Volume-1 Issue-5

Download 26
Total Views 1158
File Size 4.00 KB
File Type unknown
Create Date September 3, 2017
Last Updated July 15, 2018

Download Abstract Book

S. No

Volume-1 Issue-5, November 2011, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

Page No.



CheeFai Tan, Ranjit Singh Sarban Singh, Siti Aisyah Anas

Paper Title:

Truck Seating Comfort: Objectify and Subjectify Measurement Approach

Abstract: Technology has changed trucks significantly over the years. Truck companies are getting interested in comfortable equipment for their employees in order to create a healthy and stimulating working environment. Due to this reason, truck manufacturers recognize comfort as one of the major selling point, as it is thought to play an important role for the buyer as well. Seat is one of the most important components of truck and they are the place where professional driver spend most of their time. The aim of the paper is to describe the measurement methods that used to improve the physiological comfort of truck driver’s seat. There will be three sections in the paper. First, the paper describes the nature of sitting comfort and discomfort. Secondly, it describes the subjective and objective measurement methods that are used to evaluate the truck seat. Thirdly, the paper proposes a methodology for the development of comfortable truck driver’s seats.

Truck Seat, Comfort, Objective measurement, Subjective Measurement.


1. Runkle, V.A., “Benchmarking Seat Comfort”, Society of Automotive Engineers, Inc., Warrendale, PA, USA, 1994, SAE Technical Paper No. 940217.
2. http://www.euractiv.com/en/transport/drivers- deplore-inconsistent-eu-working-time-laws/article-

3. 166879. Accessed on 10 November 2007.

4. Hertzberg, H.T.E. 1972, “The Human Buttocks in Sitting: Pressures, Patterns, and Palliatives”, Society of Automotive Engineers, Inc., New York, NY, USA, 1972, SAE Technical Paper no. 72005.

5. Kolich, M., “Review: A Conceptual Framework Proposed to Formalize the Scientific Investigation of Automobile Seat Comfort”, Applied Ergonomics, Vol. 39, No. 1, 2008, pp. 15-27.

6. Shen, W. and Parsons, K. C., “Validity and Reliability of Rating Scales for Seated Pressure Discomfort”, International Journal of Industrial Ergonomics, Vol. 20, 1997, pp. 441-461.

7. De Looze, M. P., Kuijt_Evers, L. F. M. and Dieen, J. V., “Sitting Comfort and Discomfort and the Relationship with Objective Measures” Ergonomics, Vol. 46, No. 10, 2003, pp. 985-997.

8. Shen, W. and Vertiz, A., “Redefining Seat Comfort”, Society of Automotive Engineers, Inc., Warrendale, PA, USA, 1997, SAE Technical Paper No. 970597.

9. Floyd, W.F. and Roberts, D.F., “Anatomical and Physiological Principles in Chair and Table Design”, Ergonomics, Vol. 2, 1958, pp. 1-16.

10. Adler, S., Friedrichs, A. and Blickhan, R., “Analysis of Driver Seated Posture to Objectively Measure Long-term Discomfort”, Proc. of 6th World Congress on Ergonomics, Maastricht, the Netherlands, 2006.

11. Helander, M. G. and Zhang, L., “Field Studies of Comfort and Discomfort in Sitting”, Ergonomics, Vo. 20, No. 9, 1997, pp. 865-915.

12. Zhang, L., “Identifying Factors of Comfort and Discomfort in Sitting”, Human Factors, Vol. 38, No. 3, 1996, pp. 377-389.

13. Viano, D.C. and Andrzejak, D. V., “Research Issues on the Biomechanics of Seating Discomfort: an Overview with Focus on Issues of the Elderly and Low-Back Pain”, Society of Automotive Engineers, Inc., Warrendale, PA, USA, 1992, SAE Paper No. 920130.

14. Gruber, G. J., “Relationship between Whole body Vibration and Morbidity Patterns among Interstate Truck Drivers”, Southwest Research Institute, San Antonio, Texas. Center for Disease Control Publication No.77-167, Cincinnati, Ohio, 1976.

15. Nawayseh, N. and Griffin, M. J., “Effect of Seat Surface Angle on Forces at the Seat Surface During Whole-body Vertical Vibration”, Journal of Sound and Vibration, Vol. 284, 2005, pp. 613-634.

16. Boggs, C. M., “Field Study to Evaluate Driver Fatigue on Air-inflated Truck Cushions”, Master of Science thesis, Faculty of the Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA, 2004.

17. Ofori-Boetang, A. B., “A Study of the Effect of Varying Air-inflated Seat Cushion Parameters on Seating Comfort”, Faculty of the Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA, 2003.

18. Gyi, D.E., Porter, J.M., and Robertson, N.K.B., “Seat pressure measurement technologies: consideration for their evaluation”, Applied Ergonomic, Vol. 27, No. 2, 1998, pp. 85–91.

19. Guenaelle, P., “One methodology to evaluate Automotive Seat Comfort”, Proc. of the Third International Conference on Vehicle Comfort and Ergonomics, Bologna , Italy, 1995, pp. 231–240.

20. Porter, J.M. and Gyi, D.E., “Interface pressure and the prediction of car seat discomfort”, International Journal of Vehicle Design, Vol. 19, No. 3, 1998, pp. 255–266.

21. Zhang, L., Helander, M. G., Drury, C. G., “Indentifying factors of comfort and discomfort in sitting”, Human Factors, Vol. 38, No. 3, 1996, pp. 377–389.

22. Ebe, K. and Griffin, M. J., “Factors affecting static seat cushion comfort”, Ergonomics, Vol. 44, No. 10, 2001, pp. 901–921.

23. Van Niekerka, J. L., Pielemeierb, W.J., Greenberg, J.A., “The use of Seat Effective Amplitude Transmissibility (SEAT) Values to Predict Dynamic Seat comfort”, Journal of Sound and Vibration, Vol. 260, 2003, pp. 867–888.

24. Falou, E. W., Duchêne, J., Grabisch, M., David Hewson, Yves Langeron, Lino, F., “Evaluation of Driver Discomfort during Long-Duration Car Driving”, Journal of Applied Ergonomics, Vol. 34, No. 3, 2003, pp. 249-255.

25. Mehta, C.R. and Tewari, V.K., “Seating Discomfort for Tractor Operators- A Critical Review”, International Journal of Industrial Ergonomics, Vol. 25, 2000, pp. 661-674.

26. Bouazara, M., Richard, M.J. and Rakheja, S., “Safety and Comfort Analysis of a 3D Vehicle Model with Optimal Non-linear Active Seat Suspension”, Journal of Terramechanics, Vol. 43, 2006, pp. 97-118.

27. Cheng, Z., Smith, J. A., Pallettiere, J. A., and Fleming, S. M., “Considerations and Experiences in Developing and FE Buttock Model for Seating Comfort Analysis”, Society of Automotive Engineers. Inc., Warrendale, PA, USA, 2007, Technical Paper No. 2007-01-2458.

28. Cengiz, T.G. and Bablik, F.C., “An On-the-road Experiment Into the Thermal Comfort of Car Seats”, Applied Ergonomics, Vol. 38, 2007, pp. 337-347.

29. Choi, H. Y., Kim, K. M., Han, J., Sah, S., Kim, S.-H., Hwang, S.-H., Lee, K. N., Pyun, J.-K., Montmayeur, N., Marca, C., Haug, E., and Lee, I., “Human Body Modeling for Riding Comfort Simulation”, Proc. of HCI2007, LNCS4561, 2007, pp.813-823.

30. Dhingra, H. S., Tewari, V. K., and Singh, S., “Discomfort, Pressure Distribution and Safety in Operator's Seat- a Critical Review," Engineering International, Vol. 5, 2003, pp. 1-16.

31. Hanson, L., Sperling, L., and Akselsson, R., “Preferred Car Driving Posture Using 3-D Information”, International Journal of Vehicle Design, Vol. 42, No. 1/2, 2006, pp. 154-169.

32. Hinz, B., Rutzel, S., Bluthner, R., Menzel, G., Wolfel, H. P., and Seidel, H. “Apparent Mass of Seated Man- First Determination with a Soft Seat and Dynamic Seat Pressure Distributions”, Journal of Sound and Vibration, Vol. 298, 2006, pp. 704-724.

33. Inagaki, H., Taguchi, T., Yasuda, E., and Iizuka, Y., “Evaluation of Riding Comfort: From the Viewpoint of Interaction of Human Body and Seat for Static, Dynamic, Long Time Driving”, Society of Automotive Engineers. Inc., Warrendale, PA, USA, 2000, Technical Paper No. 2000-01-0643.

34. Jang, H.-K., Choi, S.-H., and Ruquet, K., “Evaluation of Discomfort due to Vertical Feet Vibration at a Driver's Sitting Posture”, Society of Automotive Engineers. Inc., Warrendale, PA, USA, 2007, Technical Paper No. 2007-01-2395.

35. Kolich, M., and Taboun, S., “Ergonomics Modeling and Evaluation of Automobile Seat Comfort”, Ergonomics, Vol. 47, No. 8, 2004, pp. 841-863.

36. Kyung, G., Nussbaum, M. A., and Babski-Reeves, K., “Driver Sitting Comfort and Discomfort (part I): Use of Subjective Ratings in Discriminating Car Seats and Correspondence Among Ratings”, International Journal of Industrial Ergonomics, doi:10.1016/j.ergon.2007.08.010, 2007.

37. Kyung, G., and Nussbaum, M. A., “Driver Sitting Comfort and Discomfort—Part II: Relationships with and prediction from interface pressure”, International Journal of Industrial Ergonomics, doi:10.1016/j.ergon.2007.08.011, 2008.

38. Kyung, G., Nussbaum, M. A., Lee, S., Kim, S., and Baek, K., “Sensitivity of Preferred Driving Postures and Determination of Core Seat Track Adjustment Ranges”, Society of Automotive Engineers. Inc., Warrendale, PA, USA, 2007, Technical Paper No. 2007-01-2471.

39. Marler, T., Yang, J., Rahmatalla, S., Abdel-Malek, K., and Harrisonq, C., “Validation Methodology Development for Predicted Posture”, Society of Automotive Engineers, Inc., Warrendale, PA, USA, 2007, Technical Paper No. 2007-01-2467.

40. Montmayeur, N., Marca, C., Cabane, C., Dwarampudi, R., Kolich, M., and Nunez, S., “Virtual Seat Comfort Engineering Through Hardness and Initial Softness Prediction”, Society of Automotive Engineers, Inc., Warrendale, PA, USA, 2007, Technical Paper No. 2007-01-2455.

41. Nilsson, H. O., “Local Evaluation of Thermal Comfort”, International Journal of Vehicle Design, Vol. 42, No. 1/2, 2006, pp. 8-21.

42. Nishimatsu, T., Hayakawa, H., Shimizu, Y., and Toba, E., “Influence of Top Coated Cloth for Sitting Comfort of Car Driver’s Seat”, Proc. of 17th IEEE Instrumentation and Measurement Technology Conference, Vol. 2, 2000, pp. 915-919.

43. Park, S. J., Kim, C.-B., Kim, C. J., and Lee, J. W., “Comfortable Driving Postures for Koreans”, International Journal of Industrial Ergonomics, Vol. 26, 2000, pp. 489-497.

44. Parakkat, J., Pallettiere, J., Reynolds, D., Sasidharan, M., and El-Zoghb, M., “Quatitative methods for determining U.S. Air Force crew cushion comfort”, Society of Automotive Engineers, Inc., Warrendale, PA, USA, 2006, Technical Paper No. 2006-01-2339.

45. Rakheja, S., Stiharu, I., Zhang, H. and Boileau, P. E., “Seated occupant interactions with seat backrest and pan, and biodynamic response under vertical vibration”, Journal of Sound and Vibration, Vol. 298, 2006, pp. 651-671.

46. Rasmussen, H., Zee, M. d., and Torholm, S., “Muscle Relaxation and Shear Force Reduction may be Conflicting: a Computational Model of Seating”, Society of Automotive Engineers, Inc., Warrendale, PA, USA, 2007, Technical Paper No. 2007-01-2456.

47. Smith, D. R., Andrews, D. M., and Wawrow, P. T., “Development and evaluation of the Automotive Seating Discomfort Questionnaire (ASDQ)”, International Journal of Industrial Ergonomics, Vol. 36, 2006, pp. 141-149.

48. Solaz, J. S., Page, A., Porcar, R., Mateo, B., Dura, J. V., Gomez, J. A., Prat, J., and Vera, P., “Functional Data Analysis as a Tool to Find Discomfort Evolution Patterns in Passenger Car Seats”, Society of Automotive Engineers, Inc., Warrendale, PA, USA, 2006, Technical Paper No. 2006-01-1296.

49. Mohd. Tamrin, S.B., Yokoyama, K., Jalaludin, J., Abdul Aziz, N., Jemoin, N., Nordin, R., Li Naing, A., Abdullah, Y., and Abdullah, M., “The Association Between Risk Factors and Low Back Pain Among Commercial Vehicle Drivers in Peninsular Malaysia: A Preliminary Result”, Industrial Health, Vol. 45, 2007, pp. 268-278.

50. Tsutsumi, H., Hoda, Y., Tanabe, S.i., and Arishiro, A., “Effect of Car Environment on Driver's Comfort and Fatigue”, Society of Automotive Engineers. Inc., Warrendale, PA, USA, 2007, Technical Paper No. 2007-01-0444

51. Verver, M.M., Van Hoof, J., Oomens, C.W.J., Wismans, J. S. H. M., and Baaijens, F.P.T., “A Finite Element Model of the Human Buttocks for Prediction of Seat Pressure Distribution”, Computer Methods in Biomechanics and Biomedical Engineering, Vol. 7, No. 4, 2004, pp. 193-203.

52. Wu, Q., Luo, S., and Sun, S., "A Computer-Aided Driving Posture Prediction System Based on Driver Comfort," Proc. of ICAT2006, 2006.

53. Zhang, Y. F., Wyon, D. P., Fang, L., and Melikov, A. K. “The Influence of Heated or Cooled Seats on the Acceptable Ambient Temperature Range” Ergonomics, Vol. 50, No. 4, 2007, pp. 586-600.

54. Zenk, R., Franz, M., and Bubb, H., “Spine load in the Context Of Automotive Seating”, Society of Automotive Engineers. Inc., Warrendale, PA, USA, 2007, Technical Paper No. 2007-01-2485

55. Uenishi, K., Tanaka, M., Yoshida, H., Tsutsumi, S., and Miyamoto, N., “Driver’s Fatigue Evaluation During Long Term Driving for Automotive Seat Development”, Society of Automotive Engineers. Inc., Warrendale, PA, USA, 2002, Technical Paper No. 2002-01-0773.

56. Park, S.-J., Lee, Y.-S., Nahm, Y.-E., Lee, J.-W., and Kim, J.-S., “Seating Physical Characteristics and Subjective Comfort: Design Considerations” Society of Automotive Engineers, Inc., Warrendale, PA, USA, 1998, Technical Paper No. 980653.

57. Mergl, C., Klendauer, M., Mangen, C., and Bubb, H., “Predicting long term riding comfort in cars by contact forces between human and seat”, Society of Automotive Engineers, Inc., Warrendale, PA, USA, 2005, Technical Paper No. 2005-01-2690.

58. Eklund,J.A.E. and Corlett,E.N., “Evaluation of Spinal Loads and Chair Design in Seated Work Tasks”, Clinical Biomechanics, Vol. 2, 1987, pp. 27-33.

59. Na, S., Lim, S., Choi, H., and Chung, M. K., “Evaluation of Driver’s Discomfort and Postural Change Using Dynamic Body Pressure Distribution”, International Journal of Industrial Ergonomics, Vol. 35, 2005, pp. 1085-1096.






Thyagaraju.GS, Umakanth P. Kulkarni

Paper Title:

SAP: Self Aware Protocol for Ubiquitous Object Communication

Abstract: The advances in computing technologies have resulted in an explosive growth in computing systems and applications [ubicomp] that impact all aspects of our life. People have an increasing desire for such ubiquitous access to information, anywhere, anyplace and anytime. This trend demands for unique protocol to establish a meaningful and useful communication across ubicomp objects. Self awareness property of the object driven automatically without human intervention makes their coexistence meaningful and is a necessary requirement of ubicomp systems. In this paper we are presenting a design and implementation of query language based self-aware protocol for ubicomp objects communication.

self aware, protocol, ubicompobject, query language, bnf , sap.


1. C.M. Robert, “Radio Frequency Identification (RFID), Elsevier Journal: Computer & Security, pp18-26-2006
2. U.P.Kulkarni et al “ Ubiquitous Object Categorization and Identity”, International Conference on Computational Intelligence for Modeling Control and Automation, and International conference on Intelligent Agents, Web Technologies and Internet Commerce( CIMCA-IAWTIC’ 06)- IEEE2006

3. lex & yacc by John Levine,Tony Mason and Doug Brown

4. U.P.Kulkarni,etal “Query Language Interface for Ubiquitous Objects” , International Conference on Computational Intelligence and Multimedia Applications 2007,IEEE , DOI 10.1109/ICCIMA.2007.331

5. M Satyanarayanan “Pervasive Computing: Vision and Challenges”.,School of Computer Science Carnegie Mellon University – IEEE Personal Communication, 2001.

6. Anthony D. Joseph, Almudena Díaz, Pedro Merino, F. Javier Rivas, Umakant P. Kulkarni, J.V.Vadavi, G.S. Thyagaraju, S.M. Joshi, and A.R. Yardi, “Mobile and Ubiquitous Objects” ,IEEE pervasive computing journal ,vol5 ,No3, July–September 2006 ,wips ,pg 57-60.





Ademola O. Adesina, Kehinde K. Agbele, Nureni A. Azeez, Ademola P. Abidoye

Paper Title:

A Query-Based SMS Translation in Information Access System

Abstract: Mobile technology has contributed to the evolution of several media of communication such as chats, emails and short message service (SMS) text. This has significantly influenced the traditional standard way of expressing views from letter writing to a high-tech form of expression known as texting language. In this paper we investigated building a mobile information access system based on SMS queries. The difficulties with SMS communication were explored in terms of the informal communication passage and the associated difficulty in searching and retrieving results from an SMS-based web search engine under its non-standardization. The query is a pre-defined phrase-based translated English version of the SMS. The SMS machine tool normalization algorithm (SCORE) was invented for the query to interface with the best ranked and highly optimized results in the search engine. Our results, when compared with a number of open sources SMS translators gave a better and robust performance of translation of the normalized SMS.

Short messaging service (SMS), spell checker, spell corrector, spell error


1. Dr. Hadeel Showket Al-Obaidy, "Building Ontology Web Retrieval System Using Data Mining," ed.
2. R. S. Monojit Choudhury, Vijit Jain, Animesh Mukherjee, Sudeshna Sarkar and Anupam Basu, "Investigation and modeling of the structure of texting language," IJDAR, vol. 10, pp. 157-174, 2007.

3. S. P. Sumalatha Ramachandran, Sharon Joseph, Vetriselvi Ramaraj, "Enhanced Trustworthy and high-Quality Infromation Retrieval System fro Web Search Engines," IJCSI International Journal of Computer Science Issues, vol. 5, 2009.

4. M. k. Wadee S. Alhalabi, Moiez Tapia, "Search Engine ranking Efficiency Evaluation Tool," Inroads-The SIGCSE Bulletin, vol. 39, pp. 97-101, 2007.

5. Available: http://www.seochat.com/c/a/Search-Engine-Optimization-Help/Mobile-Search-Engine-Optimization/

6. G. Pass, et al., "A Picture of Search," First Intl. Conf. on Scalable Information Systems, 2006.

7. D. E. Rose and D. Levinson, "Understanding User Goals in Web Search," 2004.

8. J. Chen, et al., "SMS-Based Contextual Web Search," presented at the Mobiheld '09 Barcelona, Spain, 2009.

9. S. K. Samanta, et al., "Automatic language translation for mobile SMS," International Journal of Information Communication Technologies and Human Development (IJICTHD), vol. 2, pp. 43-58, 2010.

10. Chien-Liang Liu, et al., "Computer assisted writing system," Expert systems and applications, vol. 38, pp. 804-811, 2010.

11. J. L.Peterson, "Computer Programs for Detecting and Correcting Spelling Errors," Communication of the ACM, vol. 23, pp. 676-687, December 1980 1980.

12. Y. How and M. Kan, "Optimizing Predictive Text Entry for Short Message Service on Mobile Phones," in In Proceedings of human computer interfaces international, 2005.

13. F. J. Damerau, "A technique for computer detection and correction of spelling errors.," Comm. ACM, vol. 7, pp. 171-176, 1964.

14. Ademola O. Adesina, et al., "Text Messaging: a tool in e-Health Services," in Southern Africa Telecommunication Networks and Applications Conference (SATNAC), Spier Estate, Stellenbosch, South Africa, 2010.

15. As cited by en.wikipedia.org/wiki/MXit.

16. C. Yu, et al., "Term Weighting in Information retrieval using the term precision model," Journal of ACM (JACM), vol. 29, pp. 152-170, 1982.

17. Chava V., et al., "System and method for in-transit SMS language translation," United States Patent 7272406.





Ram Kumar, Sarvesh Kumar, Kolte V.S

Paper Title:

A Model for Intrusion Detection Based on Undefined Distance

Abstract: In this paper, we introduced the intrusion detection system and the uncertain theory, and point out two important prerequisite that the IDS work normally must depend on, and in view of the prerequisite, the paper proposed a solution which is based on uncertain distance and the active defense technology anti-host intrusion. The solution can distinguish normal event from the unknown event efficiently, and can detect unknown event. This paper proposed the active defense technology anti-host intrusion based on uncertain distance. The system can not only judge normal event, but also can detect unknown event. The system can judge whether an event is harmful, and can store the eigenvector of suspicious event to “normal event set” or “intrusion event set” automatically.

Intrusion event; Active Defense; Uncertain theory; Uncertain distance; Intrusion Detection; Intrusion Event; Anti-Host; Intrusion Event Set


1. Yu, XD, Research on active defence technology with virus based on improved K-Nearest Neighbor Algorithm. 2008 PROCEEDINGS OF INFORMATION TECHNOLOGY AND ENVIRONMENTAL SYSTEM SCIENCES: ITESS 2008, VOL 4,2008,: 724-726.
2. Teng Shaohua, a study on object-monitoring-based distributed and collaborative intrusion detection[D]. The degree of Doctor of Philosophy, Faculty of Electromechanical Engineering Guangdong University of Technology. (in Chinese)

3. B. Liu, Theory and Practice of Uncertain Programming, 2nd ed., Springer-Verlag,Berlin, 2009

4. Yu, XD, Research on Active Defence Technology with Host Intrusion Based on K-Nearest Neighbor Algorithm of Kernel . FIFTH INTERNATIONAL CONFERENCE ON INFORMATION ASSURANCE AND SECURITY, VOL 1, PROCEEDINGS,2009,: 411-414

5. DENG Lujuan, LIU Tao, GAN Yong, XIONG Kun. Active Defence Technology with Virus Based on Differentiation and Hiding Process Computer Engineering, March 2007. Vol.33 No.5.117-119. (in Chinese)






Satyajit Saha, Kamal Bera, Paresh Chandra Jana

Paper Title:

Growth time dependence of size of nanoparticles of ZnS

Abstract: Growth time is an important factor in the growth of nanoparticles of a semiconducting material. With this view ZnS is synthesized at different duration of times by chemical method.Nanocrystalline ZnS is synthesized at room temperature by a cost effective chemical reduction method. The dispersed as grown samples in ethanol are characterized using electron diffraction techniques. Simultaneously optical absorption, photoluminescence of these samples is studied at room temperature. The increase of particle sizes and decrease of band gap of the as prepared ZnS nanoparticles are observed with increase in growth time. The size of the nanoparticles depends on growth time. An increase in band gap is observed in each case as compared to bulk ZnS. An attempt is made to correlate the structural, optical properties.

ZnS nanoparticles, Microstructural properties, Optical properties, Photoluminescence


1. Arnim Henglein,”Small-particle research: physico- chemical properties of extremely small colloidal metal and semiconductor particles”, A.Chem Rev. 1989, 89(8),pp. 1861-1873.
2. Michael L. Steigerwald, Louis E. Brus “Semiconductor crystallites: a class of large molecules”, Acc. Chem. Res., 1990, 23 (6), pp. 183–188.

3. Bawendi MG, Steigerwald ML, Brus LE, “The Quantum-Mechanics of Larger Semiconductor Clusters (Quantum Dots)”,Annual Review of Physical Chemistry 1990, 41:pp. 477-496.

4. Y. Wang and N. Herron. “Nanometer-Sized Semiconductor Clusters: Materials Synthesis, Quantum Size Effects,and Photophysical Properties”, J. Phys. Chem. 1991, 95,pp.525-532.

5. Weller, H. “Quantized Semiconductor Particles…”, Adv. Mater. 1993, 5, 88.

6. A. P. Alivisatos “Semiconductor Clusters, Nanocrystals, and Quantum Dots”, Science 16February, 1996: Vol. 271 no. 5251 pp. 933-937.

7. Alexander Eychmüller “Structure and Photophysics of Semiconductor Nanocrystals”, J. Phys. Chem. B, 2000, 104 (28), pp.6514–6528.

8. H. Okuda, J. Dakada, Y. Iwasaki, N. Hashimoto, C.Nagao, IEEE Trans. Consumer Electron.1990, 36, 436.

9. J. Ghrayeb, T.W. Jackson, R. Daniels, D.G. Hopper,Proc. SPIE 3057 (1997) 237.

10. J.C. Barton, P.W. Ranby, J. Phys. E 1977, 10, 437 .

11. Ye Changhui, Fang Xiaosheng, Li Guanghai and Zhang Lide, Appl. Phys. Lett. 2004, 85 ,15, 3035.

12. O A Korotchenkov, A Cantarero, A P Shpak, Yu A Kunitskii, A I Senkevich, M O Borovoy and A B Nadto chii, Nanotechnology 2005, 16, 2033.

13. A.Z. Obidin, et al., Soviet Journal of Quantum Electronics .1988, 18, 1764;

14. S.K. Mandal, S. Chaudhuri, A.K. Pal, “Thin Solid Films”, 1999, 209,230.

15. R. Thielsch, T. Böhme, H. Böttcher, “Optical and Structural Properties of Nanocrystalline ZnS-SiO2 Composite Films”, Phys. Status Solidi, A, 1996,155, pp.157-170.

16. Wei Chen, Zhanguo Wang, Zhaojun Lin, and Lanying Lin “Absorption and luminescence of the surface states in ZnS nanoparticles”, J. Appl. Phys,1997, 82, 3111 .

17. S. M. Scholz, R. Vacassy, J. Dutta, H. Hofmann, and M. Akinc, “Mie scattering effects from monodispersed ZnS nanospheres”, J. Appl. Phys. 1998, 83, 7860.

18. J. Nanda, Sameer Sapra, and D. D. Sarma, Nirmala Chandrasekharan and Gary Hodes, “Size-Selected Zinc Sulfide Nanocrystallites: Synthesis, Structure, and Optical Studies”, Chem. Mater, 2000, 12 (4), pp.1018–1024.

19. Ming Tan, Weiping Cai, and Lide Zhang, “Optical absorption of ZnS nanocrystals inside pores of silica”, Appl. Phys. Lett. 1997, 71, 3697.

20. B. Bhattacharjee, D. Ganguli, S. Chaudhuri, and A. K. Pal, “Synthesis and optical characterization of sol–gel derived zinc sulphide nanoparticles confined in amorphous silica thin films”, Materials Chemistry and Physics, Volume 78, Issue 2, 17 February 2003, pp.372-379.

21. Hao-Ying Lu, Sheng-Yuan Chu and Soon-Seng Tan, “The characteristics of low-temperature-synthesized ZnS and ZnO nanoparticles”, Journal of Crystal Growth, Volume 269, Issues 2-4, 1 Sep2004, pp.385-391.

22. Yu Zhao, J.-M. Hong and J.-J. Zhu, “Microwave-assisted self-assembled ZnS nanoballs”, Journal of Crystal Growth Volume 270, Issues 3-4, 1 October 2004, Pages 438-445.

23. Yonghong Nia, Gui Yina, Jianming Hongb and Zheng Xu, “Rapid fabrication and optical properties of zinc sulfide nanocrystallines in a heterogeneous system”, Materials Research Bulletin Volume 39, Issue 12, 4 October 2004, Pages 1967-1972.

24. J.F. Xu, W. Ji, J.Y. Lin, S.H. Tang and Y.W. Du, “Preparation of ZnS nanoparticles by ultrasonic radiation method”, Applied Physics A: Materials Science & Processing Volume 66, Number 6,1998,pp. 639-641,

25. Jianfeng Chen, , a, Yaling Lia, Yuhong Wanga, Jimmy Yunb and Dapeng Caob, “Preparation and characterization of zinc sulfide nanoparticles under high-gravity environment”, Materials Research Bulletin ,Volume 39, Issue 2, 2 February 2004, Pages 185-194

26. W. Wang, I.N. Germanenko and M.S. El-Shall,“Room Temperature Synthesis and Characterization of Nanocrystalline CdS, ZnS andCdxZn1-xS”, Chem. Mater.2002.14, pp.3028-3033.

27. P.S.Kirev “Semiconductor Physics"






E. Raja, K.V. Ramana

Paper Title:

Implementation of Multilayer AHB Busmatrix for ARM

Abstract: The multi-layer AHB busmatrix (ML-AHB busmatrix) proposed by ARM is a highly efficient on chip bus that allows parallel access paths between multiple masters and slaves in a system. However, the ML-AHB busmatrix of ARM offers only transfer-based fixed-priority and round-robin arbitration schemes. In this paper, we present one way to improve the arbiter implementation of the ML-AHB busmatrix. The proposed arbiter, which is Self-motivated (SM), selects one of the nine possible arbitration schemes based upon the priority-level and the desired transfer length from the masters so that arbitration leads to the maximum performance. Our SM arbitration scheme has the following advantages: 1) It can adjust the processed data unit; 2) it changes the priority policies during runtime; and 3) it is easy to tune the arbitration scheme according to the characteristics of the target application.

ML-AHB busmatrix, Self-motivated Arbiter, fixed-priority arbitration, round-robin arbitration.


1. M. Drinic, D. Kirovski, S. Megerian, and M. Potkonjak, “Latencyguided on-chip bus-network design,” IEEE Trans. Comput.-Aided Design Integr. Circuits Syst., vol.25, no.12, pp. 2663–2673, Dec. 2006.
2. S. Y. Hwang, K. S. Jhang, H. J. Park, Y. H. Bae, and H.J.Cho, “An ameliorated design method of ML-AHB busmatrix,”ETRI J., vol. 28, no. 3, pp. 397–400, Jun. 2006.

3. ARM, “AHB Example AMBA System,” 2001 [Online].

4. IBM, New York, “32-bit Processor Local Bus Architecture Specification,” 2001.

5. R. Usselmann, “WISHBONE interconnect matrix IP core,” Open-Cores, 2002. [Online].

6. N.-J. Kim and H.-J. Lee, “Design of AMBA wrappers for multiple clock operations,” in Proc. Int. Conf. ICCCAS,Jun.2004, vol. 2, pp.1438–1442.

7. D. Flynn, “AMBA: Enabling reusable on-chip designs,” IEEE Micro, vol. 17, no. 4, pp. 20–27, Jul./Aug. 1997.






Gunja Varshney, Uma Soni

Paper Title:

Color –Based Image Retrieval in Image Database System

Abstract: Image Databases (IDBs) are a special kind of Spatial Databases where a large number of images are stored and queried. IDBs find a plethora of applications in modern life, e.g. in Medical, Multimedia, Educational Applications, etc. Data in an IDB may be stored in raster or vector format. Each of these data formats has certain properties and, in several cases, the choice between them is a challenge. Raster data lead to fast computing of several operations and they are well suited to remote sensing. On the other hand, they have a fixed resolution, leading to limited detail. In this article, we focus on raster data. We present the design and architecture of an Image Database System where several query types are supported. These include: queries about the additional properties (descriptive information) that have been recorded for each image (e.g. which images have been used as covers of children’s books), queries about the color characteristics (color features) of the images (e.g. find the images that depict vivid blue), queries by example, or sketch (e.g. a sample image is chosen, or drawn by the user and images color-similar to this sample are sought). Color retrieval is achieved by utilizing color histograms. The development of our system is based on non-specialized tools: a relational database, Visual Basic and the computer’s file system. The user interface of the system aims at increased ease of use. It permits the management of the collection of images and the effective querying of the images by all the above query types and their combinations.

Image Databases, Color Information, Query Processing, Color-Based Retrieval, Spatial Databases, Image Retrieval


1 Athanasakos K. C., Doulamis A. D. and Karanikolas N. N. “A Signature Tree Content-based Image Retrieval System”, Proc. 3IA'2007 - 10th International Conference on Computer Graphics and Artificial Intelligence, May 30-31, 2007, Athens, Greece, pp. 181-191
2 Datta R., Li J., Wang J.Z., (2005) “Content-Based Image Retrieval - Approaches and Trends of the New Age,'' Proceedings of the 7th International Workshop on Multimedia Information Retrieval, in conjunction with ACM International Conference on Multimedia, Singapore, pp. 253 - 262

3 Johansson, B. (2000) “A survey on: Contents Based Search in Image Databases”, technical report LiTH-ISY-R-2215, Linköping University

4 Long F., Zhang H.J., and Feng D.D. (2003) “Fundamentals of Content-Based Image Retrieval”, Multimedia Information Retrieval and Management- Technological Fundamentals and Applications, D. Feng, W.C. Siu, and H.J. Zhang (Eds.), Springer, 2003

5 Moutousidis E., A System for Content-Based Image Retrieval, Master’s thesis submitted for approval, Helenic Open University (2007).

6 Rui Y., Huang T.S. and Chang S.-F. (1999), “Image Retrieval: Current Techniques, Promising Directions and Open Issues”, Journal of Visual Communication and Image Representation, 10(1): p. 39-62

7 Schettini, R., Ciocca, G., & Zuffi, S. (2001). A survey of methods for colour image indexing and retrieval in image databases. In L. W. MacDonald & M. R. Luo (Eds.), Color imaging science: exploiting digital media. Chichester, England: Wiley, J. & Sons Ltd.

8 Valova I., Rachev B. & Vassilakopoulos M., Optimization of the Algorithm for Image Retrieval by Color Features, Proc. CompSysTech’ 2006, II.17 1-5

9 Vassilakopoulos M., Corral A., Rachev B., Valova I. & Stoeva M., IMAGE DATABASE INDEXING TECHNIQUES, to appear in the Encyclopedia of Geoinformatics






A. Subramani, A. Krishnan

Paper Title:

Node Mobility Tracking In Mobile Ad-Hoc Networks in their Geographical Position (Dynamic Networks)

Abstract: In mobile ad-hoc network, nodes of position change due to dynamic nature. There should be a provision to monitor behavior and position of the on the regular basis. In this paper, importance of management schemes in ad-hoc networks is studied. Further, mobility models and reviewed and classified by incorporating real life applications into an account. This essay explores a model for the operation of an ad hoc network and the effect of the mobile nodes. The model incorporates incentives for users to act as transit nodes on multi-hop paths and to be rewarded with their own ability to send traffic. The essay explores consequences of the model by means of simulation of a network and illustrates the way in which network resources are allocated to user according to their geographical position. The mobile nodes are explored in this essay, where nodes have incentives to collaborate. Mobility and Traffic pattern of mobility models are generated by using AnSim Simulator.

Mobile Adhoc Network, Mobility Management, Mobility Model, Classification, Location Management, AnSim.


1. C.E. Perkins, “ Ad hoc Networking”, Addison Wesley 2001.
2. C.K. Toh, “ Adhoc Mobile Wireless Networks: Protocols and Systems”, Printice Hall, New Jersy, 2002.

3. Dr. Yogesh Chaba and Naresh Kumar Medishetti, “ Routing Protocols in Mobile Ad hoc Network – A Simulation Study Final”, JCS Vol 1. No. 1. August 2005.

4. David B. Johnson, David A. Maltz, “ Dynamic Source Routing in Ad hoc Networks”, In Mobile computing, Vol. 353 (1996), pp. 153-181.

5. J. Broch, D.A. Maltz, D.B. Johanson, Y.C. Hu, and J. Tetcheva, “ A Performance comparision of multi-hop wireless ad hoc network routing protocols”, IEEE/ACM, Mobicom’98, pp.16-28, 1998.

6. C.E. Perkind, E. M. Royer, s.R. Das, and M.K. Marina, “ Performance comparison of two on-demand routing protocols for ad hoc networks”, IEEE Pers. Commun. Vol.8. No.1. pp. 16 -28, 2001,

7. Byung Jae Kwak, Nah-Oak Song, and Leonard E. Miller, “A standard measure of mobility for evaluating mobile ad hoc network performance”, IECE TRANS. COMMUN. E86-B, 2003.

8. D. Shukla, Mobility models in ad hoc networks, Master’s thesis, KReSIT-ITT Bombay, Nov. 2001.

9. T. Camp, J. Boleng, and V. Dvies, “ A survey of mobility for ad hoc networks research, Wireless Communication and Mobile Computing (WCMC); Special issue on Mobile Ad hoc Networking: Research, Trends and Applications, pages pp. 483-502, 2002.

10. X. Gong, M. Gerla, G. Pei, and C. C. Chiang.” A group moibility model for an ad hoc wireless network”, Proc. ACM/IEEE, MSWiM’99, pp. 53-60, 2000.

11. Xiaoyan hong, Mario Gerla, Guangya Pei, and Ching-Chuan Chiang, “ A gourp mobility model for ad hoc wireless networks”, August 1999 in Proceedings for the ACM International workshop on Modeling and Simulation of Wireless and Mobile systems (MSWim)

12. D. Johnson and D. Tse, “ the dynamic source routing protocol for mobile ad hoc networks (DSR)”, Feb. 2002, IETF internet Draft draft-ietf-manet-dsr-07.txt.

13. F.P. Kelly, A.K. Maulloo and D.K.H. Tan. “ Rate control for communication networks shadow prices, proportional fairness and stability”, Journal of the Operational Research society, 49(3), 237-252, March 1998.

14. Miguel sanchez. “Node movment models in ad hock networks”, July 15, 1999, IETE MANET Mailing list.

15. B Ling and Z. Haas, Personal Communication, February 4 2000.

16. Akbar Momeni, Karim Khaghil Essmaeili, “CALM; Cellular Aggregated Location Management in Mobile Ad hoc Networks”, 978-1-4244-5824-0/$26.00c_2010IEEE.

17. Zygmunt J. Haas Ben Liang, “ Ad hoc Mobility Management with Uniform Quorum systems”, Identifier S 1063-6692(99)03633-X.1998, IEEE/ACM transaction on Netwoking.






Vijay Khare, Jayashree Santhosh, Sneh Anand, Manvir Bhatia

Paper Title:

Brain Computer Interface Based Real Time Control of Wheelchair Using Electroencephalogram

Abstract: In this study, eight electrodes were to capture Electroencephalogram (EEG) from the brain to build a brain computer interface (BCI) based real time control for wheelchair to help the severely handicapped persons. To achieve this goal Wavelet Packet Transform (WPT) was used for feature extraction of the relevant frequency bands from EEG signals. Radial Basis Function network was used to classify the pre defined movements such as rest, forward, backward, left and right of the wheelchair. The experiment results confirmed that this system can provide a convenient manner to real time control a wheelchair.

Electroencephalogram (EEG), Wavelet Packet Transform (WPT), Radial Basis Function neural network (RBFNN), Brain computer interface (BCI), Rehabilitation, and Wheelchair Controller


1. F. Lotte, M. Congedo, A. Lecuyer, F. Lamarche, B Arnaldi, “A Review of Classification algorithms for EEG bases brain computer interface,” Journal of neural Engineering, Vol. 4, R1-R13, 2007.
2. R. Boostani, B. Graimann, M.H.Moradi, G. Plurfscheller, “ Comparison approach toward finding the best feature and classifier in cue BCI, “Med. Bio. Engg., Computer Vol 45, pp403-413, 2007.

3. J.R.Wolpaw, N.Birbaumer, D.J Mc Farland, G.Plurtscheller, T.M. Vaughan, “Brain computer Interfaces for communication and control,” Clinical Neurophys. , Vol 113, pp 767-791, 2002.

4. G.Pfurtschelle, D.Flotzinger, and J.Kalcher, “Brain Computer interface-A new communication device for handicapped people,” J Microcomput. Applicate.,vol. 16,.pp.293-299,1993.

5. J.R Wolpaw, T.M.Vaughan and E.Donchin, “EEG Based Communication prospects and problems,”IEEE, Trans.Rehab.Eng. Vol.4,.pp.425-430, Dec.1996.

6. Z. A. Keirn and J. I. Aunon, “A new mode of communication between man and his surroundings,” IEEE Trans. Biomed. Eng., Vol. 37, No. 12, pp. 1209–1214, Dec. 1990.

7. K. Tanaka. K. Matsunaga, N.Kanamori, S.Hori, and H.O.Wang, “Electroencephalogram based control of a mobile robot,” in proc. IEEE Int. Symp. Computational Intell.. Robot.Autom Kobe, Japan, pp 670-675, Jul.2003.

8. Kyuwan choi &Andrazej Cichocki , “Control a Wheelchair by Motor Imagery in Real Time,” IDEAL 2008,LNCS 5326 ,pp 330-337,Springer Verlag Berlin Heidelberg 2008.

9. F. Galan, M.Nuttan, E.Lew, P.W.Ferrez,G.Vanacker ,J.Philip,J.del R.Millan, “A brain actuated wheelchair :Asynchronous and nojn invasive brain computer interfaces for continuous control of robot” ,Clinical Neurophysiology,Vol 119,pp2159-2169,2008.

10. A.T.C. Au and R.F. Kirsch, “EMG based prediction of shoulder and elbow kinematics in able –bodies and spinal cord injured individual,” IEEE Trans. Rehab.Eng.vol.8 no.4 pp 471-480 Dec.2000.

11. J. Millan et.al., “Noninvasive brain actuated control of a mobile robot by human EEG,” IEEE Trans. Biomed. Eng , vol. 51, no 6,pp.1026-1033,June 2004.

12. R.Bare et al, “E.O.G guidance of a wheeelchair using neural network,” in porc.Int.Conf. Pattern recognition, Barcelona Spain, pp4668-4672, 2000.

13. Robert Leeb et al., Self paced (Asynchronous) BCI-Contol of a wheelchair in virtual environment :Acase study with tetraplegic”Computational Intelligent & Neuroscience.Vol 2007,Aritical ID 79642, 2007

14. R.Palaniappan, “Brain computer interface design using band powers extracted during mental task,” proceeding of the 2ndInternational IEEE EMBS Conference on Neural Engineering pp321- 324, 2005.

15. G. Pfurtscheller, C. Neuper, A. Schlogl, and K. Lugger, “Separability of EEG signals recorded during right and left motor imagery using adaptive auto regressive parameters, IEEE. Trans. on rehabilitation Engineering, Vol 6 ,No3, pp.316-325, 1998.

16. Jayashree Santhosh, Manvir Bhatia, S. Sahu, Sneh Anand ,Quantitative EEG analysis for assessment to plan a task in ALS patients, a study of executive function (planning) in ALS, Cognitive brain research Vol 22, pp 59-66, 2004.

17. A.R. Nikolaev. and A.P. Anokhin , “EEG frequency ranges during reception and mental rotation of two and three dimensional objects.”, Neuroscience and Bheaviour physiology , Vol. 28, No-6,1998.

18. Osaka M, “Peak alpha frequency of EEG during a mental task: task difficulty and hemisphere difference,” Psychophysiology, Vol.21, pp 101-105, 1984.

19. C.S.Li and H.Wang , “Wavelet transform for on –off switching BCI device,” 7th Asian-Pacific Conference on Medical and Biological Engineering, Beijing, China, Vol 19,pp 363-365, 22–25 April 2008

20. Bao-Guo Xu & Ai Guo Song , “Pattern recognition of motor imagery EEG using wavelet transform,”Jouranl of Biomedical Science &Engineering Vol 1,pp 64-67,2008.

21. Elisabeth Larsson, Krister Åhlander, and Andreas Hall, “Multi-dimensional option pricing using radial basis functions and the generalized Fourier transform,” In J. Comput. Appl. Math., 2008.

22. Ulrika Pettersson, Elisabeth Larsson, Gunnar Marcusson, and Jonas Persson, “Improved radial basis function methods for multi-dimensional option,” In J. Comput. Appl. Math., 2008.

23. S.Chen, C.F.N. Cowan, and P. M. Grant, "Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks," IEEE Transactions on Neural Networks, vol. 2, No. 2, pp. 302-309, March 1991.

24. Vijay khare, Jayashree Santhosh and Sneh Anand Manvir Bhatia,“Controlling wheelchair using Electroencephalogram (EEG)”, International Journal of Computer Science and Information Security, Vol. 8, No.2, 2010

25. V. Khare, J. Santhosh, S. Anand, M. Bhatia, “Performance Comparison of Three Artificial Neural Network Methods for Classification of Electroencephalograph Signals of Five Mental Tasks” Journal Biomedical Science & Engineering, vol. 3, no 2,pp200-205, 2009.






Vivek Kapur, M.M.Raghuvanshi, A.B.Maidamwar

Paper Title:

Real Time Implementation of Speech codec G.729 Using CS-ACELP on TM 1000 VLIW DSP processor

Abstract: Conjugate structure algebraic CELP (G.729) is a voice codec that compresses speech signal based on model parameter of human voice. This paper deals with implementation of a speech-coding algorithm CS-ACELP using ITU-T’s G.729 recommendation and optimize it for real-time implementation on a Very Long Instruction Word (VLIW) Digital Signal Processor (DSP) Central Processing Unit (CPU). Very long instruction word or VLIW refers to a CPU architecture designed to take advantage of instruction level parallelism (ILP). A processor that executes every instruction one after the other (i.e. a non-pipelined scalar architecture) may use processor resources inefficiently, potentially leading to poor performance

G.729, CS-ACELP, DSP processor


1. Real-Time Implementation and Optimization of ITU-T’s G.729 Speech Codec Running At 8kbitsBec Using CS-ACELP On TM-1000 VLIW DSP CPU.
2. Salami et al: ‘Design and Description of CS-ACELP: A toll quality 8kb/s speech coder’, IEEE trans Speech Audio Process, 1996.

3. ITU-T G.729: ‘Coding of speech at 8 kb/s using CS-ACELP’, 1996.

4. Kataoka et al: ‘An 8 kb/s speech coder based on conjugate structured CELP’, IEEE int. conf. acoustic, speech, signal processing, 1993.

5. Kataoka et al: ‘LSP and gain quantization for proposed ITU-T 8 kb/s speech coding standard’, IEEE workshop on speech coding, 1995.

6. Shaw Hwa Hwang: ‘ Computational improvement for G.729 standard’, 2003.

7. A. B. Roach, “Session Initiation Protocol (SIP) -specific event notification,” RFC 3265, June 2002.

8. A. Johnston, S. Donovan, R. Sparks, C. Cunningham, and K. Summers, “Session Initiation Protocol (SIP) Public Switched Telephone Network (PSTN) call flows,” RFC 3666, December 2003.

9. R. Sparks, “The Session Initiation Protocol (SIP) refer method,” RFC 3515, April 2003.

10. ITU-T Recommendation P.862, “Perceptual evaluation of speech quality (PESQ): An objective method for end-to-end speech quality assessment of narrow-band telephone networks and speech codecs,” Feb. 2001.

11. ITU-T Recommendation P.862 Amendment 1, “Source code for reference implementation and conformance tests,” March 2003.






Koti Mudela

Paper Title:

Auto Generation of Embedded C Code from Transfer Function

Abstract: Nowadays the implementation of control systems in the embedded world is becoming more prevalent. All the processes handled by the control systems are performed by the designed embedded controllers. The continuous or analogue control systems are converted to discrete control systems because discrete control systems can perform logical manipulation and complex mathematical computations by using appropriate programming techniques.

Embedded controllers, discrete control systems, control systems.


1. Modern Control Engineering Katshuko OGATA.
2. Control system fundamentals William S Levine.

3. Signals and Systems, 2nd Edition Simon Haykin Barry Van Veen.

4. Weigert T., Weil F., van den Berg A., Dietz P., Marth K. Automated Code Generation for Industrial-Strength Systems // Computer Software and Applications COMPSAC '08. 3 2nd Annual IEEE International Conference. – July 28, 2008 – Aug. 1, 2008. – P. 464–472.

5. Toeppe S., Bostic D., Ranville S., Rzemien K. Automatic code generation requirements for production automotive powertrain applications // Computer Aided Control System Design. Proceedings of the IEEE International Symposium. – 22–27 Aug., 1999. – P. 200–206.

6. http://www.eetimes.com/design/signal-processing-dsp/4007112/Why-is-Autocode-Generation-Slow-to-Adoption.

7. Sampling Moments and Reconstructing Signals of Finite Rate of Innovation: Shannon meets Strang-Fix Pier Luigi Dragotti and Martin Vetterli and Thierry Blu IEEE Transactions on Signal Processing, SP EDICS: DSP-WAVL (Wavelet theory and applications) and DSP-SAMP (Sampling, Extrapolation and Interpolation)

8. RCGES: Retargetable Code Generation for Embedded Systems_ Trong-Yen Lee, Yang-Hsin Fan, Tsung-Hsun Yang, Chia-Chun Tsai, Wen-Ta Lee, and Yuh-Shyan Hwang Institute of Computer, Communication, and Control, National Taipei University of Technology, Taipei, Taiwan.

9. Pont, M.J. (2001) "Patterns for time-triggered embedded systems: Building reliable applications with the 8051 family of microcontrollers", ACM Press / Addison-Wesley. ISBN: 0-201-331381. [Reprinted 2003]

10. "Embedded C", Pont, M.J. (2002) Addison-Wesley. ISBN: 0-201-79523X.

11. Class notes and material from design of discrete systems module Prof N B Jones.






B. Manikyala Rao, P.N.S.Lakshmi

Paper Title:

Performance Analysis of IEEE 802.11 MAC and Physical Layer on Saturation Throughput

Abstract: An analytical model that accounts for the positions of stations with respect to the Access Point (AP) while evaluating the performance of 802.11 MAC layer. This paper is based on the Bianchi’s model where the performance of 802.11 MAC layer is computed using a discrete time Markov chain, but where all stations are implicitly assumed to be located at the same distance to the AP. In this model, given the position of one station, we compute its saturation throughput while conditioning on the positions of the other concurrent stations. Further, this model provides the total saturation throughput of the medium and describe the model numerically and show that the saturation throughput per station is strongly dependent not only on the station’s position but also on the positions of the other stations, and confirm that a station achieves a higher throughput when it is closer to the AP but bring out that there is a distance threshold above which the throughput decrease is fast and significant. When a station is far from the AP compared to the other stations, it will end up by contending for the bandwidth not used by the other stations. This model is a good tool to dimension 802.11 wireless access networks and to study their capacities and their performances.

MAC Layer, Access point, throughput.


1. IEEE 802.11 WG, part 11a/11b/11g , “Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications,”, Standard Specification,IEEE, 2009.
2. Giuseppe Bianchi, “Performance Anaylsis of the IEEE 802.11 Distributed Coordination Function”, IEEE Journal on Selected Areas in Communications.

3. C. H. Foh, M. Zukerman, “Performance Analysis of the IEEE 802.11 MAC Protocol”, Proceedings of the EW 2006 Conference, Italy.

4. H. Wu, Y. Peng, K. Long, J. Ma, “Performance of Reliable Transport Protocol over IEEE 802.11 Wireless LAN: Analysis and Enhancement”, Proc. of IEEE INFOCOM, vol.2, pp. 599-607, 2007.

5. H. Kim and J. Hou, “Improving protocol capacity with model-based frame scheduling in IEEE 802.11-operated WLANs”, ACM MobiCom 2003, Sep. 2008.

6. P. Chatzimisios, V. Vitsas and A. C. Boucouvalas, “Throughput and Delay analysis of IEEE 802.11 protocol”, IEEE IWNA, UK, 2005.

7. Z. Helkov, B. Spasenovski, “Saturation Throughput-Delay Analysis of IEEE 802.11 DCF in Fading Channel”, ICC 2008.

8. P. Chatzimisios, V. Vitsas and A. C. Boucouvalas,“ Performance Analysis of IEEE 802.11 DCF in Presence of Transmission Errors”, ICC 2007, Paris.





Tomas Skripcak, Pavol Tanuska, Nils Schmeisser

Paper Title:

Design and Implementation of Interactive Visualisation Configuration using Interaction Paradigms in Virtual Reality Environment

Abstract: This article is aimed on the specific task of interaction with an immerse visualisation application. The first part of the article provides basic introduction into interaction paradigms in 3D space. After that classification and description of standard interaction tasks are presented. We introduce our view of matter on relations between the 3D interaction and standard interaction techniques. The second part describes hardware and software components of our VR system. Furthermore an overview of the architecture and implementation details of system for interactive visualisation configuration is discussed. We describe design specifications of a 3D UI, which helps to make interaction less error prone for inexperienced users. A specific solution for performing numerical input is also provided. The main goal of the article is to describe how 3D user interface paradigms can be implemented in the VR system.

3D interaction, virtual reality, visualisation.


1. D. Bowman, “3D user interfaces : theory and practice.” Boston: Addison-Wesley, 2005.
2. D. A. Bowman and L. F. Hodges, “An Evaluation of Techniques for Grabbing and Manipulating Remote Objects in Immersive Virtual Environments,” p. 35--38, 1997.

3. Oxford Dictionaries Online, “Definition of confusion.” [Online]. Available: http://oxforddictionaries.com/definition/confusion. [Accessed: 11-Aug-2011].

4. A. Craig and ScienceDirect (Online service), Developing virtual reality applications foundations of effective design. Burlington, MA. ;;Oxford :: Morgan Kaufmann,, 2009.

5. Chair of Multimedia Technology, “Component-oriented three-dimensional interactive graphical applications CONTIGRA.” [Online]. Available: http://www.mmt.inf.tu-dresden.de/Forschung/Projekte/CONTIGRA/index_en.xhtml. [Accessed: 21-Jul-2011].

6. J. S. Pierce, A. S. Forsberg, M. J. Conway, S. Hong, R. C. Zeleznik, and M. R. Mine, “Image plane interaction techniques in 3D immersive environments,” in Proceedings of the 1997 symposium on Interactive 3D graphics, New York, NY, USA, 1997, p. 39–ff.

7. T. Skripcak, P. Tanuska, and N. Schmeisser, “Interactive calibration and registration of electromagnetic tracking system for virtual reality,” in International Doctoral Seminar Proceedings [May 15 -17, 2011 in Smolenice Castle], Trnava, 2011, p. 365--375.

8. OpenNI, “Introducing OpenNI.” [Online]. Available: http://www.openni.org/. [Accessed: 20-Jul-2011].

9. Microsorft, “Kinect” [Online]. Available: http://www.xbox.com/en-US/kinect. [Accessed: 20-Jul-2011].

10. J. E. Kyprianidis, “Lib3ds - lib3ds is an overall software library for managing 3D-Studio Release 3 and 4 ‘.3DS’ files. - Google Project Hosting.”[Online]. Available: http://code.google.com/p/lib3ds/. [Accessed: 20-Jul-2011].

11. Wikipedia, “Natural user interface.” [Online]. Available: http://en.wikipedia.org/wiki/Natural_user_interface. [Accessed: 12-Aug-2011].

12. V. V. Kindratenko, NCSA libTrCalibr 2.00. National Center for Supercomputing Applications: , 2000.

13. Natural User Interface Group,“NUI Group.” [Online]. Available: http://nuigroup.com/go/. [Accessed: 20-Jul-2011].

14. Studierstube project, “OpenTracker.” [Online]. Available: http://studierstube.icg.tugraz.at/opentracker/. [Accessed: 21-Jul-2011].

15. Bowman et. al., “The Art and Science of 3D Interaction,” in Virtual Reality Conference, IEEE, Los Alamitos, CA, USA, 2000, p. 294.

16. C. Cruz-Neira, D. J. Sandin, T. A. DeFanti, R. V. Kenyon, and J. C. Hart, “The CAVE: audio visual experience automatic virtual environment,” Communications of the ACM, vol. 35, pp. 64–72, Jun-1992.

17. I. Poupyrev, M. Billinghurst, S. Weghorst, and T. Ichikawa, “The go-go interaction technique: non-linear mapping for direct manipulation in VR,” in Proceedings of the 9th annual ACM symposium on User interface software and technology, New York, NY, USA, 1996, pp. 79–80.

18. E. Kruijff, “Unconventional 3D user interfaces for virtual environments.”

19. A. Olwal, S. Feiner, and L. Kjelldahl, “Unit—A Modular Framework for Interaction Technique Design, Development and Implementation.,” 2002.

20. R. Stoakley, M. J. Conway, and R. Pausch, “Virtual Reality on a WIM: Interactive Worlds in Miniature,” p. 265--272, 1995.

21. L. Zuhl, “Visualisierung von Laser-Plasma-Simulationen,” Diploma Thesis, Technishe Universitat Dresden, 2011.

22. R. M. Taylor, T. C. Hudson, A. Seeger, H. Weber, J. Juliano, and A. T. Helser, “VRPN,” in Proceedings of the ACM symposium on Virtual reality software and technology - VRST ’01, Baniff, Alberta, Canada, 2001, p. 55.

23. Kitware, “VTK - The Visualization Toolkit.” [Online]. Available: http://www.vtk.org/. [Accessed: 20-Jul-2011].





Mohammed Ali Hussain, R. Satya Prasad

Paper Title:

A Survey on Growth and Success of E-Commerce in Recent Trends

Abstract: This paper represents the literature survey report on growth and success of e-commerce. It helps to analyze the growth of e-commerce and the success factors to be considered in the growth of business. In E-congress survey, the author (Nick Bontis 2000) had analyzed the trends in shopping on-line. The accelerating total revenue obtained from 1999 to 2003 was analyzed. The analysis concluded that software, music (CDs) and literature (books and magazines) continue to dominate on-line purchases; however, new industries are finding their place on the web as well. Travel and tourism companies, grocery and department store chains and non apparel specialty stores were making their presence known. It was stressed that the companies must undertake the beginning of an implementation plan to involve in e-commerce.

Shopping online, E-Commerce, E-Congress survey.


1. Asokan, Article of e-commerce, 2005, e-commerce journal.
2. Daniel N. Ownwanne, kamal nayan Agarwal, 2007, Outsourcing ecommerce effects in global market and its statistical survey.

3. Daniel J. Pare, 2002, B2B e-commerce services in developing countries, London School of economics.

4. Jeweler’s circular keystone, 2008, The Internet and how retailers use it in day-to-day business life.

5. China Internet Network Information Centre CINIC, 2008, online survey on typical applications of the internet.

6. Dan Muse, 2006, “On-line shopping to grow- Are you Ready”, Ecommerce Journal.





Pradeep Kumar, Amit Kolhe

Paper Title:

Design & Implementation of Low Power 3-bit Flash ADC in 0.18µm CMOS

Abstract: This paper describes the design and implementation of a Low Power 3-bit flash Analog to Digital converter (ADC). It includes 7 comparators and one thermometer to binary encoder. It is implemented in 0.18um CMOS Technology. The pre-simulation of ADC is done in T-Spice and post layout simulation is done in Microwind3.1. The response time of the comparator equal to 6.82ns and for Flash ADC as 18.77ns.The Simulated result shows the power consumption in Flash ADC as is 36.273mw .The chip area is for Flash ADC is 1044um2 .a

CMOS, Comparator , Flash ADC, T-Spice


1. P. E. Allen and D. R. Holberg, “CMOS Analog Circuit Design,” 2nd edition ISBN 0-19-511644-5
2. Wen-Ta Lee, Po-Hsiang Huang, Yi-Zhen Liao and Yuh-Shyan Hwang, “A New Low Power Flash ADC Using Multiple-Selection Method,” 1-4244-0637-4/07 ©2007 IEEE

3. Chia-Nan Yeh and Yen-Tai Lai, “A Novel Flash Analog- to-Digital Converter,” 978-1-4244-1684-4/08 ©2008 IEEE

4. R. Jacob Baker, Harry W. Li, David E. Boyce, “CMOS Circuit Design Layout and Simulation,” ISBN 0-7803- 3416-7, IEEE Press, 445 Hose Lane, P.O.Box 1331,Piscataway,NJ08855-1331 USA

5. Gu and W. M. Snelgrove, “A novel self-calibrating theme for videorated2-step flash analog-to-digital converter,” in Proc. ISCAS 1992, pp.601-604.

6. Liang Rong,"Systematic Design of a Flash ADC for UWB Applications", Proceedings of the 8th Design (ISQED'07), 2007.





Sanchayan Mukherjee

Paper Title:

Application of Truncated Pyramid Model in Determination of Escape Velocity of Particles of Different Diameters in Varying Conditions

Abstract: The behavior of the particles in a riverbank is actually a function of inter-particle distances, radii of the particles and the volume of water entrapped between them. It is also different for pure and saline water. In the present paper a deterministic method has been suggested for calculation of the cohesive force between the adjacent particles. It has been shown that the cohesive force is actually a function of all those parameters. The escape velocity is an important parameter in determination of other relevant parameters like volumetric rate of bank erosion and entrainment rate. The results indicate that the escape velocity of a particle on a riverbank increases no further after a certain value of the volume of the water bridge between the pairs of particles is attained. The results fall well in line with those obtained by previous researchers.

Cohesive force, escape velocity, inter-particle distance, liquid (water) bridge.


1. W. M Cornelis., D. Gabriels and R. Hartmann, “A parameterisation for the threshold shear velocity to initiate deflation of dry and wet sediment”. Geomorphology. vol. 59, 2004, pp. 43 – 51.
2. J. G. Duan, “Analytical approach to calculate rate of bank erosion”. Journal of Hydraulic Engineering, ASCE, 131(11), 2005, pp. 980 – 989.

3. J. P. Julian and R. Torres, “Hydraulic erosion of cohesive riverbanks”. Geomorphology. vol. 76, 2006, pp.193 – 206.

4. R. Zhang and J. Li, “Simulation on mechanical behavior of cohesive soil by Distinct Element Method”, Journal of Terramechanics. vol. 43, 2006, pp. 303 – 316.

5. F. Soulie, M. S. El Youssoufi, F. Cherblanc and C. Saix, "Capillary cohesion and mechanical strength of polydisperse granular materials”. The European Physical
Journal, vol. 21, 2006, pp. 349 – 357.

6. F. Mu and X. Su, “Analysis of liquid bridge between spherical particles”, China Particuology, vol. 5, 2007, pp. 420 – 424.

7. Z. Grof, C. J. Lawrence and F. Stepanek, “The strength of liquid bridges in random granular materials”. Journal of Colloid and Interface Science, vol. 319, 2008, pp. 182–192.

8. A. Kudrolli, “Sticky sand”. Nature Materials, vol. 7, March 2008, pp. 174 – 175.

9. S. Mukherjee and A. Mazumdar, “Study of effect of the variation of inter-particle distance on the erodibility of a riverbank under cohesion with a new model”, Journal of Hydro-environment Research, 4(3), 2010, pp. 235-242.

10. S. Mukherjee and A. Mazumdar, “Influence of Inter-Particle Distance, Entrapped Water Volume and Salinity of Water on the Escape Velocity of Particles on a Riverbank”, Engineering, 3(7), 2011, 763-770.

11. S. Poorni, R. Miglani, M. R. Srinivasan and R. Indira, “Comparative evaluation of the surface tension and the ph of calcium hydroxide mixed with five different vehicles: an in vitro study”. Indian Journal of Dental Research, 20(1), 2009, pp. 17-20.

12. D. P. Bakker, J. W. Klijnstra, H. J. Busscher and H. C. Van der Mei, “The effect of dissolved organic carbon on bacterial adhesion to conditioning films adsorbed on glass from natural seawater collected during different seasons”. Biofouling, 19(6), 2003, pp. 391-397.
13. J. Morgan, Introduction to University Physics, Allyn and Bacon, Inc., Boston, 1963, pp. 13 - 37.





R. Harikumar, S.N. Shivappriya

Paper Title:

Analysis of QRS Detection Algorithm for Cardiac Abnormalities – A Review

Abstract: This work investigates and compares a set of efficient techniques to extract and select striking features from the ECG data applicable in automatic cardiac beat classification. Each method was applied to a pre-selected data segment from the MIT-BIH (Massachusetts Institute of Technology / Beth Isrel Hospital) database. The classification and optimization of different heart beat methods were performed based upon the extracted features (morphological and statistical feature). The morphological features were found as the most important for arrhythmia classification. However, because of ECG signal variability in different patients, the statistical approach is favoured for a precise and robust feature extraction. Among all these feature extraction, feature selection, classification and optimization techniques, SVM based PSO gives higher classification accuracy with curse of dimensionality.

Cardiac beat classifier, Feature Extraction, Feature Selection, SVM, PSO


1. J. Pan AndW. J. Tompkins, “A real-time QRS detection algorithm,” IEEE Trans. Biomed. Eng., vol. BME-32, no. 3, pp. 230–236, Mar. 1985.
2. P. de Chazal, M. O’Dwyer, and R. B. Reilly, “Automatic classification of heartbeats using ECG morphology and heartbeat interval features,” IEEE Trans. Biomed. Eng., vol. 51, no. 7, pp. 1196–1206, Jul. 2004.

3. P. Zarychta, F. E. Smith, S. T. King, A. J. Haigh, A. Klinge, D. Zheng, S. Stevens, J. Allen, A. Okelarin, P. Langley, and A. Murray, “Body surface potential mapping for detection of myocardial infarct sites,” in Proc. IEEE Comput. Cardiol., Sep./Oct. 2007, pp. 181–184.

4. B.-U.Kohler, C.Henning, and R.Orglmeister, “The principles of software QRS detection,” IEEE Eng. Med. Biol. Mag., vol. 21, no. 1, pp. 42–57, Jan./Feb. 2002.

5. D. Zhang, “Wavelet approach for ECG baseline wander correction and noise reduction,” in Proc. 27th IEEE Annu. Conf. Eng. Med. Biol., Sep. 2005, pp. 1212–1215.

6. C. Li, C. Zheng, and C. F. Tai, “Detection of ECG characteristic points using wavelet transforms,” IEEE Trans. Biomed. Eng., vol. 42, no. 1, pp. 21–28, Jan. 1995.

7. M. Faezipour, T. M. Tiwari, A. Saeed, M. Nourani, and L. S. Tamil, “Wavelet-based denoising and beat detection of ECG signal,” in Proc. IEEE-NIH Life Sci. Syst. Appl. Workshop,, Apr. 2009, pp. 100–103.

8. Y. H. Hu, S. Palreddy, andW. J. Tompkins, “A patient adaptive ECG beat classifier using a mixture of experts approach,” IEEE Trans. Biomed.Eng., vol. 44, no. 9, pp. 891–900, Sep. 1997.

9. M. H. Song, J. Lee, S. P. Cho, K. J. Lee, and S. K. Yoo, “Support vector machine based arrhythmia classification using reduced features,” Int. J. Control, Autom., Syst., vol. 3, no. 4, pp. 571–579, Dec. 2005.

10. R. Besrour, Z. Lachiri, and N. Ellouze, “ECG beat classifier using support vector machine,” in Proc. 3rd IEEE Int. Conf. Inf. Commun. Technol.: From Theory Appl., Apr. 2008, pp. 1–5.

11. Brune S, Gonska BD, Flei schmann C, Belles B, Belles G, Kreuzer H. “Prevalence of late ventricular potentials in hypertensive patients” J Cardiovasc Pharmacol. 1991;17 Suppl2:S146-7.

12. Biffi A, Ansalone G, Verdile L , Fernando F, Caselli G, Ammirati F “ Ventricular arrhythmias and athlete's heart- Role of signal-averaged electrocardiography”. Europ-ean Heart Journal .1996; 17: 557-63.

13. Mikhled Alfaouri and Khaled Daqrouq, ECG Signal Denoising By Wavelet Transform Thresholding, American Journal of Applied Sciences, 2008

14. MIT-BIH Arrhythmia Database. (2010). [Online]. Available: www.physionet.org/physiobank/database/mitdb

15. N.V. Thakor andY. S.Zhu, “Applications of adaptive filtering to ECG analysis: Noise cancellation and arrhythmia detection,” IEEE Trans. Biomed. Eng., vol. 38, no. 8, pp. 785–794, Aug. 1991.

16. J. P. Martinez, S. Olmos, and P. Laguna, “Evaluation of a wavelet-based ECG waveform detector on the QT database,” Comput. Cardiol, vol. 27, pp. 81–84, Sep. 2000.

17. P. M. Agante and J. P.Marques de Sa, “ECG noise filtering using wavelets with soft-thresholding methods,” Comput. Cardiol, vol. 26, pp. 535–538, Sep. 1999.

18. J. S. Sahambi, S. N. Tandon, and R. K. P. Bhatt, “Using wavelet transforms for ECG characterization: An on-line digital signal processing system,” IEEE Eng. Med. Biol. Mag., vol. 16, no. 1, pp. 77–83, Jan./Feb. 1997.

19. H. A. N. Dinh, D. K. Kumar, N. D. Pah, and P. Burton, “Wavelets for QRS detection,” in Proc. 23rd IEEE EMBS Int. Conf., Oct. 2001, pp. 1883– 1887.

20. Elif Derya Übeyli “Feature Extraction for Analysis of ECG Signals”, International Conference on IEEE EMBS Vancouver, British Columbia, Canada, August, 2008

21. Sung-Nien Yu, Kuan-To Chou, “Combining Independent Component Analysis and Back propagation Neural Network for ECG Beat Classification”, Proceedings of the 28th IEEE EMBS Annual International Conference, 2006.

22. Wei Jiang, and Seong G. Kong, “Block-Based Neural Networks for Personalized ECG Signal Classification”, IEEE Transcation on Neural Network. Vol 18, No. 6, November 2007

23. Farid Melgani, and Yakoub Bazi, “Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization” IEEE Transaction on Information Technology in Biomedicine, Vol. 12, No. 5, September 2008

24. Edoardo Pasolli, and Farid Melgani, “Active Learning Methods for Electrocardiographic Signal Classification” IEEE Transactions On Information Technology In Biomedicine, Vol. 14, No. 6, November 2010

25. Osowski S, et al. “Support Vector Machine-Based Expert System for Reliable Heartbeat Recognition” IEEE Trans. Biomed. Eng. 2004:51:582-9.

26. S. Osowski and T. H. Linh, “ECG beat recognition using fuzzy hybrid neural network,” IEEE Trans. Biomed. Eng., vol. 48, no. 11, pp. 1265–1271, Nov. 2001.

27. Kiranyaz, and Moncef Gabbouj “A Generic and Robust System for Automated Turker Ince, Serkan Patient-Specific Classification of ECG Signals”, IEEE Transaction on Biomedical Engineering, Vol. 56, No. 5, May 2009

28. Jehan Zeb Shah, Naomie bt Sali, “Neural Networks and Support Vector Machines Based Bio-Activity Classification”, Proceedings of the International Conference on Natural Resources Engineering & Technology, 24-25th July 2006, Putrajaya, Malaysia..

29. Hyejung Kim, Refet Firat Yazicioglu, Patrick Merken,

30. L. Bruzzone and D. Prieto, “A technique for the selection of kernel function parameters in RBF neural networks for classification of remote sensing images,” IEEE Trans. Geosci. Remote. Sens., vol. 37, no. 2, pp. 1179– 1184, Mar. 1999.

31. R. Duda, P. Hart, and D. Stork, “Pattern Classification’, 2nd ed. New York: Wiley, 2001.

32. Kennedy and R. C. Eberhart, “Swarm Intelligence”. San Mateo, CA: Morgan Kaufmann, 2001.

33. R. Mark and G. Moody, “MIT-BIH Arrhythmia Database Directory,” [Online]. Available: http://ecg.mit.edu/dbinfo.html

34. Martínez JP, Almeida R, Olmos S, Rocha AP, Laguna P. A wavelet-based ECG delineator: evaluation on standard databases. IEEE Trans Biomed Eng 2004;51(4):570-581.

35. S.Karpagachelvi, Dr.M.Arthanari, Prof. & Head, M.Sivakumar, “ECG Feature Extraction Techniques- A Survey Approach” (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 1, April 2010

36. L. Khadra, A. S. Al-Fahoum, and S. Binajjaj, “A quantitative analysis approach for cardiac arrhythmia classification using higher order spectral techniques,” IEEE Trans. Biomed. Eng., vol. 52, no. 11, pp. 1840–1845, Nov. 2005.

37. T. Inan, L. Giovangrandi, and J. T. A. Kovacs, “Robust neural network based classification of premature ventricular contractions using wavelet transform and timing interval features,” IEEE Trans. Biomed. Eng., vol. 53, no. 12, pp. 2507–2515, Dec. 2006.





Bal Mukund Sharma, A. N. Tiwari, K. P. Singh

Paper Title:

Analysis and Simulation of Active Clamped Quasi-Resonant DC Link Inverter

Abstract: This paper proposes a simulation developed to use pulse width modulation (PWM) technique with the active clamped quasi- parallel resonant dc link (ACQPRDCL) inverter in order top increase the advantages of the PWM which allows to reduce the switching losses of the power devices. The new ACQPRDCL ensures zero crossing for a time period, and at any time required for soft switching (SS) and PWM operation of the inverters, respectively. The principle of operation is analyzed and verified by PSPICE simulations.

Keywords: PWM, ACQPRDCL, ACQPRDCL inverter, soft switching, soft switching inverter, simulation.


2. D. M. Divan and G. Venkataramanan, Design Methodologies for Soft Switched Inverters, IEEE Trans. on Ind. Appl., Vol.29, no.1, 1993, pp. 126-135.

3. D. M. Divan, L. Malesani and P. Tenti, V. Toigo, A Synchronized Resonant DC Link Converter for Soft-Switched PWM, IEEE Trans. on Ind. Appl., Vol.29, No.5, 1993, pp. 940-948.

4. J. He, N. Mohan and B. Wold, Zero-Voltage-Switching PWM Inverter for High- Frequency DC-AC Power Conversion, IEEE Trans. on Ind. Appl., Vol.29, No.5, 1993, pp. 959-968.

5. L. Malesani, P. Tenti, P. Tomasin and V. Toigo, High Efficiency Quasi-Resonant DC Link Three-Phase Power Inverter for Full-Range PWM, IEEE Trans. on Ind. Appl., Vol.31, No.1, 1995, pp. 141-147.

6. V. V. Deshpande and S. R. Doradla, A New Topology for Parallel Resonant DC Link with Reduced Peak Voltage, IEEE Trans. on Ind. Appl., Vol.32, No.2, 1996, pp. 301-307.

7. Y. C. Jung, H. L. Liu and G. H. Cho, Soft Switching Space Vector PWM Inverter Using a New Quasi-Parallel Resonant DC Link, IEEE Trans. on Power Electron., Vol.11, No.3, 1996, pp. 503-510.

8. S. Chen and T. A. Lipo, A Novel Soft-Switched PWM Inverter for AC Motor Drives, IEEE Trans. on Power Electron., Vol.11, No.4, 1996, pp. 653-659.

9. V. V. Deshpande, S. R. Doradla and D. M. Divan, A Current-Prediction Scheme for the PRDCL Inverter-Fed Induction Motor Drive, IEEE Trans. on Power Electron., Vol.12, No.1, 1997, pp. 64-69.

10. V. V. Deshpande and S. R. Doradla, A Detailed Study of Losses in the Reduced Voltage Resonant Link Inverter Topology, IEEE Trans. on Power Electron., Vol.13, No.2, 1998, pp. 337-344.

11. Y. Chen, A New Quasi-Parallel Resonant DC Link for Soft-Switching PWM Inverters, IEEE Trans. on Power Electron., Vol.13, No.3, 1998, pp. 427-433.

12. V. Pickert and C. M. Johnson, Three-Phase Soft-Switching Voltage Source Converters for Motor Drives. Part 1: Overview and Analysis, IEE Proc.-Electr. Power Appl., Vol.14, No.2, 1999, pp. 147-154.

13. H. Bodur, I. Aksoy, H. Obdan, "A New Actively Clamped Quasi Parallel Resonant DC Link Circuit", 9th National Conferences on Electrical-Electronics-Computer Engineering,19-23 September 2001, Kocaeli, p.p. 183-186.

14. H. Obdan, H. Bodur, I. Aksoy, N. Bekiroglu, G. Yıldırmaz, “A New Parallel Resonant DC Link for Soft Switching Inverters”, Electric Power Components and Systems, vol.33, no.2, pp. 159-169, Feb 2005.





Bal Mukund Sharma, A. N. Tiwari, K. P. Singh

Paper Title:

Design and Simulation of Soft Switched Converter Fed DC Servo Drive

Abstract: Resonant switching is preferred over hard switching to minimize the switching losses. Resonant switching DC-DC converters are attractive for power supply in many applications due to their distinct advantages such as high efficiency, high frequency operation, compact structure, low EMI, etc.as compared to hard-switched converters. The paper deals with design and simulation of ZCS-Quasi resonant converter fed DC servo drive using Matlab Simulink. The salient feature of QRC is that the switching devices can be either switched on at zero current or switched off at zero current, so that switching losses may be zero, with low switching stresses, low volumes and high power density. The output of QRC is regulated by varying the switching frequency of the converter.

Keywords: Quasi resonant converter (QRC), Zero current switching (ZCS), Pulse width modulation (PWM), Low power factor load, and Power supply.


1. K.H. Liu, Ramesh Oruganti, and F.C.Y. Lee: “Quasi-resonant Converters Topology and Characteristics”, IEEE Transactions on Power electronics, Vol. PE-2, No. 1, pp. 62 – 71, Jan. 1987.
2. D. M. Divan, “The resonant DC link converter—A new concept in static power conversion,” IEEE

3. Trans. Ind. Applicat., vol. 25, pp. 317–325, Mar./Apr. 1989.

4. J. He and N. Mohan, “Parallel resonant dc link circuit—A novel zero switching loss topology with minimum voltage stresses,” in Conf. Rec. IEEE PESC’89, pp. 1006–1012, 1989.

5. G.H. Cho, and J.G. Cho: “Cyclic Quasi-resonant Converter: a New Group of Resonant Converters Suitable for Higher Performance DC/DC and AC/AC Conversion Applications”, proc IEEE IECON, pp. 956 – 963, 1990.

6. R.B. Ridely, W.A. Tabisz, F.C.Y. Lee, and V. Vorperian: “Multi Loop Control of Quasi-resonant Converters”, IEEE Transactions on Power Electronics, Vol. 6, No. 1, pp. 28 – 38, Jan. 1991.

7. J. S. Lai and B. K. Bose, “An inductionmotor drive using an improved high frequency resonant link inverter,” IEEE Trans. Power Electron., vol. 6, pp. 504–513, July 1991.

8. G. Venkataramanan and D. M. Divan,“Pulse width modulation with resonant dc link converters,” IEEE Trans. Ind. Applicat., vol. 29, pp. 113–120, Jan./Feb. 1993.

9. S.R. Reddy, and C. Chellamuthu: “Performance of a DC Motor Fed from Series and Parallel Quasi resonant Converters, International Journal of Power and Energy Systems”, Vol. 17, No. 3, pp. 161 – 168, 1997.

10. L.K. Wong, F.H. Leung, and P.K.S. Tam: “A Simple Large Signal non Linear Modeling Approach for Fast Simulation of Zero Current Switching Quasi-resonant Converters”, IEEE Transactions on Power Electronics, Vol. 12, No. 3, pp. 437 – 442, May 1997.

11. L. Bo-Tao, L. and Yim-Shu: “Novel Actively-clamped Zero Current Switching Quasi-resonant Converters, IEEE International Symposium on Circuits and Systems”, Hong Kong, 9-12, Vol. 2, pp. 869 – 872, June 1997.

12. S. Arulselvi, C. Subhashini, and G. Ume: “A New Push-pull Zero Voltage Switching Quasiresonant Converter: Topology, Analysis and Experimentation”, IEEE
INDICON 2005, 11-12 pp. 482 – 486, Dec. 2005.

13. S. Arulselvi, G. Ume, and B. Kalaranjani: “Design and Simulation of Model based Controllers for Quasi Resonant Converters using Neural Networks”, India International Conference on Power Electronics 2006, IICPE 2006, Chennai, India, 19-21 pp. 197 – 202, Dec. 2006.

14. K.N. Rao, and V.C.V. Reddy: “Implementation of FM-ZCS-Quasi Resonant Converter fed DC servo drive”, Journal of Theoretical and Applied Information Technology, Vol. 5, No. 4, pp. 432 – 436, April 2009.

15. K.N. Rao, and V.C.V. Reddy: “Digital Simulation of FM-ZCS-Quasi Resonant Converter Fed DC Servo Drive using Matlab Simulink”, IESPEEE09 , SRM University, Chennai, India.





Kamal Jain, Payal

Paper Title:

A Review Study On Urban Planning & Artificial Intelligence

Abstract: AI Techniques are best techniques for dealing with complex and dynamic problems of urban studies. They give a better way for analysis of urban growth. Major objective of this study is to review recent developments in the field of urban planning & AI. The purpose of this article is to explore how artificial echniques have been applied for urban dynamic planning processes. For this, the authors have reviewed the application of AI techniques in urban land dynamics. This paper discusses how cellular Automata, Fuzzy Logic, Neural Networks have been applied for Urban Planning due to the unpredictability, instability, uncomputability, irreducibility and emergence that exists in the process of urban evolution. The authors conclude that AI based approaches offer possible solutions for urban dynamics.

Cellular Automata, Fuzzy Logic, Neural Networks.

1. Colonna, A., Stephano, V., Lombardo, S., Papini, L., and Rabino, A. (1998), Learning cellular automata: modelling urban modelling, In Proceedings of the 3rd international conference on GeoComputation, University of Bristol, Bristol, UK.
2. Engelen (1997) Wh.ite, R., Engelen, G. and UIjee I.,1997. The use of constrained cellular automata for high resolution modeling of urban land-use dynamics. Environment and planning B, Vol. 24, pp. 323-343

3. Fuller, R. (2000) Introduction to neuro-fuzzy systems. New York: Physica-Verlag.

4. Dr. Kamal Jain , Mulugeta Feyissa, "Urban Growth Prediction Using Remote sensing and artificial intelligence techniques,2008.

5. Khalid Al-Ahmadia, Alison Heppenstallb, Linda Seeb and Jim Hoggb ,Modeling Urban Growth Dynamics using Cellular Automata and GIS

6. Li, X. and yeh, A.G.O.,2000. Modelling sustainable urban development by the integration of constrained Cellular Automata. International Journal of Geographical Information science, Vol. 14(2), pp 131-152

7. M. Roscia, D. Zaninelli, Gh. Lazaroiu, Sustainable urban model through fuzzy logic weights

8. Pijanowskia, B. C., Brown, D. G., Shellitoc, B. A., and Manikd, G. A., 2002. Using neural networks and GIS to forecast land use changes: a land transformation model. Computers, Environment and Urban Systems, 26(6), pp. 553-575.

9. Sui, D. Z. (1998) GIS-based urban modelling: practices, problems, and prospects. International Journal of Geographical Information Science, 12(7), 651-671.

10. Takeyama and Couclelis (1997), From Cellular automata to urban models; new principles for model development and implementation. Environment and planning B: Planning & Design,I,pp. 165-174

11. Ward, D. P., Murray, A. T., and Phinn, S. R. (1999), An optimized cellular automata approach for sustainableurban development in rapidly urbanizing regions, In Proceedings of the 4th international conference on GeoComputation, Marry Washington College, Fredericksburg, Virginia, USA. Ward, et al., 1999

12. Hegde N. P. , Murlikrishna, and ChalapatiRao, Integration of CA & GIS for simulating Land Use Changes

13. Mª Carmen Ruiz Puente1, The Development of a New Methodology Based on GIS and Fuzzy Logic to Locate Sustainable Industrial Areas





Malek Alzaqebah, Salwani Abdullah

Paper Title:

The Bees Algorithm for Examination Timetabling Problems

Abstract: Bees Algorithm (BA) is a population-based algorithm inspired by the honey bees forage for food. The algorithm presents a neighbourhood search associated with a random search which can be used for optimisation problems. In the basic version of BA, when a bee finds a food source, it returns to the hive and shares the information with other bees. Later, the bees will decide how many of them should fly towards the food source, depending on its quality (the quality represents the fitness value). In this paper, we have proposed to investigate the using of BA for examination timetabling problems, in addition a modification on the algorithm has been applied by replacing the fitness value by probability value. Experimental results indicate that the proposed approach produces promising results in solving examination timetabling problems and also show that the modified bees’ algorithm outperforms the basic bees algorithm when tested on the same problems.

Bees Algorithm, Examination Timetabling Problems.


1. D.T. Pham, A. Ghanbarzadeh, E. Koc, S. Otri, S. Rahim and M . Zaidi, (2005) The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK.
2. D.T. Pham, E. Kog, A. Ghanbarzadeh, S. Otri, S. Rahim, M. Zaidi (2006), The Bees Algorithm – A Novel Tool for Complex Optimisation Problems, IPROMS 2006 Proceeding 2nd International Virtual Conference on Intelligent Production Machines and Systems, Oxford, Elsevier.
3. S. Abdullah, and E.K. Burke, (2006) A Multi-start large neighbourhood search approach with local search methods for examination timetabling. In The International Conference on Automated Planning and Scheduling (ICAPS 2006) (Eds. Long, D., Smith, S.F., Borrajo, D. & McCluskey, L.), 6-10 June, Cumbria, UK, , 334-337.
4. S. Abdullah, S. Ahmadi, E.K. Burke and M. Dror (2007) Investigating Ahuja Orlin’s large neighbourhood search approach for examination timetabling. OR Spectrum, 29(2), 351-372.

5. S. Abdullah, E.K. Burke and B. McCollum, (2007) Using a Randomised Iterative Improvement Algorithm with Composite Neighbourhood Structures for University Course Timetabling. In Metaheuristics: Progress in complex systems optimization (Operations Research / Computer Science Interfaces Series), Chapter 8. published by Springer, ISBN:978-0-387-71919-1.

6. S. Abdullah, H. Turabeih and B. McCollum, (2009) A hybridization of electromagnetic like mechanism and great deluge for examination timetabling problems, HM2009: 6th International Workshop on Hybrid Metaheuristics, Udine, 60-72.

7. L. Bao, J. Zeng, (2009) Comparison and Analysis of the Selection Mechanism in the Artificial Bee Colony Algorithm. HIS (1): 411-416

8. E.K. Burke, Y. Bykov, J.P. Newall and S. Petrovic, (2004) A time-predefined local search approach to exam timetabling problem. IIE Transactions, 36(6), 509 528.

9. E. K. Burke, A. J. Eckersley, B. McCollum, S. Petrovic and R. Qu, (2010), Hybrid variable neighbourhood approaches to university exam timetabling, European Journal of Operation Research. 206(1), 46-53.

10. S. Camazine, J.L. Deneubourg, N. Franks, J. Sneyd, G. Theraulaz and E. Bonabeau. Self-Organization in Biological Systems. Princeton University Press, Princeton, 2003 NJ.

11. M. Caramia, P. Dell’Olmo and G.F. Italiano, (2001) New algorithms for examination timetabling. Algorithms Engineering 4th International Workshop, Proceedings WAE 2001, Saarbrücken, Germany, Springer Lecture Notes in Computer Science, vol. 1982. 230-241.

12. M.W. Carter (1986) A survey of practical applications of examination timetabling algorithms. Operations Research, 34(2): 193-202.

13. M.W. Carter, , G. G. Laporte, and S.Y. Lee, (1996) Examination Timetabling: Algorithmic Strategies and Applications. Journal of the Operational Research Society 47, 373-383.

14. F. Kang, J. Li, and Q. Xu, “Structural inverse analysis by hybrid simplex artificial bee colony algorithms,” Computers & Structures, vol. 87, no. 13-14, pp. 861–870, 2009

15. R. Lewis (2008) A survey of metaheuristic-based techniques for university timetabling problems. OR Spectrum, 30(1). 167-190.

16. Pan, Tasgetiren, Q-K., Suganthan, M. F., P. N., Chua, T. J (2010) A Discrete Artificial Bee Colony Algorithm for the Lot-streaming Flow Shop Scheduling Problem, Information Sciences, Elsevier, Netherlands, In Press.

17. D. T. Pham, A. A. Afify, and E. Koç, (2007). Manufacturing cell formation using the Bees Algorithm. In Proceedings of the Third International Virtual Conference on Intelligent production machines and systems (IPROMS 2007), Whittles, Dunbeath, Scotland.

18. D.T. Pham, M. Castellani, (2009), The Bees Algorithm – Modelling Foraging Behaviour to Solve Continuous Optimisation Problems. Proc. ImechE, Part C, 223(12): 2919-2938.

19. R. Qu, E.K. Burke, B. McCollum, and L.T.G. Merlot, (2009) A survey of search methodologies and automated system development for examination timetabling. Journal of Scheduling, 12. 55-89.

20. A. Singh, (2009) An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem, Applied Soft Computing 9 625–631. Timetabling (PATAT 2008)

21. Y. Yang, and S. Petrovic, (2005). A novel similarity measure for heuristic selection in examination timetabling. In E. K. Burke & M. Trick (Eds.), Lecture notes in computer science: Vol. 3616. Practice and theory of automated timetabling V: selected papers from the 5th international conference (pp. 377–396). Berlin: Springer,

22. B. McCollum, A. Schaerf, B. Paechter, P. McMullan, R. Lewis, A.J. Parkes, L. Gaspero, R. Qu, E.K. Burke, (2010). Setting the research agenda in automated timetabling: the second international timetabling competition. INFORMS. J. Computing, 22: 120-130.

23. E.K. Burke, D.G. Elliman, P.H. Ford, R.F. Weare, (1996). Examination Timetabling in British Universities – A Survey. In EK.Burke and P.Ross (eds.), The Practice and Theory of Automated Timetabling: Selected Papers from the 1st International Conference, pp 76-90. Lecture Notes in Computer Science, 1153. Springer.

24. T. Muller, (2009) ITC2007 Solver Description: A Hybrid Approach. Annals of Operation Research. 172(1): 429-446.

25. M. Atsuta, N. Nonobe, T. Ibaraki (2007). ITC2007 Track 1: An Approach using general CSP solver. www.cs.qub.ac.uk/itc2007.

26. A. Pillay (2007). Developmental approach to the examination timetabling problem.www.cs.qub.ac.uk/itc2007.

27. C. Gogos, G. Goulas, P. Alefragis, E. Housos, (2009). Pursuit of Better Results for the Examination Timetabling Problem Using Grid Resources, CI-Sched '09. IEEE Symposium Computat. Intelligence Scheduling, 48-53.





Mahdi Ebrahimzadeh, Farzan Rezaei, Siavash Rezaei

Paper Title:

A New Active Inductor and Its Application to Wide Tuning Range LC Oscillator

Abstract: This paper presents a new structure to reduce noise in an active inductor. Our structure is based on a local common mode feedback. The proposed active inductor is used for implementation a wide tunable low phase noise LC oscillator. By varying control voltage of active inductor from 0.85 V to 1.6 V, the oscillation frequency changes from 86 MHz to 1.137 GHz. The presented structure improves the value of phase noise in the oscillator based on it about 5.5 dBc/Hz in 1 MHz offset frequency. The proposed oscillator is simulated with TSMC 0.18 µm CMOS technology.

Active Inductor, High Quality Factor, LC Oscillator, Wide Tuning Range, Phase Noise


1. 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.
2. Byunghun Min, Hanggeun Jeong, "5-GHz CMOS LC VCOs With Wide Tuning Ranges," IEEE Microwave and Wireless Components Letters, vol. 15, no. 5, pp. 336-338, May 2005.

3. C.-M. Hung, N. Barton, "Low phase noise wide tuning range digitally-controlled LC oscillator using switchable inductor," Electronics Letters, vol. 45, no. 17, pp. 890-892, Aug. 2009.

4. Tomar, A., Pokharel, R., Kanaya, H., Yoshida, K, "Design of digitally controlled LC oscillator with wide tuning range in 0.18um TSMC CMOS technology," Microwave Conference, 2008. APMC 2008. Asia-Pacific, pp. 1-4, Dec. 2008.

5. Wenhua Tan, Guican Chen, Hong Zhang, "A 1-GHz LC Voltage-Controlled Oscillatorwith High Linearity and Wide Range," Electron Devices and Solid-State Circuits, 2008. EDSSC 2008. IEEE International Conference on, pp. 1-4, Dec. 2008.

6. Kao, H.L., Yang, D.Y., Chang, Y.C., Lin, B.S., Kao, C.H., "Switched resonators using adjustable inductors in 2.4/5 GHz dual-band LC VCO," Electronics Letters, vol. 44, no. 4, pp. 299-300, Feb. 208.

7. Axel D. Berny, Ali M. Niknejad, Robert G. Meyer, "A 1.8-GHz LC VCO With 1.3-GHz Tuning Range and Digital Amplitude Calibration," IEEE Journal of Solid State Circuits, vol. 40, no. 4, pp. 909-917, Apr. 2005.

8. Liang-Hung Lu, Hsieh-Hung Hsieh,Yu-Te Liao, "A Wide Tuning-Range CMOS VCO With a Differential Tunable Active Inductor," IEEE Transactions on Microwave Theory and Techniques, vol. 54, no. 9, pp. 3462-3468, Sep. 2006.

9. M.M. Reja, I.M. Filanovsky, K. Moez, "Wide tunable CMOS active inductor," Electronics Letters, vol. 44, no. 25, p. 1461–1463, Dec. 2008.

10. F. Yuan, CMOS Active Inductors and Transformers Principle, Implementation, and Applications. New York, USA: Springer, 2008.

11. Craninckx, J. Steyaert, M., “Low-Noise Voltage-Controlled Oscillators Using Enhanced LC-Tanks” IEEE Transactions on Circuits and Systems II, Analog and Digital Signal Processing, vol 42, no 12, pp 794 - 804, December 1995.

12. Y. Wu, M. Ismail, H. Olsson, "CMOS VHF/RF CCO based on active inductors," Electronics Letters, vol. 37, no. 8, pp. 472-473, Apr. 2001.

13. Yong-Ho Cho; Song-Cheol Hong; Young-Se Kwon, "A novel active inductor and its application to inductance-controlled oscillator," IEEE Transactions on Microwave Theory and Techniques, vol. 45, no. 8, pp. 1208-1213, Aug. 1997.

14. Thanachayanont, A., Payne, A, "CMOS floating active inductor and its applications to bandpass filter and oscillator designs," Circuits, Devices and Systems, IEE Proceedings, vol. 147, pp. 42-48, Feb. 2000.

15. Haiqiao Xiao, Schaumann, R, "A low-voltage low-power CMOS 5-GHz oscillator based on active inductors," Electronics, Circuits and Systems, 2002. 9th International Conference on, vol. 1, pp. 231-234, Sep. 2002.






M.Vijaya Kumar, C.Chandrasekar

Paper Title:

GIS Technologies in Crime Analysis and Crime Mapping

Abstract: Common and geographic issues, such as location of Apartments or neighbourhoods’ with different financial status within an area, can manipulate the patterns and rate of crime incidents in that area. Crime analysis and crime mapping, achieved by GIS, have a major role in reducing crime and improving the effective police activities. Chennai district includes significant attentions of evening, late-night and shopping occasions in Tamil Nadu. Using knowledge such as where and when crime occurs, this paper highlights what the inputs and outputs of crime analysis could be. The relationship between financial characteristics and crime rate for police areas of Tamil Nadu was inquired in to. Then, spatial GIS analyses using Spatial Analyst, Hotspot Analysis was performed. The findings of this paper give the possible incident sites.

Crime analysis, Hotspot analysis, data manipulation, spatial crime distribution


1. HOME DEPARTMENT TAMIL NADU POLICE POLICY NOTE FOR 2006-2007.Tamil Nadu Police Service Annual Statistical Report 2006 – 2007. Statistical Summary.
2. Impact Assessment of Modernization of Police Forces (MPF) (From 2000 to 2010). Bureau of Police Research and Development Ministry of Home Affairs 2010

3. EXECUTIVE SUMMARY of Tamil Nadu Police Statistical Report 2002 – 2009. Statistical Summary.

4. CRIME REVIEW TAMIL NADU 2007. State Crime Records Bureau Crime CID, Chennai Tamil Nadu.

5. Tamil Nadu Police Website http://www.tnpolice.gov.in/

6. Crime Mapping in India: A GIS implementation in Chennai City Policing, Geographic Information Sciences, Vol.10, No1, June 2004. Jaishankar K., and Debarati.

7. Chennai City Map: http://www.mapsofindia.com/maps/tamilnadu/chennai-map.htm

8. E. Akpinar, N. Usul “Geographic Information Systems Technologies in Crime Analysis and Crime Mapping”, proceedings. ESRI.

9. Canter, P.R. (1998) “Geographic Information Systems and Crime Analysis in Baltimore Country, Maryland” in Crime Mapping and Crime Prevention, Weisburd, D. and McEwen T. (eds.), Criminal Justice Press, Monsey, New York, USA, 157-190.

10. Chaffin, J. W. (2004) Criminal Intelligence Analyst, Hillsborough County Sheriff's Office, Hillsborough County Traffic Crash Management System, Traffic Analysis Program, Tampa, FL, interview on 4th March 2004.

11. Cohen, L.E and Felson, M. (1979) Social Change and Crime Rate Trends: A Routine Activity Approach, American Sociological Review.

12. ICJIA (Illinois Criminal Justice Information Authority) (1996) Spatial and Temporal Analysis of Crime Program (STAC) User Manual, Chicago.

13. Lab P. S. (2000) Crime Prevention: Approaches, Practices and Evaluations, Anderson Publishing Co., Cincinnati,OH.

14. Ractiffe, J.H. and McCullagh, M.J. (1999) “Hotbeds of crime and the search for spatial accuracy”, Geographical Systems, 1, 385-398.

15. Weisburd, D and McEwen, T. (1998) Crime Mapping and Crime Prevention, Crime Prevention Studies, Vol. 8, Criminal Justice Press, Monsey, New York, USA

16. Wieczorek, W. and Hanson, C. E. (1997) “New Modeling Methods, Geographic Information Systems and Spatial Analysis”, Journal of Alcohol Health and Research World, Vol. 21 (4), pp. 331-339.






J. Sreedhar, S. Viswanadha Raju, A. Vinaya Babu

Paper Title:

Query Processing for Content Based Image Retrieval

Abstract: In this paper, we investigated image retrieval based on image content, Content Based Image Retrieval (CBIR) and proposed a framework to characterize the image content and similarity between the images. Our paper discusses the CBIR problem and the solution. Due to the enormous increase in image database sizes, the need for the development of CBIR systems arose. Firstly, this paper outlines feature extraction methods for color and texture. The extracted features for each image in the database used as the basis for similarity between the images. We built user interface based on Java, in which user can easily select query image and view top ten retrieved images based on decreasing order. We extended our approach to sub image retrieval also. Our results report that HSV based color features and contrast based texture features outperform than RGB based features and results reported in the paper are convincing.

Colour, Texture, Query, CBIR.


1. N. S. Chang and K. S. Fu, A Relational Database System for Images, Technical Report TREE 79-28, Purdue University, May 1979.
2. N. S. Chang and K. S. Fu, Query-by pictorial-example, IEEE Trans. on Software Engineering SE-6(6), 1980.

3. S.-K. Change Pictorial database systems, IEEE Computer, 1981.

4. S.-K. Chang, C. W. Yan, D. C. Dimitroff, and T. Arndt, An intelligent image database system, IEEE Trans. Software Eng. 14(5), 1988.

5. H. Tamura and N. Yokoya, Image database systems: A survey, Pattern Recognition 17(1), 1984.

6. S.-K. Chang and A. Hsu, Image information systems: Where do we go from here? IEEE Trans. On Knowledge and Data Engineering 4(5), 1992.

7. V. N. Gudivada and J. V. Raghavan, Special issue on content-based image retrieval systems, IEEE Computer Magazine 28(9), 1995.

8. A. Pentland and R. Picard, Special issue on digital libraries, IEEE Trans. Patt. Recog. and Mach. Intell.,1996.

9. A. D. Narasimhalu, Special section on content-based retrieval, Multimedia Systems, 1995.

10. Special issue on visual information management (R. Jain, Guest Ed.), Comm. ACM, Dec. 1997.

11. B. Schatz and H. Chen, Building large scale digital libraries, Computer, 1996.

12. C. S. McCamy, H. Marcus, and J. G. Davidson, A colour-rendition chart, Journal of Applied Photographic Engineering 2(3), 1976.

13. M. Miyahara, Mathematical transform of (r,g,b) colour data to munsell (h,s,v) colour data, SPIE Visual Commun. Image Process. 1001, 1988.

14. J. Wang, W.-J. Yang, and R. Acharya, Colour clustering techniques for colour-content-based image retrieval from image databases, in Proc. IEEE Conf. on Multimedia Computing and Systems, 1997.

15. M. Swain and D. Ballard, Colour indexing, International Journal of Computer Vision 7(1), 1991.

16. M. Ioka, A Method of Defining the Similarity of Images on the Basis of Colour Information, Technical Report RT-0030, IBM Research, Tokyo Research Laboratory, Nov. 1989.

17. W. Niblack, R. Barber, and et al., The QBIC project: Querying images by content using colour, texture and shape, in Proc. SPIE Storage and Retrieval for Image and Video Databases, Feb. 1994.

18. M. Stricker and M. Orengo, Similarity of colour images, in Proc. SPIE Storage and Retrieval for Image and Video Databases, 1995.

19. J. R. Smith and S.-F. Chang, Single colour extraction and image query, in Proc. IEEE Int. Conf. on Image Proc., 1995.

20. J. R. Smith and S.-F. Chang, Tools and techniques for colour image retrieval, in IS & T/SPIE Proceedings, Vol. 2670, Storage & Retrieval for Image and Video Databases IV, 1995.

21. Cao LiHua, Liu Wei, and Li GuoHui, “Research and Implementation of an Image Retrieval Algorithm Based on Multiple Dominant Colours ”,Journal of Computer Research & Development, Vol 36, No. 1,pp.96-100,1999.

22. Song Mailing, Li Huan, “An Image Retrieval Technology Based on HSV Colour Space”, Computer Knowledge and Technology, No. 3,pp.200-201, 2007.

23. D.C. He and Li Wang. Texture filters based on texture spectrum. Pattern Recognition, 24(12):1187–1195, 1991.






Arihant Khicha, Neeti Kapoor

Paper Title:

A Consistent Protected Structural Design for Mobile Agents In Open Network Systems

Abstract: A system in which user programs (the agent) may willingly and separately travel from one the host to the mobile agent server is a mobile-agent system. A large exploitation of mobile agent systems is not possible without gratifying security structural design. The attack of a visiting code by a malicious host is the major barrier facing wide exploitation of mobile agents. The fact that host computers have complete control over all the programs of a visiting agent makes it very hard to protect agents from untrusted hosts. This has resulted to restricted exploitation of mobile agents to acknowledged hosts in congested networks where the agent’s security is assured. However, this restriction negates the original major concept of sovereignty on the basis of which mobile agent technology is established. This paper proposed a dynamic protected structural design for mobile agents systems, using Platform Registry and Travel Diary Protection Scheme. The scheme protects and allows mobile agents to travel liberally in open networks environment.

Mobile Agents, Travel Diary, Security, Platform Registry


1. Wahbe R., S, Lucco, T.E. Anderson and Graham S.L., 1993, Efficient Software Based Fault Isolation, In Proceedings of the 14th ACM Symposium on Operating Systems Principle, pp 203-216, ACM
2. Jacob Y. Levy, John K. Ousterbhout and Brent B Welch, 1997, The safe Tcl Security Model Technical Report, Sun Microsystems

3. Sreekanth V., S Ramchandram and A. Govardhan, 2010, Mobile Agent Security and Key Management Technique, Journal of Computing, Vol. 2, Issue 9, ISSN 2151-9617

4. Neelesh Kumar Panthi and Chaudhari Neelesh Kumar Panthi, 2010, Securing Mobile Agent using Dummy and Monitoring Mobile Agent, International Journal of Computer Science and Information Technologies, Vol 1 (4), pp 208-211

5. Sarvarnl Islam Rizvi, Zinat Sultana, Bio Sun and Mid Washiqul Islam, 2010, Security of Mobile Agent in Ad Hoc Network using Threshold Cryptography, World Academy of Science, Engineering and Technology, Vol 30, pp 424-427

6. Sreekanth V., Ranchandra S., and Gavardhan A.,2008, A Novel Approach for Securing and Integrity of Mobile Agents, ICCBN, IISC, Bangalore

7. Tomas Sander and Christian F. Tschudin, 1998, Protecting Mobile Agent against Malicious Hosts, In Giovanni Vigna, Mobile Agent Security, pp. 44-60, Springer-Verlag, Herdeberg Germany

8. Gray R.S., 1995, A Transportable Agent System, In proceedings of CIKM 95 Workshop on Intelligent Information Agents 14

9. Dierks T. and Rescorla E., 2006, The Transport Layer Security Protocol Version, In RFC 4344, IETF

10. Vigna G., 1998, Cryptographic Traces for Mobile Agents, In Mobile Agent and Security, Vol., 1419 of Lecture Notes in Computer Science, pp 137-153, Springer Verlag

11. Hohl F., 1998, Time Limited Blackbox Security: Protecting Mobile Agent from Malicious Hosts, In Mobile Agent and Security, Vol., 1419 of Lecture Notes in Computer Science, pp 92-113, Springer Verlag

12. Sander T. and Tschudin C.F., 1998, Protecting Mobile Agents against Malicious Host, In Mobile Agent and Security, Vol., 1419 of Lecture Notes in Computer Science, Springer Verlag

13. Tan H.K. and Morean L 2001, Trust Relationships in a Mobile Agent System, In Mobile Agent, Vol., 2240 of Lecture Notes in Computer Science, pp 15-30, Springer Verlag

14. Carles Garrigne Ollivera Bellaterra, 2008, Contribution to Mobile Agent Protection, PhD Thesis, Universtat Ant Onoma, De Barcelona

15. Wikipedia the Free Encyclopedia Serialization, http://en.wikipedia.org/wiki/serialization





R. Sangeetha, B. Kalpana

Paper Title:

Performance Evaluation of Kernels in Multiclass Support Vector Machines

Abstract: In recent years, Kernel based learning algorithm has been receiving increasing attention in the research domain. Kernel based learning algorithms are related internally with the kernel functions as a key factor. Support Vector Machines are gaining popularity because of their promising performance in classification and prediction. The success of SVM lies in suitable kernel design and selection of its parameters. SVM is theoretically well-defined and exhibits good generalization result for many real world problems. SVM is extended from binary classification to multiclass classification since many real-life datasets involve multiclass data. In this paper, we propose an optimal kernel for one-versus-one (OAO) and one-versus-all (OAA) multiclass support vector machines. The performance of the OAO and OAA are evaluated using the metrics like accuracy, support vectors, support vector percentage, classification error, and speed. The empirical results demonstrate the ability to use more generalized kernel functions and it goes to prove that the polynomial kernel’s performance is consistently better than other kernels in SVM for these datasets.

Support Vector Machine, Multiclass Classification, Kernel function, One versus One, One versus All.


1. J. Han and M. Kamber, Data Mining—Concepts and Technique, 2nd ed. San Mateo, CA: Morgan Kaufmann, 2006.
2. P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining. Reading, MA: Addison-Wesley, 2005.

3. V. Vapnik, An overview of statistical learning theory, IEEE Trans. on Neural Networks, 1999.

4. N. Cristianini and J. Shawe-Taylor, Introduction to Support Vector Machines, Cambridge University Press, 2000.
5. B. Schölkopf and A. Smola, Leaning with Kernels, MIT Press, 2001.
6. C. J. C. Burges, A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 1998, pp 56–89.

7. Corinna Cortes and V. Vapnik, Support-Vector Networks, Machine Learning, 1995.

8. J. Manikandan , B.Venkataramani,Study and evaluation of a multi-class SVM classifier using diminishing learning technique, Neurocomputing, 2010.

9. Anna Wang, Wenjing Yuan, Junfang Liu, Zhiguo Yu, Hua Li, A novel pattern recognition algorithm: Combining ART network with SVM to reconstruct a multi-class classifier, Computers and Mathematics with Applications, 2009.

10. Vojtech Franc, Václav Hlavá, Statistical Pattern Recognition Toolbox for Matlab, 2009.

11. Ralf Herbrich, Learning kernel classifiers: theory and algorithms, MIT Press, Cambridge, Mass, ISBN 026208306X, 2001.

12. Sangeetha, R., Kalpana, B, A comparative study and choice of an appropriate kernel for support vector machines, In: Das, V.V., Vijaykumar, R. (eds.) ICT 2010. CCIS, vol. 101,pp. 549–553. Springer, Heidelberg (2010)

13. Sangeetha, R., Kalpana, B, Optimizing the Kernel Selection for Support Vector Machines using Performance Measures, In: A2CWiC 2010, ISBN: 978-1-4503-0194-7,2010

14. G.F. Smits , E.M Jordaan,Improved SVM Regression using Mixtures of Kernels, IJCNN '02. Proceedings of the International Joint Conference on Neural Networks, 2002.

15. J Weston, C Watkins, Multi class support vector machines, Technical Report.

16. XIA Guo-en and SHAO Pei-ji.”Factor Analysis Algorithm with Mercer Kernel”, IEEE Second International Symposium on Intelligent Information Technology and Security Informatics, 2009.





Arpit Singhal, Mandeep Singh

Paper Title:

Speckle Noise Removal and Edge Detection Using Mathematical Morphology

Abstract: Mathematical morphology is a new subject established based on rigorous mathematical theories. In the basis of set theory, mathematical morphology is used for image processing, analyzing and comprehending. It is a powerful tool in the geometric morphological analysis and description. Noise removal and edge detection are very important pre-processing steps. For removing speckle noise nonlinear filtering techniques are better then liner filtering techniques image processing applications, at removing noise without affecting thin and small image features. One structure for designing nonlinear filters is mathematical morphology. Also the need of edge detection is to find the discontinuities in depth, discontinuities in surface orientation, changes in material properties and variations in scene illumination. Again mathematical morphological operations are used for edge detection and enhancement .This paper describes removal of speckle noise presented in images and then to obtain the useful edges in the output image obtained after noise removed using mathematical morphology.

Mathematical morphology, speckle noise, edge detection.


1. Rafael C. Gonzalez, Richard E. Woodes, and Steven L. Eddins, DIGITAL IMAGE PROCESSING, published by Pearson Education (Singapore) Pvt. Ltd.
2. Dr. H. B. Kekre and Ms. Saylee M. Gharge, “Image Segmentation using Extended Edge Operator for Mammography Images”, International Journal on Computer Science and Engineering, Vol. 02, No. 04, 2010, 1086-1091.

3. Bhadauria H. S., Dewal M. L., “Comparison of edge detection Techniques on Noisy Abnormal Lung CT Image before and after Using Morphological Filter”, Published in International Journal of Advanced Engineering & Application, Jan 2010.

4. S.Sudha,G.R.Suresh and R.Sukanesh, “Comparative Study on Speckle Noise Suppression Techniques for Ultrasound Images”, International Journal of Engineering and Technology Vol. 1, No., April, 2009 1793-8236.

5. Scott T. Acton, Janelle A. Molloy and Yongjian Yu, “Three- Dimensional Speckle Reducing Anisotropic Diffusion”, IEEE Conference Record of the 37thAsilomar Conference on Signals, Systems and Computers,2003, vol. 2. p.: 1987- 1991.

6. J. Serra, Ed., Image Analysis and Mathematical Morphology, Vol. 2, Theoretical Advances. New York: Academic, 1988.

7. Maragos P., “Differential Morphology and Image Processing”, IEEE Trans Image Processing, vol. 5, pp. 922–937, June 1996.

8. M Rama Bai, “A New Approach For Border Extraction Using Morphological Methods”, International Journal of Engineering Science and Technology, vol. 2(8), 2010, 3832-3837.

9. Li-Hui Jiang, Zhen-Ni Jin, Fan Zhang,Rui-Hua Liu, “A New Algorithm For Speckle Suppression Using Mathematical Morphology And Adaptive Weighted Technique”, proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong,19-20 August 2007.

10. T.Ratha Jeyalakshmi and K.Ramar, “A Modified Method for Speckle Noise Removal in Ultrasound Medical Images”, International Journal of Computer and Electrical Engineering, Vol. 2, No. 1, February, 2010 1793-8163.

11. Ahmed S. Mashaly, Ezz Eldin F. AbdElkawy, Tarek A. Mahmoud, “Speckle Noise Reduction in SAR Images using Adaptive Morphological Filter”, 10th International Conference on Intelligent Systems Design and Applications, 2010.

12. Richard Alan Peters, “A New Algorithm for Image Noise Reduction Using Mathematical Morphology”, IEEE Transaction on Image processing .vol. 4. NO.5. MAY 1995.

13. M. N. Nobi and M. A. Yousuf, “A New Method to Remove Noise in Magnetic Resonance and Ultrasound Images”, Journal Of Scientific Research, 3 (1), 81-89 (2011).

14. Chaofeng Li1,2 and Alan C. Bovik, “Three-Component Weighted Structural Similarity Index”, SPIE, 7242-24 V. 1 (p.1 of 9), 2008.

15. Zhao Yuqian, Gui Wei-hua, Chen Zhencheng, Tang Jing-tian, and Li Ling-yun., “Medical Images Edge Detection Based on Mathematical Morphology”, ”, Proceedings of the IEEE Engineering in Medicine and Biology 27th Annual International Conference Shanghai, China, PP: 6492 – 6495, 2005.

16. Yuqian Zhao, Weihua Gui and Zhencheng Chen, “Edge Detection Based on Multi-Structure Elements Morphology”, Proceedings of the 6th World Congress on Intelligent Control and Automation Dalian, China, vol.-2, PP: 9795 – 9798, 2006.

17. M Rama Bai , Dr V Venkata Krishna and J SreeDevi, “A new Morphological Approach for Noise Removal cum Edge Detection”, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 6, November 2010.

18. J.-A. Jiang, C.-L. Chuang, Y.-L. Lu and C.-S. Fahn, “Mathematical- morphology-based edge detectors for detection of thin edges in low-contrast regions”, The Institution of Engineering and Technology,2007.

19. Deng Shiwei and Yuan Baozong, “Range Image Segmentation Using Mathematical Morphology”, IEEE Region 10 Conference on TENCON Proceedings on Computer, Communication, Control and Power Engineering, vol-2, PP: 1009-1011, 1993.

20. Xueshun Wang, Dawei Qi and Yuanxiang Li, “Edge Detection of Decayed Wood Image Based on Mathematical Morphological Double Gradient Algorithm”, Proceedings of the IEEE International Conference on Automation and Logistics Qingdao, China, September 2008.





S. N. Geethalakshmi, P. Subashini, S. Ramya

Paper Title:

A Study on Detection and Classification of Underwater Mines Using Neural Networks

Abstract: Mine detection and classification using side scan sonar imagery is a challenging problem. As opposed to the majority of techniques, several Neural-network-based methods for the detection and classification of mines and mine like objects have been proposed. Detection and classification of underwater objects in sonar imagery is a complicated problem, due to various factors such as variations in operating and environmental conditions, presence of spatially varying clutter, variations in target shapes, compositions and orientation. Moreover, bottom features such as coral reefs, sand formations, and the attenuation of the sonar signal in the water column can totally obscure a mine-like object. Side scan sonar is a proven tool for detection of underwater objects. In order to overcome such complicated problems detection and classification system is needed. This method is able to extrapolate beyond the training data and successfully classify mine-like objects (MLOs). Five basic components of detection and classification techniques are considered namely data preprocessing, segmentation, feature extraction, detection and classification. In this paper nearly fifteen research papers of neural network techniques have been reviewed.

Segmentation, Feature extraction, Side scans sonar, Image classification, Underwater mine detection, Neural networks.


1. Anthony, R. Castellano, and Brian, C. Gray, “Autonomous Interpretation of Side Scan Sonar Returns” General Dynamics Electric Boat Division.
2. F. Langner, C. Knauer, W. Jans, and A. Ebert, “Side Scan Sonar Image Resolution and Automatic Object Detection, Classification and Identification “2009 IEEE.

3. Payam Saisan, Shubha Kadambe, “Shape Normalized Subspace Analysis for Underwater Mine Detection” 2008 IEEE.

4. James D. Tucker a, Mahmood R. Azimi-Sadjadi a, and Gerry J. Dobeck b” Canonical Coordinates for Detection and Classification of Underwater Objects from Sonar Imagery”2007 Naval Surface Warfare Center Panama City Panama City, FL, USA 32407-7001.

5. W. Kenneth Stewart, Min Jiang, and Martin Marra “A Neural Network Approach to Classification of Side scan Sonar Imagery from a Miocene Ridge Area “Member, zee, IEEE journal of oceanic engineering, vol. 19, no. 2, April 1994.

6. Ali Pezeshki, Mahmood R, Azimi-Sadjadi, and Louis L. Scharf, IEEE” Undersea Target Classification Using Canonical Correlation Analysis Life Fellow, Senior Member, IEEE

7. F. Langner, C. Knauer, W. Jans and A. Ebert, “Side Scan Sonar Image Resolution and Automatic Object Detection, Classification and Identification” 2009 IEEE.

8. C. Shang, and K. Brown, “Feature e-based texture Classification of side-scan sonar Images using a neural network Approach” Electronics Letters 5th November 1992.

9. Bryan Thompson, Jered Cartmill, Mahmood R, Azimi-Sadjadi, and Steven G. Schock “A Multichannel canonical correlation analysis feature extraction with application to buried underwater target classification” 2006 International Joint Conference on Neural Networks.

10. Changjing Shang, and Keith Brown” Texture Classification of Side-Scan Sonar Images with Neural Networks” 1993 the Institution of Electrical Engineers. Printed and published by the IEEE. Savoy Place, London WCPR OBL. UK.

11. Vinod Chandran, Steve Elgar, and Anthony Nguyen,” Detection of Mines in Acoustic Images Using Higher Order Spectral Features” Student Member, IEEE journal of oceanic engineering, vol. 27, no3, July 2002.

12. Payam Saisan, Shubha Kadambe, “Shape Normalized Subspace Analysis for Underwater Mine Detection” ©2008 IEEE HRL Laboratories, LLC3011 Malibu Canyon Road, Malibu, CA 90265.

13. Rebecca T. Quintal, John E. Kiernan, John Shannon Byrne, Paul S. Dysart “Automatic Contact Detection in Side-scan Sonar Data” 2010 IEEE.

14. G. J. Dobeck, "Algorithm fusion for the detection and classification of sea mines in the very shallow water region using side-scan sonar imagery," SPIE Proc., vol. 4038, pp. 348-361, April 2000.

15. M. Neumann, C. Knauer, B. Nolte, W. Jans and A. Ebert, “Target Detection of Man Made Objects in Side scan Sonar Images–Segmentation based False Alarm Reduction “,Acoustics’08, Paris, 2008.





Malek Alzaqebah, Salwani Abdullah

Paper Title:

Comparison on the Selection Strategies in the Artificial Bee Colony Algorithm for Examination Timetabling Problems

Abstract: This paper presents an investigation of selection strategies upon the Artificial Bee Colony (ABC) algorithm in examination timetabling problems. ABC is a global stochastic optimisation algorithm that is based on the behavior of honey bee swarms. Onlooker bees in ABC algorithm choose food source based on the proportional selection strategy. In this paper, three selection strategies are introduced (i.e. disruptive, tournament and rank selection strategies), in order to improve the diversity of the population and avoid the premature convergence in the evolutionary process. Experimental results show that the modified ABC with the three selection strategies outperforms the ABC algorithm alone. Among the selection strategies, the disruptive selection strategy shows the better performance when tested on standard benchmark examination timetabling problem.

Artificial Bee Colony Algorithm, Examination Timetabling problems, Selection Strategies.


1. S. Abdullah, E.K. Burke and B. McCollum, (2007). Using a Randomised Iterative Improvement Algorithm with Composite Neighbourhood Structures for University Course Timetabling. In Metaheuristics: Progress in complex systems optimization (Operations Research / Computer Science Interfaces Series), Chapter 8. Springer, ISBN:978-0-387-71919-1.
2. S. Abdullah, S. Ahmadi, E.K. Burke and M. Dror, (2007). Investigating Ahuja-Orlin’s large neighbourhood search approach for examination timetabling. OR Spectrum, 29(2), 351-372.

3. L. Bao and J. Zeng, (2009). Comparison and Analysis of the Selection Mechanism in the Artificial Bee Colony Algorithm. HIS (1) 411-416

4. A. Baykasoglu, L. Ozbakır and P. Tapkan, (2007). Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem, Swarm Intelligence: Focus on Ant and Particle Swarm Optimization, I-Tech Education and Publishing.

5. T. Blickle and L. Thiele, (1995). A Mathematical Analysis of Tournament Selection, Proc. of the Sixth International Conference on Genetic Algorithms, San Francisco, CA, pp. 2-8.

6. E. K. Burke, A. J. Eckersley, B. McCollum, S. Petrovic and R. Qu, (2010), Hybrid variable neighbourhood approaches to university exam timetabling, European Journal of Operation Research. 206(1), 46-53.

7. E.K. Burke, D.G. Elliman, P.H. Ford and R.F. Weare, (1996). Examination timetabling in British universities - A survey. In E.K. Burke and P. Ross. (eds). Selected Papers from 1st International Conference on the Practice and Theory of Automated Timetabling. Springer Lecture Notes in Computer Science, vol. 1153, 76-92.

8. E E.K. Burke and J.P. Newall. Solving examination timetabling problems through adaptation of heuristic orderings. Annals of Operations Research, 129, (2004) 107-134.

9. M. Caramia, P. Dell’Olmo and G.F. Italiano, (2001). New algorithms for examination timetabling. Algorithms Engineering 4th International Workshop, Proceedings WAE, Saarbrücken, Germany, Springer Lecture Notes in Computer Science, vol. 1982. (2001) 230-241.

10. M.W. Carter, G. Laporte and S.Y. Lee, (1996). Examination Timetabling: Algorithmic Strategies and Applications. Journal of the Operational Research Society 47, 373-383.

11. D. Karaboga, and B. Basturk, (2007). Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems, LNCS: Advances in Soft Computing: Foundations of Fuzzy Logic and Soft Computing, Vol: 4529/2007, Springer- Verlag, IFSA 789-798.

12. D., Karaboga, (2005). An Idea Based On Honey Bee Swarm for Numerical Optimization, Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department.

13. T. Kuo, and S. Y. Huang, (1997). Using Disruptive Selection to Maintain Diversity in Genetic Algorithms, Applied Intelligence, vol.7, no.3, 257-267.

14. R. Qu, E.K. Burke, B. McCollum and L.T.G. Merlot, (2009). A survey of search methodologies and automated system development for examination timetabling. Journal of Scheduling, 12. 55-89.

15. R. Qu, E.K. Burke, B. McCollum and L.T.G. Merlot,( 2009). A survey of search methodologies and automated system development for examination timetabling. Journal of Scheduling, 12. 55-89.

16. N. R. Sabar, M. Ayob and G. Kendall, (2009). Solving examination timetabling problems using honey-bee mating optimization (ETP-HBMO). In: Proceedings of the 4th Multidisciplinary International Scheduling Conference: Theory andAp- plications (MISTA 2009), 10–12 Aug 2009, Dublin, Ireland. 399–408.

17. A.G. Song, and J.R. Luo, (1999). A Ranking Based Adaptive Evolutionary Operator Genetic Algorithm, Acta Electronica Sinica, vol.27 no.1, 85-88.

18. H. Turabieh, and S. Abdullah, (2011). A Hybrid Fish Swarm Optimisation Algorithm for Solving the Examination Timetabling Problems. Learning and Intelligent Optimisation Workshop (LION 5), Rome, LNCS, Springer-Verlag Berlin.

19. H. Turabieh and S. Abdullah, (2011). An Integrated Hybrid Approach to the Examination Timetabling Problem. OMEGA - The International Journal of Management Science, doi.org/10.1016/j.omega.2010.12.005.

20. F. Von and Karl, (1974). Decoding the Language of the Bee, Science, Volume 185, Issue 4152, 663-668.

21. Y. Yang and S. Petrovic, (2005). “A novel similarity measure for heuristic selection in examination timetabling,” Lecture Notes in Comput. Sci., vol 3616, pp. 334-353. [Practice and Theory of Automated Timetabling V, 2004].






Anubhuti Khare, Manish Saxena, Manorma Kushwah

Paper Title:

On the Application of Wireless Sensor Networks in industrial Plant Energy Evaluation and Planning Systems

Abstract: Energy evaluation and planning are important in industry for overall energy savings. Traditionally these functions are realized in wired systems formed by communication cables and various types of sensors. However,the installation and maintenance of these cables and sensors is usually much more expensive than the cost of the sensors themselves. Recent advances in wireless communications, micro-electro-mechanical systems, and highly integrated electronics allowed the introduction of wireless sensor networks (WSN). WSNs have unique functional characteristics that enables low cost energy evaluation and planning in industrial plants. This paper proposes a closedloop energy evaluation and planning system with the WSN architecture. The importance of the proposed scheme lies in its non-intrusive, intelligent, and low cost nature. As the focus of this paper, the properties and architecture of the WSN in this application are discussed in detail. The applicability of the proposed system is analyzed and potential challenges are addressed. Finally, a demo system is constructed and experimental results are presented.

Wireless sensor networks, IEEE 802.15.4, efficiency estimation, condition monitoring, experimental implementations.


1. B. Lu, T. G. Habetler, and R. G. Harley, “A survey of efficiency estimation methods of in-service induction motors with considerations of condition monitoring,” in Proc. 2005 International Electric Machine and Drive Conference (IEMDC’05), May 2005, pp.1365-1372.
2. IEEE Standard Test Procedure for Polyphase Induction Motors and Generators, IEEE Std. 112-1996, Sept. 1996.

3. A. Wallance, A. Von Jouanne, E. Wiedenbrug, E. Matheson, and J. Douglass, “A laboratory assessment of in-service and non-intrusive motor efficiency testing methods,” Electric Power Components and Systems, vol. 29, 2001, pp. 517-529.

4. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks: a survey,” Computer Networks, vol. 38, no. 4, 2002, pp. 393-422.

5. Department of Energy, “2002 industrial wireless technology for the 21st century,” Office of Energy Efficiency and Renewable Energy Report, Department of Energy, Washington D.C., 2002.

6. B. Lu, L. Wu, T. G. Habetler, R. G. Harley, and J. A. Gutiérrez, “On the application of wireless sensor networks in condition monitoring and energy usage evaluation for electric machines,” in Proc. 31st Annual Conference of the IEEE Industrial Electronics Society (IECON’05), Nov. 2005.

7. B. Lu, L. Wu, T. G. Habetler, R. G. Harley, and J. A. Gutiérrez, “On the application of wireless sensor networks in condition monitoring and energy usage evaluation for electric machines,” to appear in the 31st Annual Conference of the IEEE Industrial Electronics Society (IECON’05), Raleigh, NC, Nov. 2005.

8. J. A. Gutiérrez, E. H. Callaway, and R. L. Barrett, “Low-rate wireless personal area networks: enabling wireless sensors with IEEE 802.15.4,” IEEE Press, New York, 2003.

9. E. Callaway, P. Gorday, L. Hester, J. A. Gutiérrez, M. Naeve, B.Heile, and V. Bahl, “Home networking with IEEE 802.15.4: adeveloping standard for low-rate wireless personal area networks,” IEEE Communications Magazine, vol. 40, no. 8, Aug. 2002, pp. 70-77.

10. IEEE Standard for Information Technology – Telecommunications and Information Exchange between Systems - Local and Metropolitan Area Networks - Specific Requirements - Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low Rate Wireless Personal Area Networks (LRWPANs),”
IEEE Std. 802.15.4-2003, Oct. 2003.

11. J.R. Holmquist, J.A. Rooks and M.E. Richter, "Practical Approach for Determining Motor Efficiency in the Field Using Calculated and Measured Values", IEEE/IAS PPIC 2003 Conference Record, pp 1-5

12. E. Agamloh, A.K. Wallace, A. von Jouanne, K.J. Anderson and J.A. Rooks, "Assessment of Non-Intrusive Motor Efficiency Estimators", IEEE/IAS PPIC 2004 Conference Record, pp 64-72.

13. J. Hsu and B. P. Scoggins, “Field test of motor efficiency and load changes through air-gap torque,” IEEE Trans. Energy Conversion, vol. 10, no. 3, Sept. 1995, pp. 477-483.

14. T. G. Habetler, R. G. Harley, R. M. Tallam, S. B. Lee, R. Obaid, and J. Stack, “Complete current-based induction motor condition monitoring: stator, rotor, bearings, and load,” in Proc. CIEP 2002 VIII IEEE International Power Electronics Congress, Oct. 2002.

15. D. B. Durocher, G. R. Feldmeier, “Future Control Technologies in Motor Diagnostics and System Wellness” IEEE/IAS PPIC 2003 Conference Record, pp98-106.





Md. Jahangir Hossain, A.N.M. Enamul Kabir, Md Mostafizur Rahman, Borhan Kabir, Md Rafiqul Islam

Paper Title:

Determination of Typical Load Profile of Consumers Using Fuzzy C-Means Clustering Algorithm

Abstract: This paper reports the Typical Load Profile of different types of consumers of distribution feeder which is based on clustering methods. Among many clustering methods, fuzzy c-means has been examined for determination of representative clusters because fuzzy logic is conceptually easy to understand. It is a well known clustering algorithm for typical load profiles determination. The results demonstrate that the proposed method is efficient for assigning Typical Load Profile (TLP) to the consumers. Moreover, the finding shows that the energy consumption can be clustered not only based on the load pattern but also load value. The results demonstrate that the proposed method is efficient for assigning TLP to the consumers.

Deregulation, Load Profile, Fuzzy Clustering Algorithm, Optimum Cluster, Probability, Neural network.


1. System Tariff Issues Working Group, "Metering, Load Profiles and Settlement in Deregulated Markets," Union of Electricity Industry 2000-220-0004, 2000.
2. "Load Profiling: An Innovate Alternative to Direct Metering When Implementing Customer Choice in the Electric Utility Industry," AEIC Load Research Committee July 1998.

3. C.S Chen, J.C Hwang, C.W Huang, Y.M Tzeng, and M.Y Chu, "Determination of Customer Load Characteristics by Load Survey System at Taipower,” IEEE Transaction on Power Delivery, vol. Vol.11, 1996.

4. C.S Chen, J.C Hwang, and C.W Huang, "Application of Load Survey Systems to Proper Tariff Design," IEEE Transaction in Power System, vol. Vol.12, 1997.

5. K.L Lo and Zuhaina Zakaria, "Electricity Consumer Classification Using Artificial Intelligence," presented at University Power Engineering Conference, Bristol, U.K,vol 1, pp. 443-447, 2004.

6. D Gerbec, S Gasperic, and F Gubina, "Comparison of Different Classification Methods for the Consumers' Load Profile Determination," presented at 17th International Conference on Electricity Distribution, CIRED, Barcelona,vol. Session 6, pp. 2003.

7. Mihai Gavrilas, Viovel Calin Sfintes, and Marius Nelu Filimon, "Identifying Typical Load Profiles Using Neural Fuzzy Model," presented at IEEE Power Engineering Society Transmission and Distribution Conference, vol. 1, pp. 421-426, 2001.

8. Miin-Shen Yang, "A Survey of Fuzzy Clustering," Math. Compute. Modeling, vol. 18, pp. 1-6, 1993.

9. S.V Allera and A.G Horsburgh, "Load Profling for Energy Trading and Settlements in the Uk Electricity Markets," presented at DistribuTECH Europe DA/DSM
Conference, London,vol, pp. 1998.

10. R Bellman, R Kalaba, and L Zadeh, "Abstraction and Pattern Classification," Journal of Math Analysis and Applications, vol. 13, pp. 1-7, 1966.

11. MohammadAlata, Mohammad Molhim, and Abdullah Ramini, “Optimizing of Fuzzy C-Means Clustering Algorithm Using GA” World Academy of Science Engineering & Technology,2008.

12. Hyun-Sook Rhee and Kyung-WhanOh, "A Validity Measure for Fuzzy Clustering and Its Use in Selecting Optimal Number of Clusters," presented at 5th IEEE International Conference on Fuzzy Systems, Seoul, Korea,vol 2, pp. 10201025, 1996.







Paper Title:

Audio Wave Steganography

Abstract: Present paper explores a new 4th bit rate LSB audio Stegnography method that reduces embedding distortion of the host audio. Using the proposed algorithm, Message bits are embedded into 4th LSB layers, resulting in increased robustness against noise addition. In addition, listening tests showed that perceptual quality of audio is higher in the case of the proposed method than in the standard LSB method.

Audio stegnography, carrier file, keyfile, payload, transmission medium.


1. [Lee and Chen2000] Lee, Y., Chen, L.: High capacity image steganographic model, IEE Proceedings on Vision, Image and Signal Processing, 147, 3, 288-294.
2. [Mintzer et al. 1998] Mintzer, F., Goertzil, G., Thompson, G.: Display of images with calibrated colour on a system featuring monitors with limited colour palettes, Proc.SID International Symposium, 377-380.

3. [Mobasseri 1998] Mobasseri, B.: Direct sequence atermarking of digital video using m-frames, Proc. International Conference on Image Processing, Chicago, IL, 399-403.

4. [Yeh and Kuo 99]Yeh, C., Kuo, C.: Digital Watermarking through Quasi m-rrays,Proc. IEEE Workshop on Signal Processing Systems, Taipei, Taiwan, 456-461.

5. S. Lyu, H. Farid, Steganalysis using color wavelet statistics and one-class support vector machines, in: SPIE Symposium on Electronics Imaging, 84.39, 29%, 81.21, 28%, 53.02, 18% 75.02, 25% 1st lsb 2nd lsb 3rd lsb 4th lsb San Jose, CA, 2004

6. M.K. Johnson, S. Lyu, H. Farid, Steganalysis of recorded speech, in: SPIE Symposium on Electronics Imaging, San Jose, CA, 2005.

7. A.Westfeld, Detecting low embedding rates, in: F.A.P. Petitcolas (Ed.), Information Hiding. 5th nternationalWorkshop, IH 2002 Noordwijkerhout,The Netherlands, October 7–9, 2002, Springer-Verlag, Berlin, 2003, pp. 324–339

8. [Zwicker 1982] Zwicker, E.: Psychoacoustics, Verlag, Berlin, Germany. N.F. Johnson, S. Jajodia, Steganalysis of images created using current steganography software, in:

9. D. Aucsmith (Ed.), Information Hiding,LNCS, vol. 1525, Springer- Verlag, Berlin, 1998, pp. 32–47.






M.VijayKumar, C.Chandrasekar

Paper Title:

Evolution of Micro, Macro, Me so Level Simulations for Spatial Analysis of Burglary in Metropolis Using Crime Mapping and GIS

Abstract: Crime is a long overwhelmed issue of human society. Especially, with the expansion of cities and population aggregation in recent years, crime problems have become a great challenge in urbanization process. Both urban policy-makers and police departments have realized the importance of a better understanding of the dynamics of crime. This study explores the spatial characteristic of crime in Chennai using Geographical Information System (GIS) and police call data of the metropolis in 2008. In the research, the combination of both applied and theoretical methods is used to analyze the characteristics of Chennai crime. To find out the crime pattern of the city, three spatial levels (i.e. macro level, me so level and micro level) are adopted. In this paper the crime is divided into two categories: violent crime and property crime. The spatial distribution of the two kinds of crimes is analyzed on a comparative basis of different properties in terms of land use type and population features, and the relevant factors of the crime pattern are investigated. The results reveal that there are some hotspots in downtown, transportation hubs, where population density is relatively high and the crime density decreases gradually away from the center. The analytical and theoretical result will undoubtedly lead to enhanced crime prevention strategies of Chennai in the future.

Crime, Spatial Pattern, GIS, Police Records; Chennai.


1 W. V. Ackerman and A. T. Murray, "Assessing spatial patterns of crime in Lima, Ohio", Cities, vol.21, no.5, pp. 423-437, 2004.
2 J. H. Liu, "Crime patterns during the market transition in china", The British Journal of Criminology, vol.45, no.5, pp. 613-633, 2005.

3 L. N. Zhang, S. F. Messner and J. H. Lu, "Criminological research in contemporary china - challenges and lessons learned from a large-scale criminal victimization survey", International Journal of Offender Theray and Comparative Criminology, vol.51, no.1, pp. 110-121, 2007.

4 I. Waller and B. Welsh, "International trends in crime prevention: Cost effective ways to reduce victimization", pp. 191-220, Oxford University Press., 1999.

5 Y. Zhuo, S. F. Messner and L. Zhang, "Criminal victimization in contemporary china: A review of the evidence and challenges for future research", Crime Law Social Change, vol.50, pp. 197-209, 2008.

6 R. H. Burke, "An introduction to criminological theory", in Mapping murder. London: Virgin books., ed. D. Canter, Virgin Books, London 2001.

7 P. J. Brantingham and P. L. Brantingham, Environmental criminology. Prospect heights, Waveland Press, Inc., 1991.

8 R. V. Clarke, "Situational crime prevention: Theory and practice", The British Journal of Criminology, vol.20, no.2, pp. 136-147, 1980.

9 L. E. Cohen and M. Felson, "Social change and crime rate trends: A routine activity approach", American Sociological Review, vol.44, pp. 588-608, 1979.

10 M. Felson, Crime in everyday life, CA: Sage, Thousand Oaks, 2002.

11 V. Ceccato, "Homicide in são paulo, brazil: Assessing spatial-temporal and weather variations", Journal of Environmental Psychology, vol.25, no.3, pp. 307-321, 2005.

12 L. Zhang, S. F. Messner and J. Liu, "A multilevel analysis of the risk of household burglary in the city of tianjin, china", The British Journal of Criminology, vol.47, pp. 918- 937, 2007.

13 L. Zhang, S. F. Messner, J. Liu and Y. A. Zhuo, "Guanxi and fear of crime in contemporary urban china", The Brithish Journal of Criminology, vol.49, pp. 472-490, 2009.

14 L. Anselin, J. Cohen, D. Cook and W. T. Gorr, G., "Spatial analysis of crime, measurement and analysis of crime and justice", in Criminal justice 2000. U.S. Department of crime and justice, pp. 213-262, U.S. Department of Justice, Washington, D. C., 2000.

15 D. F. B. Rosemary and L. N. Amanda, "Alcohol-related crime and disorder across urban space and time: Evidence from a british city", Geoforum, vol.33, no.2, pp. 239-254, 2002.

16 R. Boba, Crime analysis and crime mapping, Sage publications, London, 2005. [17] David T and Herbert, The geography of urban crime longman, New York, 1982.

17 E. Groff, D. Weisburd and N. A. Morris, "Where the action is at places: Examining spatio-temporal patterns of juvenile crime at places using trajectory analysis and gis ", Putting Crime in its Place, pp. 61-86, 2009.

18 D. L. Weisburd, S. Bushway, C. Lum and S. M. Yang, "Trajectories of crime at places: A longitudinal study of street segments in the city of seattle", Criminology, vol.42, no.2, pp. 283-321, 2004.

19 J. G. J. van Schaaik and J. J. van der Kemp, "Real crimes on virtual maps: The application of geography and gis in criminology ", in Geospatial technology and the role of location in science, ed. R. v. d. V. a. N. v. M. Henk J. Scholten, pp. 217-237, Springer Netherlands, 2009.

20 H. T. Kim, S. B. Kim, J. S. Go, Y. D. Eo and B. K. Lee, "Building 3d geospatial information using airborne multi-looking digital camera system", Journal of Convergence Information Technology, vol.5, no.1, pp. 15-22, 2010.

21 Y.-J. C. Shih-Kai Tsai, L.-D. Chou and T.-Y. Wang, "An indoor wayfinding system based on geo-coded qr codes for individuals with cognitive impairments", Journal of Convergence Information Technology, vol.2, no.4, pp. 71-77, 2007.

22 J. Corcoran, G. Higgs, C. Brunsdon, A. Ware and P. Norman, "The use of spatial analytical techniques to explore patterns of fire incidence: A south wales case study", Computers Environment and Urban Systems, vol.31, no.6, pp. 623-647, 2007.

23 K. Harries, "Extreme spatial variations in crime density in baltimore county, md", Geoforum, vol. 37, no. 3, pp. 404-416, 2006.

24 Y. M. Lu, "Getting away with the stolen vehicle: An investigation of journey-after-crime", The Professional Geographer, vol.55, no.4, pp. 422-433, 2003.

25 C. Lum, "The geography of drug activity and violence: Analyzing spatial relationships of non-homogenous crime event types", Substance Use & Misuse, vol.43, no.2, pp. 179-201, 2008.

26 R. A. Martin, D. K. Rossmo and N. Hammerschlag, "Hunting patterns and geographic profiling of white shark predation", Journal of Zoology, vol.279, no.2, pp. 111-118, 2009.

27 V. Ceccato, "Crime in a city in transition: The case of tallinn, estonia", URBAN STUDIES vol.46, no.8, pp. 1593-1610, 2009.

28 Y. Mao and S. Dai, "Crime spatial analysis and enviromental characteristic-take shanghai as example", Journal of City Planning,2006.

29 Z. W. Shen, "Development of pudong:New pattern of urbanization in china", Shanghai Economic Research, vol.12, pp. 22-24, 1994.

30 Chennai Municipal Statistics Bureau, Chennai statistics yearbook 2005, Statistical Press, Chennai, 2005.

31 Chennai Municipal Statistics Bureau, Chennai statistics yearbook 2007,Chennai, 2007.

32 A. L. Nelson, R. D. F. Bromley and C. J. Thomas, "Identifying micro-spatial and temporal patterns of violent crime and disorder in the British city centre", Applied Geography, vol.21, no.3, pp. 249-274, 2001.

33 B. W. Silverman, "Density estimation for statistics and data analysis", New York: Chapman and Hall, 1986.

34 N. Levine, "Crimestat iii (version 3.0): A spatial statistics program for the analysis of crime incident locations", in Crimestat iii (version 3.0): A spatial statistics program for the analysis of crime incident locations, ed. N. Levine, The National Institute of Justice, Washington, D. C., 2004.

35 S. McLafferty, D. Williamson and P. G. McGuire, "Identifying crime hot spots using kernel smoothing", in Analyzing crime patterns: Frontiers of practice, ed. V. Goldsmith, McGuire, P.G., Mollenkoph, J.H., Ross, T.A. (Eds). pp. 77-85, Sage Publications, Thousand Oaks, CA, 2000.

36 Y. Harada and T. Shimada, "Examining the impact of the precision of address geocoding on estimated density of crime locations", Computers & Geosciences, vol.32, no.8, pp. 1096-1107, 2006.

37 Tamil Nadu Police Website http://www.tnpolice.gov.in/

38 Chennai City Map: http://www.mapsofindia.com/maps/tamilnadu/chennai-map.htm






Ira Joshi, Abhishek Joshi, R.P.Gupta

Paper Title:

Dual Frequency Circularly Polarized Microstrip Antenna

Abstract: Design and analysis of a single-feed arrangement of rectangular patches is proposed, which is capable of pro¬viding dual frequency circular polarization along with broadband performance. An antenna is designed on a glass epoxy FR-4 substrate with overall thickness of the structure less than 8 mm or 0.11A0. Axial ratio bandwidth better than 1.04 % and impedance bandwidth better than 10.89% may be achieved with the proposed geometry. Measured gain and axial ratio variations of the pro¬posed antenna with frequency are compared to simulated results for better understanding. The measured E- and H-plane radiation patterns in the entire impedance bandwidth are identical in shape, and the direction of maximum radiations is normal to the patch geometry. In the entire axial ratio bandwidth range of the pro¬posed antenna, the E-plane left circularly polarized patterns are nearly 15 dB.

An antenna is designed on a glass epoxy FR-4 substrate with overall thickness of the structure less than 8 mm or 0.11A0.


1. K. L. Wong, Compact and Broadband Microstrip Antennas. New York: Wiley, 2002.
2. R. Garg, P. Bhartia, I. Bahl, and A. Ittipiboon, Microstrip Antenna De¬sign Handbook. Norwood, MA: Artech House, 2001.

3. K. L. Wong and W. Hsu, "Broadband triangular microstrip antenna with U-shaped slot," Electron. Lett, vol. 33, pp. 2085-2087, 1997.

4. G. Z. Rafi and L. Shafai, "Wideband V-slotted diamond-shaped mi¬crostrip patch antenna," Electron. Lett., vol. 40, no. 19, 2004.

5. D. Bhardwaj, D. Bhatnagar, S. Sancheti, and B. Soni, "Design of square patch antenna with a notch on FR4 substrate," Microw., An-tennas Propag., vol. 2, no. 8, pp. 880-885, 2007.

6. V. Sharma, S. Shekhawat, V. K. Saxena, J. S. Saini, K. B. Sharma, B. Soni, and D. Bhatnagar, "Right isosceles triangular microstrip antenna with narrow L-shaped slot," Microw. Opt. Technol. Lett, vol. 51, no. 12, pp. 3006-3010, 2009.

7. F. Yane, X. X. Zhane, X. Ye, and Y. Rdhmat-Samii, "Wide-band E-shaped parch antennas for wireless communications," IEEE Trans. Antennas Propag., vol. 49, no. 7, pp. 1094-1100, Jul. 2001.

8. S. Egashira and E. Nishiyama, "Stacked microstrip antennas with wide bandwidth and high gain," IEEE Trans. Antennas Propag., vol. 44, no. 11, pp. 1533-1534, Nov. 1996.

9. J. Ollikainen and P. Vainikainen, "Design and bandwidth optimization of dual-resonant patch antennas," Radio Lab., Helsinki Univ. Technol., Espoo, Finland, Rep. S 252, 2002. [10] L. C. Shen, "The elliptical microstrip antenna with circular polariza¬tion," IEEE Trans. Antennas Propag., vol. AP-29, no. 1, pp. 90-94, Jan. 1981.

10. IE3D Software, Release 8. Zeland Software, Inc., Fremont, CA.

11. D. Bhardwaj, D. Bhatnagar, S. Sancheti, and B. Soni, "Stacked patch circular polarized microstrip antena," Microw., An¬tennas Propag.






Omesh Wadhwani, Amit Kolhe, Sanjay Dekate

Paper Title:

Recognition of Vernacular Language Speech for Discrete Words using Linear Predictive Coding Technique

Abstract: Vernacular language spoken in various countries creates a limitation on software associated with speech recognition. This paper is an attempt to overcome such problem. The suggested work makes use of Linear Predictive Technique for better interpretation of spoken words. The rule based structure of fuzzy suits very well with closeness of vernacular speech recognition. In this paper we study the feasibility of Speech Recognition with fuzzy neural Networks for discrete Words Different Technical methods are used for speech recognition. Most of these methods are based on transfiguration of the speech signals for phonemes and syllables of the words. We use the expression "word Recognition" (because in our proposed method there is no need to catch the phonemes of words.). In our proposed method, LPC coefficients for discrete spoken words are used for compaction and learning the data and then the output is sent to a fuzzy system and an expert system for classifying the conclusion. The experimental results show good precisions. The recognition precision of our proposed method with fuzzy conclusion is around 90 percent.

Automatic Speech Recognition, Feature Extraction, Linear Predictive Coding, LPC Coefficients, Vernacular, Words Recognition, Word error rate


1. L. R. Rabiner and R. W. Schafer, Digital Speech Processing. Prentice-Hall, 1978.
2. M. Forsberg. 2003. Why Speech Recognition is Difficult. Chalmers University of Technology.

3. J. R. Deller, J. H. L. Hansen, J. G. Proakis. Discrete-time Processing of Speech Signals. IEEE Press. 1993.

4. Thiang, Suryo Wijoyo. Speech Recognition using LPC and Artificial Neural Network for controlling the movements of robot, 2011.

5. N.Uma Maheswari, A.P.Kabilan, R.Venkatesh “Speech Recognition system based on phonemes using neural networks”. JCSNS International Journal of Computer Science and Network Security, Vol.9 No.7, July 2009.

6. Asim Shahzad, Romana Shahzadi, Farhan Aadil “Design and software implementation of efficient speech recognizer”. International Journal of Electrical & Computer Sciences IJECS-IJENS Vol. 10.

7. R. Rodman, Computer Speech Technology. Artech House, Inc. 1999, Norwood, MA 02062.

8. J. Wang, “Fundamentals of erbium-doped fiber amplifiers arrays (Periodical style—Submitted for publication),” IEEE J. Quantum Electron., submitted for publication.

9. C. H. Lee; F. K. Soong; K. Paliwal “An Overview of Speaker Recognition Technology”. Automatic Speech and Speaker Recognition: Advanced Topics. Kluwer Academic Publishers, 1996, Norwell, MA.

10. S. R. Jang, "Neuro Fuzzy Modeling Architectures, Analyses and Applications". University of California, Berkeley, Canada, 1992.

11. P. P. Bonissone, "Adaptive Neural Fuzzy Inference Systems (ANFIS): Analysis and Applications", technical report, GE CRD, Schenectady, NY USA, 2002.

12. P. Eswar, S.K. Gupta, C. Chandra Sekhar, B. Yegnanarayana and K. Nagamma Reddy, An acoustic phonetic expert for analysis and processing of continuous speech in Hindi, in Proc. European Conf. on Speech Technology, Edinburgh, vol. 1 (1987) 369-372.

13. J.P. Haton, Knowledge based approach in acoustic phonetic decoding of speech, in: H. Niemann, M. Lang and G. Serger, Eds., Recent Advances in Speech Understanding and Dialog Systems, NATO-ASI Series, vol. 46, (1988) 51-69.

14. W.A. Lea, Ed., Trends in Speech Recognition (Prentice Hall, Englewood Cliffs, N J, 1980).






Dwarka Prasad, H.C. Sharma

Paper Title:

Significance Of Step And Touch Voltages

Abstract: Abstract: Step and touch voltages play an important role when designing high voltage substation. Step and touch potentials near high voltage substation due to severe ground faults present a hazard to anyone in proximity to substation when a fault occurs. A primary issue of concern is hazardous step and touch voltage is that arise during fault situations, hence a reliable and efficient solution to the problem is essential. Although personnel safety is of primary concern, effect of electric current, resistance of the human body and tolerable voltage criteria considerations are also essential in the design system to ensure the protection of personnel and equipment. This study will briefly explain the significance of step and touch voltages.

Step and Touch Voltages, Grounding System, Substation, Safety


1 Mohamed Nayel, Zhao Jie, He Jin Liang, Cai Zongyuan, Wang Qi , “Study of Step and Touch Voltages in Resistive/Capacitive Ground due to Lighting Stroke”, CEEE 2006, Dalian, 2P1-07, pp 56-60.
2 Heppe.R.J., “Step Potentials and Body Currents near Grounds in Two Layer Earth” ,IEEE Transaction on Power Apparatus and System Voltages, vol.PAS-98, No.1, pp.45-59, Jan./ Feb.1979.

3 Romulad K., Mukhedar D., “Field Measurement of Touch and Step Voltages”, IEEE Transaction on Power Apparatus and System Voltages,vol.PAS-103, No.11, November 1984, pp.3286-3294.

4 IEEE-SA Standard Boards 2000, IEEE Guide for Safety in AC Substation Grounding, IEEE Std 80-2000, IEEE Incorporate.

5 He J., Zeng R., Gao Y., Tu Y., Sun W., Zou J., and Guan Z. ,“Seasonal Influences on Safety of Substation”, IEEE Transaction on Power Delivery,vol.18, No.3, July 2003, pp. 788-795.

6 IEEE Std 81-1983, IEEE Guide for Measuring Earth Resistivity, Ground Impedance, and Earth Surface Potentials of a Ground System.

7 Huang L, Chen X., Yan H.,“Study of Unequally Spaced Grounding Grids”,IEEE Transactions on Power Delivery, vol. 10, No.2, April 1995, pp. 716 – 722.

8 Spuntunpong K., Chartratana S., “Design of Grounding System for GIS indoor Substation”, IEEE Region 10th Conference, volume C ,21-24 Nov.2004, pp.413-416.

9 Ghoneim Sherif, Hirsch Holger, Elmorshedy Ahdab, Amer Rabah, “Optimum Grounding Grid Design by using an Evolutionary Algorithm”, IEEE Power Enginerering Society General Meeting, 2007, 24-28 June 2007, pp1-7.

10 Dalziel,C.F., Lagen, J.B., and Thurston, J.L., “Electric Shock,” AIEE Transactions on Power Apparatus and Systems, vol. 60, pp. 1073–1079, 1941.

11 Dalziel,C.F., and Massogilia,F.P., “Let-go Currents and Voltages,” AIEE Transactions on Power Apparatus and Systems, vol. 75, part II, pp. 49–56, 1956.

12 Sakis Meliopoulos, A. P., Patel Shashi, Cokkinides, G. J., “A New Method and Instrument for Touch and Step Voltage Measurements”, IEEE Transactions on Power Delivery, vol. 9, No. 4, October 1994, pp. 1850-1860.

13 Chow, Y.L., Salama, M.M.A., Djogo, G., “Thevenin Source Resistances of the Touch,Transferred and Step Voltages of a Grounding System” IEE Proceedings Gener. Transin. Distrib., vol. 146, No. 2, March 1999, pp. 107-114.






Ramanpreet Singh, Sukhwinder Singh

Paper Title:

Detection of Rogue Base Station Using MATLAB

Abstract: This paper considers the problem of detecting rogue base station in WiMAX/802.16 networks. A rogue base station is an attacker station that duplicates a legitimate base station. The rogue base station puzzles a set of subscribers who try to get service which they believe to be a legitimate base station. It may lead to disturbance in service. The strategy of attack depends on the type of network. Our approach is based on the inconsistencies in sensitivity and received signal strength (RSS) reports received by mobile stations can be seen if a rogue Base Station (BS) is present in a network. These reports can be assessed by the legitimate base stations, for instance, when a mobile station undertakes a handover towards another BS. A new algorithm for detecting a rogue base station is described in this paper.

MATLAB, Received Signal Strength (RSS), Rogue Base Station detection and Sensitivity.


1. Jamshed Hasan, “Security Issues of IEEE 802.16 (WiMAX)” Originally published in the Proceedings of 4th Australian Information Security Management Conference, Edith Cowan University, Perth, Western Australia, Page(s): 1-10, 2006.
2. Syed Shabih Hasan and Mohammed Abdul Qadeer “Security Concerns in WiMAX”, IEEE First Asian Himalayas International Conference, Page(s): 1-5, November 2009.

3. Michel Barbeau and Jean-Marc Robert, “Rogue-Base Station Detection in WiMax/802.16 Wireless Access Networks”, Page(s): 1-14.

4. “Optimizing Your WiMAX Device Investment” WHITE PAPER: WiMAX CPE, Page(s): 1-11.

5. Sang-Eon Kim, Byung-Soo Chang, Sang Hong Lee and Dae Young Kim, “Rogue AP Detection in the Wireless LAN for Large Scale Deployment”, SYSTEMICS, CYBERNETICS AND INFORMATICS VOLUME 4 - No 5, Page(s): 78-85.

6. Alaaedine CHOUCHANE, Slim REKHIS, and Noureddine BOUDRIGA “Defending against Rogue Base Station Attacks Using Wavelet Based Fingerprinting”, IEEE/ACS International Conference, Page(s): 523-530, May 2009.

7. Ekram Hossain, “IEEE802.16/WiMAX-Based Broadband Wireless Networks: Protocol Engineering, Applications, and Services”, IEEE Fifth Annual Conference on Communication Networks and Services Research, Page(s): 1-2, 2007.

8. Sanjeev Dhawan, “Analogy of Promising Wireless Technologies on Different Frequencies: Bluetooth, WiFi, and WiMAX” 2007, IEEE 2nd International Conference on Wireless Broadband and Ultra Wideband Communications, Page(s): 1-9.

9. Lang Wei-min, Wu Run-sheng and Wang jian qiu, “A Simple Key Management Scheme Bsaed on WiMAX”, IEEE Computer Science and Computational Technology, ISCSCT '08. International Symposium, Page(s): 3-6, Dec. 2008.

10. LANG Wei-min, ZHONG Jing-li and LI Jian-Jun, “Research on the Authentication Scheme of WiMAX”, IEEE Wireless Communications, Networking and Mobile Computing, WiCOM '08. 4th International Conference, Page(s): 1-4, 2008.

11. Jim Martin, Bo Li, Will Pressly and James Westall, “WiMAX Performance at 4.9 GHz”, IEEE Aerospace Conference, Page(s): 1-8, March 2010.

12. Mussa Bshara and Leo Van Biesen, “Available Measurement in Current WiMAX Networks and Positing Opportunities”, Page(s): 580-585, Sep. 2009.

13. Pal Gronsund, Ole Grondalen, Tor Breivik and Paal Engelstad, “Fixed WiMAX Field Trial Measurements and the Derivation of a Path Loss Model”, Page(s): 1-6.

14. Mussa Bshara and Leo Van Biesen, “Localization in WiMAX Networks Depending on The Available RSS-based Measurements”, International Journal on Advances in Systems and Measurements, volume 2 no 2&3, Page(s): 214-223, 2009.

15. www.intel.com/technology/wimax/

16. www.wimaxforum.org/resources/featured-research

17. www.wimax industry.com/wimaxwhitepapers.htm

18. http://4g-wirelessevolution.tmcnet.com






Iqbal Singh, Meenakshi Bansal

Paper Title:

Monitoring Water Level in Agriculture Using Sensor Networks

Abstract: Recent advances in communications technology and wireless sensor networks made new trends to emerge in agriculture sector. One such new trend is Precision Agriculture. In this paper we are giving brief outline of using Wireless Sensor Networks (WSN) in Monitoring water level in the farm area for Precision Agriculture. This algorithm offers a maximum opportunity of delivery of water level information packets/signals to base station as it also computes a threshold as well as does calculates values based on transmission range. This over all computational mechanism helps us to build a robust mechanism for delivery of information to base station thus, reducing the packet loss. Our algorithm which picks up the information for water level can be further optimized by using optimization algorithms, which lead to smoothening of packet delivery ratio, thereby increasing the packet delivery ratio by choosing the right cost path with the help of optimization techniques like genetic algorithm, neural networks.

Precision Agriculture, wireless sensor networks, topology under control, stationary base station.


1. Izzatdin Abdul Aziz, Mohd Hilmi Hasan, Mohd Jimmy Ismail, Mazlina Mehat and Nazleeni Samiha Haron, “Remote Monitoring in Agricultural Greenhouse Using Wireless Sensor and Short Message Service (SMS)”, International Journal of Engineering & Technology IJET Vol: 9, page(s): 1-12, 2009.
2. Anurag D, Siuli Roy and Somprakash Bandyopadhyay, “Agro-sense: precision agriculture using sensor-based wireless mesh networks”, Indian Institute of Management Calcutta, page(s): 1-5, 2007.

3. Sandhyasree Thaskani and Rammurthy, “Application of topology under control wireless sensor networks in precision agriculture”, International institute of information technology, page(s): 1-14, April 2010.

4. Ning Wang, Naiqian Zhang and Maohua Wang, “Wireless sensors in agriculture and food industry—Recent development and future perspective”, published in Computers and Electronics in Agriculture, page(s): 1-14, 2006.

5. Akyildiz, I.F. and Xudong Wang, “A Survey on Wireless Mesh Networks”, IEEE Communications Magazine, September 2005.

6. George W. Irwin, Jeremy Colandairaj and William G. Scanlon, “An Overview of Wireless Networks in Control and Monitoring”, International Conference on Intelligent Computing, Kunming , CHINE (2006) , Vol. 4114, page(s): 1061-1072, 2006.

7. Tseng Chwan-Lu, Jiang Joe-Air, Lee Ren- Guey, Lu Fu-Ming, Ouyang Cheng-Shiou, Chen Yih-Shaing and Chang Chih-Hsiang, “Feasibility study on application of GSM–SMS technology to field data acquisition, Computers and Electronics in Agriculture”, Vol. 53, Issue 1, page(s): 45-59, 2006.

8. X. Zuo, W. Gao, G. Zhang, J. Zhao, Y. Zhu and D. Xia, “Design of Environmental Parameters Monitoring System for Watermelon Seedlings Based on Wireless Sensor Networks”, Applied mathematics and information sciences”, page(s): 243-250, march 2011.

9. www.Tekscan.com/Load-Cell.html

10. www.campbellsci.com/water-level-stage-flow

11. www.campbellsci.com/cs475-radar-water-level






Anubhuti Khare, Manish Saxena, Rishabh Dubey

Paper Title:

Real-Time Threshold-Voltage Control Theory for Low Power VLSI under Variable Supply Voltage

Abstract: In the recent sub-9Onm VLSI generation, a fluctuation exists in a supply voltage due to IR-drop and inductance effects. A supply voltage fluctuation in VISI chips causes large variations in the logic delay time and power consumption. However, in conventional low-power VLSI architecture such as variable threshold voltage CMOS (VTCMOS), the threshed voltage of the transistor is fixed in advance at the system design level. As a result, VTCMOS can't compensate a supply voltage fluctuation. By employing an adaptive threshold voltage control (ATVC), minimization of power consumption under a time constraint is achieved even in the presence of n supply voltage fluctuation. Optimal granularity is discussed to minimize the total power consumption.

Low Power, VLSI, Threshold-Voltage Control, VTCMOS


1. Amir H. Ajami er al.. “Analysis of IR-dmp scaling with implications for deep submicron P/G network designs,”IEEE Intemational Symposium on Quality Electronic Design (ISQED). pp. 35-40, (March 2M)3).
2. R. Saleh et al.. “Clock skew vcrihcation in h e presence of lRR-drop in the power distrihlioo nehvcrk,” tEEE Trans. on Computer-Aided Design, vol. 19, pp.635-1544, (Nov. 2000).

3. James W. Tscbaru et al.. “Adaptive body bia for reducing impacts of die-tdic and withidie parameter variations on microprocessor frequency and leakage,” IEEE J. Solid-State Circuits. vol. 37. pp.1396-1402, (Nov 2002).

4. A. Kcshavmi et al.: ‘Effectiveness of rcvcrsc body bias for leakage contro! in scaled dual Vt CMOS ICs,” Roc. ISLPED. pp.207-212, (Aug. 2001).

5. Y. Oowaki er 01.. “A sub0.lpm circuit design wilh subsuau-overbiasing.” in IEEE ISSCC Dig. Tech. Papers. pp.88-89, (1998).

6. T.Kuroda et al., ”A 0.9-V. 150-MHz. IC-mW, 4mm2. 2-D discrete cosine transform core pmssor with variable threshold-voltage (vr) scheme.” EEE I. Solid-state Circuiis. vol. 31. pp.1770-1779. (Nov. 1996).






R. M. Potdar, Chhetal Chowhan

Paper Title:

Comparison of Topologies of Shunt Active Power Filter Implemented On Three Phase Four-Wire System

Abstract: Supply of uninterrupted sinusoidal voltage of constant magnitude is the most important aim of the electrical distribution system, but this task is very tough and is becoming more difficult due to the increasing size and number of non-linear and poor power factor loads. Some of the most important causes of poor power quality is harmonics and high neutral current. Harmonics and neutral current deteriorate power quality as well as affect the system at large and makes significant impact. In this paper a control scheme based on PI controller has been proposed for generation of reference current to mitigate the harmonics and neutral current for two different topologies. The proposed methodology not only reduces the complexity but also offers simplicity to implement and increases reliability of the system. Analysis and simulation of three phase four wire shunt active power filter under balanced and unbalanced load condition have been done using MATLAB/ SIMULINK and detailed simulation level results have been presented to validate the proposed methodology.

Shunt active power filter, Voltage source inverter, PI controller, THD


1. Filipe Ferreira, Luís Monteiro, João L. Afonso, Carlos Couto, “A Control Strategy for a Three-Phase Four-Wire Shunt Active Filter”, IECON 2008, page(s): 411 - 416 10-13 Nov. 2008.
2. Yang Han, Wen-Xiang Song, Lin Xu, Gang Yao, Li-Dan Zhou, M. M. Khan, Chen Chen, “Experimental Investigation of the Operation Characteristics of 3-Phase 3-Wire Active Power Filter”, Circuits and Systems, APCCAS 2008. Page(s): 97 – 100, 2008 IEEE.

3. N. Mendalek, “Modeling and Control of Three-Phase Four-Leg Split-Capacitor Shunt Active Power Filter”, ACTEA 2009, Page(s): 121 – 126, July 15-17, 2009.

4. Norani Atan and Zahrul Faizi Hussien ,“An Improvement of Active Power Filter Control Methods in Non-Sinusoidal Condition”, 2nd IEEE International Conference on Power and Energy (PECon 08), December 1-3, 2008, Johor Baharu, Malaysia.

5. Po-Tai Cheng ,Yung-F'u Huang, Chung-Chuan Hou, “A new harmonic suppression scheme for three-phase four-wire distribution systems”, Pages 1287-1293,2001 IEEE.

6. Conor A. Quinn, Ned Mohan, “Active Filtering of Harmonic Currents in Three-phase, Four-Wire Systems with Three-phase and Single-phase Non-Linear Loads”,1992 IEEE, Page(s): 829 - 836 APEC '1992.

7. María Isabel Milanés Montero, Enrique Romero Cadaval, “Comparison Of Control Strategies For Shunt Active Power Filters In Three-Phase Four-Wire Systems”, IEEE Transactions On Power Electronics, Vol. 22, No. 1, January 2007.

8. Tian Yue-xin, Cao Hai-yan and Zhu Yan-ping, “Research on Three-Phase Four-wire Shunt Active Power Filter for Harmonic and Reactive Compensation”, ICEMS 2008, page(s): 4174 – 4177, 17-20 Oct. 2008.

9. H.Y. Kanaan, A. Hayek, S. Georges, K. A1-Haddadt, “Averaged Modeling, Simulation and Linear Control Design of a PWM Fixed Frequency Three-Phase Four-Wire Shunt Active Power Filter for a Typical Industrial Load, Power Electronics, Machines and Drives”, IET International Conference PEMD 2006.

10. Cheng-Che Chen and Yuan-Yin Hsu, “A Novel Approach to the Design of a Shunt Active Filter for an Unbalanced Three-Phase Four-Wire System under Nonsinusoidal Conditions,” IEEE Trans. on Power Delivery, Vol. 15, No. 4, Oct. 2000, pp 1258-1264.

11. C.A. Quinn, Ned Mohan & H. Mehta, “A four-wire, current-controlled converter provides harmonic neutralization in three-phase, four-wire systems,” IEEE 1993, pp 841-846.





T. Jayasree, D. Sam Harrison, T. Sree Rangaraja

Paper Title:

Automated Classification of Power Quality Disturbances using Hilbert Huang Transform and RBF Networks

Abstract: This paper presents Radial Basis Function Neural Network based approach for automatic Power Quality (PQ) disturbance classification. The input features of the Neural Network are extracted using Hilbert Huang Transform (HHT) and they are given as input to the Radial Basis Function Neural network. The data required to develop the network are generated by creating various faults in a test system. The proposed method requires less number of features and less memory space without losing its original property.



1. Ameen M.Gargoom, Nesmi Ertugrul and Wen.L.Soong (2008), ‘Automatic Classification and Characteriszation of power Quality events’, IEEE Trans. Power Delivery, Vol.23, No.4. pp.2417-2425 , 1995
2. Anton V. Shupletsov, Irine V and Hom Chneko , ‘Wavelet Packet Transform for Power Quality Factors Measurement’, Proc. of 5th International Serabian Workshop and Tutorial (EDM 2004), July 15, Erlagol, pp. 145-146.

3. Axelberg P.G.V, Gu I.Y and Bollen M.H.J ‘Support Vector Machine for classification of voltage disturbances’ IEEE Trans. Power Delivery, Vol.22 No.3 pp. 1297-1303, 2007

4. Borras D, Castilla M, Moreno and N, Montano JC . ‘Wavelet and Neural structure: A new tool for diagnostic of power system disturbances’, IEEE Trans. Industry application Vol.37 No.1, pp. 184-190, 2000.

5. Daubuchies I ‘The Wavelet Transform, Time Frequency localization and signal analysis’, IEEE Trans. Information Theory, Vol.36 pp. 961-1005, 1990.

6. Dhas P.K, Panigragi B.K, Sahoo D.K and Panda G, ‘Power Quality Disturbance Data compression detection and Classification using Integrated spline wavelet and S-Transform’, IEEE Trans. Power Delivery, Vol.18 No.2 pp.1-6, 2003.

7. Dhas P,K, Panigrahi B.K and Panda G, ‘Power Quality Analysis using S-Transform’, IEEE Trans. Power Delivery, Vol. 18, No 2, pp.406-411, 2003.

8. Devaraj D, Yegnanarayana B and Ramar K, ‘Radial Basis Function Networks for fast contingency ranking’, Electrical Power and Energy systems’, Vol. 24 pp.387-395, 2001.

9. Gouda A.M, Salama M.A, Sultan M.R, and Chikhani A.Y ‘Power Quality Detection and Classification Using Wavelet Multiresolution Signal Decomposition’, IEEE Trans. Power Delivery, Vol.14, No 4, pp. 1469-1476, 1999.

10. Gouda A.M, Kanoun S.H, Salama M.M.A and Chikkani A.Y, ‘Wavelet-Based signal Processing for disturbance classification and measurement’, IEE Proc. Generation Transmission and Distribution, Vol.149 No.3, pp. 310- 317, 2002.

11. He Zheng-you, Chen Xiaoqing and Zhang Bin , ’Wavelet Entropy Measure definition and its application for Transmission line fault Detection’ Proc. International conference on Power system technology. pp 1-5, 2006.

12. IEEE standards Board. ‘IEEE standard 1159-1995, IEEE Recommended practice for monitoring Electric power quality’, IEEE Inc. New June 14 1995.

13. Irene Yu-Hua Gu and Emmanouil Styvaktakis, ‘Bridging the gap: Signal processing for power quality applications’, Electric Power Systems research, Vol. 66 pp. 83-96, 2003.

14. Hong-Tzer and Chiung-Chou Liao , ‘A Denoising scheme for Enhancing Wavelet Based Power Quality monitoring System’, IEEE Trans on Power Delivery, Vol.16, No.3 pp. 353- 360, 2001.

15. Mario Oleskovicz, Denis V.Coury, Odilon Delmont Felho, Wesley F.Usida, Aariano A.F.M.Carneiro and Leandro R.S.pires. Power Quality Analysis applying a hybrid methodology with wavelet transforms and neural networks. Electrical Power and Energy Systems Vol. 31, pp. 206-212, 2009.

16. Ming Zhang, Kai-Chang Li and Wei-Bing Hu , ‘Automated Classification of Power Quality Disturbances using the S-Transform’, Proc. Int. Con. Wavelet Analysis and Pattern Recognition, Hong kong 30-31, August 2008. pp. 321-326, 2008.

17. Huang J, Negnevitsky and Nguyen ‘A Neural–Fuzzy Classifier for recognition of Power Quality disturbances ‘, IEEE Trans Power Delivery, Vol. 17, pp. 609-616, 2002.





Om Prakash Yadav, Vivek Chandra, Pushpendra Singh

Paper Title:

Design and Analysis of an efficient Technique for Compression of ECG Signal

Abstract: Data compression is a common requirement for most of the computerized applications. There are number of data compression algorithms, which are dedicated to compress different data formats. Even for a single data type there are number of different compression algorithms, which use different approaches. This paper examines lossless data compression algorithms and compares their performance. A set of selected algorithms are examined and implemented to evaluate the performance in compressing text data. The paper is concluded by stating which algorithm performs well for ECG Signal.

Data compression, Lossy and Lossless Compression, ECG, Compression Ratio, Compression factor, Compression time, PRD.


1. Laguna P, Jané R, Caminal P. Automatic detection of wave boundaries in multilead ECG signals: Validation with the CSE Database. Computers and Biomedical research 1994; 27:45-60.
2. Sivannarayana M. Redy DC. Biorthogonal wavelet transform for ECG parameters estimation. Medical Engineering and Physics ELSEVIER 1999; 21:167-174.

3. Bereksi-Reguig F, Chouakri SA. Computerised Cardiac Arrhythmia Detection, AUTOMEDICA. 1998;17:41-58.

4. Khalid Sayood, Introduction to data compression, Morgan Kaufmann, 3rd edition (2005).

5. Blelloch, E., 2002. Introduction to Data Compression, Computer Science Department, Carnegie Mellon University.

6. Phillips, Dwayne, "LZW Data Compression," The Computer Applications Journal, Circuit Cellar Ink, vol. 27, June/July 1992, pp. 36-48.

7. Kesheng, W., J. Otoo and S. Arie, 2006. Optimizing bitmap indices with efficient compression, ACM Trans. Database Systems, 31: 1-38.

8. Kaufman, K. and T. Shmuel, 2005. Semi-lossless text compression, Intl. J. Foundations of Computer Sci., 16: 1167-1178.

9. N. Ahmed, T. Natarajan and K. R. Rao (1974): Discrete Cosine Transform. IEEE Trans. Trans. On Computers. C-23, 90-93.

10. Mrs. S. O. Rajankar and Dr. S. N. Talbar (2010): An Optimized Transform for ECG Signal Compression. In Proc. Of Int .Conf. on Advances in Computer Science, 94-96.

11. M. Clausen and U. Baum (1993): Fast Fourier Transforms. BI-Wiss.-Verl.

12. L. Auslander, E. Feig and S. Winograd (1984): Abelian Semi-simple Algebras and Algorithms for the Discrete Fourier Transform. In Advances in Applied Mathematics.5, 31-55.

13. Tinku Acharya and Ajoy K. Roy. Image Processing Principles and Applications. John Wiley.

14. S. Chan and K. Ho (1990): Direct Methods for computing discrete sinusoidal transforms. IEEE Proceedings, 137, 433-442.

15. G. Steidl and M. Tasche (1991): A Polynomial approach to Fast algorithms for Discrete Fourier –cosine and Fourier-sine Transforms. In Mathematics in Computation, 56 (193), 281-296.

16. E. Feig and S. Winograd (1992): Fast Algorithms for Discrete Cosine Trnsforms. IEEE Tran. On Signal Processing.vol-40(9), pp 2174-2193.

17. Xuancheng Shao and Steven G. Johnson (May 10, 2007): Type-II/III DCT/DST algorithms with reduced number of arithmetic operations. Preprint submitted to Elsevier.

18. J. Abenstein and W. Tompkins (1982): A new data-reduction algorithm for real time ECG analysis. IEEE Tran. On Biomed. Engg., 29(BME-1):4, 3-8.

19. K. R. Rao and P. Yip (1990): Discrete cosine transform – algorithms, advantages, applications, San Diego: Academic Press.

20. Al-Nashash, H. A. M., 1994, "ECG data compression using adaptive Fourier coefficients estimation", Med. Eng. Phys., Vol. 16, pp. 62-67

21. Bradie, Brian., 1994, "Wavelet Packet Based Compression of Single Lead ECG", Scheduled to appear in IEEE Transactions on Biomedical Engineering

22. Hamilton, Patrick S., 1991, "Compression of the Ambulatory ECG by Average Beat Subtraction and Residual Differencing", IEEE Transactions on Biomedical Engineering, Vol. 38, No. 3., pp. 253-259.

23. S. M. S. Jalaleddine, C. G. Hutchens, R. D. Strattan and W. A. Coberly. ECG Data Compression Techniques – A Unified Approach. IEEE Trans. on Biomedical Eng., vol. 37, 4 (April 1990), pp. 329-341.






P.M. Reshmi, A.G. Kunjomana, K.A. Chandrasekharan, M. Meena, C.K. Mahadevan

Paper Title:

Structural, Electrical and Mechanical Properties of GaTe for Radiation Detector Applications

Abstract: Single crystals of gallium monotelluride (GaTe) have been grown by the horizontal freezing technique. The lattice parameters, crystallite size and lattice strain were evaluated from the x-ray diffraction (XRD) studies. Energy dispersive analysis by x-rays (EDAX) was performed on these samples to confirm the stoichiometry and chemical homogeneity. The layer-feature of GaTe was examined using a scanning electron microscope (SEM). Dielectric and AC conductivity measurements were carried out in the temperature range 313–423 K. The dielectric constant ε1 and dielectric loss tanδ were determined. Microindentation analysis was done on the cleaved planes of GaTe for different applied loads (5–35 g), to understand its mechanical behaviour. The Vickers microhardness has been computed and its correlation with energy gap Eg of the grown crystals was investigated. The energy gap of GaTe crystals was found to be ~1.69 eV, suitable for using it as high energy radiation detectors.

AC conductivity, dielectric constant, gallium monotelluride, horizontal freezing, microhardness, SEM.


1. V.P. Gupta, V.K. Srivastava, “Opto-electronic properties of gallium chalcogenides” J. Phys. Chem. Solids, vol. 42, 1981, pp.1071-1077.
2. H.S. Guder, B. Abay, H. Efeoglu, Y.K. Yogurtcu, “Photoluminescence characterization of GaTe single crystals” J. Lumin., vol. 93, 2001, pp.243-248.

3. A.M. Conway, C.E. Reinhardt, J. Nikolic, A.J. Nelson, T.F. Wang, K.J. Wu, A. Payne, A. Mertiri, G. Pabst, R. Roy, K.C. Mandal, P. Bhattacharya, Y. Cui, M. Groza, A. Burger, “Exploration of GaTe for gamma detection” IEEE Nuclear Science Symposium Conference Record, N24-326, 2007, pp.1551-1555.

4. G.A. Gamal, M.M. Nassary, S. Hussein, A.T. Nagat, “Single crystal growth and electrical properties of gallium monotelluride” Cryst. Res. Technol., vol. 27, 1992, pp. 629-635.

5. O.A. Balitskii, B. Jaeckel, W. Jaegermann, “Surface properties of GaTe single crystals” Phys. Letter. A, vol. 372, 2008, pp.3303-3306.

6. S. Pal, D.N. Bose, “Growth, characterization and electrical anisotropy in GaTe-a natural semiconducting superlattice” Bull. Mater. Sci., vol. 17, 1994, pp.1039-1047.

7. V. Augelli, C. Manfredotti, R. Murri, R. Piccolo, A. Rizzo, L. Vasanelli, “Hall effect in GaTe single crystals” Solid State Commun., vol. 21, 1977, pp 575-578.

8. G.B. Abdullaev, E.S. Guseinova, B.G. Tagiev, “Electric conductivity studies of p-GaTe polycrystals and single crystals in strong electric fields” Phys. Stat. Sol., vol. 20, 1967, pp.421-425.

9. M.M. Nassary, “Peculiarities of electric and thermoelectric properties of GaTe” Turk. J. Phys., vol. 30, 2006, pp.95-102.

10. M. Abdel Rahman, A.E. Bela, “Single crystal growth and optical energy gap of gallium telluride” J. Phys. Chem. Solids, vol. 61, 2000, pp.925-929.

11. M. Meena, C.K. Mahadevan, “Growth and electrical characterization of L-arginine added KDP and ADP single crystals” Cryst. Res. Technol., vol. 43, 2008, pp.166-172.

12. V.P. Gupta, A. Gupta, V.K. Srivastava, “Electronic properties of gallium chalcogenides (GaS, GaSe, and GaTe)” Phys. Stat. Sol. (b), vol. 108, 1981, pp.323-327.

13. R.S. Madatov, A.I. Najafov, T.B. Tagiev, A.R. Mobili, “Effect of gamma irradiation on the electrical conductivity of the layered compound GaS” Inorg. Matter., vol. 44, 2008, pp.100-103.

14. M. Priya, C.K. Mahadevan, “Studies on multiphased mixed crystals of NaCl, KCl and KI” Cryst. Res. Technol., vol. 44, 2009, pp.92-102.

15. J.J. Gilman, “The science of hardness testing and its research applications”, American Society for Metals, Ohio, 1971, pp.51.

16. T. Sato, H. Matsumoto, K. Nakano, M. Konno, M. Fukui, I. Nagaoki, Y. Taniguchi, “Application of lattice strain analysis of semiconductor device by nano-beam diffraction using the 300 kV Cold-FE TEM” J. Phys. Conference Series, vol. 241, 2010, pp. 1-4.

17. O. Oda, “Compound semiconductor and bulk materials and characterizations” World Scientific Publishing Co. Pte. Ltd., London, 2007, pp.53-55.

18. N.A. Hegab, M.A. Afifi, H.E. Atyia, M.I. Ismael, “AC conductivity and dielectric properties of amorphous Ge15Se60X25 (X=As or Sn) films” Acta Phys. Pol. A, vol. 119, 2011, pp.416-422.

19. A.S. Riad, M.T. Korayem, T.G. Abdel-Malik, “AC conductivity and dielectric measurements of metal-free phtalocyanine thin films dispersed in polycarbonate” Physica B, vol. 270, 1999, pp.140-147.

20. M.A.M. Seyam, “Dielectric relaxation in polycrystalline thin films of In2Te3” Appl. Surf. Sci., vol. 181, 2001, pp.128-138.

21. A.G. Kunjomana, K.A. Chandrasekharan, “Dislocation and microindentation analysis of vapour grown Bi2Te3-xSex whiskers” Cryst. Res.Technol., vol. 43, 2008, pp.594-598.

22. E.M. Onitsch, “Systematic metallographic and mineralogic structures” Mikroscopic, vol. 2, 1947, pp.131-138.






R Bhagya, Pramodini D V, A G Ananth

Paper Title:

Transmission characteristics of 4x4 MIMO system with OFDM multiplexing and Markov Chain Monte Carlo Receiver

Abstract: A detailed analysis of the performance of 4×4 Multiple Input Multiple Output (MIMO) antenna system has been carried out using Orthogonal Frequency Division Multiplexing (OFDM) techniques. The transmission characteristics are determined for BPSK and 16-QAM digital modulation. Additive White Gaussian Noise (AWGN) has been used for the channel. On the receiver side, Markov Chain Monte Carlo (MCMC) receiver techniques have been employed for computing the BER performance. The simulation results show that, for BER of ~10-4, the SNR achieved are significantly higher. The results indicate for BPSK modulation the SNR ~ 9 dB, and for 16-QAM modulation the SNR ~13 dB. The MIMO- OFDM multiplexing scheme show a overall improvement of ~ 4.0 dB for BER values of 10-4 between BPSK and 16-QAM modulation. A comparison of the performance of present MIMO-OFDM multiplexing system with MIMO-CDMA system and common MCMC receiver indicates that, the MIMO-OFDM Multiplexing exhibits a better BER performance for 16-QAM digital modulation. The simulations results are presented and discussed in the paper.

Multiple Input Multiple Output (MIMO), Orthogonal Frequency Division Multiplexing (OFDM), Phase Shift Keying (PSK), Quadrature Amplitude modulation (QAM), Markov Chain Monte Carlo (MCMC) Bit Error Rate (BER), Signal to Noise Ratio (SNR), Code Division Multiple Access (CDMA).


1. B. Farhang-Boroujeny, H. zhu, and Z. Shi, “Markov chain monte carlo Algorithms for CDMA and MIMO communication systems,” IEEE Trans. Signal Process, vol. 54, no. 5, pp. 1896-1909, May 2006.
2. Helmut Bölcskei, “MIMO-OFDM Wireless Systems: Basics, Perspectives and Challenges,” IEEE Wireless Communications, Volume 13, Issue 4, August 2006.

3. B. Walsh, “Markov chain monte carlo and Gibbs Sampling,” Lecture notes for EEb 581, version 26, April 2004.

4. A. Paulraj, D. Gore, R. Nabar, and H. Bolcskei, “An overview of MIMO communications A key to gigabit wireless,” Proc. IEEE, vol. 92, no. 2, pp. 198–218, Feb. 2004.

5. B. Muquet, Z. Wang, G. B. Giannakis, M. de Courville, and P. Duhamel, “Cyclic prefixing or zero padding for wireless multicarrier transmissions?” IEEE Trans. Commun., vol. 50, no. 12, pp. 2136–2148, Dec. 2002.

6. Mosier, R. R., and Clabaugh, R.G.,” A Bandwidth Efficient Binary Transmission System,” IEEE Trans., Vol. 76, pp. 723 - 728, Jan. 1958.

7. S. Ma and T. S. Ng“Semi-Blind Time-Domain Equalization for MIMO-OFDM Systems,” IEEE Trans. Vehicular Technology., vol. 57, no. 4, pp. 2219-2227 Jul, 2008.

8. Heidi Steendam, Marc Moeneclaey, “Optimization of OFDM on Frequency-Selective Time Selective Fading Channels,” IEEE Trans. Signal Processing, vol. 32, no. 10, pp. 1431–1441, Sep. 1998.

9. S. B. Weinstein, “Data Transmission by Frequency-Division Multiplexing Using the Discrete Fourier Transform,” IEEE Transactions on Communication Technology, Volume 19, Issue 5, October 1971

10. David R. Pauluzzi and Norman C. Beaulieu, “A Comparison of SNR Estimation Techniques for the AWGN Channel,” IEEE Transactions on Communications, Volume 48, Issue 10, October 2000.






Tripti Arjariya, Shiv Kumar, Rakesh Shrivastava, Dinesh Varshney

Paper Title:

Data Mining and It’s Approaches towards Higher Education Solutions

Abstract: The major objective of the research is advancement of knowledge and theoretical understanding of the relations among variables for the study and development using data mining in higher education system and its solution for Madhya Pradesh state. New knowledge takes three main forms: Exploratory research: which structures and identifies new problems, Constructive research: develops solutions to a problem, Empirical research: tests the feasibility of a solution using empirical evidence. As per the research methodology, one can have two distinct methods of research either primary or secondary. This study is a survey type of research followed by the developmental study of data mining in higher education Madhya Pradesh state. The terms basic or fundamental indicate that, through theory generation, basic research provides the foundation for further, sometimes-applied research. As there is no guarantee of short-term practical gain, researchers may find it difficult to obtain funding for basic research. In this research we come to know that how the data mining approaches and issues are helpful for the development and the solutions of higher education in Madhya Pradesh state.

Knowledge development, data mining approaches and issues, higher education system.


1. Cave, M., Kogan, M. and Hanney, S. (1990), “The scope and effects of performance measurement in British higher education, in F. J. R. C.” Dochy, M. S. R. Segers and W. H. F. W. Wijnen (Eds.), “Management Information and Performance Indicators in Higher Education,”Van Gorcum and Comp, 48–49.
2. Fielden, J., and Abercromby, K. (2000), “UNESCO Higher Education Indicators Study: Accountability and International Co-operation in the Renewal of Higher Education”, Georgia Professional Standards. UNESCO, Paris.

3. Han, J. and Kamber, M. (2001), “Data Mining: Concepts and Techniques”, Simon Fraser University, Organ Kaufmann.

4. Johnstone, J.N. (1976), “Indicators of the Performance of Educational Systems. UNESCO”, International Institute for Educational Planning, Paris.

5. Luan, J. (2002a), “Data mining and knowledge management in higher education – potential applications”, In Proceedings of AIR Forum, Toronto, Canada.

6. Luan, J. (2002b), “Data Mining Application in Higher Education”, SPSS Executive Report.






Tripti Arjariya, Vijay K Chaudhari, Dinesh Varshney

Paper Title:

Identifying and Resolving Higher Educational Problems using Data Mining Technique

Abstract: The objective of the present paper is advancement of knowledge and theoretical understanding of the relations among variables for the study and development using data mining in higher education system and its solution for Madhya Pradesh state. New knowledge takes three main forms: Exploratory research: which structures and identifies new problems, Constructive research: develops solutions to a problem, Empirical research: tests the feasibility of a solution using empirical evidence. The research methodology includes two distinct methods of research either primary or secondary. This study is a survey based followed by the developmental study of data mining in higher education Madhya Pradesh state. The terms indicate that, through theory generation, basic research provides the foundation for the better exploration of data mining research. Based on the information analyzed, it is stressed that the data mining approaches and issues, the survey on different data sources regarding educational status of school and colleges, the various policies of government of scholarships are helpful for the development and the solutions of higher education in Madhya Pradesh state in India.

Knowledge development, data mining approaches and issues, higher education system.


1. Cave, M., Kogan, M. and Hanney, S. (1990), The scope and effects of performance measurement in British higher education, in F. J. R. C. Dochy, M. S. R. Segers and W. H. F. W. Wijnen (Eds.), Management Information and Performance Indicators in Higher Education, Van Gorcum and Comp, 48–49.
2. Fielden, J., and Abercromby, K. (2000), UNESCO Higher Education Indicators Study: Accountability and International Co-operation in the Renewal of Higher Education, Georgia Professional Standards. UNESCO, Paris.

3. Han, J. and Kamber, M. (2001), Data Mining: Concepts and Techniques, Simon Fraser University, Organ Kaufmann.

4. Johnstone, J.N. (1976), Indicators of the Performance of Educational Systems. UNESCO: International Institute for Educational Planning, Paris.

5. Luan, J. (2001), Data mining and knowledge management, a system analysis for establishing a tiered knowledge management model (TKMM), In Proceedings of Air Forum, Toronto, Canada.

6. Luan, J. (2002a), Data mining and knowledge management in higher education – potential applications, In Proceedings of AIR Forum, Toronto, Canada.

7. Luan, J. (2002b), Data Mining Application in Higher Education, SPSS Executive Report.

8. Luan, J., Zhao, C.M. and Hayek, J. (2004). Use data mining techniques to develop institutional typologies for NSSE, National Survey of Student Engagement.

9. Modern Data Warehousing, Mining and Visualization Core Concepts, George M. Marakas.

10. Wako, T. N. (2003), Basic Indicators of Educational System’s Performance, National Educational Statistics Information Systems, UNESCO, Harare, Zimbabawe.

11. Yang, M., Goldstein, H., Rath, T. and Hill, N. (1999), The use of assessment data for school improvement purposes, Oxford Review of Education, 25, 469–483.

12. Zhang Yofeng, Wu Jinhong, Wang Cuibo, Automatic Competitive Intelligence Collection Based On Semantic web mining IEEE 2007

13. Wang Jain, Li Zhuo, Research and Realization of Long Distance Education platform based on Web Mining, IEEE 2009

14. Chang-xin Song, Ke Ma ,Applications of Data Mining in the Education resource based on XML, International Conference on Advanced Computer Theory and Engineering, 2008

15. Intelligent Web Mining Model to Enhance Knowledge Discovery on the Web, Sunil Kr.Pandey1 , R.B.Mishra2, Proceedings of the Seventh International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT'06) 0-7695-2736-1/06 © 2006 IEEE

16. Learning Object Models from Semi structured Web Documents Shiren Ye and Tat-Seng Chua; IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 18, NO. 3, MARCH 2006

17. A Flexible Structured-based Representation for XML Document Mining Anne-Marie Vercoustre, Mounir Fegas, Saba Gul, and Yves Lechevallier; rXiv:cs/0607012v1 [cs.IR] 5 Jul 2006

18. Mao-sang Lin, Hui Zhang, Zhang Gua Yo, An ontology for supporting data mining process, IMACS CESA, 2006

19. Survey Paper on Top 10 algorithms in data mining; XindongWu • Vipin Kumar • J. Ross Quinlan • Joydeep Ghosh • Qiang Yang • Hiroshi Motoda • Geoffrey J. McLachlan • Angus Ng Bing Liu • Philip S. Yu • Zhi-Hua Zhou • Michael Steinbach • David J. Hand • Dan Steinberg Published online: 4 December 2007 © Springer-Verlag London Limited 2007

20. Amy M. Hightower, Hajime Mitani, Christopher B. Swanson, State Policies That Pay “ Survey of School Finance Policies and Outcome” , Copyright © 2010 by Editorial Projects in Education

21. Geeta Gandhi Kingdon, “The progress of school education in India “, GPRG-WPS-071, March 2007






Harish Kumar, Neel Kamal

Paper Title:

Steady State Analysis of Self-Excited Induction Generator

Abstract: The paper deals with the steady state analysis of self-excited induction generator using Genetic Algorithm, Pattern Search and Quasi-Newton optimization techniques. The performance of an induction generator, maintaining a constant terminal voltage is analyzed under resistive loads. Further the paper deals with effects of various system parameters on the steady state performance of an induction generator. Simulated results obtained from various optimization techniques are compared graphically. The comparison of results, had lead to their comparative importance.

Induction Generator, Optimization Techniques, steady state analysis


1. D. Seyoum, C. Grantham & F. Rahman, “The dynamic analysis and control of a self-excited induction generator driven by a wind turbine”, Proceedings IEEE-IAS, USA, Oct. 2002.
2. S.S Murthy, B.P. Singh, C. Nagamani & K.V.V. atyanarayana, “Studies on the use of conventional induction motor as self- excited induction generators”, IEEE Trans. EC, Vol. 3, No. 4, pp 842-848, Dec. 1988.

3. A.K. Tandon, S.S. Murthy & G.J. Berg, “Steady state analysis of capacitor self-excited induction generator”, IEEE Trans. On PAS, Vol. PAS-103, No. 3, March 1984.

4. L. Shridhar, Bhim Singh & C.S. Jha, “A step towards improvements in the characteristics of self excited induction generator”, IEEE Trans. EC, Vol. 8, No. 1, pp 40-46, March 1993.

5. S.P. Singh, Bhim Singh & M.P. Jain, “Performance characteristics and optimum utilization of a cage machine as capacitor excited induction generator”, IEEE Trans. EC, Vol. 5, No. 4, pp 679-684, Dec. 1990.

6. S.S. Murthy, O.P. Malik & A.K. Tandon, “Analysis of self- excited induction generators”, Proc. IEE, Vol. 129, Pt. C., No. 6, pp 260-265, Nov. 1982.

7. Y.N. Anagreh & I.S. Al-Kofahi, “Genetic algorithm-basedperformance analysis of self-excited induction generator”, International Journal of Modeling and Simulation, Vol. 26, No. 2, pp 175-179, 2006.

8. Genetic Algorithm Manual.

9. Pattern Search Manual.

10. Quasi-Newton Manual

11. A.K. Al Jabri & A.L. Alolah, “Capacitance requirement for isolated self-excited induction generator”, IEE proceedings, Vol. 137, Pt. B, No. 3, pp 154-159, May 1990. induction generator”, IEEE Trans. EC, Vol. 10, No. 3, pp 516-523, Sept. 1995.

12. Dheeraj Joshi, K.S. Sandhu & M.K. Soni, “Constant voltage constant frequency operation for a self-excited induction generator”, IEEE Trans. EC, Vol. 21, No. 1, March 2006.

13. R. Bansal, T. Bhatti, & D. Kothari, “Bibliography on the application of induction generators in non-conventional energy systems”, IEEE Trans. EC, 18(3), pp 433-439, 2003.






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

Paper Title:

Routing Centric NoC Design for High Performance Multimedia Application

Abstract: NoC has been proposed as a solution for the communication challenges in the nanoscale regime of SoC. In order to tackle design complexity and to facilitate reuse, systems are typically required to be built from pre-designed and pre-verified homogenous or heterogeneous building blocks such as programmable RISC cores, DSPs, memory blocks. Most SoC platforms are special-purpose tailored to the domain-specific requirements of their application, which communicate in a very specific, mostly irregular way. In this work, we propose a methodology for routing centric Network-on-Chip design for High Performance Multimedia Application. The proposed methodology exploits a priori knowledge of the applications communication characteristic to generate an optimized network topology along with chosen routing function compliant routing tables to improve communication performance by improved traffic load distribution.

NoC, SoC, ULSI, on-chip networks, application optimized NoC.


1. W. J. Dally and B. Towles, “Route Packets, Not Wires: On-Chip Interconnection Networks,” IEEE Proceedings of 38th Design Automation Conference (DAC), 2001, pp. 684–689.
2. 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,” Proceedings of Very Large Scale Integration (VLSI) Annual Symposium (ISVLSI 2002), 2002, pp. 105–112.

3. C. Glass and L. Ni, “The Turn Model for Adaptive Routing,” Proceedings of 19th International Symposium on Computer Architecture, May 1992, pp. 278– 287.

4. M. D. Schroeder et al. “Autonet: A High-Speed Self-Configuring Local Area Network Using Point-to-Point Links,” Journal of Selected Areas in Communications, vol. 9, October 1991.

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

6. Pinto et al. “Efficient Synthesis of Networks on Chip”. Proceedings of ICCD, October 2003, pp. 146-150.

7. W. H. Ho and T. M. Pinkston, “A Methodology for Designing Efficient On-Chip Interconnects on Well-Behaved Communication Patterns,” Proceedings of HPCA, February 2003, pp. 377-388.

8. K. Srinivasan et al. “An Automated Technique for Topology and Route Generation of Application Specific On-Chip Interconnection Networks,” Proceedings of ICCAD 2005, 2005.

9. K. Srinivasan and K. S. Chatha, “ISIS: A Genetic Algorithm based Technique for Custom On-Chip Interconnection Network Synthesis,” Proceedings of 18th International Conference on Very Large Scale Integration (VLSI) Design, Kolkata, India, 2005, pp. 623-628.

10. T. Ahonen et al. “Topology Optimization for Application Specific Networks on Chip,” Proceedings of SLIP 2004, 2004.

11. J. Hu, and R. Marculescu, “Energy-Aware Mapping for Tile-based NOC Architectures under Performance Constraints,” proceedings of ASP-DAC 2003, Jan 2003.

12. R. P. Dick, D. L. Rhodes and W. Wolf, “TGFF: Task Graphs for Free,” Proceedings of International Workshop on Hardware/Software Codesign, March 1998.

13. Lavina Jain, B. M. Al-Hashimi, M. S. Gaur, V. Laxmi and A. Narayanan, “NIRGAM: A Simulator for NoC Interconnect Routing and Application Modelling, Proceedings of DATE 2007, 2007.

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

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

16. A. B. Kahng, B. Li, L. S. Peh and K. Samadi, “Orion 2.0: A Fast and Accurate NoC Power and Area Model for Early-Stage Design Space Exploration,” Proceedings of DATE’09, 2009, pp. 423–428.

17. S. Kumar, A. Jantsch, J.P. Soininen, M. Forsell, M. Millberg, J. Öberg, K. Tiensyrjä, A. Hemani, “A Network on Chip Architecture and Design Methodology”, In IEEE Annual Symposium on VLSI, April 2002.

18. R. Holsmark, S. Kumar, “Design Issues and Performance Evaluation of Mesh NoC with Regions”, In IEEE NorChip, Oulu, Finland, pp. 40-43, Nov. 2005.





Suman Rathi, Rajeshwar Lal Dua, Parmender Singh

Paper Title:

Spectrum Sensing in Cognitive Radio using MIMO Technique

Abstract: In this paper one of the most important cognitive radio task i.e. spectrum sensing is explained in detail. Cognitive radio is an intelligent wireless communication technology in order to increase the spectrum efficiency. Increasing efficiency of the spectrum usage is an urgent need as an intrinsic result of the increasing demand for higher data rates, better quality of services and higher capacity. There are several spectrum sensing techniques proposed in literature for cognitive radio based systems like Non cooperative and cooperative spectrum sensing. But there are some practical challenges and limitations in these techniques. So this paper provides the idea behind the MIMO concept in cognitive radio where multiple antennas can be placed both on primary user and secondary user and results evaluate the performance of its implementation. With the emergence of MIMO system, multipath were effectively converted into benefit for communication system. The probability of detection increases and the probability of false alarm decreases as given below in the simulation section.

Cognitive Radio, Spectrum hole, Spectrum Sensing, MIMO, Cooperative


1. Federal Communications Commission’s, “Spectrum policy task force report (ET Docket No. 02-135),” Nov. 2002.
2. J. Mitola, III and G. Q. Maguire, Jr., "Cognitive radio: making software radios more personal," Personal Communications, IEEE [see also IEEE Wireless Communications], vol. 6, pp. 13, 1999.

3. Thesis on “Spectrum sensing techniques for Cognitive radio systems with multiple Antennas” by Refik Fatih U¨ STOK Submitted to the Graduate School of Engineering and Sciences of ˙Izmir Institute of Technology June 2010,Pg No 16.

4. Coopertive communication for cognitive radio networks,Ben Letaif, K. Wei Zhang, Dept of Electronics & Comput. Engg.,Hong Kong Univ.of Sci.and Tehn.,Kowloon, China, Procedings of the IEEE,May 2009 Vol.97 Issue:5,Pages:878-893.

5. Ghurumuruhan Ganesan and Ye (Geoffrey),”Cooperative spectrum sensing in cognitive radio networks” New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005. 2005 First IEEE International Symposium, pp. 137 – 143, 8-11 Nov. 2005

6. Thesis report on “Cognitive Radios – Spectrum Sensing Issues”by Amit Kataria presented to the Faculty of the Graduate School at the University of Missouri-Columbia.

7. S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 23, pp. 201–220, Feb. 2005.

8. Abbas Taherpour, Masoumeh Nasiri-Kenari, and Saeed Gazor,“Multiple antenna spectrum sensing in cognitive radios”,IEEE Transactions on Wireless Communications, vol. 9 , (February 2010), pp. 814-823,2010

9. A. Taherpour, S. Gazor, and M. Nasiri-Kenari, “Wideband spectrum sensing in unknown white Gaussian noise," IET Commun., vol. 2, pp. 763-771, Dec. 2008.

10. R.P Singh and S.D Sapre, Communication Systems, Second Edition, TMH, p. No 2






Manisha V. Pinto, Kavita Asnani

Paper Title:

Stock Price Prediction Using Quotes and Financial News

Abstract: This paper provides a framework for predicting stock magnitude and trend for making trading decisions by making use of a combination of Data Mining and Text Mining methods. The prediction model predicts the stock market closing price for a given trading day ‘D’, by analysing the information rich unstructured news articles along with the historical stock quotes. In particular, we investigate the immediate impact of the news articles on the time series based on Efficient Market Hypothesis (EMH). Key phrases provide semantic metadata that summarize and characterize documents. This framework incorporates Kea [1], an algorithm for automatically extracting key phrases from news articles. The prediction power of the Neural Network is used for predicting the closing price for a given trading day. The Neural Network is trained on the extracted key phrases and the stock quotes using the Back propagation Algorithm.

Stock Market, Dow Jones Industrial Average, Key Phrase Extraction Algorithm (KEA), Neural Network, Back Propagation Algorithm


1. Ian H. Witten,* Gordon W. Paynter,* Eibe Frank,* Carl Gutwin† and Craig G. Nevill –Manning, KEA: Practical Automatic Keyphrase Extraction
2. Petr Kroha, Thomas Reichel and Bj¨orn Krellner, Text Mining for Indication of Changes in Long-Term Market Trends

3. Robert P. Schumaker, An Analysis of Verbs in Financial News Articles and their Impact on Stock Price

4. Ramon Lawrence, Using Neural Networks to Forecast Stock Market Prices

5. K. Senthamarai Kannan, P. Sailapathi Sekar, M.Mohamed Sathik and P. Arumugam, Financial Stock Market Forecast using Data Mining Techniques

6. Garth Garner, Prediction of Closing Stock Prices

7. Mateusz, KOBOS, Jacek and Mańdziuk, Artificial Intelligence Methods In Stock Index Prediction With The Use Of Newspaper Articles

8. Manoel C. Amorim Neto, Victor M. O. Alves, Gustavo Tavares, Lenildo Arag˜ao Junior, George D. C. Cavalcanti and Tsang Ing Ren Stock Price Forecasting Using Exogenous Time Series and Combined Neural Networks

9. Gil Rachlin' ,Mark Last' , Dima Alberg' and Abraham Kandel2 ADMIRAL: A Data Mining Based Financial Trading System,

10. Marc-André Mittermayer, Forecasting Intraday Stock Price Trends with Text Mining Techniques

11. Moshe Koppel and Itai Shtrimberg, Good News or Bad News? Let the Market Decide

12. M. I. Yasef Kaya and M. Elif Karsl_gil, Stock Price Prediction Using Financial News Articles

13. E.F. Fama, Long Term Returns and Behavioral Finance, Social Science Research Network.

14. E.F. Fama, Efficient Capital Markets: A Review of Theory and Empirical Work, Journal of Finance, 25 (May 1970): 383-417.

15. E.F. Fama, Efficient Capital Markets: II, Journal of Finance, 46 (December 1991): 1575-1617.





R.I. Minu, K.K. Thyagharajan

Paper Title:

Scrutinizing The Video And Video Retrieval Concept

Abstract: Digital video information is some kind of proof for the events happening in our mankind. As capturing such kind of multimedia information is ease of work due to hand-held technology there are tremendous amount of such video in our Internet database. So, obviously we are in need of some technique for retrieving these data. This paper will give an initial analysis on the structure of digital video and the overall overview of video retrieval procedure which will give effective retrieval result.

Mpeg7, Video Retrieval, CBIR, Semantic Web


1. D. Zhong, S.-F. Chang, “Video Object Model and Segmentation for Content-Based Video Indexing”,IEEE Intern. Conf. on Circuits and Systems, June, 1997, Hong Kong.
2. S. Chang, W. Chen, H. Meng, H. Sundaram, D. Zhong, (1998, September), “A fully automated content based video search engine supporting spatio-temporal queries”, IEEE Trans. Circuis and Systems for Video Technology, 8(5), 602-615.

3. M.Swani and D.Ballard,"Color indexing",International Journal of Computer Vision,vol. 7,no.1,00 11-32, 1991.

4. Jing Huang, S.R.Kumar,M.Mitra,Wei-Jing ZhuR.Zbih,"Image indexing using color correlograms",IEEE Processdings on Computer vision and Pattern Recognition, pp.762-768,1997

5. R.Mehtotra and J.E.Gray, "Similar shape retrieval in shape data mangement",IEEE Computer, vol.28,pp.57 -62,Sept. 1995

6. B.S.Manjunath and W.Y.Ma."Texture features for browsing and retrieval of image data",IEEE Transaction on Pattern Analysis and Machine Intelligence,vol.18,pp.837-842,Aug.1996

7. ISO/IEC JTC1/SC29/WG11N6828 Palma de Mallorca, “MPEG-7 Overview (version 10)” October 2004

8. Ivica Dimitrovski,Suzana Loskovska,Gorgi Kakasevki and Ivan Chorbev”Video content Based Retrieval System”IEEE The international Conference on “Computer as a Tool” Pg.978 -983,Sep 07

9. Hong lu and Yap-Peng Tan'"An effective Post-Refinement Method for Shot Boundary Detection",IEEE Transactions on Circuits and systems for Video Technology,Vol.15,No.11,Nov05,pp.1407 - 1420.

10. W. Wolf, “Key frame selection by motion analysis,” in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, vol. 2, 1996, pp. 1228–1231.

11. M. Yeung, B.-L. Yeo, and B. Liu, “Extracting story units from long programs for video browsing and navigation,” in Proc. IEEE Int. Conf. Multimedia Computing and Systems, 1996, pp. 296–305.

12. X. B. Gao and X. O. Tang, “Unsupervised video-shot segmentation and model-free anchorperson detection for news video story parsing,” IEEE Trans. Circuits Syst. Video Technol., vol. 12, no. 9, pp. 765–776, Sep. 2002.

13. H. J. Zhang, Y. H. Gong, S. W. Smoliar, and S. Y. Tan, “Automatic parsing of news video,” in Proc. Int. Conf. Multimedia Computing and Systems, 1994, pp. 45–54.

14. Y.-P. Tan, D. D. Saur, S. R. Kulkarni, and P. J. Ramadge, “Rapid estimation of camera motion from MPEG video with application to video annotation,” IEEE Trans. Circuits Syst. Video Technol., vol. 10, no. 1, pp. 133–146, Feb. 2000.

15. R. Lienhart, “Comparison of Automatic Shot Boundary Detection Algorithms”, Proc. SPIE Vol. 3656, p.290-301, Storage and Retrieval for Image and Video Databases VII, Editor(s), 1998

16. J. S. Boresczky and L. A. Rowe, “A Comparison of Video Shot Boundary Detection Techniques. Storage & Retrieval for Image and Video Databases IV”, SPIE 2670, pp. 170-179, 1996.

17. S. Jun, S. Park, “An Automatic Cut Detection Algorithm Using Median Filter and Neural Network”, ITC-CSCC 2000, Vol. 2, pp 1049-1052.

18. D. Zhong, “Segmentation, Index and Summarization of Digital Video Content,” Doctoral Dissertation, Columbia University, 2001.

19. Houng Lu,Yap-Peng Tan,”An effective post refinement method for shot boundary detection”IEEE Transactions on circuits and systems for video technology,Vol 15,No.11,Nov 05

20. W. Wolf, “Key frame selection by motion analysis”, Proceedings of IEEE International Conference Acoustic, Speech, and Signal Processing, 1996

21. M. Yeung and B. Liu, “Efficient matching and clustering of video shots”, Proceedings of IEEE International Conference Image Processing, 1995

22. Girgensohn and J.Borecky “Time constrained key frame selection technique”, Proceedings of IEEE International Conference Multimedia Computing and Systems, 1999.

23. Pascal Kelm, Sebastian Schmiedeke, and Thomas Sikora “FEATURE-BASED VIDEO KEY FRAME EXTRACTION FOR LOW QUALITY VIDEO SEQUENCES”, Proceeding of IEEE WIAMIS 2009 conference

24. R. Datta, D. Joshi, J. Li, and J. Z. Wang. Image retrieval: Ideas, inuences, and trends of the new age. In ACM Computing Surveys, 2008Letter Symbols for Quantities, ANSI Standard Y10.5-1968.

25. M. Worring and G. Schreiber. Semantic image and video indexing in broad domains. IEEE Transactions on Multimedia, 9(5):909{911, August 2007

26. Shneiderman B and Kang H (2000) Direct Annotation: A Drag-and- Drop Strategy for Labeling Photos. In: Proc. International Conference Information Visualisation (IV2000). London, England.

27. B.S.Manjunath,Jens-Rainer Ohm,Vinod V.Vaudevan and Akio Yamada “Color and Texture Descriptors” IEEE Transaction on Circuits and Systems for Video technology, VOL. 11, NO. 6, JUNE 2001

28. Kuen-Long Lee,Ling-Hwei Chen “An efficient computation method for the texture browsing descriptor of MPEG-7”,Science Direct Image and Vision Computation 23(2005)

29. Yong Man Ro, Munchurl Kim, Ho Kyung Kang, B.S. Manjunath, and Jinwoong Kim,” MPEG-7 Homogeneous Texture Descriptor”, ETRI Journal, Volume 23, Number 2, June 2001

30. Dong Kwon Park Chee Sun Won and Soo-Jun Park. Efficient use of mpeg-7 edge histogram descriptor. ETRI Journal, 24(2), 2002.

31. Bober, M. Price, W. Atkinson, J. ,” The contour shape descriptor for MPEG-7 and its applications”, International Conference on Consumer Electronics, 2000. ICCE. 2000 Digest of Technical Papers.

32. Carla Zibreira and Fernando Pereira.” Image Description and Retrieval Using MPEG-7 Shape Descriptors “,Springer Publication Book on Research and Advanced Technology for Digital Libraries, 200
33. Mokhtarian, F., Bober, M.,” Curvature Scale Space Representation: Theory, Applications, and MPEG-7 Standardization”,Computational Image and Vision Vol25,2003

34. Patrick Ndjiki-Nya, Oleg Novychny and Thomas Wiegand “VIDEO CONTENT ANALYSIS USING MPEG-7 DESCRIPTORS”,”Merging Mpeg-7 descriptors for image content analysis” and “A Mpeg-7 aided segmentation tool for content based video coding”,IEEE Conference ICASSP,2004

35. Mojsilovic, B. Rogowitz, Capturing image semantics with low-level descriptors, Proceedings of the ICIP, September 2001, pp. 18–21

36. Ying Liu, Dengsheng Zhang, Guojun Lu,Wei-Ying Ma ,” A survey of content-based image retrieval with high-level semantics”, Science Direct, Pattern Reorganization ,40,2007

37. Jianjiang Lu,Qi Xiao,Tianzhong Zhao, Yafei Zhang and Yanhui Li,”Image Annotation Based On Bi-Coded Chromosome Genetic Algorithm for Feature Selection”, IEEE proceesing,International Conference on Information and Automation,2008

38. Lei Shi,Guochang Gu,Haibo Liu,Jing Shen,”A Semantic annotation algorithm based on image regional object ontology”,2008 IEEE International Conference on Computer Science and Software Engineering

39. Hui Hui Wang,Dzulkifli Mohamad,N.A.Ismail,”Image Retrieval:Techniques,Challenge and Trend”,World Academy of Science,Engineering and Technology 2009

40. Lei Wang,Latifur Khan,Li Lie and Weili Wu,”Automatic Image Annotation and Retrieval using Weighted Feature Selection”, Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering (ISMSE’04)

41. Thyagharajan K. K. & R. Harikrishnan,” Contentbased Bandwidth Aware Hierarchical Video Summarization”,International Journal of Computer Science, System Engineering and Information technology, Volume 3 Number 1 January-June 2010, Pages: 197-203

42. R.I.Minu and K.K.Thyagharajan "Automatic image classification using SVM Classifier"CiiT International Journal of Data Mining Knowledge Engineering,July issue,2011

43. G.Nagarajan, Prof.K.K.Thyagharajan,"A Survey on the Ethical Implications of Semantic Web Technology": Journal of Advanced Reasearch in Computer Engineering,Vol.4No.1 jan-june 2010

44. R.I.Minu,Prof.K.K.Thyagharajan,"Evolution of Semantic Web and Its Ontology": Second Conference on Digital Convergence 2009.






M. Nazimuzzaman, Himadri S. Saha, Md. Imdadul Islam, M. R. Amin

Paper Title:

Performance Comparison of Uplink Cognitive Cellular Network under Rayleigh and Nakagami-m Fading Environments

Abstract: In this paper we consider a mobile cellular network where two types of users: primary user (PU) and cognitive user (CU) share the entire spectrum of the base station (BS). Opportunistic scheduling scheme of CU is widely used to alleviate interference between CU and PU users. Recent literature deals with such networks under Rayleigh fading environment. The objective of this paper is to determine the performance of such networks under Nakagami-m fading environment and to compare the results with the results for the Rayleigh fading model. The paper shows the comparison of average bit error rate (BER) and mean channel capacity of target transmission rate taking outage probability as a parameter. It is found that for comparatively lower value of the outage probability the Nakagami-m fading has higher BER than the corresponding Rayleigh fading case whereas for higher values of the outage probability the situation becomes reverse. It is further observed that the channel capacity under Nakagami-m fading environment is better than the Rayleigh fading environment. The paper depicts the real-time performance with some explanations.

PU, CU, average BER, mean channel capacity, opportunistic spectrum access, target transmission rate.


1. Xin Kang,Rui Zhang,Ying- Chang Liang, “Optimal Power Allocation for Cognitive Radio under Primary User’s Outage Loss Constraint,” H. Simpson, Dumb Robots, 3rd ed., Springfield: UOS Press, 2004, pp.6-9.
2. Federal Communication Comission, “Spectrum Policy Task Force,” Rep.,ET Docket no. 02- 135, Nov 2002 .

3. D. Cabric,I. D. O. Donnell,M. S. W. Chen, and R. W. Brodersen, “Spectrum Sharing Radios,” IEEE Circuits and Systems Mag.,vol. 6, July 2006, pp. 30-45.

4. Simon Haykin, “Cognitive Radio: Brain-Empowered Wireless Communications,”IEEE Journal on Selected Areas in Communications, vol. 23 Feb 2005 pp. 201-220.

5. Yan Chen,Guanding Yu,Zhaoyang Zhang,Hsiao-Hwa Chen and Peiliang Qiu, “On Cognitive Radio Networks with Opportunistic Power Control Strategies in Fading Channels,” IEEE Trans.on Wireless Communications, vol. 7 July,2005 pp. 2752-2761.

6. M. Gastpar, “On capacity under received-signal constraints,” in Proc. 42nd Annual Allerton Conf. Comm.,Control Comp.,Sept. 2004. Available: citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1. 122.7634.pdf

7. M. Gastpar, “On capacity under received and spatial spectrum-sharing constraints,” IEEE Trans. Inform .Theory, vol. 53, Feb. 2007 pp. 471-487 .

8. A. Ghasemi and E.S. Sousa, “Fundamental limits of spectrum-sharing constraints,” IEEE Trans.Wireless Commun.,vol. 6, Feb. 2007, pp.649-658.

9. A. Ghasemi and E.S. Sousa, “On Performance of multiuser diversity is SISO and MIMO wireless communicaiton,” in Proc. IEEE PIMRC’03, Sept. 2003.

10. C.- J. Chen and L.-C Wang,“A unified capacity analysis for wireless systems with joint multiuser scheduling and antenna diversity in Nakagami fading channels,”IEEE Trans.Commun., vol. 54, Mar, 2006 pp. 469-478.

11. G. Song and Y. Li, “Asymptotic throughput analysis for channel-aware scheduling,” IEEE Trans. Commun., vol. 54, Oct. 2006, pp. 1827-1834.

12. Q. Ma and C. Tepedelenlioglu, “Practical multiuser diversity with outdated channel feedback,” IEEE Trans. Veh. Tech., vol. 54, vol.54 , July 2005, pp. 1334-1345.

13. V.K.N. Lau and M. Jiang, “Performance analysis of multiuser downlink space-time scheduling for TDD systems with imperfect CSIT,” IEEE Trans. Veh. Technol., vol.55, Jan. 2006, pp 296-305.

14. Dong Li, “Performance Analysis of Uplink Cognitive Cellular Networks with Opportunistic Scheduling,” IEEE Commun. Lett., vol. 14, Sep. 2010, pp. 827-829.






Kamal Ahmed, Himadri S. Saha, Mustafa M. Hussain, M. R. Amin

Paper Title:

Comparison between the Performance of PUSC and FFR Network

Abstract: This work is an approach to solve the limitations of WiMax with three operational band frequencies. As the license fee of the frequency band goes higher and not easy to get allotment, it is wise to look for solution to make effective use of the available band. This can be done by several features that will lead to a network to the peak of performance with limited resource. One of the proposed solutions to maximize the capacity and upgrading the performance in this literature is using fractional frequency reuse (FFR) with MIMO (Multiple-Input Multiple-Output) technique. MIMO is a revolutionary technique to overcome the limitations of capacity and coverage of a WiMax network. In this paper, first we discuss about the features which will give a perfect overview of this FFR technology. Then a simulator is used to compare the performance of the FFR technology with partial usage of sub-channels (PUSC) technique. The simulation results are compared to find the best feature to see whether this feature really works in a real RF environment. Finally, the whole approach is discussed with the limitations and future proposals.

WiMax, MIMO (multi-input multi-output), CINR (carrier to interference-plus-noise ratio), RSSI (received signal strength indicator), CAPEX (capital expenditure).


1. “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 and Corrigendum1,”Available:http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1603394&abstractAccess=no&userType=inst
2. Masood Maqbool, Marceau Coupechoux, Philippe Godlewski ,“Effect of Distributed Subcarrier Permutation on Adaptive Beamforming in WiMAX Networks,”IEEE Vehicular Technology Conference, October 2008, pp.1-5 [68th IEEE Vehicular Technology Conference,2008 VTC 2008 fall.].

3. Q. Ni et al. , “Investigation of Bandwidth Request Mechanisms under Point-to-Multipoint Mode of WiMAX Networks,”IEEE Commun. Mag.,vol 45 issue 5, May 2007 pp. 132-138.

4. R. Giuliano et al., “Reuse Distance for an OFDMA-based Cellular System,” IEEE PIMRC ’06, Helsinki, Finland, Sept. 2006, pp. 1–5 [IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communication].

5. “Mobile WiMAX Part I: A Technical Overview and Performance Evaluation,” WiMAX Forum, Feb. 2006 pp 1-53, Available: http://www.wimaxforum.org

6. Marc C. Necker, “Local Interference Coordination in Cellular OFDMA Networks,” IEEE Vehicular Technology Conference, Oct. 2007, pp. 1741–1746 [IEEE 66th Vehicular Technology Conference,2007].

7. H. Jia et al, “On the Performance of IEEE 802.16 OFDMA System Under Different Frequency Reuse and Subcarrier Permutation Patterns,” IEEE International Conference on Communication, June 2007, pp. 5720–5725.

8. R. Giuliano et al. , “Analysis of Fractional Frequency Reuse Planning Strategy for WiMAX Systems,” IEEE Wireless Rural Emerg. Commun. Conf., Rome, Italy, Oct. 2007, pp 60-65.

9. V. H. MacDonald, “Advanced Mobile Phone Service: The Cellular Concept,”The Bell System Technical Journal, vol. 58, no. 1, Jan. 1979, pp. 15–41.

10. Vladimir Bykovnikov, “The Advantages of SOFDMA for WiMAX” by Intel Corporation 2005, pp-1-10 Available: http://www. my.com.nthu.edu

11. Romeo Giuliano and Cristiano Monti, “Wimax Fractional Frequency Reuse for Rural Environment” IEEE Wireless Communications,vol 15, June 2008, pp. 60-65.

12. Throdore S. Rapaport, “Wireless Communications: Principles and Practice”, Prentice Hall, Upper Saddle River, NJ, 2nd Edition, 2002.

13. Simon Haykin, Michael_Moher, “Modern Wireless Communications”, Pearson Education,4th Edition 2005.





Naveen Choudhary

Paper Title:

Constant Bit Rate Traffic Investigation for Network-on-Chip

Abstract: Scalable Networks on Chips (NoCs) are needed to match the ever-increasing communication demands of large-scale Multi-Processor Systems-on-chip (MPSoCs) for high-end wireless communications applications. The heterogeneous nature of on-chip cores, and the performance efficiency requirements typical of high end computing devices call for efficient NoCs architecture which eliminate much of the overheads connected with general-purpose communication architectures. This paper evaluates the performance of regular and Irregular NoC for constant bit rate traffic pattern for various routing algorithms such as X-Y, O-E, Up*/down*. The performance of NoC with varying number of cores is evaluated on the systemC based discrete event, cycle accurate NoC performance simulator.

NoC, SoC, simulation, traffic pattern, Topology


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

3. P. Guerrier, A. Greiner, “A generic architecture for on-chip packet-switched interconnections,” in Proceedings of the Automation and Test in Europe Conference and Exhibition, 2000, pp. 250-256.

4. International Technical Roadmap for Semiconductors. Available: http://public.itrs.net/, 2004.

5. IBM Inc. CoreConnect Bus Architecture. http://www-3.ibm.com/chips/ products/coreconnect.

6. ARM Inc. AMBA On-Chip Bus Standard. http://www.arm.com/products/solutions/AMBAHomePage.html.

7. D. Flynn, “AMBA: enabling reusable on-chip designs,” in IEEE Micro, 17(4):20-27, July-Aug 1997.

8. 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,” Proceedings of Very Large Scale Integration (VLSI) Annual Symposium (ISVLSI 2002), 2002, pp. 105–112.

9. J. Duato, S. Yalamanachili and L. Ni, Interconnection Networks: An Engineering Approach. Morgan, 2003.

10. W. Dally, “Performance analysis of k-ary n-cube interconnection networks,” in IEEE Trans. Comput.39, 6 (June) , 1990, pp. 775–785.

11. G. M. Chiu, “The odd-even turn model for adaptive routing,” Parallel and Distributed Systems, IEEE Transactions on, vol. 11, no. 7, Jul 2000, pp. 729–738.

12. M. D. Schroeder et al., “Autonet: A high-speed self-configuring local area network using point-to-point links,” Journal on Selected Areas in Communications, vol. 9, Oct. 1991.

13. A. Jouraku, A. Funahashi, H. Amano, M. Koibuchi, “L-turn routing: an adaptive routing in irregular networks,” in International Conference on Parallel Processing, Sep. 2001, pp. 374-383.

14. 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, Aug. 2004, pp. 343-352.

15. J. Wu, L. Sheng, “Deadlock-free routing in irregular networks using prefix routing,” DIMACS Tech. Rep. 99-19, Apr. 1999.

16. V. L.Cherkasova, T. Rokicki, “Fibre channel fabrics: evaluation and design,” in Proceedings of the 29 International Conference on System Sciences, Vol. 1, 3-6 Jan. 1996, pp. 53-62.

17. J. C. Sancho et al., “A flexible routing scheme for networks of workstations,” in the International Conference on High Performance Computing, Oct. 2000.

18. F. Silla, J. Duato, “High-performance routing in networks of workstations with irregular topology,” in IEEE Transactions on Parallel and Distributed Systems, vol. 11, july 2000, pp. 699-719.

19. Naveen Choudhary, M.S. Gaur, V. Laxmi, “Irregular NoC Simulation Framework: IrNIRGAM,” in IEEE proceedings of International conference on Emerging Trends in Networks and Computer Communications (ETNCC 2011), April 22-24, 2011, Udaipur, India, pp. 1-5.

20. 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.

21. Naveen Choudhary, MS Gaur, Vijay Laxmi, and Virendra Singh, “Genetic algorithm based topology generation for application specific network-on-chip”, IEEE International Symposium on Circuits and Systems (ISCAS) 2010, Paris, France, May 2010, pp. 3156 – 3159.

22. J. Hu, and R. Marculescu, “Energy-Aware Mapping for Tile-based NOC Architectures under Performance Constraints,” proceedings of ASP-DAC 2003, Jan 2003.






Soumen Biswas, Sarosij Adak

Paper Title:

OFDMA- PON: High Speed PON Access System

Abstract: Principal requirements and technology trends of advanced DSP for high-speed, real-time Orthogonal Frequency Division Multiple Access (OFDMA)-based PON are analyzed. Key benefits emerge from component integration, mass production and parallel activity in long-haul fiber systems. Future PON technologies are highly cost-efficient to remain attractive and practical. OFDM technology that is well-suited for future PON Systems. But it requires advanced Digital Signal Processing (DSP). Moreover, we provide an analysis of primary cost factors in a practical DSP-based OFDMA-PON implementation and survey the most recent achievements in this domain. Due to the combination of highly attractive advanced features and favorable related trends in long haul fiber transmission, OFDMA-PON can be regarded as a very promising solution for future PON-based access.

OFDM, OOFDM, Passive Optical Network (PON).


1. J. Kani et al., “Next-Generation PON-Technology Roadmap and General Requirements,” IEEE Commun. Mag., vol. 47, no. 11, Nov. 2009.
2. P. P. Iannone et al., “A 40 Gb/s CWDM-TDM PON With a Cyclic CWDM Multiplexer/Demultiplexer,” Proc. 2009 ECOC, paper 8.5.6.

3. F. J. Effenberger et al., “Next-Generation PON-Candidate Systems for Next-Generation PON,” IEEE Commun. Mag., vol. 47, no. 11, Nov. 2009.

4. J. Kani, “Next-Generation PONs: An Operator’s View,” Proc. 2009 ECOC, paper 5.7.4.

5. D. Qian et al., “108 Gb/s OFDMA-PON with Polarization Multiplexing and Direct Detection,” Proc. 2009 OFC/NFOEC, paper PDPD5.

6. M. Kuschnerov et al., “DSP for Coherent Single-Carrier Receivers,” IEEE/OSA J. Lightwave Tech., vol. 27, no. 16, Aug. 2009.

7. T. N. Duong et al., “High Bit Rate Transmission for NGPON by Direct Modulation of DFB laser Using Discrete Multi-Tone,” Proc. 2009 ECOC, paper 7.5.4.

8. N. Cvijetic, L. Xu and T. Wang, “Adaptive PMD Compensation Using OFDM in Long-Haul 10Gb/s DWDM Systems,” Proc. 2007 OFC/NFOEC, paper OTuA5.

9. N. Cvijetic, S. G. Wilson, and D. Qian, “System Outage Probability Due to PMD in High-Speed Optical OFDM Transmission,” IEEE/OSA J. Lightwave Tech., vol. 26, no. 14, July 2008.

10. D. Qian et al., “Single-Wavelength Upstream 108 Gb/s OFDMA-PON Transmission,” Proc. 2009 ECOC, paper PD 3.3.

11. D. Qian et al., “41. 25 Gb/s Real-Time OFDM Receiver for Variable-Rate WDM-OFDMA-PON Transmission,” Proc. 2010 OFC/NFOEC, paper PDPD9.






N. Indhumadhi, G. Padmavathi

Paper Title:

Enhanced Image Fusion Algorithm Using Laplacian Pyramid and Spatial frequency Based Wavelet Algorithm

Abstract: The aim of image fusion is to combine relevant information from two or more source images into one single image such that the single image contains most of the information from all the source images. The successful fusion of images acquired from different modalities or instruments is of great importance in many applications, such as medical imaging, microscopic imaging, remote sensing, computer vision and robotics. The algorithm begins by applying 2D-DWT to decompose the input images. The lower approximations are subjected to pixel-based Laplacian fusion algorithm. The SF algorithm combined with wavelet fusion algorithm is used for higher approximations. The new sets of detailed and approximate coefficients from each image are then added to get the new fused coefficients. The final step then performs inverse DWT with the new coefficients to construct the fused image. Experimental results demonstrate the proposed fusion algorithm can obtain the quality output image, both in visual effect and objective evaluation criteria. Four performance metrics, namely, Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR) and speed of fusing images, were used during experimentation. All the experiments showed that the proposed hybrid model is an improved version to fuse images when compared with pixel-based and wavelet-based algorithms.

2D-DWT, pixel-based Laplacian fusion algorithm, SF, CWD.


1. Blum, R.S. and Yang, J. (2003) Image fusion using the expectation-maximization algorithm and a Gaussian mixture model, In Advanced Video-Based Surveillance Systems, Foresti, G. L., Regazzoni, C. S. and Varshney, P. K., Eds., Kluwer, Boston.
2. Laliberte, F., Gagnon, L. and Sheng, Y. (2003) Registration and fusion of retinal images an evaluation study, IEEE Trans. Med. Imaging, Vol.22, No.5, Pp.661–673.

3. Li, H., Manjunath, B.S., Sanjit. K.. Mitra. H.. Li, B.S., and Mitra, S.K. (1994) Multisensor Image Fusion Using the Wavelet Transform, Proc. first international conference on image processing, ICIP 94, Austin, Texas, Vol. I, Pp.51-55.

4. Li, S.T. and Yang, B. (2008) Multifocus image fusion using region segmentation and spatial frequency, Image and Vision Computing, Vol. 26, Pp. 971-979.

5. Reese, C.E. and Bender, E.J. (2001) Multi-spectral/image fused head tracked vision system for driving applications, Proc. SPIE, Vol. 4361, Pp.1–11.

6. Shechtman, E. (2000) Sequence Fusion Based on 3D Pyramids, Project Report, http://www.wisdom.weizmann.ac.il/~elishe/SF_technical_report.html, Last Access Date: August, 2011.

7. Simone, G., Farina, A., Morabito, F. C., Serpico, S. B. and Bruzzone, L. (2002) Image fusion techniques for remote sensing applications, Inf. Fusion, Vol.3, No.1, Pp.3–15.

8. Vekkot, S. and Shukla, P. (2009) A Novel Architecture for Wavelet based Image Fusion, World Academy of Science, Engineering and Technology, Vol. 57, Pp. 372-379.

9. Xue, Z. and Blum, R.S. (2003) Concealed weapon detection using color image fusion, The Sixth International Conference on Image Fusion. Queensland, Australia, Pp.622–627.






Gajanand Gupta

Paper Title:

Algorithm for Image Processing Using Improved Median Filter and Comparison of Mean, Median and Improved Median Filter

Abstract: An improved median filter algorithm is implemented for the de-noising of highly corrupted images and edge preservation. Mean, Median and improved mean filter is used for the noise detection. Fundamental of image processing, image degradation and restoration processes are illustrated. The pictures are corrupted with different noise density and reconstructed. The noise is Gaussian and impulse (salt-and pepper) noise. An algorithm is designed to calculate the PSNR and MSE. The result is discussed for Mean, Median and improved Median filter with different noise density.



1. K. Jain, “Fundamentals of Digital Image Processing” , Prentice Hall of India, First Edition,1989
2. Rafael C.Gonzalez and Richard E. woods, “Digital Image Processing”, Pearson Education, Second Edition,2005

3. K. S. Srinivasan and D. Ebenezer, “A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises,” IEEE Signal Processing Letters, Vol. 14, No. 3, March 2007.

4. S. Indo and C. Ramesh, “A Noise Fading Technique for Image Highly Corrupted with Impulse Noise,” International Conference on Computing: Theory and Applications, PP. 627-632, March 2007.

5. T. Song, M. Gabbouj, and Y. Neuvo, “Center Weighted Median Filters: Some Properties and Applications in Image Processing,” Signal Processing, Vol. 35, No. 3, PP. 213-229, 1994

6. R. Yang, L. Lin, M. Gabbouj, J. Astola, and Y. Neuvo, “Optimal Weighted Median Filters Under Structural Constraints,” IEEE Trans. Signal Processing, Vol. 43, PP. 591-604, Mar 1995

7. Pei-Eng Ng and Kai-Kuang Ma, “A Switching Median Filter with BDND for Extremely Corrupted Images”, IEEE Trans Image Processing, Vol. 15, No. 6, PP. 1506-1516, June 2006

8. Jafar Ramadhan Mohammed, “An Improved Median Filter Based on Efficient Noise Detection for High Quality Image Restoration,”,IEEE Int. Conf ,PP. 327 – 331, . May 2008

9. Xiaoyin Xu, Eric L. Miller, Dong bin Chen and Mansoor Sarhadi, “ Adaptive Two-Pass Rank Order Filter to Remove Impulse Noise in Highly Corrupted Images”, IEEE Trans Image Processing,Vol.13,No.2, PP.238-247, February 2004.






M. Poorna Chandar, Mayank Sharma, M.V.Vijaya Saradhi

Paper Title:

Study On Enhancing Blog Quality Using Social Connectivity

Abstract: Blogging has become a useful way for people to publish data on the Internet, which contains posts, comments, trackbacks etc. They can also share latest news and opinions. As several blogs have risen up on the web, the users have the problem to identify which of the blogs contain useful information & data. My attempt in this paper is to present a road map in ranking blogs. In the first blush, it appears that this exercise is the same as ranking the general web ranking but actually the blogs in Blogosphere are not amenable to using these same algorithms. An approach based on social connectivity between bloggers i.e. Relationships among bloggers and different important features could help in enhancing blog quality. In our approach we are adding “Time” Factor by analyzing these blogs relationships at different time intervals would help us to identify the impact of blogs in Blogosphere.

Blogosphere, Blogs, Ranking and Analyzing.


1. DiMicco, J., Millen, D., Geyer, W., Dugan, C., Brownholtz, B. 2008. Motivations for Social Networking at Work. ACM CSCW’08.
2. Blood, R. Weblogs: A History and Perspective. Rebecca’s Pocket, Sept. 7, 2000; www.rebeccablood.net/essays/weblog_history.html.

3. Herring, S. C., Kouper, I., Scheidt, L. A., & Wright, E. (2004).Women and children last: The discursive construction of weblogs.In L. Gurak et al. (Eds.), Into the Blogosphere: Rhetoric, C o m m u n i t y , a n d C ultureofWeblogs. http://blog.lib.umn.edu/blogosphere/women_and_children.html

4. L. Page et al., The PageRank Citation Ranking: Bringing Order to the Web,tech.report,Stanford,InfoLab,1999;http://ilpubs.stanford.edu:8090/422/1/1999-66.pdf

5. J.M. Kleinberg, “Authoritative Sources in a Hyperlinked Environment,” J. ACM, vol. 46, no. 6, 1999, pp. 604–632.

6. K. Fujimura, T. Inoue, and M. Sugisaki, “The EigenRumor Algorithm for Ranking Blogs,” Trusting Agents for Trusting Electronic Societies, LNCS 3577, Springer, 2005, pp. 59–74.

7. B.A. Nardi et al., “Why We Blog,” Comm. ACM, vol. 47, no. 12, 2004, pp. 41–46.

8. A. Chin and M. Chignell, “A Social Hypertext Model for Finding Commu¬nity in Blogs,” Proc. 17th Conf. Hypertext and Hypermedia, ACM Press, 2006, pp. 11–22.

9. T. Lento et al., “The Ties that Blog: Examining the Relationship Between Social Ties and Continued Participation in the Wallop Weblogging System,” Proc. Workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynam¬ics, ACM Press, 2006; www.blogpulse.com/www2006-workshop/papers/ Lento-Welser-Gu-Smith-TiesThatBlog.pdf.

10. N. Ali-Hasan and L.A. Adamic, “Expressing Social Relationships on the Blog through Links and Comments,” Proc. Int’l Conf. Weblogs and Social Media (ICWSM 07), AAAI Press, 2007; www.icwsm.org/papers/2--Ali-Hasan --Adamic.pdf.

11. T. Furukawa et al., “Social Networks and Reading Behavior in Blogosphere,” Proc. Int’l Conf. Weblogs and Social Media (ICWSM), AAAI Press, 2007; www. icwsm.org/papers/2--Furukawa-Matsuo-Ohmukai-Uchiyama-Ishizuka.pdf.

12. E. Adar et al., “Implicit Structure and the Dynamics of Blogspace,” Proc. Workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics, ACM Press,2004; www.blogpulse.com/papers/www2004adar.pdf.

13. A. Kritikopoulos, M. Sideri, and I. Varlamis, “BlogRank: Ranking Weblogs Based on Connectivity and Similarity Features,” Proc. 2nd Int’l Workshop Advanced Architectures and Algorithms for Internet Delivery and Applications, ACM Press, no. 8, 2006; http://doi.acm.org/10.1145/1190183.1190193.

14. Chih-Lu Lin and Hung-Yu Kao,”Blog Popularity Mining Using Social Interconnection Analysis” Published by the IEEE Computer Society,41-49,2009; http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5432133.

15. N. Agarwal et al., “Identifying the Influential Bloggers in a Community,” Proc. Int’l Conf. Web Search and Web Data Mining, ACM Press, 2008, pp. 207–218.

16. M.A. Tayebi, M.S. Hashemi, and A. Mohades, “B2Rank: An Algorithm for Ranking Blogs Based on Behavioral Features,” ProcInt’l Conf. Web Intelli¬gence, IEEE CS Press, 2007; http://doi.ieeecomputersociety.org/10.1109/ WI.2007.81.






A K Malik, Yashveer Singh

Paper Title:

An Inventory Model for Deteriorating Items with Soft Computing Techniques and Variable Demand

Abstract: Inventory has always been the foundation of conducting business in any organization. Holding and managing of an inventory is essential for efficient and smooth running of any business organization be it a manufacturing industry, an educational institute, a five star hotel, a hospital and a printing press etc. The proper utilization of space is also a critical component in business world, whether one is a manufacturer, retailer or a wholesaler. Business organizations mainly focus on improving the customer services and reduce the inventory costs in such a manner so that profit can be maximum. Our objective in this paper is to provide a general review for the application of soft computing techniques like fuzzy logic and genetic algorithms to use for improve the effectiveness and efficiency for various aspect of inventory management.

Inventory Control, Demand, Enterprise Resource Planning (ERP), Soft Computing, Genetic Algorithm, Fuzzy Decision.


1. E. A. Silver, R. Peterson, (1985) Decision Systems for Inventory Management and Production Planning, John Wiley & Sons, New York.
2. Ghare, P.M., Schrader, G.P., (1963). A model for an exponentially decaying inventory. Journal of Industrial Engineering, 14 (5), 238–243.

3. H. J. Zimmermann. Fuzzy Set Theory and Its Applications. Kluwer-Nijho®, Hinghum, Netherlands, 1985.

4. J. F. Bard, J. T. Moore. (1990) Production Planning with Variable Demand. Omega, vol. 18, no. 1, pp. 35-42.

5. M. Papadrakakis, N. D. Lagaros. (2003) Soft Computing Methodologies for Structural Optimization. Applied Soft Computing, vol. 3, no. 4, pp. 283-300.

6. Malik, A. K. and Garg C. P. (2010), “Supply chain management-an overview”, International Journal of Logistics and Supply chain management, Vol. 2, No. 2, 2010, 97-101.

7. Malik, A.K, Singh, S.R. and Gupta, C.B., (2008). An inventory model for deteriorating items under FIFO dispatching policy with two warehouse and time dependent demand, Ganita Sandesh 22(1), 47-62.

8. R. P. Sundarraj, S. Talluri. (2003) A Multi-period Optimization Model for the Procurement of Component-based Enterprise Information Technologies. European Journal of Operational Research, vol. 146, no. 2, pp. 339-351.

9. S. D. Levi, P. Kaminsky & S. E. Levi, (2000) Designing and managing the supply chain. (McGraw-Hill, Illinois).

10. Singh, S.R., Malik, A.K., (2010). Optimal ordering policy with linear deterioration, exponential demand and two storage capacity, International Journal of Mathematical Sciences, 9(3-4), 513-528.

11. Singh, S.R. and Malik, A.K. (2011), “An inventory model with stock dependent demand with two storages capacity for non-instantaneous deteriorating items, International Journal of Mathematical Sciences and Applications”,1(3), 1255-1260.

12. Singh S.R. and Singh C. (2008). Fuzzy Inventory Model for finite rate of Replenishment using Signed Distance Method. International Transactions in Mathematical Sciences and Computer, 1(1), 27-34.

13. S. Avraham. Enterprise Resource Planning (ERP): the Dynamics of Operations Management, Kluwer Academic Publishers, Boston, 1999.

14. W. H. Ip, Y. Li, K. F. Man, K. S. Tang., (2000) Multi-product Planning and Scheduling Using Genetic Algorithm Approach. Computers & Industrial Engineering, vol. 38, no. 3, pp. 283-296.

15. Yao J.S. and Lee H.M., (1999). Fuzzy inventory with or without backorder for fuzzy order quantity with trapezoidal fuzzy number. Fuzzy Sets and Systems, 105, 311-337.

16. Y. Yi, D. Wang, (2003). Soft Computing for Scheduling with Batch Setup Times and Earliness-tardiness Penalties on Parallel Machines. Journal of Intelligent Manufacturing, vol. 14, no. 3-4, pp. 311-322.

17. Yung, K. L., W. Ip and D. Wang (2007). Soft Computing Based Procurement Planning of Time-variable Demand in Manufacturing System. International Journal of Automation and Computing, 04 (1), 80-87.

18. Y.W. Zhou, (2003). A Multi-warehouse Inventory Model for Items with Time-varying Demand and Shortages. Computers & Operations Research, vol. 30, no. 14, pp. 2115-2134.

19. Z. T. Balkhi, L. Benkherouf, (2004). On an Inventory Model for Deteriorating Items with Stock Dependent and Time-varying Demand Rates. Computers & Operations Research, vol. 31, no. 2, pp. 223-240.






Devendra Kr.Tripathi, H.K.Dixit, N.K.Shukla

Paper Title:

Comparative Performance Study in 32 Multiplexed Channels Optical Transmission in Bit Rates 10, 20, 30,40Gbps with NZDSF

Abstract: In this paper we have done comparative performance study for four different optical systems, each of thirty two multiplexed channels and spaced 100GHz. Multiplexed systems operating at 10, 20, 30 and40Gb/s/ch with non return-to-zero (NRZ) signal. The transmitted power is kept constant while the bit-rate and the length of the fiber are varied and the observations are based on the modeling and numerical simulation of optimum dispersion-managed transmission link .Performance study is done for variable fiber span length for NZDSF.It is observed that at low bit rate (10Gb/s/ch) per channel multiplexed optical system shows much better performance metrices (Q, BER, eye pattern) for variable fiber span. But with increase in per channel bit rate over10Gb/s/ch viz 20 Gb/s/ch, 30Gb/s/ch and 40Gb/s/ch transmission performance degrades on the increase of fiber length, it is much higher for 40Gb/s/ch multiplexed optical system as compared to other systems operating on 20, 30Gb/s/ch bit rate.



1. C. A. Brackett, “Dense wavelength division multiplexing networks: Principles and applications,” IEEE J. Selected Areas Communications, vol. 8, pp. 948–964, August 1990.
2. Meccozi, A.,” Wiesenfeld, J. M.: "The roles of semiconductor optical amplifiers in optical networks". Opt. Photon., 3, 2001.

3. Li, G. L.; Yu, P. K. L., "Optical Intensity Modulators for Digital and Analog Applications". J. Lightwave Technol. 21 ,2003.

4. Govind P. Agrawal, “Fiber-Optic Communication Systems”, third ed, Academic Press, New York, 2002.

5. Govind P. Agrawal, “Nonlinear Fiber Optics”, third ed, Academic Press, New York, 2001.

6. D. Breuer, K. Petermann, “Comparison of NRZ- and RZ- modulation format for 40-Gb/s TDM standard-fiber systems”, IEEE Photon. Technol. Lett. ,pp.398,9 ,1997.

7. M.I. Hayee, A.E. Willner, “NRZ versus RZ in 10-40-Gb/s dispersion managed WDM transmission systems”, IEEE Photon. Technol. Lett. Photon. Technol. Lett., pp. 991–993, 11 1999.

8. G. Bosco, A. Carena, V. Curri, Member, R. Gaudino, P. Poggiolini, “On the use of NRZ, RZ, and CSRZ modulation at 40 Gb/s with narrow DWDM channel spacing”, IEEE J. Lightwave Technol.,pp , 1694–1704, 20 , 9,2002.

9. Md. SaifulIslam, S. P Majumder “Effects of polarization mode dispersion on cross-phase modulation in a WDM optical transmission system”, Elsiever OptiK, pp., 24-28,122, 2011.

10. M.S. Islam, S.P.Majumder, “Influence of polarization mode dispersion on the effect of cross-phase modulation in intensity modulation – direct detection WDM transmission system,” in: Proceedings of International Conference on Computer and Communication Engineering (ICCCE2008), vol. 1, pp. 247–251, 2008






K.Bala Souri, K.Hima Bindu, K.V. Ramana Rao

Paper Title:

A Built-In Self-Repair Scheme for Random Access Memories with 2-D Redundancy

Abstract: Built-in self-repair (BISR) technique has been widely used to repair embedded random access memories (RAMs). This paper presents a reconfigurable BISR (ReBISR) scheme for repairing RAMs with different sizes and redundancy organizations. An efficient redundancy analysis algorithm is proposed to allocate redundancies of defective RAMs. In the ReBISR, a reconfigurable built-in redundancy analysis (ReBIRA) circuit is designed to perform the redundancy algorithm for various RAMs. Also, an adaptively reconfigurable fusing methodology is proposed to reduce the repair setup time when the RAMs are operated in normal mode. Experimental results show that the ReBISR scheme can achieve high repair rate (i.e., the ratio of the number of repaired RAMs to the number of defective RAMs). The area cost of the ReBISR is very small, which is only about 2.7% for four RAMs (one 4 Kbit RAM, one 16 Kbit RAM, one 128 Kbit RAM, and one 512 Kbit RAM). Moreover, the time overhead of redundancy analysis is very small. Embedded memories are among the most widely used cores in current system-on-chip (SOC) implementations. Memory cores usually occupy a significant portion of the chip area, and dominate the manufacturing yield of the chip. Efficient yield-enhancement techniques for embedded memories thus are important for SOC. In this paper we present a built-in self-repair (BISR) scheme for semiconductor memories with 2-D redundancy structures. The BISR design is composed of a built-in self-test (BIST) module and a built-in redundancy analysis (BIRA) module. Our BIST circuit supports three test modes: the 1) main memory testing, 2) spare memory testing, and 3) repair modes. The BIRA module executes the proposed redundancy analysis (RA) algorithm for RAM with a 2-D redundancy structure, i.e., spare rows and spare columns.

Built-in self-test, built-in self-repair, built-in redundancy-analysis, memory testing, semiconductor memory.


1. S. E. Schuster, “Multiple word/bit line redundancy for semiconductor memories”, IEEE Journal of Solid- State Circuits, vol. 13, no. 5, pp. 698–703.
2. M. Horiguchi, J. Etoh, M. Masakazu, K. Itoh, and T. Matsumoto, “A flexible redundancy technique for high-density DRAM’s”, IEEE Journal of Solid-State Circuits, vol. 26, no. 1, pp. 12–17.

3. T. Yamagata, H. Sato, K. Fujita, Y. Nishimura, and K. Anami, “A distributed globally replaceable redundancy scheme for sub-half-micron ULSI memories and beyond”, IEEE Journal of Solid-State Circuits, vol. 31, no. 2, pp. 195–201, Feb. 1996.

4. I. Kim, Y. Zorian, G. Komoriya, H. Pham, F. P. Higgins, and J. L. Lweandowski, “Built in self repair for embedded high density SRAM”, in Proc. Int. Test Conf. (ITC), Oct. 1998, pp. 1112–1119.

5. S. Runyon, “Testing big chips becomes an internal affair”, IEEE Spectrum, pp. 49–55, Apr. 2006.

6. C.-T. Huang, J.-R. Huang, C.-F. Wu, C.-W. Wu, and T.-Y. Chang, “A programmable BIST core for embedded DRAM”, IEEE Design & Test of Computers, vol. 16, no. 1, pp. 59–70, Jan.-Mar. 2009.






R. Rajeshwara Rao, A. Prasad, Ch. Kedari Rao

Paper Title:

Robust Features for Automatic Text-Independent Speaker Recognition Using Gaussian Mixture Model

Abstract: In this paper, robust features for text-independent speaker recognition has been explored. Through different experimental studies, it is demonstrated that the speaker related information can be effectively captured using Gaussian mixture Models (GMMs). The study on the effect of feature vector size for good speaker recognition demonstrates that, feature vector size in the range of 20-24 can capture speaker discrimination information effectively for a speech signal sampled at 16 kHz, it is established that the proposed speaker recognition system requires significantly less amount of data during both during training as well as in testing. The speaker recognition study using robust features for different mixtures components, training and test duration has been exploited. We demonstrate the speaker recognition studies on TIMIT database.

Gaussian Mixture Model ( GMM), MFCC, Robust Features, Speaker.


1. S.Furui, “An overview of speaker recognition technology”, in Automatic Speech and Speaker Recognition (C.-H. Lee, F. K. Soong, and K. K. Paliwal, eds.), ch. 2, pp. 31-56, Boston: Kluwer Academic, 1996.
2. D. A. Reynolds, “The effects of handset variability on speaker recognition performance: Experiments on switch board corpus,” in Proceedings of IEEE Int. Conf. Acoust., Speech, and Signal Processing, pp. 113-116, 1996.

3. Dempster, A., Laird, N., and Rubin, D., “Maximum likelihood from incomplete data via the EM algorithm,” Journal of the Royal Statistical Society, vol. 39, pp. 1-38,

4. Makhoul, J., 1975. Linear prediction: a tutorial review. Proc. IEEE 63, 561-580.

5. Molau, S., Pitz, M., Schluter, R., and Ney, H., “Computing Mel-frequency cepstral coefficients on the power spectrum,“ Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing ( ICASSP), vol. 1, pp. 73-76, May. 2001.

6. Picone, J. W., “Signal modeling techniques in speech recognition,” Proceedings of IEEE, vol. 81, no.9, pp. 1215-1247, Sep. 1993.

7. Doddington.G., Speaker Recognition based on idiolectal differences between speakers. In Proc. 7th European Conference on Speech Communication and Technology (Eurospeech 2001) (Aalborg, Denmark, September 2001), pp. 2521 – 2524.

8. Andrews.W., Kohler.M., Campbell.J., Godfrey.J., and Hernandez –Cordero.J. Gender-dependent phonetic refraction for speaker recognition. In Proc. Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP 2002) (Orlando, Florida, USA, May 2002), vol. 1, pp. 149-152.

9. Campbell.W., Campbell.J., Reynolds.D., Jones.D., and Leek.T., Phonetic speaker recognition with support vector machines. In Advances in Neural Information Processing Systems 16, S.Thrun, L.Saul, and B.Scholkopf, Eds. MIT Press, Cambridge, MA, 2004.

10. Adami.A, Mihaescu.R, Reynolds.D, and Godfrey.J., Modelling Prosodic dynamics for speaker recognition. In Proc. Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP 2003) ( Hong Kong, China, April 2003), pp. 788-791.

11. Chen, Z.-H., Liao, Y.-F., and Juang, Y.-T. Eigen-Prosody analysis for robust speaker recognition under mismatch handset environment. In Proc. Int. Conf. on Spoken language processing (ICSLP 2004) (Jeju, South Korea, October 2004), pp. 1421-1424.

12. Shriberg, E., Ferrer, L., Kajarekar, S., Venkataraman, A., and Stolcke, A. Modeling Prosodic feature sequences for speaker recognition. Speech Communication 46, 3-4 (July 2005), 455-472.

13. Leung, K., Mak, M., Siu, M., and Kung, S. Adaptive articulatory feature – based conditional pronunciation modeling for speaker verification. Speech Communication 48, 1 (January 2006), 71-84.

14. MA, B., Zhu, D., Tong, R., and Li, H. Speaker cluster based GMM tokenization for Speaker recognition. In Proc. Interspeech 2006 (ICSLP) (Pittsburgh, Pennsylvania, USA, September 2006), pp. 505-508.

15. Torres-Carrasquillo, P., Reynolds, D., and Jr., J. D. Language identification using Gaussian mixture model tokenization. In Proc. Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP 2002) (Orlando, Florida, USA, May 2002), vol. 1, pp. 757-760.

16. Xiang, B. Text-independent speaker verification with dynamic trajectory model. IEEE Signal Processing Letters 10 (May 2003), 141 -143.

17. Jin, Q., Schultz , T., and Waibel, A. Speaker identification using multilingual phone strings. In Proc. Int. Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP 2002) (Orlando, Florida, USA, May 2002), vol. 1, pp. 145-148.

18. Zissman, M. Comparison of four approaches to automatic language identification of telephone speech. IEEE Trans. on Speech and Audio Processing 4, 1 (January 1996), 31-44.

19. MA, B., Li, H., and Tong, R. Spoken language recognition with ensemble classifier. IEEE Trans. Audio, Speech and Language Processing 15, 7 (September 2007), 2053-2062.)

20. Markel, J., Oshika, B., and A.H. Gray, J. Long-term feature averaging for speaker recognition. IEEE Trans. Acoustics, Speech, and Signal Processing 25, 4 (August 1977), 330-337.

21. Kinnunen, T., Hautamaki, V., and Franti, P. On the use of long-term average spectrum in automatic speaker recognition. In 5th Int. Symposium on Chinese Spoken Language Processing (ISCSLP’06) (Singapore, December 2006), pp.559-567.

22. Tomi Kinnunen., and Haizhou Li., An overview of Text-Independent Speaker Recognition: from Features to Supervectors. Speech Communication, July 1, 2009.

23. Gish, H., Krasner, M., Russell, W., and Wolf, J., “ Methods and Experiments for text-independent speaker recognition over telephone channels,” Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 11, pp. 865-868, Apr. 1986.

24. Reynolds, D.A., and Rose, R.C., “Robust Text-Independent Speaker Identification using Gaussian Mixture Models,” IEEE-Transactions on Speech and Audio Processing, vol. 3, no.1, pp. 72-83,1995.

25. A. P. Dempster, N.M. Laird, and D.B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm”, J. Royal Statist. Soc. Ser. B. (methodologies), vol. 39, pp. 1-38, 1977.

26. K.N. Stevens, Acoustic Phonetics. Cambridge, England: The MIT Press, 1999.






Srikanth Pothula, Sk Nayab Rasool

Paper Title:

C-Pack: A High-Performance Microprocessor Cache Compression Algorithm

Abstract: Microprocessor designers have been torn between tight constraints on the amount of on-chip cache memory and the high latency of off-chip memory, such as dynamic random access memory. Accessing off-chip memory generally takes an order of magnitude more time than accessing on-chip cache, and two orders of magnitude more time than executing an instruction. Computer systems and microarchitecture researchers have pro- posed using hardware data compression units within the memory hierarchies of microprocessors in order to improve performance, energy efficiency, and functionality. Furthermore, as we show in this paper, raw compression ratio is not always the most important metric. In this work, we present a lossless compression algorithm that has been designed for fast on-line data compression, and cache compression in particular. The algorithm has a number of novel features tailored for this application, including combining pairs of compressed lines into one cache line and allowing parallel compression of multiple words while using a single dictionary and without degradation in compression ratio. We reduced the proposed algorithm to a register transfer level hardware design, permitting performance, power consumption, and area estimation. Permitting performance, power consumption, and area estimation. Experiments comparing our work to previous work are described.

Cache compression, effective system-wide compression ratio, hardware implementation, pair matching, parallel compression.


1. R. Alameldeen and D. A. Wood, “Adaptive cache compression for high-performance processors,” in Proc. Int. Symp. Computer Architecture, Jun. 2004, pp. 212–223.
2. E. G. Hallnor and S. K. Reinhardt, “A compressed memory hierarchy using an indirect index cache,” in Proc. Workshop Memory PerformanceIssues, 2004, pp. 9–15.

3. P. Pujara and A. Aggarwal, “Restrictive compression techniques to increase level 1 cache capacity,” in Proc. Int. Conf. Computer Design, Oct. 2005, pp. 327–333.

4. L. Yang, H. Lekatsas, and R. P. Dick, “High-performance operating system controlled memory compression,” in Proc. Design Automation Conf., Jul. 2006, pp. 701–704.






C.Saranya, G.Padmavathi

Paper Title:

Enhanced 2-Dimensional to 3-Dimensional Conversion for Medical Images

Abstract: The advent of modern 3D technological devices and the desire to create 3D images from the numerous 2D images have increased tremendously. Image-based 3D-modeling techniques for creating a 3D representation of a scene from one or more 2D images have received great attention and methods to improve the conversion process have been probed by both academicians and researchers. This research study focuses on particular conversion method recommended that facilitates various image processing algorithms to improve the conversion of 2D images to 3D images. The primary steps involved are Motion, Edge detection and image segmentation, Depth estimation and Shift algorithm. A weighted motion detection registration method is used to calculate the difference between the current image frame and the previous image frame. During edge detection, Sobel edge detector is used to detect the edges. The result of motion detection and edge detection are combined together and then a gray level closing is performed to make the edges connected and smooth. An edge registration module is used to store the motion and edge information in the memory. The segmentation process uses two algorithms, namely, K-Means and Mean Shift. An enhanced connected component algorithm which improves the traditional algorithm to use Max-Tree is used to create refined components. The final step of the proposed algorithm is the shift algorithm, which reconstructs the 3D image. To prove the efficiency of the proposed algorithm, several experiments are conducted. Various parameters like Root Mean Square Error (RMSE), Peak Signal to Noise Ratio, Standard Deviation and Speed of conversion are used to analyze the performance and efficiency of the proposed conversion algorithm. The experimental results proved that the depth map generated by mean-shift algorithm and enhanced connected component produce efficient and improved results.

RMSE, Dept Estimation, Edge detection, Shift Algorithm, Sobel Edge Detector


1. Barron, J.L., Fleet, D.J. and Beauchemin, S.S. (1994) Performance of optical flow techniques. International Journal of Computer Vision, Vol.12, Pp.43–77.
2. Chang, Y.L., Fang, C.Y., Ding, L.F., Chen, S.Y. and Chen, L.G. (2007) Depth map generation for 2D-to-3D conversion by short-term motion assisted color segmentation, ICME 2007, Pp.1958-1961.

3. Cornelis, N., Leibe, B., Cornelis, K. and Van Gool, L. (2006) 3d city modeling using cognitive loops, Video proceedings of CVPR (VPCVPR), Pp. 1-8.

4. Criminisi, A., Reid, I. and Zisserman, A. (2000) Single view metrology, International Journal of Computer Vision, Vol. 40, Pp.123–148.

5. Das, S. and Ahuja, N. (1995) Performance analysis of stereo, vergence, and focus as depth cues for active vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, No.12, Pp.1213–1219.

6. Delage, E., Lee, H. and Ng, A. Y. (2005) Automatic single-image 3d reconstructions of indoor Manhattan world scenes, 12th International Symposium of Robotics Research (ISRR), Pp.305-321

7. Forsyth, D. A. and Ponce, J. (2003) Computer vision: a modern approach, New York: Prentice Hall.

8. Fu, Y., Chen, X. and Guo, H. (2009) A new Connected Component Analysis Algorithm Based on Max-Tree, Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE Comp. Society, Pp.843-845.

9. Guichard, F. and Morel, J.M. (2002) Mathematical morphology “almost everywhere”, Proceedings of ISMM, Csiro Publishing, Pp.293–303.

10. Jung, J.I. and Ho, Y.S. (2010) Depth map estimation from single-view image using object classification based on Bayesian learning, 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON), Tampere, Pp.1-4.

11. Meer, P. and Georgescu. B. (2001) Edge detection with embedded confidence. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No.12, Pp. 1351–1365.

12. Michels, J., Saxena, A. and Ng, A. Y. (2005) High speed obstacle avoidance using monocular vision and reinforcement learning, 22nd international conference on machine learning (ICML), Pp. 593-600.

13. Nagai, T., Naruse, T., Ikehara, M. and Kurematsu, A. (2002) HMM-based surface reconstruction from single images, IEEE International Conference on Image Processing (ICIP), Vol. 2, Pp. II-561 - II-564.

14. Quartulli, M. and Datcu, M. (2001) Bayesian model based city reconstruction from high resolution ISAR data, IEEE/ISPRS joint workshop remote sensing and data fusion over urban areas, Pp. 58 – 63.

15. Salembier, P., Oliveras, A. and Garrido, L. (1998) Antiextensive connected operators for image and sequence processing, IEEE Trans. on Image Processing, Vol.7, No.4, Pp.555–570.

16. Scharstein, D. and Szeliski, R. (2002) A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, Internationa Journal of Computer Vision, Vol.47, No.1, Pp. 7–42.

17. Scharstein, D. and Szeliski, R. (2003) High-accuracy stereo depth maps using structured light, Computer vision and pattern recognition (CVPR), Vol.1, Pp.195-202.

18. Steinbach, E., Hanjalic, A. and Girod, B. (1996) 3D Motion and Scene Structure Estimation with Motion Dependent Distortion of Measurement Windows, Proceedings [EEE International Conference Image Processing, ' 96, Vol. 1, Pp. 61-64.

19. Thrun, S. and Wegbreit, B. (2005) Shape from symmetry, International conference on computer vision (ICCV), Vol. 2, Pp. 824.1831.

20. Torresani, L. and Hertzmann, A. (2004) Automatic non-rigid 3D modeling from video, European conference on computer vision, 299-312.

21. Wilkinson, M.H.F. and Westenberg, M.A. (2001) Shape preserving filament enhancement filtering, Proc. of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Pp. 770–777.






Rahul Dubey, Sanjeev Sharma

Paper Title:

An Agent Based Energy Efficient Local Monitoring

Abstract: Local monitoring is the one of the powerful technique for improving the security in multihope Wireless Sensor Network (WSN).Although it is a good technique for security purpose in WSN but it has a major drawback that it is costly in terms of energy consumption which make overhead for the energy constrained system such as WSN. In WSN environment, the scarce power resources are typically addressed through sleep- wake scheduling of nodes but sleep-wake technique is vulnerable even to simple attacks .In this paper a new technique is proposed that is not only energy efficient but also a secure technique which combine the sleep wake up scheduling with local monitoring which we call the OD –AEELMO (On Demand Agent Based Energy Efficient Local Monitoring ).it enables sleep- wake management in secure manner even in face of adversarial nodes that choose not to awaken nodes responsible for monitoring their traffic.

Sensor networks, local monitoring, sleep/wake techniques, malicious node.


1. S. Marti, T.J. GiulI, K.Lai, and Baker, “Mitigating Routing Misbehaviour in Mobile Ad Hoc networks,” proc MOBICOM, pp. 255-265, 2000.
2. I.Khalil, S. Bagchi, and N.B. , Shroff, “Design and Analysis of A Protocol for Detection and Isolation of the wormhole attack in Multihop Wireless Networks,” IEEE/Create Net Secure Comm. , pp. 89-100, sept. 2005.

3. Y. Haung and W. Lee, “A Cooperative Intrusion Detection System for Ad Hoc and Sensor Networks,” pp. 135-147, 2003

4. A. Silva, M.Martin, B.Rocha, A. Loureiro, L.Ruiz and H.wong,” Decentralize Intrusion Detection in Wireless Sensor Networks, Proc. First ACM Workshop Quality of Service and Security in Wireless and Mobile Networks, pp. 16-23, 2005.

5. A.A. Pirzada and C.Mcdonald,”Establishing Trust In Pure Ad Hoc Networks,” Proc. 27th Australasian Computer Science Conf.(ACSC ’04), pp. 47-54, 2004.

6. s. Buchegger and J.-Y. Le Boudec,” Performance Analysis of The CONFIDANT Protocol; Cooperation of NodesFareness in Distributed Ad Hoc networks,” Proc. Symp. Mobile Ad Hoc Networking and Computing (MOBIHOC), pp. 80-91, 2002.

7. Khalil, S. Bagchi, and C. Nina-Rotaru, “Dicas: Detection Diagnosis and Isolation of Control Attacks in Sensor Networks,”IEEE/Create Net Secure Comm., pp. 89-100, Sept. 2005.

8. S.J. Lee and M. Gerla, “Split Multipath Routing with Maximally Disjoint Paths in Ad Hoc Networks,” Proc. IEEE Int’l Conf. Comm. (ICC), pp. 3201-3205, 2001.

9. Abhisek Pandey and R.C. Tripathi”A Survey On WSN Security” proc IJCA/0975-8887/volume 3-No 2, June 2010.

10. B. Chen, K. Jamieson, H. Balakrishnan, and R. Morris, “Span: An Energy-Efficient Coordination Algorithm for Topology Maintenance in Ad Hoc Wireless Networks,” Proc. MOBICOM ’01, 2001.

11. W. Ye, J. Heidemann, and D. Estrin, “An Energy Efficient MAC Protocol for Wireless Sensor Networks,” Proc. IEEE INFOCOM, pp. 88-97, 2002.

12. A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler, and J. Anderson, “Wireless Sensor Networks for Habitat Monitoring,” Proc. ACM Int’l Workshop Wireless Sensor Networks and Applications pp. 1567-1576, 2002.

13. R. Naik, S. Biswas, and S. Datta, “Distributed Sleep-Scheduling Protocols for Energy Conservation in Wireless Networks,” Proc. 38th Ann. Hawaii Int’l Conf. System Sciences (HICSS), pp. 285b-285b 2005.

14. J.W. Hui, Z. Ren, and B. Krogh, “Sentry-Based Power Management in Wireless Sensor Networks,” Proc. Second Int’l Workshop Information P rocessing in Sensor Networks (IPSN), pp. 458-472, 2003

15. S. Liu, K. Fan, and P. Sinha, “Dynamic Sleep Scheduling Using Online Experimentation for Wireless Sensor Networks,” Proc Analysis of Wireless Sensor Networks (Symmetric), 2005 Third Int’l. Workshop Measurement, Modelling and Performance.

16. L. Gu and J.A. Stankovic, “Radio-Triggered Wake-Up Capability ForSensorNetworks,”Proc. IEEE Real-Time and Embedded Technology and Applications Sym., (RTAS), pp. 27-36, 2004

17. Y.H. Chee, “Ultra Low Power Transmitters for Wireless Sensor Networks,” PhD dissertation, Univ. of California, 2006

18. B. Cook, A. Berny, S. Lanzisera, A. Molnar, and K. Pister, “Low-Power 2.4-GHz Transceiver with Passive RX Front-End and 400 mV Supply,” IEEE J. Solid-State Circuits, vol. 41, no. 12, pp. 2757 2766, Dec. 2006

19. N. Pletcher, “Ultra-Low Power Wake-Up Receivers for Wireless Sensor Networks” PhD dissertation, Univ. of California, 2008. AS3931Product_brief_0204.pdf, 2011

20. http://www.austriamicrosystems.com/03products/data.

21. I. Khalil and S. Bagchi, “MISPAR: Mitigating Stealthy Packet Networks,” Proc. Fourth ACM SecureComm. pp. 1-10, 2008 Dropping in Locally Monitored Multi-Hop Wireless Ad Hoc.






NayabRasool Shaik, Srikanth Pothula

Paper Title:

Design of Open Core Protocol

Abstract: As more and more IP cores are integrated into an SOC design, the communication flow between IP cores has increased drastically and the efficiency of the on-chip bus has become a dominant factor for the performance of a system. The on-chip bus design can be divided into two parts, namely the interface and the internal architecture of the bus. In this work we adopt the well-defined interface standard, the Open Core Protocol and focus on the design of the internal bus architecture. We develop an efficient bus architecture to support most advanced bus functionalities defined in open core protocol , including burst transactions, lock transactions, pipelined transactions, and out-of-order transactions. We first model and design the on-chip bus with transaction level modeling for the consideration of design flexibility and fast simulation speed. We then implement the RTL models of the bus for synthesis and gate-level simulation. Experimental results show that the proposed TLM model is quite efficient for the whole system simulation and the real implementation can significantly save the communication time.



1. Advanced Microcontroller Bus Architecture (AMBA) Specification Rev 2.0 & 3.0
2. Open core protocol specifications http://www.ocpip.org/home.

3. Y.-T. Kim, T. Kim, Y. Kim, C. Shin, E.-Y. Chung, K.-M. Choi, J.-T.Kong, S.-K.Eo, "Fast and Accurate Transaction Level Modeling of an Extended AMBA2.0 Bus Architecture," Design, Automation, and Test in Europe, pages 138-139, 2005.

4. G. Schirner and R. Domer, "Quantitative Analysis of Transaction Level Models for the AMBA Bus," Design, Automation, and Test in Europe, 6 pages, 2006.

5. C.-K. Lo and R.-S. Tsay, "Automatic Generation of Cycle Accurate and Cycle Count Accurate Transaction Level Bus Models from a Formal Model," Asia and South Pacific Design Automation Conference, pages 558-563, 2009.

6. N.Y.-C. Chang, Y.-Z. Liao and T.-S. Chang, "Analysis of Shared-link AXI," IET Computers & Digital Techniques, Volume 3, Issue 4, pages 373-383, 2009.

7. IBM Corporation, "Prioritization of Out-of-Order Data Transfers on Shared Data Bus," US Patent No. 7,392,353, 2008.

8. David C.-W. Chang, I.-T. Liao, J.-K. Lee, W.-F. Chen, S.-Y. Tseng and C.-W. Jen, "PAC DSP Core and Application Processors," International Conference on Multimedia and Expo, pages 289-292, 2006.

9. CoWare website, http://www.coware.com





Poonam Tyagi, Ravinder Prakash Gupta, Rakesh Kumar Gill

Paper Title:

Comparative Analysis of Cluster Based Routing Protocols used in Heterogeneous Wireless Sensor Network

Abstract: Energy Efficient are one of the most practical solutions in order to handle with the requirements of large-scale wireless sensor networks (WSN). Energy consumption is one of the basic problem of WSNs. In this paper we have compare the performance of (DEEC) Distributed Energy Efficient routing protocol in terms of energy consumption, alive nodes, and packet transmission .We propose a new approach to exploit efficiently the network energy, by increasing the network lifetime.We have used a clustering technique so that the energy consumption of the network decreases, packet transmission increases and alive nodes increases. Simulation results reveal that the lifetime of proposed algorithm is 40% longer than DEEC and shows the no of packets transmission increases as compare to DEEC

Wireless Sensor Networks, Clustering Protocol, Energy Efficiency, Cluster-Head Selection, Network lifetime


1. W.R. Heinzelman, A.P. Chandrakasan, H. Balakrishnan, “An application specific protocol architecture for wireless microsensor networks”, IEEE Transactions on Wireless Communications, Vol. 1, No.4, pp. 660–670, 2002.
2. S. Lindsey, C.S. Raghavenda, “PEGASIS: Power Efficient Gathering in Sensor Information Systems”, in Proceeding of the IEEE Aerospace Conference, Big Sky, Montana, March 2002.

3. O. Younis, S. Fahmy, “HEED: A Hybrid, Energy-Efficient, Distributed clustering approach for ad hoc sensor networks”, IEEE Transactions on Mobile Computing, Vol. 3, No.4, pp. 660–669, 2004.

4. G. Smaragdakis, I. Matta, A. Bestavros, “SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks”, in Second International Workshop on Sensor and Actor Network Protocols and Applications (SANPA 2004), 2004.

5. M. Ye, C. Li, G. Chen, J. Wu, “EECS: An Energy Efficient Cluster Scheme in Wireless Sensor Networks”, in IEEE International Workshop on Strategies for Energy Efficiency in Ad Hoc and Sensor Networks (IEEE IWSEEASN2005), Phoenix, Arizona, April 7–9, 2005.

6. Sajid Husain, Abdul W. Martin, “Hierarchical Cluster based Routing in Wireless Sensor Networks”, Journal of Networks, Academy Publisher, Vol. 2, No. 5, pp. 87-97, 2007.

7. A. Manjeshwar and D. P. Agrawal, “TEEN: A routing protocol for enhanced efficiency in wireless sensor networks”, in Proceedings of IPDPS, pp. 2009–2015, 2001.

8. Manjeshwar and D. P. Agrawal, “APTEEN: A hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks”, in Proceedings of IPDPS, pp. 195–202, 2002.

9. L. Qing, Q. X. Zhu, and M. W. Wang, “Design of a distributed energy efficient clustering algorithm for heterogeneous wireless sensor networks,” Computer Communication, Elsevier, Vol. 29, No. 12, pp. 2230–2237, 2006.

10. W. R. Heinzelman, A. Chandrakasan and H. Balakrishnan, “Energy Efficient Communication Protocol for Wireless Microsensor Networkings”, in Proceedings of 33rd Hawaii International Conference of System Sciences (HICSS ’00), January 2000.

11. E. Perkins and P. Bhagwat, “Highly Dynamic Destination-Sequenced Distance-Vector Routing (DSDV) for Mobile Computers”, Computer Communications, pp. 234-244, October 1994.

12. P. Krishna, N.H. Vaidya, M. Chatterjee, D. Pradhan, “A Cluster-based approach for Routing in Dynamic Networks”, ACM SIGCOMM Computer Communication Review, Vol. 27 No. 2, pp. 49–65, 1997.

13. Estrin, R. Govindan, J. Heidemann, S. Kumar, “Next Century Challenges: Scalable Coordination in Sensor Networks”, in Proceedings of the 5th Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom’99), pp. 263–270, August 1999.

14. Y. Yao and J. Gehrke, “The cougar approach to in network query processing in sensor networks”, in SIGMOD Record, September 2002.

15. S. Bandyopadhyay, E.J. Coyle, “An energy efficient hierarchical clustering algorithm for wireless sensor networks”, in Proceeding of INFOCOM 2003, April 2003.

16. S. Tilak, N. Abu-Ghazaleh, and W. Heinzelman, “A Taxonomy of Wireless Microsensor Network Models”, ACM Mobile Computing and Communications Review, 2002.

17. K. Akkaya and M. Younis, “A survey on routing protocols for wireless sensor networks”, Elsevier Ad Hoc Network, Vol. 3, No. 3, pp. 325-349, 2005

18. J. N. Al-Karak and A. E, Kamal, “Routing techniques in wireless sensor networks: A Survey”, IEEE Wireless Communications, December 2004.






Upasana Pandey, S. Chakraverty, Bhawna Juneja, Ashima Arora, Pratishtha Jain

Paper Title:

Semantic Document Classification using Lexical Chaining & Fuzzy Approach

Abstract: We propose a novel approach to classify documents into different categories using lexical chaining. In this paper we present a text categorization technique that extracts lexical features of words occurring in a document. Two kinds of lexical chains based on the WordNet and Wikipedia reference sources are created using the semantic neighborhood of tokens. The strength of each lexical chain is determined with the help of TF/IDF, category keyword strength and relative position of tokens in the document. Each category is assigned a weight depending upon the value obtained after the lexical chain computation. Fuzzy logic is incorporated to generate a range for each category using a triangular membership function. The document belongs to the category which satisfies the range criteria. Lexical chaining has large applicability in automated email spam filtering, topic spotting, email routing.

Lexical Chaining, TF-IDF, Wikipedia, WordNet.


1. Mohammed Abdul Wajeed, Dr. T.Adilakshmi, ”Text Classification using Machine learning”.A Journal of Theoretical and Applied Information Technology, Vol 7. No. 2, year 2009.
2. Yongguang Bao, Daisuke Asai1, Xiaoyong Du, Kazutaka Yamada1 and Naohiro Ishii1, “An Effective Rough Set-Based Method for Text Classification”, In Proc. Of IDEAL 2003, LNCS 2690, pp. 545-552, 2003.

3. Cheng Hua Li and Soon Choel Park ,“Text Categorization Based on Artificial Neural Networks”, In Proc of ICONIP 2006, Part III, LNCS 4234, p.p. 302-311, 2006

4. Hu Jin-zhu , Shu Jiang-bo,Huang Yu-ying , “Text Feature Extraction based on Extension of Topic Words and Fuzzy Set”.In Proc of 2008 Intl. Conference on Computer Science and Software Engineering.

5. Marco Ernandes et al, “ An Adaptive context Based algorithm For Term Weighting” , In Proc. of the 20th international joint Conference on Artificial Intelligence, 2748-2753 , 2007

6. http://www.cvisiontech.com/index.php?option=com_content&id=370&task=view&Itemid=312

7. Diwakar Padmaraju et al, “Applying Lexical Semantics to Improve Text Classification”, http://web2py.iiit.ac.in/publications/default/download/inproceedings. Pdf.9ecb6867-0fb0-48a5-8020-0310468d3275.pdf

8. http://en.wikipedia.org/wiki/Information_science.

9. Gerard Salton, A. Wong, and C. S. Yang. 1975. A vector space model for automatic indexing. Commun. ACM, 18(11):613–620.

10. Gregory Silber and Kathleen F. McCoy. 2002. Efficiently computed lexical chains as an intermediate representation for automatic text summarization. Computational Linguistics, 28(4):487–496.

11. Regina Barzilay and M. Elhadad. 1997. Using lexical chains for text summarization. In proceedings of the Intelligent Scalable Text Summarization Workshop (ISTS’97), ACL, Madrid, Spain.

12. Graeme Hirst and David St-Onge. 1997. Lexical chains as representation of context for the detection and correction of malapropisms. In C. Fellbaum, editor, WordNet:An electronic lexical database and some of its applications. The MIT Press, Cambrige, MA.

13. Stephen J Green. 1998. Automatically generating hypertext in newspaper articles by computing semantirelatedness. In D.M.W. Powers, editor, NeMLaP3/CoNLL98: New Methods in Language Processing and Computational Natural Language.

14. Jane Morris and Graeme Hirst. 1991. Lexical cohesion computed by thesaural relations as an indicatorof the structure of text. Computational Linguistics, 17(1):21–48.

15. Kirkpatrick. 1998. Roget’s Thesaurus of English Words and Phrases. Penguin.

16. hristiane Fellbaum, editor. 1998. WordNet: An Electronic Lexical Database. MIT Press.

17. Stephen Bloehdron et al,“Boosting for Text Classification with Semantics Features”,In Proc of the MSW 2004 workshop at the 10th ACMSIGKDD Conference on Knowledge,Discovery and Data Mining, AUG (2004), p.p.70-87http://www.webkb.org/interface/categSearch.html?categField=sports

18. http://www.WordNet-online.com/

19. Libby Barak et al, ”Text Categorization from Category Name via Lexical Reference” , In Proc. Of NAACL HLT 2009: Short papers, Pages 33-36, June 2009.

20. Building Semantic Kernels for Text Classification using Wikipedia by Pu Wang, Department of Computer Science, George Mason University,2007.

21. http://en.Wikipedia.org/wiki/fuzzylogic

22. Zadeh, L. A. 1973. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on Systems, Man, and Cybernetics,

23. El-Sayed M. El-Alfy, Fares S. Al-Qunaieer, ”A Fuzzy Similarity approach for Automated Spam filtering”. In proc. of the 2008 IEEE/ACS International Conference on Computer Systems and Applications-Volume 00, Pages 544-550, 2008.

24. Choochart Haruechaiyasak, Mei-Ling Shyu, Shu-Ching Chen, “Web Document Classification Based on Fuzzy Association”, In Proc of the 26th International Computer Software and Applications Conference on Prolonging Software Life: Development and Redevelopmen, 2002.

25. www.wikipeida.org.

26. Learning to Link with Wikipedia by David Milne, Department of Computer Science, University of Waikato, 2008.

27. Q. Wang, yi Guan, X. Wang, “SVM Based Spam Filter with Active and Online Learning”, In Procs. of the TREC Conference, 2006.

28. I. Androutsopoulos et al., “Learning to filter spam email: a comparison of a naïve Bayes and a memory based approach,” In Procs of the workshop “Machine Learning and Textual Information Access”, 4th European Conference on Principles and Practice of Knowledge discovery in Databases, 2000.

29. Johan Hovold, “Naïve Bayes Spam Filtering Using Word Position Based Attributes”, International conference of Email and Anti spam, 2005.

30. http://people.csail.mit.edu/jrennie/20Newsgroups/






B.V.S.S.R.S.Sastry, K.Akshitha, M.V.Vijaya Saradhi

Paper Title:

Establishing Security for Group Communication in Multicast Environment

Abstract: Multicast is an internetwork service that provides efficient delivery of data from a source to multiple receivers. It reduces the bandwidth requirements of the network and the computational overhead of the host devices. This makes multicast an ideal technology for communication among a large group of participants. Secure group communications involves many service types include teleconferencing, pay TV and real-time delivery of stock quotes. IP multicast is the traditional mechanism to support multicast communications. Multicast security includes group membership control, secure key distribution, secure data transfer and copyright protection. This paper is an overview of the schemes proposed for group key management, authentication and watermarking in wired networks with fixed members and wireless networks with mobile members.

Multimedia, key management, authentication, multicast security, watermarking, access control, copyright protection.


1. Balenson D, McGrew D and Sherman A (1999) Key Management for Large Dynamic Groups: One-Way Function Trees and Amortized Initialization. Internet Draft (work in progress), February 26, 1999.
2. Ballardie A (1996) Scalable Multicast Key Distribution. RFC 1949, May 1996.

3. Banerjee S and Bhattacharjee B (2001) Scalable Secure Group Communication over IP Multicast. International Conference on Network Protocols, Riverside, CA, November 10-14, 2001.

4. Bauer M, Canetti R, Dondeti L and Lindholm F (2002) Group Key Management Architecture, Internet Draft, IETF MSEC WG, February 2002.

5. Becker C and Willie U (1998) Communication Complexity of Group Key Distribution. 5th ACM Conference on Computer and Communications Security, San Fransisco, CA, November 1998.

6. Bell A (1999) The Dynamic Digital Disk. IEEE Spectrum, Volume 36, Number 10, October 1999, pp. 28-35.

7. Bloom JA, Cox IJ, Kalker T, Linnartz JPMG, Miller ML and Traw CBS (1999) Copy Protection for DVD Video. Proceedings of the IEEE, Vol. 87, No. 7, July 1999, pp. 1267-1276.

8. Blundo C, De Santis A, Herzberg A, Kutten S, Vaccaro U and Yung M (1997) Perfectly-Secure Key Distribution for Dynamic Conferences. Information and Computation, December 1997.

9. Boneh D, Durfee G and Franklin M (2001) Lower Bounds for Multicast Message Authentication. Proceedings of Eurocrypt 2001, Lecture Notes in Computer Science, Vol. 2045, Springer-Verlag, 2001, pp. 437-452.

10. Boyd C (1997) On Key Agreement and Conference Key Agreement. Information Security and Privacy, Second Australasian Conference, ACISP'97, Sydney, NSW, Australia, July 7-9, 1997, pp.294-302.

11. Briscoe B (1999) MARKS: Multicast Key Management Using Arbitrarily Revealed Key Sequences. First International Workshop on Networked Group Communication, November 1999.

12. Brown I, Perkins C and Crowcroft J (1999) Watercasting: Distributed Watermarking of Multicast Media. First International Workshop on Networked Group Communication (NGC ’99), Pisa, November 17-20, 1999.

13. Bruschi D and Rosti E (2000) Secure Multicast in Wireless Networks of Mobile Hosts and Protocols and Issues. ACM Baltzer MONET Journal, Special Issue on Multipoint Communication in Wireless Networks, 2000.

14. Canetti R, Garay J, Itkis G, Micciancio D, Naor M and Pinkas B (1999) Multicast Security: A Taxonomy and Some Efficient Constructions. Proceedings of IEEE INFOCOM, Vol. 2, New York, March 1999, pp. 708-716.

15. Caronni G, Waldvogel M, Sun D and Plattner B (1998) Efficient Security for Large and Dynamic Groups. Technical Report No. 41, Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, February 1998.

16. Caronni G and Schuba C (2001) Enabling Hierarchical and Bulk-Distribution for Watermarked Content. The 17th Annual Computer Security Applications Conference, New Orleans, LA, December 10-14, 2001.

17. Chang I, Engel R, Kandlur D, Pendakaris D and Saha D (1999) Key Management for Secure Internet Multicast Using Boolean Function Minimization Techniques. Proceedings of IEEE INFOCOM, Vol. 2, New York, March 1999.

18. Chiou GH and Chen WT (1989) Secure Broadcast Using the Secure Lock. IEEE Transactions on Software Engineering, 15(8), August 1989, pp. 929-934.

19. Chor B, Fiat A, Naor M and Pinkas B (2000) Tracing Traitors. IEEE Transactions on Information Theory, Vol. 46, No. 3, May 2000.

20. Chu H, Qiao L and Nahrstedt K (1999) A Secure Multicast Protocol with Copyright Protection. Proceedings of IS&T/SPIE Symposium on Electronic Imaging: Science and Technology, January 1999.






K.Akshitha, B.V.S.S.R.S.Sastry, Dr. M.V.Vijaya Saradhi

Paper Title:

Establishing Job Scheduling and Checkpointing in Multi-Cluster Systems

Abstract: Multi-site parallel job schedulers can improve average job turn-around time by making use of fragmented node resources available throughout the grid. By mapping jobs across potentially many clusters, jobs that would otherwise wait in the queue for local resources can begin execution much earlier; thereby improving system utilization and reducing average queue waiting time. Recent research in this area of scheduling leverages user-provided estimates of job communication characteristics to more effectively partition the job across system resources. In this paper, we address the impact of inaccuracies in these estimates on system performance and show that multi-site scheduling techniques benefit from these estimates, even in the presence of considerable inaccuracy. While these results are encouraging, there are instances where these errors result in poor job scheduling decisions that cause network over-subscription. This situation can lead to significantly degraded application performance and turnaround time. Consequently, we explore the use of job check pointing, termination, migration, and restart (CTMR) to selectively stop offending jobs to alleviate network congestion and subsequently restart them when (and where) sufficient network resources are available. We then characterize the conditions and the extent to which the process of CTMR improves overall performance.

parallel job scheduling; check pointing; migration; clusters; grid scheduling


1. Jones WM. Using checkpointing to recover from poor multi-site parallel job scheduling decisions. The 5th Workshop on Middleware for Grid Computing at the ACM/IFIP/USENIX 8th International Middleware Conference, 2007.
2. Bucar AID, Epema DHJ. The performance of processor co-allocation in multicluster systems. 3rd International Symposium on Cluster Computing and the Grid, 2003; 302-309.

3. Ernemann C, Hamscher V, Streit A, Yahyapour R. Enhanced algorithms for multi-site scheduling. Grid Computing - GRID 2002, Third International Workshop, Baltimore, MD, USA, November 18, 2002, Proceedings, 2002; 219-231.

4. Qin J, Bauer MA. An improved job co-allocation strategy in multiple HPC clusters. 21st International Symposium on High Performance Computing Systems and Applications (HPCS 2007), 2007.

5. Jones WM, Pang LW, Stanzione D, Ligon III WB. Characterization of bandwidth-aware meta-schedulers for co-allocating jobs across multiple clusters. Journal of Supercomputing, Special Issue on the Evaluation of Grid and Cluster Computing Systems, vol. 34, Springer Science and Business Media B.V, 2005; 135-163.

6. Ngubiri J, van Vliet M, Nijmegen RU. Group-wise performance evaluation of processor co-allocation in multi-cluster systems. Job Scheduling Strategies for Parallel Processing. Springer Verlag, 2007. To appear in Lect. Notes Comput. Sci.

7. Weizhe Z, Binxing F, Mingzeng H, Xinran L, Hongli Z, Lei G. Multisite co-allocation scheduling algorithms for parallel jobs in computing grid environments. Science in China Series F: Information Sciences 2006; 49(6):906-926, doi:10.1007/s11432-006-2034-2.

8. Lee CB, Schwartzman Y, Hardy J, Snavely A. Are user runtime estimates inherently inaccurate? Job Scheduling Strategies for Parallel Processing. Springer Verlag, 2004; 253-263. Lect. Notes Comput. Sci.vol. 3277.

9. Chiang SH, Arpaci-Dusseau A, Vernon MK. The impact of more accurate requested runtimes on production job scheduling performance. Job Scheduling Strategies for Parallel Processing. Springer Verlag, 2002; 103- 127. Lect. Notes Comput. Sci. vol. 2537.

10. Jackson D, Snell Q, Clement M. Core algorithms of the Maui scheduler. Job Scheduling Strategies for Parallel Processing. Springer Verlag, 2001; 87-102. Lect. Notes Comput. Sci. vol. 2221.

11. Jones WM. The impact of error in user-provided bandwidth estimates on multi-site parallel job scheduling performance. The 19th IASTED International Conference on Parallel and Distributed Computing and Systems (PDCS 2007), 2007.

12. Koo R, Toueg S. Checkpointing and rollback-recovery for distributed systems. IEEE Transactions on Software Engineering 1987; 13:23-31.

13. Deconinck G, Vounckx J, Lauwereins R, Peperstraete JA. Survey of backward error recovery techniques for multicomputers based on checkpointing and rollback. International Journal of Modeling and Simulation 1998; .

14. Oliner AJ, Sahoo RK, Moreira JE, Gupta M. Performance implications of periodic checkpointing on large- scale cluster systems. IPDPS '05: Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 18, IEEE Computer Society:Washington, DC, USA, 2005;299.2, doi:http://dx.doi.org/10.1109/IPDPS.2005.337.

15. Jones WM, Daly JT, DeBardeleben NA. Application resilience: Making progress in spite of failure. The Workshop on Resiliency in High-Performance Computing held in conjunction with the 8th IEEE International Symposium on Cluster Computing and the Grid (CCGrid2008), 2008.

16. Daly JT. A higher order estimate of the optimum checkpoint interval for restart dumps. Future Generation Computer Systems Janurary 2006; 22:303-312.

17. Weil AM, Feitelson DG. Utilization, predictability, workloads, and user runtime estimates in scheduling the IBM SP2 with back_lling. IEEE Transactions Parallel Distributed Systems 2001; 12(6):529-543.

18. Tsafrir D, Etsion Y, Feitelson DG. Back_lling using system-generated predictions rather than user runtime estimates. IEEE Transactions on Parallel and Distributed Systems 6 2007; 18:789-803
19. Plank JS, Thomason MG. Processor allocation and checkpoint interval selection in cluster computing systems. J. Parallel Distrib. Comput. 2001; 61(11):1570-1590, doi:http://dx.doi.org/10.1006/jpdc.2001.1757.

20. Feitelson DG, Nitzberg B. Job characteristics of a production parallel scienti_c workload on the NASA Ames iPSC/860. Job Scheduling Strategies for Parallel Processing. Springer-Verlag, 1995; 337-360. Lect. Notes Comput. Sci. vol. 949.






K.Hima Bindu, K.Bala Souri, K.V. Ramana Rao

Paper Title:

MAC Architecture – Accumulator Based on Booth Encoding Parallel Multiplier

Abstract: The MAC provides high speed multiplication with accumulative addition. In this paper, we study the various parallel MAC architectures and then implement a design of parallel MAC based on some booth encodings such as radix-4 booth encoder and some final adders such as CLA, Kogge stone adder and then compare their performance characteristics. The one most effective way to increase the speed of a multiplier is to reduce the number of the partial products. Although the number of partial products can be reduced with a higher radix booth encoder, but the number of hard multiples that are expensive to generate also increases simultaneously. To increase the speed and performance, many parallel MAC architectures have been proposed. Parallelism in obtaining partial products is the most common technique used in this implemented architecture.

Radix-4 Booth multiplier, CLA, multiplier and- accumulator (MAC).


1. J. J. F. Cavanagh, “Digital Computer Arithmetic”, New York: McGraw- Hill, 2004.
2. K. Hwang. Computer Arithmetic – Principles, Architecture and Design. School of Electrical Engineering 1979.

3. Chung Nan Lyu & David W. Matula Redundant Binary Booth Recoding Proceedings of the 12th Symposium on Computer Arithmetic (ARITH '95) 1995 IEEE.

4. B. Cherkauer and E. Friedman. A Hybrid Radix-4/Radix-8 Low Power, High Speed Multiplier Architecture for Wide Bit Widths. In IEEE International Symposium on Circuits and Systems, volume 4, pages 53–56, 1996.

5. Yajuan He and Chip-Hong Chang, “A New redundant binary booth encoding for fast 2n-bit multiplier design”, IEEE Transaction on circuit and systems, Vol. 56, No.6, June 2009.






Md. Selim Hossain, A. N. M. Enamul Kabir, Debabrata Kumar Karmokar

Paper Title:

Wire Type Multiband Strip Antenna for WiMAX /WLAN Operations

Abstract: This paper presents a wire type multiband strip antenna for WiMAX and WLAN operations by means of numerical simulations. The dimension of the antenna is 39×30 mm2 and provides an impedance bandwidth of 300 MHz (2350-2650 MHz) and 130 MHz (5490-5620 MHz) at lower and upper frequency band respectively, which fully covers the 2.4, 2.5 and partially covers the 2.3, 5.5 GHz bands. The antenna contains very high peak gains of 8.25, 8.43, 8.46 and 6.43 dBi at 2.3, 2.4, 2.5 and 5.5 GHz band respectively with less than 0.2, 0.1, 0.3 and 1.5 dBi gain variations within the 10 dB return loss bandwidth. The VSWR of the antenna varies between 1.20~1.80 and 1.06~1.83 at the lower and upper resonant frequency bands respectively and the peak values of the return loss are -20.85 and -30.71dB. The antenna has good omnidirectional radiation characteristics at E-plane and H-plane.

Strip Antenna, Multiband Antenna, Numerical Electromagnetic Code (NEC), WiMAX, WLAN.


1. C. -Y. Pan, T. -S. Horng, , W. -S. Chen, and C. -H. Huang, “Dual Wideband Printed Monopole Antenna for WLAN/WiMAX Applications”, IEEE Antennas and Wireless Propagation Letters, Vol. 6 pp. 149-151, 2007.
2. L. Pazin, N. Telzhensky, and Y. Leviatan, , “Multiband Flat-Plate Inverted-F Antenna for Wi-Fi/WiMAX Operation”, IEEE Antennas and Wireless Propagation Letters, Vol. 7, pp. 197-200, 2008.

3. J. -W. Wu, Y. -D. Wang, H. -M. Hsiao, and J. -H. Lu, “T-Shaped Monopole Antenna with Shorted L-Shaped Strip-Sleeves for WLAN 2.4/5.8-GHz Operation”, Microwave and Optical Technology Letters, Vol. 46, No. 1, pp. 65-69, 2005.

4. W. -C. Su, and K. -L. Wong, “Internal PIFA’s for UMTS/WLAN/WiMAX Multi-Network Operation for a USB Dongle”, Microwave and Optical Technology Letters, Vol. 48, No. 11, pp. 2249-2253, 2006.

5. Y. Song, Y. -C. Jiao, G. Zhao, and F. -S. Zhang, “Multiband CPW-Fed Triangle-Shaped Monopole Antenna for Wireless Applications”, Progress in Electromagnetics Research, PIER 70, pp. 329–336, 2007.

6. P. -Y. Lai and K. -L. Wong, “Capacitively Fed Hybrid Monopole/Slot Chip Antenna for 2.5/3.5/5.5 GHz WiMAX Operation in the Mobile Phone”, Microwave and Optical Technology Letters, Vol. 50, No. 10, pp. 2689-2694, 2008.

7. K. -L. Wong, T. -Y. Wu, S. -W. Su, and J. -W. Lai, “Broadband Printed Quasi-Self-Complementary Antenna for 5.2/5.8 GHz WLAN Operation”, Microwave and Optical Technology Letters, Vol. 39, No. 6, pp. 495-496, 2003.

8. J. Jung,, H. Lee and Y. Lim, “Compact Monopole Antenna for Dual ISM-Bands (2.4 and 5.8 GHz) Operation”, Microwave and Optical Technology Letters, Vol. 51, No. 9, pp. 2227-2229, 2009.

9. S. -Y. Sun, S. -Y. Huang and J. -S. Sun, “A Printed Multiband Antenna for Cellphone Applications”, Microwave and Optical Technology Letters, Vol. 51, No. 3, pp. 742-744, 2009.

10. S. -W. Su, K. -L. Wong, and H. -T. Chen, “Broadband Low-Profile Printed T-Shaped Monopole Antenna for 5-GHz WLAN Operation”, Microwave and Optical Technology Letters, Vol. 46, No. 3,pp. 243-245, 2004.

11. Y.-S. Shin and S.-O. Park, “A novel compact PIFA for Wireless Communication applications”, IEEE Region 10 Conference 2007, pp. 1-3, 2007.

12. G. J. Burke and A. J. Poggio, “Numerical Electromagnetic Code-2”, Ver. 5.7.5, Arie Voors, 1981.

13. M. –C. T. Huynh, “A Numerical and Experimental Investigation of Planar Inverted-F Antennas for Wireless Communication Applications”, M.Sc. Thesis, Virginia Polytechnic Institute and State University, 2000.

14. K. Hirisawa and M. Haneishi, “Analysis, Design, and Measurement of small and Low-Profile Antennas”, Artech House, Boston, 1992.






Md. Selim Hossain, Debabrata Kumar Karmokar, Khaled Mahbub Morshed

Paper Title:

Compact Zigzag Inverted-F Antenna with Matching Network for Wi-Fi Operation in Portable Devices

Abstract: This paper presents a compact zigzag inverted-F antenna with matching network for Wi-Fi operation in portable devices. The antenna occupies a very small area of 8 × 18 mm2 and provides high gain of 6.91 dBi with the peak return loss of -49.75 dB, VSWR of 1.006 and input impedance of 50.2045 Ω at 5.5 GHz. This antenna covers a 10 dB return loss bandwidth of 609 MHz (5250 MHz~5859 MHz). In addition an impedance matching pie filter is used to ensure impedance matching between the source and the antenna. As well, the antenna has acceptable radiation characteristics.

Inverted-F antenna (IFA), Compact Antenna, Matching Network (MN), Zigzag IFA, Voltage Standing Wave Ratio (VSWR), Wi-Fi.


1. L. Pazin, N. Telzhensky and Y. Leviatan, “Multiband Flat-Plate inverted-F antenna for Wi-Fi/WiMAX operation”, IEEE antennas and wireless propagation letters, Vol. 7, pp. 197-200, 2008.
2. J. -W. Wu, Y. -D. Wang, H. -M. Hsiao and J. -H. Lu, “T-Shaped Monopole Antenna with Shorted L-Shaped Strip-Sleeves for WLAN 2.4/5.8-GHz Operation”, Microwave and Optical Technology Letters, Vol. 46, No. 1, pp. 65-69, 2005.

3. K. -L. Wong, and L. -C. Chou, “Internal Composite Monopole Antenna for WLAN/WiMAX Operation In A Laptop Computer”, Microwave and Optical Technology Letters, Vol. 48, No. 5, pp. 868-871, 2006.

4. K. L Chung, T. H. Mak, and W. Y. Tam, “A Modified Two-Strip Monopole Antenna for Wi-Fi and WiMAX Applications”, Microwave and Optical Technology Letters, Vol. 51, No. 12, pp. 2884-2886, 2009.

5. K. -L.Wong and L. –C. Chou, “Internal Wideband Metal-Plate Antenna for Laptop Application”, Microwave and Optical Technology Letters, Vol. 46, No. 4, pp. 384-387, 2005.

6. K. -L Wong, L. -C. Chou and C. –M. Su, “Dual-Band Flat-Plate Antenna with a Shorted Parasitic Element for Laptop Applications”, IEEE Transactions on Antennas and Propagation, Vol. 53, No. 1, pp. 539-544, 2005.

7. W. C. Su and K. -L Wong, “Internal PIFAS for UMTS/WLAN/WiMAX Multi-Network Operation for a USB Dongle” Microwave and Optical Technology Letters, Vol. 48, No. 11, pp. 2249-2253, 2006.

8. L. -C. Chou and K. -L. Wong, “Uni-Planer Dual –Band Monopole Antenna for 2.4/5 GHz WLAN Operation in a Laptop Computer” IEEE Transactions on Antennas and Propagation, Vol. 55, No. 12, pp. 3739-3741, 2007.

9. C. –Y. Pan,T. –S. Horng, W. –S. Chen, and C. –H. Huang, “Dual Wideband Printed Monopole Antenna for WLAN/ WiMAX Applications” IEEE antennas and wireless
propagation letters, Vol. 6, pp. 149-151, 2007.

10. G. J. Burke, and A. J. Poggio, “Numerical Electromagnetic Code-2”, Ver. 5.7.5, Arie Voors, 1981.





Shaik Ayesha, B.V. Ramana, K.V. Ramana Rao

Paper Title:

Floating-Point FPGA: Architecture Performance and Modeling

Abstract: This paper presents a novel architecture for domain-specific FPGA devices. This architecture can be optimized for both speed and density by exploiting domain-specific information to produce efficient reconfigurable logic with multiple granularity. In the reconfigurable logic, general-purpose fine grained units are used for implementing control logic and bit-oriented operations, while domain-specific coarse-grained units and heterogeneous blocks are used for implementing data paths; the precise amount of each type of resources can be customized to suit specific application domains. Issues and challenges associated with the design flow and the architecture modeling are addressed. Examples of the proposed architecture for speeding up floating point applications are illustrated. We assume that current proposed architecture can achieve 2.5 times improvement in speed and 18 times reduction in area on average, when compared with traditional FPGA devices on selected floating point benchmark circuits.

Architecture, field-programmable gate array (FPGA), Floating Point, Application domains, modeling.


1. Kuon and J. Rose, “Measuring the gap between FPGAs and ASICs,” in Proc. FPGA. New York, NY, USA: ACM Press, 2006, pp. 21–30.
2. R. Saleh, S. Wilton, S. Mirabbasi, A. Hu, M. Greenstreet, G. Lemieux, P. Pande, C. Grecu, and A. Ivanov. System-on-chip: Reuse and integration. Proceedings of the IEEE, 94(6):1050–1069, June 2006.

3. B. Quinton and S. Wilton. Post-silicon debug using programmable logic cores. In Int. Conf. on Field-Programmable Technology, pages 241–247, Dec. 2005.

4. S. Wilton, N. Kafafi, J. Wu, K. Bozman, V. Aken’Ova, and R. Saleh. Design considerations for soft embedded programmable logic cores. IEEE Journal of Solid-State Circuits, 40(2):485–497, Feb. 2005.

5. A. Yan and S. Wilton. Product-term based synthesizable embedded programmable logic cores. IEEE Trans. on VLSI, 14(5):474–488, May 2006.

6. E. Ahmed and J. Rose, “The effect of LUT and cluster size on deepsubmicron FPGA performance and density,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 12, no. 3, pp. 288–298, Mar. 2004.

7. Michael J. Beauchamp, Scott Hauck, Keith D.Underwood, and K. Scott Hemmert, (Feb 2008)“Architectural Modifications to Enhance the Floating- Point Performance of FPGAs”, IEEE Transactions on Very Large Scale Integration (VLSI)Systems vol. 16. 2.






Vahid Nourani

Paper Title:

Application of Continues Wavelet Transform to Investigate the Resolution Effect of Digital Elevation Model on Topographic Index Distribution

Abstract: Current progress in computer, geographic information system (GIS) and remote sensing technology has led to the LIDAR (light detection and ranging) data, as a new generation of high-resolution digital elevation model (DEM). Although such a high resolution DEM can be a useful tool for terrain analysis, high amount of noise is also expected to be existent in a mega-data set. To deal with this problem in this study, continues wavelet transform (CWT), as a mathematical filter, was employed to extract slope, specific area and topographic index which are important parameters in hydro-environmental studies at different resolutions. In this way the proposed methodology was applied to 1-m resolution LIDAR DEM of the Elder Creek River watershed where is a sub-basin of the South Folk Eel River basin at California. The results were also compared favorably with the results of a classic method. The result indicates that smoothing ability of the wavelet can be a promising DEM processing alternative to cope with the noise and pits of a high resolution DEM.

DEM, Elder Creek River, GIS, LIDAR Topography, Wavelet transform.


1. J.F. O’Callaghan, and D.M. Mark, “The extraction of drainage networks from digital elevation data”, Comput. Vis. Graph. Im. Proc., Vol.28, 1984, pp. 28– 344.
2. P.F. Quinn, K.J. Beven, P. Chevallier, and O. Planchon, “The prediction of hill slope flow paths for distributed modeling using digital terrain models”, Hydrol. Process., Vol.5, 1991, pp.59– 80.

3. P.F. Quinn, K.J. Beven, and R. Lamb, “The ln (a/tanb) index: How to calculate it and how to use it within the TOPMODEL framework”, Hydrol. Process., Vol.9, 1995, pp.161– 182.

4. D.G. Tarboton, “A new method for the determination of flow directions and upslope areas in grid digital elevation models”, Water Resour. Res., Vol.33, No.2, 1997, pp.309– 319.

5. J. Seibert, and B.L. McGlynn, “A new triangular multiple flow direction algorithm for computing upslope areas from gridded digital elevation models”, Water Resour. Res., Vol.43, 2007, 04501, doi:10.1029/2006WR005128.

6. X. Zhang, N.A. Drake, J. Wainwright, and M. Mulligan, “Comparison of slope estimates from low resolution DEMs: scaling issues and a fractal method for their solution”, Earth Surf. Proc. Land., Vol.24, 1999, pp.763–779.

7. R. Sorensen, U. Zinko, and J. Seibert, “On the calculation of the topographic wetness index: evaluation of different methods based on field observations”, Hydrol. Earth Syst. Sc., Vol.10, 2006, pp.101–112.

8. D.M. Wolock, and C.V. Price, “Effects of digital elevation model map scale and data resolution on a topography-based watershed model”, Water Resour. Res., Vol.30, 1994, pp. 3041–3052.

9. P. Bruneau, C. Gascuel-Odoux, P. Robin, P. M´erot, and K. Beven, “Sensitivity to space and time resolution of a hydrological model using digital elevation data”, Hydrol. Process., Vol.9, 1995, pp.69–81.

10. M. Franchini, J. Wendling, C. Obled, and E. Todini, “Physical interpretation and sensitivity analysis of the TOPMODEL”, J. Hydrol., Vol.175, 1996, pp.293–338.

11. G.R. Hancock, “The use of digital elevation models in the identification and characterization of catchments over different grid scales”, Hydrol. Process., Vol.19, No.9, 2005,1727–1749.

12. G. Mendicino, and A. Sole, “The information content theory for the estimation of the topographic index distribution used in TOPMODEL”, Hydrol. Process., Vol.11, 1997, pp.1099–1114.

13. G-M. Saulnier, K. Beven, and C. Obled, “Digital elevation analysis for distributed hydrological modelling: reducing scale dependence in effective hydraulic conductivity values”, Water Resour. Res., Vol.33, 1997, pp.2097–2101.

14. W. Zhang, and D.R. Montgomery, “Digital elevation model grid size, landscape representation, and hydrologic simulations”, Water Resour. Res., Vol.30, 1994, pp.1019–1028.

15. R., Sorensen, and J. Seibert, “Effects of DEM resolution on the calculation of topographical indices: TWI and its components”, J. Hydrol., Vol.347, 2007, pp.79–89.

16. V. Nourani, and A. Mano, “Semi-distributed flood runoff model at the sub-continental scale for south-western Iran”, Hydrol. Process., Vol.21, 2007, pp. 3173–3180.

17. X. Cai, and D. Wang, “Spatial autocorrelation of topographic index in catchments”, J. Hydrol., Vol.328, No.3–4, 2006, pp.581–591.

18. K.J. Beven, R. Lamb, P.F. Quinn , R. Romanowicz , and J. Freer, “TOPMODEL. In Computer Models of Watershed Hydrology”, Singh V.P. (ed.), Water Resource Publications, Colorado, 1995.

19. P. Passalacqua, P. Tarolli, and E. Foufoula-Georgiou, “Testing space-scale methodologies for automatic geomorphic feature extraction from LIDAR in a complex mountainous landscape”, Water Resour. Res., Vol.46, 2010, W11535.

20. J. T. Bjorke, and S. Nilsen, “Wavelets applied to simplification of digital terrain models”, Int. J. Geogr. Inf. Sci., Vol.17, No.7, 2003, pp.601-621.

21. E. Foufoula–Georgiou, and P. Kumar, Wavelet in Geophysics, Academic Press, New York 1995.

22. B. Lashermes, E. FoufoulaGeorgiou, and W. E. Dietrich, “Channel network extraction from high resolution topography using wavelets”, Geophys. Res. Lett., Vol.34, 2007, L23S04.

23. D., Wang, and X. Cai, “Scale and resolution effects of topographic index by 2-D continuous wavelet transform”, Proc. Int. Conf. Flood Forecasting Water Resour. Assess., 2006, Beijing, China.

24. D. Labat, “Recent advances in wavelet analyses: part 1–A review of concepts”, J. Hydrol., Vol.314, 2005, pp.275–288.

25. V. Nourani, M.T. Alami, and M.H. Aminfar, “A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation”, Eng. Appl. Artif. Intell., Vol.22, 2009a, pp.466–472.

26. V., Nourani, M. Komasi, and A. Mano, “A multivariate ANN-wavelet approach for rainfall-runoff modeling”, Water Resour. Manag., Vol.23, No.14, 2009b, pp.2877–2894.

27. S.G. Mallat, A Wavelet Tour of Signal Processing. Second Edition. Academic Press, San Diego, 1998.

28. P. S. Addison, The Illustrated Wavelet Transform Hand-Book, Institute of Physics Publishing, London, 2002.

29. V. Nourani, V.P. Singh, and H. Delafrouz, “Three geomorphological rainfall– runoff models based on the linear reservoir concept”, Catena, Vol.76, No.3, 2009c, pp.206–214.

30. N.R. Pradhan, Y. Tachikawa, and K. Takara, “A downscaling method of topographic index distribution for matching the scales of model application and parameter identification”, Hydrol. Process., Vol.20, 2006, pp.1385–1405.

31. S.N. Lane, R.M. Westaway, and D.M. Hicks, “Estimation of erosion and deposition volumes in a large gravel-bed, braided river using synoptic remote sensing”, Earth Surf. Proc. Land., Vol.28, 2003, pp.249–271.

32. S.N. Lane, C.J. Brookes, M.J. Kirkby, and J. Holden, “A network index based version of TOPMODEL for use with high resolution digital topographic data”, Hydrol.
Process., Vol.18, 2004, pp.191–201.
33. R. Ibbitt, and R. Woods, “Re-scaling the topographic index to improve the representation of physical processes in catchment models”, J. Hydrol., Vol.293, 2004, pp.205–218.

34. D.M. Wolock, and G.J. McCabe, “Differences in topographic characteristics computed from 100- and 1000-m resolution digital elevation model data”, Hydrol. Process., Vo.14, No.6, 2000, pp.987–1002.