management, Repositories, flourishing mechanism, information industry, patterns


References:

1.       http//www.google.com for study materials.
2.       Data Mining by Margaret H.Dunham.Introductory and Advanced Topics Margaret H. Dunham.2002 Publisher: Prentice Hall pp 85-88

3.       Data Mining Concepts and Techniques  second edition by Jiawei Han and Micheline Kamber pp 494-499

4.       J. Han, M. Kamber, and A. K. H. Tung. Geographic Data Mining and Knowledge Discovery, chapter Spatial Clustering Methods in Data Mining: A Survey, pages 15-29. Taylor and Francis, 2001

5.       Data Mining: Concepts and Techniques, 2 Edition ... August 26, 2010.

6.       "iSAX 2.0: Indexing and Mining One Billion Time Series," icdm, pp.58-67, 2010 IEEE International Conference on Data Mining, 2010

7.       In Proceedings of the 12th ACM SIGKDD in the International conference on Knowledge discovery and data mining (KDD '06). ACM, New York, NY, USA, 748-753.

8.       M.J.Zaki Efficient Enumeration of frequent sequences. In proc 7th int. conf information and knowledge Management 98 pages 68-75 washing ton DC, nov 1998.

9.       Fabio Aioli, Ricardo Cardin, Fabrizio Sebastiani, Alessandro Sperduti,”Preferential Text Classification: Learning Algorithms and evaluation measures”, Springer – Inf Retrieval 2009.

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23.

Authors:

H. Abdul Shabeer, R.S.D.Wahidabanu

Paper Title:

Cell Phone Accident Avoidance System While Driving

Abstract:    Every year, innumerable road accidents and deaths take place due to distracted driving. Large number of studies shows mobile phone usage while driving was the major reason for distracted driving. With the aim of preventing road accidents due to mobile phone usage while driving, we propose a highly efficient automatic electronic system for early detection of incoming or outgoing call, an antenna located on the top of driver seat used for detecting when the driver uses mobile phone and a low range mobile jammer with its range covers only driver seat which prevent drivers mobile phone from receiving signals from base stations.

Keywords:
   Mobile Phone Detection, Risk of using mobile phone while driving, Mobile Jammer.


References:

1.       Alm H, Nilsson L. Changes in driver behaviour as a function of handsfree mobile phones: a simulator study. Accident Analysis and Prevention, 26:441–451.
2.       An investigation of the safety implications of wireless communication in vehicles. Department of Transport, National Highway Traffic Safety Administration, Washington

3.       Redelmeier DA, Tibshirani RJ. Association between cellular-telephone calls and motor vehicle collisions. New England Journal of Medicine, 336:453–458.

4.       The risk of using a mobile phone while driving. Birmingham, Royal Society for the Prevention of Accidents.

5.       Walsh, S.P., White, K.M., Hyde, M.K., Watson, B., 2008. Dialling and driving: factors influencing intentions to use a mobile phone while driving. Accident Analysis & Prevention 40, 1893–1900.

6.       Charlton, S.G., 2009. Driving while conversing: cell phones that distract and passengers who react. Accident Analysis & Prevention 41, 160–173.

7.       Strayer, D.L., Drews, F.A., 2007. Cell-phone-induced driver distraction. Current Directions in Psychological Science 16, 128–131.

8.       Loeb, P.D., Clarke, W.A., 2009. The cell phone effect on pedestrian fatalities. Transportation Research Part E: Logistics 45, 284–290.

9.       Nasar, J., Hecht, P., Wener, R., 2008. Mobile telephones, distracted attention, and pedestrian safety. Accident Analysis & Prevention 40, 69–75.

10.     Neyensa, D.M., Boyle, L.N., 2007. The effect of distractions on the crash types of teenage drivers. Accident Analysis & Prevention 39, 206–212.

11.     http://turnoffthecellphone.com/

12.     Kriegl, J. Location in Cellular Networks. Diploma Thesis, University of Technology Graz, Australia.

13.     Scourias, Overview of Global System for Mobile Communications. http://www.shoshin.uwaterloo.ca/~jscouria/GSM/gsmreport.html

14.     http://high-technology-market.com/2008/12/prevent-und


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24.

Authors:

H. B. Kekre, Tanuja Sarode, Sudeep D. Thepade, Supriya Kamoji

Paper Title:

Performance Analysis of Various Window Sizes for Colorization of Grayscale Images using LBG and KFCG Vector Quantization Codebooks in RGB and Kekre’s LUV Color Spaces

Abstract:    Colorization is a computer aided process of adding colors to a grayscale image or videos. The paper presents use of assorted window sizes and their impact on colorization of grayscale images using Vector Quantization (VQ) Codebook generation techniques in different color spaces such as RGB and Kekre’s LUV. The paper also analyses the performance of Vector Quantization Algorithms Linde Buzo and Gray Algorithm (LBG) and Kekre’s Fast Codebook Generation Algorithm (KFCG) for colorization of grayscale images. Experimentation is conducted on both RGB and Kekre’s LUV color space for the different pixel windows of sizes 1x2, 2x1, 2x2, 2x3, 3x2, 3x3, 1x3, 3x1, 2x4, 4x2, 1x4 and 4x1 to compare results obtained across various grid sizes

Keywords:
   Color palette, Color spaces, Vector Quantization, LBG, KFCG.


References:

1.       V. Karthikeyani, K. Duraisamy, Mr.P.Kamalkakkannan, "Conversion of grayscale image to color image with and without texture synthesis", IJCSNS International journal of Computer science and network security, Vol.7 No.4 April 2007.
2.       E.Reinhard, M. Ashikhmin, B. Gooch and P Shirley, “Colour Transfer between images”, IEEE Transactions on Computer Graphics and Applications 21, 5, pp. 34-41.

3.       Rafael C. Gonzalez & Paul Wintz, “ Digital Image Processing”, Addison Wesley Publications,    May 1987.

4.       A. Hertzmann, C. E Jacobs, N. Oliver, B. Curless and D.H. Salesin, “image Anologies”, in the proceedings of ACM SIGGRAPH 2002, pp. 341-346.

5.       G. Di Blassi, and R. D. Reforgiato, “Fast colourization of gray images”, In proceedings of Eurographics Italian Chapte, 2003.

6.       H.B.Kekre, Sudeep. D. Thepade,  “Color traits transfer to gray scale images”, in Proc of IEEE International conference on Emerging Trends in Engineering and Technology, ICETET 2008 Raisoni College of Engg, Nagpur.

7.       R. M. Gray, "Vector quantization", IEEE ASSP Mag., pp. 4-29, Apr11984.

8.       Y. Linde, A. Buzo, and R. M. Gray, "An algorithm for vector quantizer design," IEEE Trans.Commun., vol. COM-28, no. 1, pp. 84¬95, 1980.

9.       H. B. Kekre, Tanuja K. Sarode, "New Fast Improved Codebook Generation Algorithm for Color Images using Vector Quantization," International Journal of Engineering and Technology, vol.1, No.1, pp. 67-77, September 2008.

10.     H. B. Kekre, Tanuja K. Sarode, "An Efficient Fast Algorithm to Generate Codebook for Vector Quantization," First International Conference on Emerging Trends in Engineering and Technology, ICETET-2008, held at Raisoni College of Engineering, Nagpur, India, July 2008, Available at online IEEE Xplore.

11.     Ahmed A. Abdelwahab, Nora S. Muharram, "A Fast Codebook Design Algorithm Based on a Fuzzy Clustering Methodology", International Journal of Image and Graphics, vol. 7, no. 2 pp. 291¬302, 2007.

12.     H. B. Kekre, Tanuja K. Sarode, "Speech Data Compression using Vector Quantization", WASET International Journal of Computer and Information Science and Engineering (IJCISE), vol. 2, No. 4, pp.: 251-254, Fall 2008. available: http://www.waset.org/ijcise.

13.     C. Garcia and G. Tziritas, "Face detection using quantized skin color regions merging and wavelet  packet analysis," IEEE Trans. Multimedia, vol. 1, no. 3, pp. 264-277, Sep. 1999.

14.     H. B. Kekre, Tanuja K. Sarode, Bhakti Raul, "Color Image Segmentation using Kekre's Fast Codebook Generation Algorithm Based on Energy Ordering Concept", ACM International Conference on Advances in Computing, Communication and Control (ICAC3-2009), 23-24 Jan 2009, Fr. Conceicao Rodrigous College of Engg., Mumbai. Available on online ACM portal.

15.     Dr.H.B. Krkre, Sudeep D. Thepade, “Image Blending in Vista Creation using Kekre’s LUV Color Space”, In Proc. of SPIT-IEEE Colloquium, Mumbai, Feb  4-5,2008.

16.     H. B. Kekre, Tanuja K. Sarode, "Speech Data Compression using  Vector Quantization", WASET International Journal of Computer and Information Science and Engineering (IJCISE), vol. 2, No. 4, 251¬254, Fall 2008. available: http://www.waset.org/ijcise.

17.     H. B. Kekre, Ms. Tanuja K. Sarode, Sudeep D. Thepade, "Image Retrieval using Color-Texture Features from DCT on VQ Codevectors obtained by Kekre's Fast Codebook Generation", ICGST-International Journal on Graphics, Vision and Image Processing (GVIP),Volume 9, Issue 5, pp.: 1-8, September 2009. Available online at http://www.icgst.com/gvip/Volume9/Issue5/P1150921752.html.

18.     H.B.Kekre, Tanuja K. Sarode, Sudeep D. Thepade, "Color-Texture   Feature based Image Retrieval using DCT applied on Kekre's Median Codebook", International Journal on Imaging (IJI),Available online at www.ceser.res.in/iji.html.

19.     Dr. H. B. Kekre, Sudeep D. Thepade, Nikita Bhandari, “Colorization of Greyscale images using Kekre’s Bioorthogonal Color Spaces and Kekre’s  Fast Codebook Generation “,CSC Advances in Multimedia An international journal (AMU), volume 1, Issue 3, pp.49-58, Available at www.cscjournals.org/csc/manuscript/journals/AMIJ/volume1/Issue3/AMU-13.pdf.

20.     Dr. H. B. Kekre, Sudeep D. Thepade,Adib Parkar, “A Comparison of Harr Wavelets and Kekre’s Wavelets for Storing Color Information in a Greyscale Images”, International Journal of Computer Applications(IJCA), Volume 1, Number 11, December 2010,pp 32-38. Available at www.ijcaonline.org/archives/volume11/number11/1625-2186.

21.     Dr. H. B. Kekre, Sudeep D. Thepade,Archana Athawale, Adib Parkar, “Using Assorted Color Spaces and pixel window sizes for Colorization of Grayscale images’,ACM International Conferences and workshops on emerging Trends in Technology(ICWET 2010), Thakur College of Engg. And Tech.,Mumbai,26-27 Feb 2010.

22.     H. B. Krekre,Sudeep Thepade, Adib Parkar, “A comparison of Kekre’s Fast Search and Exhaustive Search for various grid sizes used for coloring a Grayscale Image” Second International conference on signal Acquisition and Processing, (ICSAP2010), IACSIT,Banglore,pp.53-57,9-10 Feb 2010.


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25.

Authors:

Sonia Goyal, Seema

Paper Title:

Region Based Contrast Limited Adaptive HE with Additive Gradient for Contrast Enhancement of Medical Images (MRI)

Abstract:   Digital Image Processing has been widely implemented in Medical Imaging. Various branches of medical science are using digital image processing as an extensive process to visualize and extract more details from the image. Quality enhancement of medical images can be performed with the help of various techniques. Contrast Enhancement is one of the most acceptable methods for enhancement of medical images. Different contrast enhancement methods like Contrast Stretching, Histogram Equalization, AHE, CLAHE are already available. Method selection depends on characteristics of image. This paper works on low contrast MRI images and presents a hybrid methodology for image enhancement. Results of the proposed algorithm have been compared against the existing major contrast enhancement techniques and Region Based Adaptive Contrast Enhancement (RBACH) on both qualitative and quantitative basis.

Keywords:
 Histogram Equalization, Adaptive, Convolution, Mask, X-Ray, Neighborhood, RBACH.


References:

1.       Chen, Soong-Der and Ramli, Abd. Rahman, “Minimum Mean Brightness Error Bi-Histogram Eualization in Contrast Enhancement”, IEEE Transactions on Consumer Electronics, Vol. 49,    No. 4, 2003, pp. 1310-1319.
2.       Zeyun, Yu. And Bajaj C. L. (2004), “A fast and adaptive method for image contrast enhancement”, IEEE international conference on image processing (ICIP), Vol. 2, pp. 1001-1004.

3.       Polesel, A.  Ramponi, G.  Mathews, V.J.  Tele Media Int. Ltd., Frankfurt, “Image Enhancement via adaptive unsharp masking”, IEEE Transactions on Image Processing, Vol. 9, Issue. 3, 2002, pp. 505-510

4.       Jafar, I. and Ying, H. (2007), “A New Method for Image Contrast Enhancement based on Automatic Specification of Local Histograms”, International Journal of Computer Science and Network Security (IJCSNS), Vol. 7, No. 7, pp 1-10

5.       Srinivasan  S. and Balram N. (2006), “Adaptive Contrast Enhancement Using local Region Stretching”, Proceedings of ASID’06, pp. 152-155

6.       Rangayan, R.M., “Biomedical Image Analysis”,  CRC Press, 2005, pp. 338, ISBN 0-8493-9695-6

7.       Kanwal, N.,   Girdhar, A.,   Gupta, S.,  “Region Based Adaptive Contrast Enhancement of Medical X-Ray Images” Proceeding of 5th International Conference (IEEE) on Bioinformatics and Biomedical Engineering, (iCBBE) 2011  , May 2011, pp 1-5, ISBN: 978-1-4244-5088-6

8.       Wei, Liyang, Kumar, Dinesh, Khemka, Animesh, Turlapti, Ram, Suri,  Jasjit  S. “Clinical Validation and Performance Evaluation of Enhancement Methods Acquired from Interventional C-ARM X-ray”, Medical Imaging 2008: Image Processing. Edited by Reinhardt, Joseph M.; Pluim, Josien P. W. Proceedings of the SPIE, Volume 6914, 2008, pp. 691426-691426-8.


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Authors:

Thyagaraju.GS, Umakanth P.Kulkarni

Paper Title:

Design and Implementation of Context Program Compiler for developing Context Aware Applications in Pervasive Computing Environment

Abstract:    With the advancement of pervasive computing, sensors technology and the wide deployment of wireless communication, there is an increasing demand for the context aware computing application. Contextual presentation is an emerging technique that has huge commercial possibilities .The theory behind the applications is complex and this make the implementation non trivial. Although some good applications / devices have been built no general solutions are available. There is no programming language or scripting language available to find solutions to context based problems. In this direction we are developing a generic context compiler and generic context programming language using which one can write and execute programs to develop any context aware applications.  In this paper we are presenting the design and implementation of context program compiler for developing context aware applications in pervasive computing environment.

Keywords:
   ubiquitous environment, Context script, pervasive, compiler, bnf.


References:

1.       Jaewoo Chang and Ahreum Kim ,” A New Context Script Language and Its Processor for Developing Context- Aware Applications in Ubiquitous Computing” , 2008 11th IEEE International Conference on Computational Science and Engineering, DOI 10.1109/CSE.2008.49
2.       Yongki Kim and Jaewoo Chang , “Design of a Context Script Language for Developing Context-Aware Applications in Ubiquitous Intelligent Environment “,2008 4th International IEEE Conference "Intelligent Systems" , 978-1-4244-1739-1/08/$25.00 © 2008 IEEE.

3.       Devdatta Kulkarni and Anand Tripathi ,” A Framework for Programming Robust Context-Aware Applications.” , IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 36 ,2010 , Digital Object Identifier no. 10.1109/TSE.2010.11

4.       S. S. Yau, and F. Karim, "Context-sensitive Middleware for Realtime Software in Ubiquitous Computing Environments," Proc. of 4th IEEE Symposium on Object-oriented Real-time Distributed Computing, pp.163-170, 2001.

5.       P. Couderc, A. M. Kermarrec, "Improving Level of Service for Mobile Users Using Context-Awareness," Proc. of 18th IEEE Symposium on Reliable Distributed Systems, pp. 24-33, 1999.

6.       CONTEXT-ORIENTED PROGRAMMING by M.L.Gassanenko SPIIRAN, St.Petersburg, Russia mlg@forth.org, mlg@iias.spb.su.

7.       CRL: A Context-aware Request Language for Mobile Computing by Alvin T.S. Chan, Peter Y.H. Wong, Siu-Nam Chuang Department of Computing, The Hong Kong Polytechnic University Hung Hom, Kowloon, Hong Kong.

8.       COPAL-ML: A Macro Language for Rapid Development of Context-Aware Applications in Wireless Sensor Networks by Sanjin Sehic, Fei Li, and Schahram Dustdar  Distributed Systems Group Information Systems Institute Vienna University of Technology.

9.       Dey.A, “Providing Architectural Support for Building Context Aware Applications”, Ph.D Thesis Dissertation, College of Computing, Georgia Tech, December 2000.

10.     Lex & yacc by John Levine,Tony Mason and Doug Brown.


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Authors:

Sumit Kumar Banchhor, S.K.Dekate

Paper Title:

Comparison of text-dependent method for Gender Identification

Abstract:    Differences of physiological properties of the glottis and the vocal track are partly due to age and/or gender differences. Since these differences are reflected in the speech signal, acoustic measures related to those properties can be helpful for automatic gender classification. Acoustics measures of voice sources were extracted from 10 utterances spoken by 20 male and 20 female talkers (aged 19 to 25 year old). The difference of speech long term features, including zero crossing rate, short time energy, and spectrum flux between male and female is studied. The result shows that the estimation of short time energy reflects more effectively, the difference in male and female voice than zero crossing rate and spectrum flux.

Keywords:
   gender classification gender identification, voice source.


References:

1.       M. M. Homayounpour, B. Mobarakabadi, N. Hamidi, “Age   interval and sex identification based on voice, using GMM and neural networks”, 11th Iranian Conference on Electrical Engineering, pp 304-311, 2003, (in persian).
2.       M. M., Homayounpour, M. H., Khosravi,., "Age Identification Using Support Vector Machine", IKT2003, pp. 615-622, 2003.

3.       Nobuaki Minematsu, Keita Yamauchi, and Keikichi Hirose,    "Automatic estimation of perceptual age using speaker modeling techniques", 8th European Conference on Speech Communication and Technology, EUROSPEECH, 2003.

4.       Peder A. Olsen, Satya Dharanipragada, "An efficient integrated gender detection scheme and time mediated averaging of gender dependent acoustic models", 8th European Conference on Speech Communication and Technology, EUROSPEECH, 2003.

5.       G. E. Peterson and H. L. Barney, “Control methods used in a study of the vowels,” The Journal of the Acoustical Society of America, vol. 24, no. 2, pp. 175–184, March 1952.

6.       B.Weinberg and S. Bennett, “Speaker sex recognition of 5- and 6-year-old children’s voices,” The Journal of the Acoustical Society of America, vol. 50, pp. 1210–1213, 1971.

7.       S. Bennett, “Vowel formant frequency characteristics of preadolescent males and females,” The Journal of the Acoustical Society of America, vol. 69, pp. 231–238, 1981.

8.       P. Busby and G. Plant, “Formant frequency values of vowels produced by preadolescent boys and girls,” The Journal of the Acoustical Society of America, vol. 97, pp. 2603–2606, 1995.

9.       T. L. Perry, R. N. Ohde, and D. H. Ashmead, “The acoustic bases for gender identification from childrens voices,” The Journal of the Acoustical Society of America, vol. 109, no. 6, pp. 2988–2998, June 2001.

10.     K. Wu and D. G. Childers, “Gender recognition from speech. part i: Coarse analysis,” The Journal of the Acoustical Society of America, vol. 90, no. 4, pp. 1828–1840, 1991.

11.     S. Lee, A. Potamianos, and S. Narayanan, “Acoustics of childrens speech: Developmental changes of temporal and spectral parameters,” The Journal of the Acoustical Society of America, vol. 105, no. 3, pp. 1455–1468, March 1999.

12.     M. Iseli, Y.-L. Shue, and A. Alwan, “Age, sex, and vowel dependencies of acoustical measures related to the voice source,” The Journal of the Acoustical Society of America, vol. 121, no. 4, pp. 2283–2295, April 2007.

13.     E. B. Holmberg, R. E. Hillman, J. S. Perkell, P. Guiod, and S. L. Goldman, “Comparisons among aerodynamic, electroglottographic, and acoustic spectral measures of female voice,” J. Speech Hear. Res., vol. 38, pp. 1212–1223, 1995.

14.     M. Iseli and A. Alwan, “An improved correction formula for the estimation of harmonic magnitudes and its application to open quotient estimation,” in Proceedings of ICASSP, Montreal, Canada, vol. 1, pp. 669–672, May 2004.

15.     H. M. Hanson and E. S. Chuang, “Glottal characteristics of male speakers: Acoustic correlates and comparison with female data,” The Journal of the Acoustical Society of America, vol. 106, pp. 1064–1077, 1999.

16.     Ruan boyao. The application of PCNN on speaker recognition based on spectrogram. Master Degree Dissertations of Wuyi University. 2008.

17.     An expert spectrogram reader: A knowledge-based approach to speech  recognition Zue, V.; Lamel, L.; Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP’86. Volume: 11, pp. 1197 – 1200, 1986

18.     Mathew J.Paiakal and Michael J.Zoran. Feature Extraction form Speech Spectrogram Using Multi-Layered Network Models. Tools for Artificial Intelligence, 1989. Architectures, Languages and Algorithms, IEEE International Workshop on Volume, Issue, 23-25, pp. 224 – 230, Oct 1989.

19.     Hideki Kawahara, Ikuyo Masuda-Katsuse and Alain de Cheveigne. Restructuring speech representations using a pitch adaptive time-frequency smoothing and an instantaneous-frequency-based F0 extraction: Possible role of a repetitive structure in sounds. Speech Communication. Volume 27, Issue 3-4, pp. 187 – 207, Apr 1999.

20.     Yang yang. Voiceprint Recognition Technology and Its Application in Forensic Expertise. Master Degree Dissertations of Xiamen University. 2007.

21.     V.W. Zue and R.A. Cole, "Experiments on Spectrogram Reading, "IEEE Conference Proceedingo, ICASSP, Washington D.C.,1979, pp. 116-119


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Authors:

H. B. Kekre, Sudeep D. Thepade, Archana A. Athawale, Paulami Shah

Paper Title:

Image Retrieval using Fractional Energy of Row Mean of Column Transformed Image with Six Orthogonal Image Transforms.

Abstract:    The thirst of better and faster retrieval techniques has always fuelled to the research in content based image retrieval (CBIR). The paper presents innovative content based image retrieval (CBIR) techniques based on feature vectors as fractional coefficients of row mean of column transformed images using Discrete Cosine, Walsh, Haar, Slant, Discrete Sine, and Hartley transforms. Here the advantage of energy compaction of transforms in low frequency coefficients in transform domain is taken to greatly reduce the feature vector size per image by taking fractional coefficients of row mean of column transformed image. The feature vectors are extracted in six different ways from the transformed image, with the first being considering all the coefficients of row mean of column transformed image  and then six reduced coefficients sets (as 50%, 25%, 12.5%, 6.25%, 3.125%, 1.5625%  of complete row mean of column transformed image) are considered as feature vectors. The six transforms are applied on the colour components of images to extract row mean of column transformed RGB feature sets respectively. Instead of using all coefficients of transformed images as feature vector for image retrieval, these six reduced coefficients sets for RGB planes are used, resulting into better performance and lower computations. The proposed CBIR techniques are implemented on a database having 1000 images spread across 10 categories. For each proposed CBIR technique 40 queries (4 per category) are fired on the database and net average precision and recall are computed for all feature sets per image transform. The results have shown performance improvement (higher precision and recall values) with fractional coefficients compared to complete transform of image at reduced computations resulting in faster retrieval. Finally Discrete Cosine Transform (DCT) surpasses all other discussed transforms in performance with highest precision and recall values for 50% of fractional coefficients.

Keywords:
   CBIR, Cosine Transform , Walsh Transform, Haar Transform, Sine Transform , Slant Transform, Hartley Transform, Fractional Coefficients, Row Mean.


References:

1.       H.B.Kekre, Sudeep D. Thepade, “Improving the Performance of Image Retrieval using Partial Coefficients of Transformed Image”, International Journal of Information Retrieval (IJIR), Serials Publications, Volume 2, Issue 1, 2009, pp. 72-79(ISSN: 0974-6285)
2.       H.B.Kekre, Sudeep D. Thepade, “Image Retrieval using Augmented Block Truncation Coding Techniques”, ACM International Conference on Advances in Computing, Communication and Control (ICAC3-2009), pp. 384-390, 23-24 Jan 2009, Fr. Conceicao Rodrigous College of Engg., Mumbai. Is uploaded on online ACM portal.

3.       H.B.Kekre, Sudeep D. Thepade, “Scaling Invariant Fusion of Image Pieces in Panorama Making and Novel Image Blending Technique”, International Journal on Imaging (IJI), www.ceser.res.in/iji.html, Volume 1, No. A08, pp. 31-46, Autumn 2008.

4.       Hirata K. and Kato T. “Query by visual example – content-based image retrieval”, In Proc. of Third International Conference on Extending Database Technology, EDBT’92, 1992, pp 56-71

5.       H.B.Kekre, Sudeep D. Thepade, “Rendering Futuristic Image Retrieval System”, National Conference on Enhancements in Computer, Communication and Information Technology, EC2IT-2009, 20-21 Mar 2009, K.J.Somaiya College of Engineering, Vidyavihar, Mumbai-77.

6.       Minh N. Do, Martin Vetterli, “Wavelet-Based Texture Retrieval Using Generalized Gaussian Density and Kullback-Leibler Distance”, IEEE Transactions On Image Processing, Volume 11, Number 2, pp.146-158, February 2002.

7.       B.G.Prasad, K.K. Biswas, and S. K. Gupta, “Region –based image retrieval using integrated color, shape, and location index”, International Journal on Computer Vision and Image Understanding Special Issue: Colour for Image Indexing and Retrieval, Volume 94, Issues 1-3, April-June 2004, pp.193-233.

8.       H.B.Kekre, Sudeep D. Thepade, “Creating the Color Panoramic View using Medley of Grayscale and Color Partial Images ”, WASET International Journal of Electrical, Computer and System Engineering (IJECSE), Volume 2, No. 3, Summer 2008. Available online at www.waset.org/ijecse/v2/v2-3-26.pdf.

9.       Stian Edvardsen, “Classification of Images using color, CBIR Distance Measures and Genetic Programming”, Ph.D. Thesis, Master of science in Informatics, Norwegian university of science and Technology, Department of computer and Information science, June 2006.

10.     H.B.Kekre, Tanuja Sarode, Sudeep D. Thepade, “DCT Applied to Row Mean and Column Vectors in Fingerprint Identification”, In Proceedings of International Conference on Computer Networks and Security (ICCNS), 27-28 Sept. 2008, VIT, Pune.

11.     Zhibin Pan, Kotani K., Ohmi T., “Enhanced fast encoding method for vector quantization by finding an optimally-ordered Walsh transform kernel”, ICIP 2005, IEEE International Conference, Volume 1, pp I - 573-6, Sept. 2005.

12.     H.B.kekre, Sudeep D. Thepade, “Improving ‘Color to Gray and Back’ using Kekre’s LUV Color Space”, IEEE International Advanced Computing Conference 2009 (IACC’09), Thapar University, Patiala, INDIA, 6-7 March 2009. Is uploaded and available online at IEEE Xplore.

13.     H.B.Kekre, Sudeep D. Thepade, “Image Blending in Vista Creation using Kekre's LUV Color Space”, SPIT-IEEE Colloquium and International Conference, Sardar Patel Institute of Technology, Andheri, Mumbai, 04-05 Feb 2008.

14.     H.B.Kekre, Sudeep D. Thepade, “Color Traits Transfer to Grayscale Images”, In Proc.of IEEE First International Conference on Emerging Trends in Engg. & Technology, (ICETET-08), G.H.Raisoni COE, Nagpur, INDIA. Uploaded on online IEEE Xplore.

15.     http://wang.ist.psu.edu/docs/related/Image.orig (Last referred on 23 Sept 2008)

16.     H.B.Kekre, Sudeep D. Thepade, “Using YUV Color Space to Hoist the Performance of Block Truncation Coding for Image Retrieval”, IEEE International Advanced
Computing Conference 2009 (IACC’09), Thapar University, Patiala, INDIA, 6-7 March 2009.

17.     H.B.Kekre, Sudeep D. Thepade, Archana Athawale, Anant Shah, PrathmeshVerlekar, SurajShirke,“Energy Compaction and Image Splitting for Image Retrieval using Kekre Transform over Row and Column Feature Vectors”, International Journal of Computer Science and Network Security (IJCSNS),Volume:10, Number 1, January 2010, (ISSN: 1738-7906) Available at www.IJCSNS.org.

18.     H.B.Kekre, Sudeep D. Thepade, ArchanaAthawale, Anant Shah, PrathmeshVerlekar,SurajShirke,“Walsh Transform over Row Mean and Column Mean using Image Dr. H. B. Kekre, Sudeep D. Thepade & Akshay Maloo International Journal of Image Processing (IJIP) Volume (4): Issue (2) 155 Fragmentation and Energy Compaction for Image Retrieval”, International Journal on Computer Science and Engineering (IJCSE),Volume 2S, Issue1, January 2010, (ISSN: 0975–3397). Available online at www.enggjournals.com/ijcse.

19.     H.B.Kekre, Sudeep D. Thepade,“Image Retrieval using Color-Texture Features Extracted from Walshlet Pyramid”, ICGST International Journal on Graphics, Vision and Image Processing (GVIP), Volume 10, Issue I, Feb.2010, pp.9-18, Available online

20.     H.B.Kekre, Sudeep D. Thepade,“Color Based Image Retrieval using Amendment Block Truncation Coding with YCbCrColor Space”, International Journal on Imaging (IJI), Volume 2, Number A09, Autumn 2009, pp. 2-14. Available online at www.ceser.res.in/iji.html (ISSN: 0974-0627).

21.     H.B.Kekre, Tanuja Sarode, Sudeep D. Thepade,“Color-Texture Feature based Image Retrieval using DCT applied on Kekre’s Median Codebook”, International Journal on Imaging (IJI), Volume 2, Number A09, Autumn 2009,pp. 55-65. Available online at www.ceser.res.in/iji.html (ISSN: 0974-0627).

22.     H.B.Kekre, Sudeep D. Thepade, “Image Retrieval using Non-Involutional Orthogonal Kekre’s Transform”, International Journal of Multidisciplinary Research and Advances in Engineering (IJMRAE), Ascent Publication House, 2009, Volume 1, No.I, pp 189-203, 2009. Abstract available online at www.ascent-journals.com (ISSN:
0975-7074)

23.     H.B.Kekre, Sudeep D. Thepade, “Boosting Block Truncation Coding using Kekre’s LUV Color Space for Image Retrieval”, WASET International Journal of Electrical, Computer and System Engineering (IJECSE), Volume 2, Number 3, pp. 172-180, Summer 2008. Available online at http://www.waset.org/ijecse/v2/v2-3-23.pdf

24.     H.B.Kekre, Sudeep D. Thepade, Archana Athawale, Anant Shah, Prathmesh Verlekar, Suraj Shirke, “Performance Evaluation of Image Retrieval using Energy Compaction and Image Tiling over DCT Row Mean and DCT Column Mean”, Springer-International Conference on Contours of Computing Technology (Thinkquest-2010), BabasahebGawde Institute of Technology, Mumbai, 13-14 March 2010, The paper will be uploaded on online Springerlink.

25.     H.B.Kekre, Tanuja K. Sarode, Sudeep D. Thepade, Vaishali Suryavanshi, “Improved Texture Feature Based Image Retrieval using Kekre’s Fast Codebook Generation Algorithm”, Springer-International Conference on Contours of Computing Technology (Thinkquest-2010), Babasaheb Gawde Institute of Technology, Mumbai, 13-14 March 2010, The paper will be uploaded on online Springerlink.

26.     H.B.Kekre, Tanuja K. Sarode, Sudeep D. Thepade, “Image Retrieval by Kekre’s Transform Applied on Each Row of Walsh Transformed VQ Codebook”, (Invited), ACM International  Conference and Workshop on Emerging Trends in Technology (ICWET 2010),Thakur College of Engg. And Tech., Mumbai, 26-27 Feb 2010, The paper is invited at ICWET 2010. Also it will be uploaded on online ACM Portal.

27.     Dr. Sudeep D. Thepade, Ph.D. Thesis, “New Approached of feature Vector Extraction for Content Based Image Retrieval”,  pp. C4-2 to C4-8, Supervisor Dr. H.B.Kekre, MPSTME, SVKM’s NMIMS (deemed to be University), Mumbai, 2011.

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Authors:

Anubhuti Khare, Manish Saxena , Shweta Tiwari

Paper Title:

Edge Detection Method for Image Segmentation – A Survey of Soft Computing Approaches

Abstract:    Soft Computing is an emerging field that consists of complementary elements of fuzzy logic, neural computing and evolutionary computation. Soft computing techniques have found wide applications. One of the most important applications is edge detection for image segmentation. The process of partitioning a digital image into multiple regions or sets of pixels is called image segmentation. Edge is a boundary between two homogeneous regions. Edge detection refers to the process of identifying and locating sharp discontinuities in an image. In this paper, the main aim is to survey the theory of edge detection for image segmentation using soft computing approach based on the Fuzzy logic, Genetic Algorithm and Neural Network.

Keywords:
   Image Segmentation, Edge Detection, Fuzzy logic, Genetic Algorithm, Neural Network.


References:

1.        Orlando J. Tobias and Rui Seara, ”Image Segmentation by Histogram Thresholding Using Fuzzy Sets”, IEEE Transactions on Image Processing, Vol.11, No.12, 2002, pp. 1457-1465.
2.        Dinesh K. Sharma, Loveleen Gaur and Daniel Okunbor, “Image Compression and Feature Extraction with Neural Network”, Proceedings of the Academy of Information and Management Sciences, Vol.11, No.1, 2007, pp. 33-38.

3.        M. Abdulghafour, ”Image segmentation using Fuzzy logic and genetic algorithms”, Journal of WSCG, vol. 11, no.1, 2003.

4.        Dao Qiang Zhanga and Song Can Chena, “A novel kernelized fuzzy C-means algorithm with application in medical image segmentation”, Artificial Intelligence in Medicine, vol. 32, 2004, pp.37-50.

5.        Lei Jiang and Wenhui Yang, ”A Modified Fuzzy C-Means Algorithm for Segmentation of Magnetic Resonance Images”, Proc. VIIth Digital Image Computing: Techniques and Applications, vol. 10-12, 2003,pp.225-231.

6.        Kanchan Deshmukh and G. N. Shinde, ” An adaptive Neuro - fuzzy system for color image segmentation”, J. Indian Inst. Sci., vol. 86, Sept.-Oct. 2006, pp.493-506.

7.        Jander Moreira and Luciano Da Fontoura Costa,”Neural-based color image segmentation and classification using self - organizing maps”, Anais do IX SIBGRAPI, 1996, pp.47-54.

8.        Mohamed N. Ahmed and Aly A. Farag, ”Two-stage neural network for volume segmentation of medical images ”, Pattern Recognition Letters, vol.18, 1997, pp.1143-1151.

9.        Gonzalo A. Ruz, Pablo A. Estevez and Claudio A. Perez, “A neurofuzzy color image segmentation method for wood surface defect detection”, Forest Products Journal, Vol.55, No.4, April 2005, pp.52-58.

10.     Mausumi Acharyya and Malay K. Kundu, “ Image Segmentation Using Wavelet Packet Frames and Neurofuzzy Tools”, International Journal of Computational Cognition, Vol.5, No.4, December 2007, pp.27-43.

11.     Ibrahiem M. M. El Emary, “On the Application of Artificial Neural Networks in Analyzing and Classifying the Human Chromosomes”, Journal of Computer Science,
vol.2(1), 2006, pp.72-75.

12.     Bouchet A, Pastore J and Ballarin V, “Segmentation of Medical Images using Fuzzy Mathematical Morphology”, JCS and T, Vol.7, No.3, October 2007, pp.256-262.

13.     Mantas Paulinas and Andrius Usinskas, “A Survey of Genetic Algorithms Applicatons for Image Enhancement and Segmentation”, Information Technology and Control, Vol.36, No.3, 2007, pp.278-284.

14.     Xian Bin Wen, Hua Zhang and Ze Tao Jiang, ”Multiscale Unsupervised Segmentation of SAR Imagery Using the Genetic Algorithm”, Sensors, vol.8, 2008, pp.1704-1711.

15.     Daniel L. Schmoldt, Pei Li and A. Lynn Abbott, ”Machine vision using artificial neural networks with local 3D neighborhoods”, Computers and Electronics in Agriculture, vol.16, 1997, pp.255-271.

16.     Ian Middleton and Robert I. Damper, “ Segmentation of magnetic resonance images using a combination of neural networks and active contour models”, Medical Engineering and Physics, vol.26, 2004, pp.71-86.


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Authors:

S. S. S. Ranjit, S. A. Anas, C. F. Tan

Paper Title:

Off-Grid System Development for House Car Pouch Lighting

Abstract:    Practically to electrify lighting system electricity supply is a necessity to power the light. Thus, solar energy is known as an alternative source to provide electricity. This paper presents an off-grid system development for house car porch lighting system. Development of the system is composed of photovoltaic panel, environmental sensors, charge controller, battery and lighting loads such as florescent lamp. The off-grid system focuses to supply electricity in small scale which is integrated with some energy saving characteristics. An auto timer and smart charge controller is integrated into the off-grid system to turn-on and turn-off the lighting at the house car pouch.  Integration of some smart functions is an ideal solution for small scale electricity supply or particularly for location which cannot be accessed by grid supply.

Keywords:
   Off-Grid System, Car Porch Lighting, Florescent Lamp, Timer Controller, Distribution Off-Grid.


References:

1.       H.P Garg, J. Prakash, “Advances in Solar Technology”, vol. III. New Delhi: Tata McGraw-Hill, 2002, pp. 402-411.
2.       http://www.indexmundi.com/malaysia/electricity_consumption.html. 1.30pm, 19 December 2010

3.       J. A. Aboagye, “An Alternative Street Lighting for UMaT Campus using LED (Light Emitting Diode) Lamps”, BSc Project Work, University of Mines and Technology, Tarkwa, May 2010.

4.       Wu Yue, Shi Changhong, Zhang Xianghong, Yang Wei, “Design of New Intelligent Street Light Control System”, 2010 8th IEEE International Conference on Control and Automation Xiamen, China, 21 December 2010.

5.       Stanislav Misak, Lucas Prokop, “Off-Grid Power Systems”, Technical University of Ostrava, Faculty of Electrical Engineering and Computer Science, Department of Electric Power Engineering, Ostrava, Czech Republic. 20th December 2010.

6.       Solomon Nunoo, Joseph C. Attachie and Charles K. Abraham, “Using Solar Power as an Alternative Source of Electrical Energy for Street Lighting in Ghana”, Department of Electrical and Electronic Engineering, University of Mines Technology, Western Region, Ghana, 19 December 2010.

7.       Anon., “Types of Lamps Used in Streetlights”, retrieved on February 10, 2010 from  http://www.eskimo.com/~jrterry/lamps.html. 19 December 2010

8.       Wu Yue, Shi Changhong, Yang Wei, “Study Of Acquisition Streetlights Background Signal By Multi-Sensor Array”, International Conference on Control, Automation and Systems 2010, 21 December 2010.

9.       Tarik M. Hussain, Ahsen M. Baig, Tarek N. Saadawi, Samir A. Ahmed, “Infrared Pyroelectric Sensor for Detection of Vehicular Traffic Using Digital Signal Processing Technique”, IEEE Transactions On Vehicular Technology, 23 December 2010.

10.     W. J. Mooney, “Optical Devices and Principles”. Englewood Cliffs, NJ: Prentice Hall, 1991, 20 December 2010.

11.     http://www.carlonchimes.us/ccus-lighting-controls-motion-activated.htm, 11.25 a.m, 23 December 2010

12.     K. Jardin and Istvan Nagy, “Modeling of a Combined Photovoltaic I Thermal Energy System”, Procs. of Power Electronics and Motion Control Conference, pp. 1754, 24 December 2010.

13.     en.wikipedia.org/wiki/Solar_cell, 21:53 p.m, 24 December  2010

14.     NowshadAmin, Lam Zi Yi and Kamaruzzaman Sopian, “Microcontroller Based Smart Charge Controller For Stand-Alone Solar Photovoltaic Power Systems”, Procs. of IEEE Photovoltaic Specialists Conference (PVSC), pp. 1094, 7-12 June 2009.

15.     Solar-batteries.net/, 1:10 a.m, 24 December 2010.


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Authors:

Sharad Kumar Tiwari, Gagandeep Kaur

Paper Title:

Analysis of Fuzzy PID and Immune PID Controller for Three Tank Liquid Level Control

Abstract:    In industrial control systems the liquid level is carrying its significance as the control action for level control in tanks containing different chemicals or mixtures is essential for further control linking set points. The three level control models are considered in our work. The conventional control algorithm is difficult to reach required control quality with more strict restriction on overshoot. Design a parameter self-tuning PID-controller based on fuzzy control, which can adjust PID-parameters according to error and change in error. Biological immune system is a control system that has strong robusticity and self-adaptability in complex disturbance and indeterminacy environments. The artificial intelligence technique of fuzzy logic and immune controller is adopted for more reliable and precise control action which incorporate the uncertain factors also. In this work the comparison of the conventional model, fuzzy model and immune feedback mechanism is clarified.

Keywords:
   Fuzzy logic, PID, immune control, artificial immunity, three level control, vertical tank.


References:

1.       Takahashi K, Yamada T. Application of an immune feedback mechanism to control systems.JSME IntJ,pp:184-191,1998
2.       Dasgupta D. Artificial immune systems and their applications.Springer-Verlag, Berlin 1999

3.       de Castro L N, Zuben F J V. Artificial immune system: part1-basic theory and application. Technical Report-RT DCA 01/99,FEEC/UNICAMP,1999

4.       Ding Yongsheng, Ren Lihong. A new fuzzy self-tuning immune feedback control system.Control And Decision,pp:443-446,2000

5.       Cominos P. and Munro N. PID controllers: recent tuning methods and design to specification, LEE Proceedings Control Theory and Application, pp:46-53,2002

6.       Huang H.P., Roan M.L. and Jeng J.C. On-line adaplive tuning for PID controllers, IEE Proceedings Control Theory and Applications, pp:60-67,2002

7.       Tan Yingzi, Shen Jiong, Lu Zhenzhong. Study of immune PID controller for surperheated system temperature control system. Proceedings of the Chinese Society for Electrical Engineering. Vol.22 No.10 pp:148-152,2002

8.       Jiao Licheng, Du Haifeng. Development and Prospect of the Artificial Immune System. ACTA ELECTRONICA SINICA,pp:1540-1548,2003

9.       XIE Fang-wei, HOU You-fu, XU Zhi-peng, ZHAO Rui: Fuzzyimmune control strategy of a hydro-viscous soft start device of a belt conveyor,2005

10.     H. Y. Sun, D. W. Yan and B. Li. "The Level Control of Three Water Tanks Based on Self-Tuning Fuzzy-PID Controller," Journal of Electrotechnical Application, Vo1.28, No. 8, pp:97-99. 2006.

11.     Wang B, Li S Y. Simulation research on of fuzzy immune nonlinear PID control. Journal of Harbin University of Commerce,  pp: 72–75,2006

12.     Wei Wang. X.Z.Gao, Changhong Wang. Fuzzy Immune PID Controller in Material-Level Control of Preheating Cylinder. International Conference on Informaticsand Control Technologies, Shenzhen,pp:52-55,2006

13.     Wei Wang, X. Z. Gao and Changhong Wang , A New Immune PID Controller in Material-Level Control, Third International Conference on Natural Computation (ICNC),IEEE,2007

14.     Zhang L, Li R H. Designing of classifiers based on immune principles and fuzzy rules. Information Sciences,pp:1836–1847,2008

15.     T. Hou. "Experimental Research on Neural Network PID Control Based on Hydraulic Pressure of 2-Container Water Tan," Journal of Lanzhou Jiaotong University, VoL28, No.3, pp:41-43,2009

16.     WangXiao-kan, SunZhong-liang, Wanglei, Feng Dong-qing Design and Research Based on Fuzzy PID-parameters Self-tuning Controller with MATLAB, International Conference on Advanced Computer Theory and Engineering,2008

17.     Zhen-Jie Yan, Yang Xue, Jian-Hua Ye, Hong Qian, Xu-Hong Yang, Main steam temperature composite control system based on variable universe fuzzy logic control integrated with immune and self-tuning PID controller, Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, Baoding, 12-15 July 2009

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32.

Authors:

Dipali Koshti, Supriya Kamoji

Paper Title:

Comparative study of Techniques used for Detection of Selfish Nodes in Mobile Ad hoc Networks

Abstract:   A MANET (Mobile Ad-hoc Network) is a self configuring system of mobile nodes connected by wireless links. MANETs are self-configuring and decentralized without having a fix infrastructure. In such a network each node acts as an end-system as well as a relay node (or router). Most of the routing algorithms designed for MANET such as AODV and DSR are based on the assumption that every node forwards every packet. But in practice some of the nodes may act as the selfish nodes. These nodes use the network and its services but they do not cooperate with other nodes. Such selfish nodes do not consume any energy such as CPU power, battery and also bandwidth for retransmitting the data of other nodes and they reserve them only for themselves. The original AODV and DSR routing algorithms can be modified to detect such selfish nodes. This paper discusses two techniques namely Reputation based technique and Credit based technique used to detect selfish nodes in MANET. This paper discusses two algorithms that are based on reputation based technique and one algorithm based on credit based technique. Finally all three techniques have been compared.

Keywords:
   MANET, Selfish nodes in MANET, Misbehaving nodes in MANET, Cooperative system in MANET.


References:

1.        L. Buttyan and J. P. Hubaux, “Stimulating Cooperation in Self- Organizing Mobile Ad Hoc Networks”, in  ACM Journal For Mobile Networks (MONET), Special Issue On Mobile Ad Hoc Networks, 2003.
2.        Dave B. Johnson and David A. Maltz, “The dynamic source routing protocol for mobile ad  hoc networks”, in Internet Draft, Mobile Ad Hoc Network (MANET) Working Group, IETF, Oslo, Norway.

3.        David B. Johnson, David A. Maltz and John Broch, “DSR: The Dynamic Source Routing Protocol for Multi-Hop Wireless Ad hoc Networks” in Ad hoc Networking edited by Charles E. Perkins, Chapter 5, pp. 139-172. (2001)

4.        Ian D. Chakeres and Elizabeth M. Belding-Royer,” AODV Routing Protocol Implementation Design” Proceedings of the International Workshop on Wireless Ad Hoc Networking,Tokyo,Japan,March 2004.

5.        V. Srinivasan, P. Nuggehalli, C.F. Chiasserini and R.R Rao,”Cooperation in wireless Ad Hoc Network”, in IEEE INFOCOM,California,USA,2003.

6.        Mrs. K. Vijaya , “ Secure 2ACK Routing Protocol in Mobile Ad hoc Networks”, TENCON.2008

7.        Zahara Safaei , Mohammad Hossein Anisi an Fatemeh Torgheh, “ A Reputation-Based Mechanisms to enforce Cooperation in MANETs”, Software, Telecommunications and Computer Networks, 2008( SoftCOM 2008.) 16th International Conference on 25-27 Sept. 2008.

8.        Cenker Demir and Cristina Comaniciu , “An Auction based AODV Protocol for Mobile Ad Hoc Networks with Selfish Nodes”,  IEEE International Conference on 24-28 June 2007.


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33.

Authors:

G. Ranganathan, R. Rangarajan, V. Bindhu

Paper Title:

Evaluation of ECG Signals for Mental Stress Assessment using Fuzzy Technique

Abstract:    This paper presents the evaluation of mental stress assessment using heart rate variability. The activity of the autonomic nervous system (ANS) is studied by means of frequency analysis of the Electrocardiogram (ECG) signal. Spectral decomposition of the Heart Rate Variability before smoking and after smoking was obtained. Mental stress is accompanied by dynamic changes in ANS activity. ECG signal analysis is popular for assessing the activities of autonomic nervous system. The approach consists of 1) Recording the ECG signals, 2) Signal processing using wavelets, 3) Fuzzy evaluation techniques to provide robustness in ECG signal analysis, 4) Monitoring the function of ANS under different stress conditions. Our experiment involves 20 physically fit persons under different conditions. Fuzzy technique has been used to model the experimental data.

Keywords:
   Adaptive Neuro Fuzzy Inference System (ANFIS), Non-linear System, Electrocardiogram (ECG), Autonomic Nervous System(ANS)


References:

1.        M. Kumar, M. Weippert, R. Vilbrandt, S. Kreuzfeld, and R. Stoll, “ Fuzzy Evaluation of Heart Rate Signals for Mental Stress Assessment,” IEEE Trans. Fuzzy Syst., vol. 15, no. 5,pp. 791-808, Oct.    2007. 
2.        S. Wu and M. J. Er, “Dynamic fuzzy neural networks—A novel approach to function approximation,”    IEEE Trans. Syst., Man., Cybern. B, vol. 30, pp. 358–364, 2000

3.        N. R. Pal, K. Pal, J. M. Keller, and J. C. Bezdek, “A possibilistic fuzzy c-means clustering algorithm,” IEEE Trans. Fuzzy Syst., vol. 13, pp. 517–530, Aug. 2005.

4.        X. Hong, C. J. Harris, and S. Chen, “Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and d-optimality,” IEEE Trans. Syst.,Man., Cybern. B, vol. 34, no. 1, pp. 598–608, 2004.

5.        N. Hjortskov, D. Rissen D, A.K. Blangsted, N. Fallentin, U. Lundberg, and K. Sogaard, “The Effect of  Mental Stress on Heart Rate Variability and Blood Pressure during Computer Work,” European   Journal  of Applied Physiology, vol. 92, pp. 84-89, June 2004.

6.        M. Akay and H. H. Szeto, “Investigating the relationship between fetus EEG, respiratory and blood pressure signals during maturation using fast wavelet transform,” Ann. Biomed. Eng., vol. 23, pp. 574–   582, 1995.

7.        W. G.Wornell and A. V. Oppenheim, “Estimation of fractal signals from noisy measurements using wavelets,” IEEE Trans. Signal Processing, vol. 40, pp. 611–623, 1992.

8.        M. Kania, M. Fereniec, and R. Maniewski, “Wavelet denoising for multi-lead high resolution ECG     signals”, Measurement Science review, vol.7.pp. 30-33, 2007.

9.        Maehmet Engin,Musa Fedakar,Erkan Zeki Engin,Mehmet Korrek, “Feature measurements of ECG   beats based on statistical classifiers”, Science direct, pp 904-912, 2007.

10.     Mikhled Alfaouri and Khaled Daqrouq, “ECG signal denoising by wavelet transform thresholding,” American journal of Applied Sciences, vol. 5(3), pp.276-281, 2008.


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34.

Authors:

L. Savadamuthu, S. Muthu, P. Vivekanandan

Paper Title:

Quality Improvement in Turning Process using Taguchi's Loss Function

Abstract:    This paper presents a advanced technique for quality improvement in turning operations. In this study, the Taguchi method is used to find the optimal cutting parameters in turning operations. The orthogonal array, the signal-to-noise ratio, and analysis of variance are employed to study the performance characteristics in turning operations of AISI 1030 steel bars using TiN coated tools. The model was developed initially for unidiameter case and then adapted to other workpiece geometries. An Adaptive Neuro Fuzzy Inference System (ANFIS) is proposed in this paper to control a constant cutting force turning process under various cutting conditions. The ANFIS consists of two parts: predictor and the fuzzy logic controller. The step size of the predictor, and the scaling factors of the fuzzy controller are adjusted for ensuring stability and obtaining optimal control performances. The Taguchi-genetic method is applied in this paper to search for the optimal control parameters of both the predictor and the fuzzy controller such that the ANFIS controller is an optimal controller. Computer simulations are performed to verify the effectiveness of the above optimal fuzzy control scheme designed by the Taguchi-genetic method. Experimental results are provided to illustrate the effectiveness of this approach.

Keywords:
   Adaptive Neuro Fuzzy Inference System (ANFIS), Taguchi-genetic method, Fuzzy controller


References:

1.       Wang, Y.-M., Luo, Y., Liang, L., 2009b. Fuzzy data envelopment analysis based upon fuzzy arithmetic with an application to performance assessment of manufacturing enterprises. Expert Systems with Applications 36 (3, Part 5205–5211.
2.       Zarandi, MH, Alaeddini, A, Turksen, IB, 2008. A hybrid fuzzy adaptive sampling—run rules for Shewhart control charts. Information Sciences 178 (4), 1152–1170.

3.       Kolarik W. Creating quality: concepts, systems, strategies, and tools. New York: McGraw-Hill; 1995.

4.       Juran J, Godfrey A. Juran’s quality handbook. New York: McGraw-Hill; 1998.

5.       Gitlow H, Oppenheim A, Oppenheim R. Quality management: tools and methods for improvement. 2nd ed. Irwin McGraw-Hill; 1995.

6.       Grant E, Leavenworth R. Statistical quality control, 6th edition New York: McGraw-Hill; 1988.

7.       Dahlgaard J, Kristensen K, Kanji G. The quality journey:  advances in total quality management series. Carfax Publishing Company; 1994.

8.       Koleske J. Paint and coating testing manual, 14th ed.  Philadelphia, PA: ASTM; 1995.

9.       Gitlow H, Gitlow S. The deming guide to quality and competitive position. Englewood Cliffs, NJ: Prentice- Hall; 1987.

10.     Lou H, Huang Y. Hierarchical decision making for proactive quality control: system development for defect reduction in   automotive coating operations.  Engineering Applications of Artificial Intelligence      2003; 16:237–50.


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35.

Authors:

R. Hari Kumar, M. Balasubramani

Paper Title:

FPGA Synthesis of Soft Decision Tree (SDT) for Classification of Epilepsy Risk Levels from Fuzzy Based Classifier Using EEG Signals

Abstract:    The objective of this paper is to design, simulate, and synthesis a simple, suitable and reliable Soft Decision Trees for classification of epilepsy risk levels from EEG signals. The fuzzy classifier (level one) is used to classify the risk levels of epilepsy based on extracted parameters like energy, variance, peaks, sharp and spike waves, duration, events and covariance from the EEG signals of the patient. Soft Decision Tree (post classifier with max-min and min-max criteria) of three models is applied on the classified data to identify the optimized risk level (singleton) which characterizes the patient’s risk level. The efficacy of these methods is compared with the bench mark parameters such as Performance Index (PI), and Quality Value (QV). A group of twenty patients with known epilepsy findings are analyzed. High PI such as 95.88 % was obtained at QV’s of 22.43 in the SDT model of (16-4-2-1) with Method-II (min-max criteria) when compared to the value of 40% and 6.25 through fuzzy classifier respectively. It was observed that the simulated and synthesized Field Programmable Gated Array (FPGA) SDT models are good post classifier in the optimization of epilepsy risk levels which is closely follows the mat lab version. The deterministic character of dynamics of the underlying system.

Keywords:
   EEG Signals, Epilepsy, Fuzzy Logic, Soft Decision Trees, Risk Levels, FPGA synthesis


References:

1.       K. P.Adlassnig, “Fuzzy Set Theory in Medical diagnosis”, IEEE Transactions on Systems Man Cybernetics, vol,16 , March 1986, pp 260-265.
2.       R.Harikumar, Dr.(Mrs.). R.Sukanesh, P.A. Bharathi, “Genetic Algorithm Optimization of Fuzzy outputs for Classification of Epilepsy Risk Levels from EEG signals,” Journal of Interdisciplinary panels I.E. (India), vol.86, no.1, May 2005,pp1-10.

3.       Leon D.Iasemidis etal., Adaptive Epileptic SeizurePrediction System, IEEE Transactions on Biomedical Engineering, May 2003,50(5): 616-627.

4.       Alison A Dingle et al, A Multistage system to  Detect epileptic form activity in the EEG,IEEE Transactions on Biomedical Engineering,1993, 40(12):1260-1268.

5.       Haoqu and Jean Gotman, A patient specific algorithm for detection onset in long-term EEG monitoring possible use as warning device, IEEE Transactions on Biomedical Engineering, February 1997,44(2): 115-122.

6.       Dr.(Mrs.).R.Sukanesh, R.Harikumar, A Simple Recurrent Supervised Learning Neural Network for Classification of Epilepsy Risk Levels from EEG Signals, I.E.India Journal of Interdisciplinary panels Vol.87,no.2,pp 37-43, Nov 2006

7.       Celement.C etal, A Comparison of Algorithms for Detection of Spikes in the Electroencephalogram,  IEEE   Transaction on Bio Medical Engineering, April 2003, 50 (4): 521-26.

8.       Nurettin Acir etal. Automatic detection of epileptiform events in EEG by a three-stage procedure based artificial neural networks, IEEE transaction on Bio Medical Engineering  January 2005,52(1):30-40.

9.       M.W.Kurzynski, “The optimal Strategy of a Tree Classifier,” Pattern Recognition, vol 16, 1983, pp 81-87.

10.     Philippe Salembier, Luis Garrido, “Binary Partition Tree as an Efficient Representation for Image Processing, Segmentation, and Information Retrieval,” IEEE Transactions on   Image processing,vol.9, no.4, April 2000,pp-561-576.

11.     K.Srirama murty and B.Yegnannarayana, “Combining Evidence from Residual Phase and MFCC Features for Speaker Recognition”, IEEE Signal Processing Letters, vol,13,no.1, 2006, pp 52-55.

12.     K.Paramasivam, R.Harikumar, K.Gunavathi Simulation of VLSI Design using Parallel Architecture for Epilepsy risk level Diagnosis in Diabetic Neuropathy, Proc. Of National conference on VLSI Design and Testing, Coimbatore, India, Feb.21st and 22nd 2003.

13.     Mahamoud A Manzoul, D.Jayabharathi , FPGA for Fuzzy Controllers, IEEE Tran. System Man Cybernetics Vol.25, No. 1 Jan. 1995.

14.     Verilog HDL Language Reference Manual, IEEE, 2001.

15.     Zoran Salcic,  High Speed Customizable Fuzzy logic Processor: Architecture and Implementation, IEEE Tran. System Man Cybernetics Vol.31, No. 6, pp 731-736, November 2001.

16.     Giuseppe ascia, Vincenzo catania, Marco Russo, Lorenzovita, Rule Driven VLSI fuzzy processor, IEEE Micro, Vol.16, no.3, pp 62-74, June 1996.

17.     C.Maxfield, “The Design Warrior Guide to FPGA,” Mentor Graphic’s Corporation and  Xilinx Inc.,USA, 2006.

18.     R. Freeman, “User-Programmable Gate Array,” IEEE Spectrum, pp.32-35, December 1988.

19.     J. Di Giacomo, “Design Methodology,” in VLSI Handbook, J. Di Giacomo, Editors, New  York: McGraw-Hill, pp. 1.3-1.9, 1989.


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36.

Authors:

Manoj Kumar, Mohammad Hussain

Paper Title:

A Framework for Performing Mutation Analysis and Deviants

Abstract:   The development of framework for safety critical area what happens, when some part of a system deviates from the intentions of designer is a critical research issue. When we apply, HAZOP technique using UML, then, we check the object-oriented design with a fault-free analysis and design. By mutation analysis and HAZOP, we find a better optimum result. The mutation method is a fault-based testing strategy that measures the quality/adequacy of testing by examining whether the test set (test input data) used in testing can reveal certain types of faults. This paper describes the UML-HAZOP technique with mutation based operator or analysis. Using this, we find more and more optimum result and solution, when we design our system with UML.

Keywords:
   Mutation Analysis, Mutation Testing, UML-HAZOP, Object-Oriented.


References:

1.        S.  Kim,  J.  A.  Clark  &  J. A. McDermid, “Class Mutation:  Mutation   Testing  for   Object  -  Oriented   Programs”,   in  Net.   Object     Days    Conference   on   Object  -  Oriented  Software  Systems, 2000.
2.        J.   H.   Andrews,   L .C.  Briand  and   Y.   Labiche,    “Is Mutation An          Appropriate    Tool    for    Testing   Experiments”,       Proceeding   27th  International  Conference   on  Software    Engineering, St Louis,  USA, 2005, pp. 402-411.

3.        R. T. Alexander, J. M. Bieman, S. Ghosh, Bixia Ji, “Mutation of Java  Objects”, To appear in Proc. IEEE  International  Symposium  Software  Reliability  Engineering (ISSRE), 2002.

4.        C.  K. Roy, J. R. Cordy, “Towards  a   Mutation -Based  Automatic   Framework   for   Evaluating   Code   Clone  Detection Tools”, ACM  International  Conference Proc. Series, Volume: 273, Publisher: ACM  Press, pp 137-140, 2008.

5.        P. Chevalley, P. Th´evenod-Fosse, “A Mutation Analysis Tool for Java  Programs”,  International     Journal     Software     Tools   Technology  Transfer ,  2003.

6.        S. Kim, J. A. Clark, and J. A. McDermid,  “The  Rigorous   Generation  of  Java  Mutation  Operators  Using  HAZOP”,  In Proceedings of the  12th  International  Conference  on  Software and Systems Engineering and their  Applications (ICSSEA’ 99), Paris, France, Dec-1999.

7.        J. Górski,  A.  Jarzębowicz,  “Detecting  Defects  in  Object - Oriented Diagrams  Using  UML - HAZOP”,  Foundations  of  Computing  and Decision Sciences, Vol. 27, No. 4, 2002.

8.        J. Górski , A. Jarzębowicz, “Development   & Validation  of a  HAZOP     Based  Inspection   of   UML    Models”,   3rd  World   Congress    for Software   Quality 26-30   Sep- 2005,

9.        M. Rausand,  ”HAZOP  Hazard  and  Operability  Study”,      System Reliability Theory (2nd ed), Wiley, pp. 1-   44. 2004.

10.     Ministry   of   Defense,   “HAZOP  Studies  on  Systems  Containing  Programmable Electronics”, Defense  Standard 00-58, Parts 1 and 2, Issue 2, May 2000.

11.     A Jarzębowicz  and  J  Górski,   ”Experimental   Comparison of UML-HAZOP    Inspection   &   Non –  Structured   Review”,    Found. of  Computing and   Decision Sciences, Vol. 30. No. 1, pp.  29-38,  2005.

12.     Dinh -Trong,  S. Ghosh,  R. France,  B. Baudry,  and  F.  Fleurey. "A  Taxonomy   of   Faults   for   UML   Designs",    In     Proceedings    of  MoDeVa'05 (Model Design and   Validation Workshop associated    to MoDELS'05),  Montego Bay, Jamaica, October 2005.

13.     S. B. A. Punuganti, P. k. Pattanaik, S. Prasad, R.  Mall, “Model-Based  Mutation  Testing  of  Object-Oriented Programs”, Proceedings of  2nd   International   Conference  on  IT  &  Business  Intelligence    (ITBI-10),  Nagpur, INDIA, November12–14, 2010.


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37.

Authors:

Tirtharaj Sen, Pijush Kanti Bhattacharjee, Debamalya Banerjee, Bijan Sarkar

Paper Title:

Noise Dose Emitted from Different Electrical Machines Compared

Abstract:   This paper deals with the study and comparison of noise dose emitted from different electrical machines. The study has been done in an electrical machine laboratory. Readings of noise parameters are taken from different machines using a noise dosimeter and the different noise related variables such as Leq (Equivalent continuous A-weighted sound level), LAV (Average sound level), LAE (Sound exposure level), TWA (Time weighted average) are compared for different ac and dc machines. Nomographic technique based on graphical analysis is used for finding out percentage noise dose and comparing that with the data collected from dosimeter. This study gives a complete measurement of noise levels and its parameters for different electrical machines ac and dc types, and hence it is a source to detect mechanical faults of the machines which are causing the noise produced. Also mechanical faults of the electrical machines are identified by analysis the frequency of noise emitting sound from the machines. These techniques are used for safeguarding the machines as well as environmental pollution.

Keywords:
   Electrical Machines, Frequency of Noise Emitting Sound, Noise Dose, Noise Dosimeter, Noise Related Parameters, Peak Exceedance Level.


References:

1.       Mum S, Geem ZW (2009). Determination of individual sound power levels of noise sources using a harmony such algoroth, International Journal of Indusrial Ergonomics,39, 366-370.
2.       Goelzer B, Hansen CH., Sehrndt G (2001). Occupational Exposure to Noise: Evaluation, Prevention and Control. Publication Series from the Federal Institute for Occupational Safety and Health, Document published on behalf of the World Health Organization, (Dortmund, Berlin).

3.       Michel P, Girard S.A, Simard M, Larocque R. Leroux T, Turcotte F (2008). Accident Analysis and Prevention J. Elevier 40, 1644 1652.

4.       Sen T, Bhattacharjee PK, Banerjee D and Sarkar B (2010). Study and Comparison of the Noise Dose on Workers in a Small Scale Industry in West Bengal, India, International Journal of Environmental Science and Development, Vol. 1, No. 4, ISSN: 2010-0264

5.       Pancholy M, . Chhapgar AF, and Singhal SP (1967). Noise Survey in Calcutta, J. Sci. Ind. Res. 26, 314–316

6.       Sen T, Bhattacharjee PK, Banerjee D and Sarkar B (2010). Running Condition Noise Dose to Auto drivers in Kolkata Metropolitan City of India in Different Seasons, International Journal of Environmental Science and Development, Vol. 1, No. 3, ISSN: 2010-0264

7.       Roy B, Santra SC, Chandra S, and Mitra B (1984), Traffic Noise Level in Calcutta, Sci. Cult.  50, 62–64.

8.       Bru¨ el, Kjær, 1998a. Technical Documentation—Sound Calibrator BK4231. Bru¨ el and Kjær, Naerum, Denmark.

9.       Bru¨ el, Kjær, 1998b. Technical Documentation—Integrating and Logging Sound level meterBK 2238 and BK 2260. Bru¨ el and Kjær, Naerum, Denmark.

10.     Basic noise calculation,April 2007,Work safe BC.

11.     Mukherjee AK, Bhattacharya SK, Ahmed S, Roy SK, Sen A, Roychowdhury S (2003). Exposure of drivers and conductors to noise, heat, dust and volatile organic compounds in the state transport special buses of Kolkata city. Transportation Research Part D 8 11–19.

12.     http://www.bksv.com/primers.aspx-introduction; as on 15th February,8.45 pm.

13.     http://www.bksv.com/Products/NoiseDoseMeters.aspx  as on 25th Feb., 2011 at 18:00 (IST)

14.     http://www.bksv.com/Products/NoiseDoseMeters.aspx  as on 20th March., 2011 at 18:30 (IST)

15.     http://www.epd.gov.hk/epd/noise_education/web/ENG_EPD_HTML/m2/types_3.html as on 20th March., 2011 at 18:30 (IST)

16.     http://www.noisenet.org/Noise_Terms_Leq.htm  as on 5th March., 2011 at 14:00 (IST).

17.     http://www.noisemeters.com/help/osha/twa.asp as on 5th March., 2011 at 14:00 (IST).

18.     http://www.epd.gov.hk/epd/noise_education/web/ENG_EPD_HTML/m2/types_3.html as on 5th March., 2011 at 14:00 (IST).

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38.

Authors:

Naveen Choudhary, M. S. Gaur, V. Laxmi

Paper Title:

Power proficient Application Specific Communication Infrastructure for Advance SoC

Abstract:    Networks-on-Chip is getting established as a communication infrastructure for future advance and complex SoC plateforms, composed of a large number of homogenous or heterogeneous processing resources.  Application specific SoC design presents the prospects for incorporating custom NoC architectures that are more suitable for a specific application, and may not be suitable for regular topologies. The precise but often different communication requirements among IP-cores of the SoC call for the design of application-specific topology of SoC for better performance with respect to communication energy, latency, and throughput. In the presented work, a methodology for the design of customized irregular topology for SoC with complex communication behavior is proposed. The proposed methodology uses the aforementioned knowledge of the application’s communication attribute to produce an power optimized network and corresponding routing tables.

Keywords:
   SoC, on-chip networks, application specific NoC,  interconnection network.


References:

1.       W. J. Dally, B.Towles,,“Route Packets, Not Wires: On-Chip Interconnection Networks,”  in IEEE Proceedings of  the 38th Design Automation Conference (DAC), pp. 684–689, 2001.
2.       L. Benini, G. DeMicheli., “Networks on Chips: A New SoC Paradigm,” IEEE Computer Vol. 35, No. 1 pp. 70–78, January 2002.

3.       S. Kumar, A. Jantsch, J.-P. Soininen, M. Forsell, M. Millberg, J. Oberg, K. Tiensyrja, and A. Hemani, “A Network on Chip Architecture and Design Methodology,” in Proceedings of VLSI Annual  Symposium (ISVLSI 2002), pp. 105–112, 2002.

4.       U. Ogras, J. Hu, R. Marculescu, “Key research problems in NoC design: a holistic perspective,” IEEE CODES+ISSS, pp. 69-74, 2005.

5.       L. Natvig, “High-level Architectural Simulation of the Torus Routing Chip,” in Proceedings of the International Verilog HDL Conference, California, pp. 48–55, Mar.
1997.

6.       J. Duato, S. Yalamanchili, L. Ni, Interconnection Networks : An Engineering Approach, Elsevier, 2003.

7.       W. Dally and C. Seitz, “Deadlock-free Message Routing in Multiprocessor Interconnection Networks,” in IEEE Transactions on Computers, pp. 547–553, 1987.

8.       C. Glass and L. Ni, “The Turn Model for Adaptive Routing,” in Proc.19 International Symposium on Computer Architecture, pp. 278– 287, May 1992.

9.       e. a. M. D. Schroeder, “Autonet: A High-Speed Self-Configuring Local Area Network Using Point-to-Point Links,” Journal on Selected Areas in Communications, vol. 9, Oct. 1991.

10.     A. Jouraku, A. Funahashi, H. Amano, M. Koibuchi, “L-turn routing: An Adaptive Routing in Irregular Networks,” in International Conference on Parallel Processing, pp. 374-383, Sep. 2001.

11.     Y.M. Sun, C.H. Yang, Y.C Chung, T.Y. Hang, “An Efficient Deadlock-Free Tree-Based Routing   Algorithm for Irregular Wormhole-Routed Networks Based on Turn Model,” in International Conference on Parallel Processing, vol. 1, pp. 343-352, Aug. 2004.

12.     J. Wu, L. Sheng, “Deadlock-Free Routing in Irregular Networks Using Prefix Routing,” DIMACS  Tech. Rep. 99-19, Apr. 1999.

13.     J.Hu, R.Marculescu,“Energy-Aware Mapping for Tile-based NOC Architectures Under Performance Constraints,” ASP-DAC 2003, Jan 2003.

14.     J. Hu, R. Marculescu, “Energy- and performance-aware mapping for regular NoC architectures,” IEEE Trans. on CAD of Integrated Circuits and Systems, 24(4), April 2005.

15.     J. Hu, R. Marculescu, “Exploiting the Routing Flexibility for Energy/Performance Aware Mapping of Regular NoC Architectures,” in Proceedings of DATE 2003, February 2003.

16.     Y. C. Chang, Y. W. Chang, G. M. Wu and S. W. Wu, “B*-Trees : A New Representation for Non-Slicing Floorplans,” in Proc. 37th Design Automation Conference, pp. 458-463, 2000.

17.     F. Silla, J. Duato, “High-Performance Routing in Networks of Workstations with Irregular Topology,” in IEEE Transactions on Parallel and Distributed Systems, vol. 11, pp. 699-719, july 2000.

18.     K. Srinivasan, K. S. Chatha, “Layout Aware Design of Mesh based NoC Architectures,” in Proceedings of 4th International Conference on Hardware Software Codesign and System Synthesis, Seoul, Korea, pp. 136-141, 2006.

19.     A. E. Eiben and J. E. Smith, Introduction to Evolutionary Computing, Springer-Verlag, Berlin, Heidelberg, 2003.

20.     R. P. Dick, D. L. Rhodes, W. Wolf, “TGFF: task graphs for free,” in Proceeding of the International Workshop on Hardware/Software Codesign, March 1998.

21.     L. Jain, B. M. Al-Hashimi, M. S. Gaur, V. Laxmi, A. Narayanan,  “NIRGAM: A Simulator for NoC Interconnect Routing and Application Modelling,” DATE 2007, 2007.

22.     H-S Wang et al., “Orion: A Power-Performance Simulator for Interconnection Network,” in Proc. International Symposium on Microarchitecture,  Nov 2002.


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39.

Authors:

D.Srinivasa Rao, B.J.M.  Ravi Kumar

Paper Title:

Performance Evaluation of Genetic Based Dynamic Clustering Algorithm over LEACH Algorithm for Wireless Sensor Networks

Abstract:    A wireless sensor network (WSN) is a wireless computing devices network consisting of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, motion, intrusion or pollutants, at different locations.  The purpose of designing these networks is gathering information from the environment and sending them to the sink node. One of the most important issues in these kinds of networks is energy efficiency. The longer the communication distance, the more energy will be consumed during transmission. So, clustering is a way to reduce energy consumption. In this paper, we propose a dynamic clustering algorithm using genetic algorithm. This algorithm takes different parameters into consideration to increase the network lifetime. The results of the conducted simulations show the high efficiency of the proposed algorithm.

Keywords:
   Wireless Sensor Networks, Clustering, Genetic algorithm, Energy Consumption.


References:

1.       j.N. Al-karaki and A.E Kamal, "Routing Techniques in Wireless Sensor networks: A  survey", IEEE  journal of wireless communications, vol. 11, No. 6, pp. 628, Dec.  2004.
2.       Y. Ossama and M. Srinivasan, "Node clustering in Wireless Sensor Networks: Recent Developments and Deployment Challenges", IEEE Network (Special issue on Wireless Sensor Networking), Vol. 20, issue 3, pp.20–25, May 2006.

3.       W.B. Heinzelman, A.p. Chandrakasan and H.Balakrishnan, "An         Application Specific Protocol Architecture for Wireless Micro sensor Networks", IEEE Transaction on Wireless Communication, 660-670, April 2002.

4.       Y. Wang, T.L.X. Yang, D. Zhang, "An Energy Efficient and balance Hierarchical unequal clustering algorithm for large scale sensor network", Inform. Technol. Journal, 28-38,8(1), 2009.

5.       I. SIM, K. Jin Choi, K. Kwon and J. Lee, "Energy Efficient Cluster Header Selection Algorithm in WSN", in the proceedings off IEEE international Conference on Complex, Intelligent and Software Intensive Systems, Pages 584-587, March 2009.

6.       S. Jin, M. Zhou and A.Wu , "Sensor Network Optimization Using a Genetic Algorithm", School of EECS, University of Central        Florida,Orland,  FL 32816.

7.       G. Riordan and S. Sampalli, "Cluster- Head Election Using Fuzzy Logic for Wireless Sensor Networks", In Proceedings of IEEE      Communication Networks and Services Research Conference, Pages 255-260,May 2005.

8.       O. Zzitoune, M . aroussi, Rziza, D. Aboutajdine,  "Stochastic Low Energy Adaptive Clustring  Hierarchy", ICGSTCNIR, volume(8), Issue(1), pp  47-51. 2008.

9.       H. Junping, J. Yuhui and D. Liang, "A Time-base Cluster-Head  Selection Algorithm for LEACH", in proceeding of IEEE  Symposium on computers and communication 2008 (ISCC 2008),   Marrakech, Morocco. July 6-9 , 2008

10.     W. Ye, J. Heidemann, and D.Estrin , "An Energy- Efficient MAC  Protocol for Wireless Sensor Networks " , in proceeding o the 21st International Annual Joint Conference of the IEEE Computers and Communication Societies (INFOCOM 2002), New York, NY, USA , June,  2002.

11.     Liu, B.; Wang, L.; Jin, Y. Advances in Differential Evolution. CHIN. J. Control Decision 2007, 22,721– 729.

12.     I.F.Akyildiz, W.Su, Y.Sankarasubramaniam, E.Cayirci,“Wireless sensor networks: a survey”, Computer Networks 38, pp. 393 422,  2002.

13.     Tatiana Bokareva, Wen Hu, SalilKanhere,”Wireless Sensor Networks for Battlefield Surveillance”, Land Warfare  Conference Brisbane, October 2006.

14.     Werner Allen, G. Johnson, J. Ruiz,”Monitoring volcanic   eruptions with a wireless sensor network “, Proceedings of the Second European workshop on Wireless Sensor Network, 2005.

15.     Junguo Zhang, Wenbin Li, Zhongxing Yin, Shengbo Liu, XiaolinGuo, “Forest fire detection system based on wireless  sensor  network “, 4th IEEE  Conference on Industria  Electronics    and Applications, 2009.

16.     Lee Angeles, Talampas Sison, “MotesArt: Wireless Sensor Network for Monitoring Relative Humidity  and Temperature in a Art Gallery “, IEEE  International Conference on networking, sensing and control,.ICNSC 2008.


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40.

Authors:

B. Bag, A. K. Jana, M. K. Pandit

Paper Title:

A Novel Dynamically Optimized Embedded Video Burst Scheduler to Enhance the System QoS

Abstract:    Real-time video processing is still now a formidable task for the strict requirement on latency control and packet loss minimization. Burst processing has come to the rescue by offering buffer less operation and separation of control and data information. In this paper a novel dynamically-optimized embedded burst scheduling method suitable for processing class-differentiated video channels has been proposed. The method is based on statistical Markov chains where the initial scheduled Markov transition probabilities are subsequently adaptively reconfigured by the central scheduler to maintain the best system Quality of Service (QoS)

Keywords:   Embedded systems, QoS, Markov process, Reconfigurable computing, Video communication.


References:

1.        M. Yoo, C. Qiao, and S Dixit, "QoS performance of optical burst switching in IP-over-WDM networks", IEEE/OSA J. Light wave Technology, Vol. 18, No. 10, Feb. 2000, pp. 2062-2071.
2.        S. J. Ben Yoo, "Optical packet and burst switching for the future photonic internet", IEEE/OSA J. Light wave Technology, Vol. 24, No. 12, Dec. 2006, pp. 4468-4492.

3.        S. Rasoul Safavian, "How to dimension wireless networks for packet data services with guaranteed QoS", Bechtel Telecommunications Technical Journal, Vol. 3, No. 1, March17-19, 2008

4.        S. Tomoyoshi, T. Kosuke, "Table-based QoS control for embedded real-time systems", Proc of the ACM SIGPLAN 1999 workshop on Languages, compilers, and tools for embedded systems, 1999, pp. 65-72.

5.        R. Jain, C. J. Hughes and S. V. Adve, "Soft Real-time Scheduling on Simultaneous multithreaded processors", Proc of 23rd Real-time systems symposium (RTSS-23), IEEE Press, 2002, pp. 134-135.

6.        A. Snaverly, D. M. Tullsen and G. Voelker, "Symbiotic job scheduling with priorities for a simultaneous multithreaded processor", Proc of 9th International Conf on  Architectural Support for programming languages and operating systems (ASPLOS-9), ACM Press, 2000, pp. 234-244.

7.        S. Addepallil, P. Andersen and G. L. Barnes, "Efficient Resource Matching in Heterogeneous Grid Using Resource Vector", Int Journal of Computer Science and Information Technology, Vol. 2, No. 3, June 2010, pp. 1-10.

8.        G. Wang, "Efficient Resource Matching in Heterogeneous Grid Using Resource Vector", IEEE Transaction on Aerospace and Electronic Systems, Vol. 46, No. 3, July 2010, pp. 1492-1502.

9.        K. S. Chan, Kwan. L. Yeung "Performance Analysis of Burst Segmentation Schemes supporting Multiple Traffic", IEEE No. 0-7803-8924-7, May 2005, pp. 495-499.

10.     S. D. Servetto, K. Nahrstedt "Video Streaming Over the Public Internet: Multiple Description Codes And adaptive Transport Protocols", 1999 IEEE No. 0-7803-5467-2/99, pp. 85-89.

11.     M. F. Ngatman, Md. A. Ngadi, J. M. Sharif "Comprehensive Study of Transmission Techniques for Reducing Packet Loss and Delay in Multimedia over IP", IJCSNS, VOL-8 No. 3, March 2008, pp-292-299

12.     Jian Li, Ye-Qiong Song  "DLB: a novel real-time QoS control mechanism for multimedia transmission", International Journal of High Performance Computing and Networking 2009 - Vol. 6, No.1 pp. 4 - 14


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41.

Authors:

Manish Kansal, Vijay Kumar, Dinesh Arora, Hardeep Singh Saini

Paper Title:

Designing & Implementation of Digital Filter for removal of Power Supply Noise

Abstract:    An ECG is a simple and useful test which records the rhythm and electrical activity of the heart of the patient that suffers from any heart disease. An ECG can detect problems you may have with your heart rhythm .It can help doctors tell if you are having a heart attack or if you’ve had a heart attack in the past. Sometimes an ECG can indicate if your heart is enlarged or thickened Digital Filter Design problem involves the determination of a set of filter coefficients to meet a set design specifications. These specifications typically consist of the width of the pass band and the corresponding gain, the width of the stop band(s) and the attenuation therein; the band edge frequencies (which give an indication of the transition band) and the peak ripple tolerable in the pass band and stop band(s).There are many techniques for selecting coefficients. We can use a spreadsheet like Microsoft Excel, or there are many design packages which will do the job. I have used MATLAB for this purpose as it is the most advanced tool for DSP applications. Also it helps to verify the design and results that comes from the hardware.

Keywords:
   FIR, IIR, Matlab, VHDL


References:

1.       Lihuang She, Zhongqiang Xu, Shi Zhang, Yuning Song.,“De-noisng Of ECG Based on EMD Improved thresholding and Mathematical Morphology”,3rd International Conference on Biomedical Engineering and Informatics, October 2010.
2.       ShanthalaS, S.Y.Kulkarni, “High speed and low power FPGA Implementation for DSP applications”,European Journal of Scientific Research, ISSN 1450-216X, vol. 31, no.1 , pp. 19-28, November 2009.

3.       Maheshs.Chavan,AGARWALA,“Comparative Study of Chebyshev I And Chebyshev II Filter used For Noise Reduction in ECG Signal”, International  Journal  of Circuits, Systems and Signal Processing Issue 1,vol. 2, December 2008.

4.       A.Peled and B. Liu.,“A New Hardware Realization of Digital Filters”, IEETrans. on Acoust., Speech, Signal Process., vol. 22, pp. 456-462, December 2007.

5.       Mahesh S. Chavan, R.A. Agarwala, M.D. Uplane,”Application of  Chebyshev II  digital filter for noise reduction in ECG Signal”, WSEAStransactions on Circuits and Systems,vol. 4, no.10, pp. 1260-1267, Oct 2005.

6.       X. Hu, L. S. DeBrunner, and V. DeBrunner, “An efficient design for FIR  filters  with Variable precision”,  IEEE Int. Symp. on Circuits and Systems,vol. 4, pp. 365-368,May 2002.

7.       S. C. Chan, W. Liu, and K. L. Ho.,“Multiplier less perfect reconstruction Modulate filterbanks with sum-of-powers-of-two coefficients”, IEEE Signal Processing Letters, vol. 8, no. 6, pp. 163-166, June 2001.

8.       Christov II., “Dynamic power line interference subtraction from biosignals”,J Med Technology, vol. 24, pp. 169-172, July2000.

9.       Y. C. Lim, R. Yang, D. Li and J. Song., “Signed-power-of-two term  allocation scheme for the design of digital filters”, IEEE Transactions on Circuits and Systems II, vol. 46, pp. 577-584, May 1999.

10.     Sanjit K. Mitra James F  Kaiser,“Handbook for Digital Signal Processing”, John Wiley & Sons, Inc 1993.

11.     VHDL, Douglas L. Perry , Second Edition, McGraw  Hill,1993 .

12.     Escalona OJ, Mitchell RH, BaldersonDE, Harron DW,“Fast and reliable QRS alignment technique for high frequency analysis of signal-averaged ECG”, Med Biology Suppl, pp. 137-146,october1993.

13.     Yi-Sheng, Zhu,et al.,“P-wave detection by an adaptive QRS-T cancellation technique”,IEEE. Friesen GM, Jannett TC, Jadallah MA,1987.

14.     Levkov C, Michov G, Ivanov R, Daskalov I., “Subtraction of 50 Hz interference from the electrocardiogram”, IEEE Trans. on Med. & Biol. Eng.,vol. 22,pp. 371-373,Dec 1984.


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42.

Authors:

Gulfishan Firdose Ahmed, Raju Barskar

Paper Title:

A Study on Different Image Retrieval Techniques in Image Processing

Abstract:   With the popularity of the network and development of multimedia technology, the traditional information retrieval techniques do not meet the users’ demand. Recently, the content-based image retrieval has become the hot topic and the techniques of content-based image retrieval have been achieved great development. In this paper, the basic components of content-based image retrieval system are introduced. Image retrieval methods based on color, texture, shape and semantic image are discussed, analyzed and compared. The semantic-based image retrieval is a better way to solve the “semantic gap” problem, so the semantic-based image retrieval method is stressed in this paper. Other related techniques such as relevance feedback and performance evaluation also discussed. In the end of paper the problems and challenges are proposed. In many areas of commerce, government, academia, and hospitals, large collections of digital images are being created. Many of these collections are the product of digitizing existing collections of analogue photographs, diagrams, drawings, paintings, and prints. Usually, the only way of searching these collections was by keyword indexing, or simply by browsing. Digital images databases however, open the way to content-based searching. In this paper we survey some technical aspects of current content-based image retrieval systems.

Keywords:
  Image retrieval, content-based image retrieval, color, texture, shape and semantic-based image retrieval.


References:

1.       R. Brunelli and O. Mich, “Histograms Analysis for Image Retrieval,” Pattern Recognition, Vol.34, No.8, pp1625–1637,2001.
2.       C. S. Fuh, S.W. Cho and K. Essig, “Hierarchical Color Image Region Segmentation for Content-Based Image Retrieval System,”IEEE Transactions on Image Processing, Vol. 9, No. 1, pp. 156–162, Jan. 2000.

3.       M. Adoram and M. S. Lew, “IRUS: Image Retrieval Using Shape,” Proceedings of IEEE International Conference on Multimedia Computing and System, Vol. 2, pp. 597–602, 1999.

4.       Yao M, Luo J H. Research on generalized computing system. Journal of System Engineering and Electronics, 1998, 9 (3): 39-43.

5.       K. Fukunaga, Introduction to Statistical Pattern Recognition, San Diego, CA, Academic Press, 1990. [1] Flicker M, Query by image and video content. The QBIC System IEEE Computer, 1995,28(9): 23-32.

6.       Stricker M A,Orengo M, Similarity of color images , Proc of SPIE, Storage and Retrieval for Image and Video Database, San Jose, CA:s.n, 1995:381-392.

7.       Flickner M, Sawhney H, N aIblack W, et al. Query by image and video content: the QBIC system. IEEE Computer, 1995, 28 (9): 23 -32.

8.       A.Natsev,R.Rastogi and K.Shim.”WARLUS: a similarity retrieval algorithm for image database,” IEEE Transaction on Knowledge and Data Engineering 16(3), March 2004.

9.       F.Jeng, M.Li, H.-J. Zhang and B. Zhang, “An efficient and effective region-based image retrieval framework,” IEEE Transaction on Image Processing 13(5), May
2004.

10.     B. Brandshaw. “Semantic based image retrieval: aprobabilistic approach,” proc, ACM Multimedia, October 2000. http://www.cs.virginia.edu/papers/MIS03.pdf

11.     W. Y. Ma. NETRA: A Toolbox for Navigating Large Image Databases. PhD thesis, Dept. of Electrical and Computer Engineering, University of California at Santa Barbara, June 1997.

12.     Wei-Ying Ma and B. S. Manjunath. Netra: A toolbox for navigating large image databases. Multimedia Systems, 7(3): 184–198, 1999.

13.     David McG. Squire, Wolfgang M¨uller, Henning M¨uller, and Thierry Pun. Content-based query ofimage databases: inspirations from text retrieval. Pattern Recognition Letters, 21:1193–1198, 2000.

14.     Gupta. Visual information retrieval: A virage perspective.Technical Report Revision 4, Virage Inc., 9605 Scranton Road, Suite 240, San Diego, CA 92121, 1997.

15.     MichaelOrtega,Yong Rui, Kaushik Chakrabarti, Sharad Mehrotra, and Thomas S. Huang. Supporting similarity queries in MARS. In Proceedings of the 5th ACM International Multimedia Conference, Seattle, Washington, 8-14 Nov. ’97, pages 403–413, 1997.

16.     R. Smith and S.-F. Chang. Querying by color regions using the VisualSEEk content-based visual query system. In M. T. Maybury, editor, Intelligent Multimedia Information Retrieval. AAAI Press, 1997.

17.     W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Petkovic, P. Yanker, C. Faloutsos, and G. Taubin. The qbic project: Quering images by content using color, texture, and shape. In Poceedings of the SPIE Conference on Storage and Retrieval for Image and Video Databases, 2-3 February ’93, San Jose, CA, pages 173
187, 1993.
18.     J. R. Smith and S.-F. Chang. Querying by color regions using the VisualSEEk content-based visual query system. In M. T. Maybury, editor, Intelligent Multimedia Information Retrieval. AAAI Press, 1997.
19.     Gulfishan Firdose Ahmed, Raju Barskar,Jyoti Bharti, Ntin Singh Rajput, ““Content Base Image Retrieval Using Fast Phong Shading”, In proceeding of IEEE, International Conference on Computational Intelligence and Communication Networks, CPS and indexed in IEEE Computer Society, pp.419-423, Novmber-2010, Bhopal, India.


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