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Volume-2 Issue-6: Published on January 05, 2013
Volume-2 Issue-6: Published on January 05, 2013

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Volume-2 Issue-6, January 2013, ISSN:  2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.



Moiz A. Hussain, G. U. Kharat

Paper Title:

Robust Human Motion Detection and Tracking In Dynamic Background 

Abstract:   Background subtraction is very important part of surveillance applications for successful segmentation of moving objects from video sequences. We present a novel & robust algorithm, for human motion detection and tracking in dynamic scenes based on background modelling technique to analyze the illumination change for detection & tracking of moving objects. Successive frame difference is taken and compared for the required set threshold for the changing pixel detection. Experimental result shows the high performance of the proposed method for human tracking in noisy backgrounds.

  object detection, tracking, video survelliance, backgoround model, illumination change.


1.        W. Hu, T. Tan, L.Wang, and S. Maybank, “A survey on visual surveillance of object motion and behaviors,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 34, no. 3, pp. 334–352, Aug. 2004.
2.        M.J. Hossain, J. Lee, and O. Chae. An Adaptive Video Surveillance Approach for Dynamic Environment. Proc. Int’l Symposium on Intelligent Signal Processing and Communication Systems: 2004. 84 – 89, 2004.

3.        Y. Dhome, N. Tronson, A. Vacavant, T. Chateau, C. Gabard, Y. Goyat, and D. Gruyer. A Benchmark for Background Subtraction Algorithms in Monocular Vision: a Comparative Study. In International Conference on Image Processing Theory, Tools and Applications (IPTA 2010). 7–10 July 2010,

4.        Madhur Mehta, Chandni Goyal, M.C. Srivastava and R.C.Jain, “ Real time object detection and tracking: Histogram matching and kalman filter approach,” IEEE 2010, 978-1-4244-5586-7.

5.        J. Lou, T.Tan, W. Hu, H. Yang,and S. H. Maybank, "3D Model-based Vehicle Tracking", IEEE Trans.on Image Processing, vol. 14, pp. 1561-1569, October 2005.

6.        Stauffer C, Grimson W. Adaptive background mixture models for real-time tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Fort Collins, Colorado, USA, IEEE, 1999: 245-252.

7.        Z. Zivkovic, “Improved adaptive Gaussian mixture model for background subtraction”, In: Proceedings of the 17th International Conference on Patter Recognition, Cambridge, United Kingdom, IEEE, 2004, 2: pp. 28-31.

8.        G. B. Yang, W. W. Chen, Q. Y. Zhou, and Z. Y. Zhang, "Optical flow approximation based motion object extraction for MPEG-2 video stream," Journal of Real-Time Image Processing, vol. 4, pp. 303-316, Nov 2009.

9.        M. Watanabe, N. Takeda, and K. Onoguchi, "Moving obstacle detection and recognition by optical flow pattern analysis for mobile robots," Advanced Robotics, vol. 12, pp. 791-816, 1999.

10.     L. Y. Li and M. K. H. Leung, "Integrating intensity and texture differences for robust change detection," IEEE Transactions on Image Processing, vol. 11, pp. 105-112, Feb 2002.

11.     M. Piccardi, "Background subtraction techniques: a review," in 2004 IEEE International Conference on Systems, Man & Cybernetics, Vols 1-7, ed, 2004, pp. 3099-3104.

12.     W. Q. Wang, J. Yang, and W. Gao, "Modeling background and segmenting moving objects from compressed video," IEEE Transactions on Circuits and Systems for Video Technology, vol. 18, pp. 670-681, May 2008.

13.     M. Mason and Z. Duric. Using histograms to detect and track objects in color video. Applied Imagery Pattern Recognition Workshop, pages 154-159, 2001.

14.     Y. Chen, C. Chen, C. Huang, Yi-Ping Hung: Efficient hierarchical method for background subtraction. Pattern Recognition 40(10): 2706–2715, 2007.

15.     Alan M. McIvor, “Background Subtraction Techniques”, 2000.

16.     A. V. Oppenheim, R. W. Schafer, and T. G. Stockham Jr., “Nonlinear filtering of multiplied and convolved signals,” Proc. IEEE, vol. 56, no. 8, pp. 1264–1291, 1968.

17.     Shaopeng Tang, Satoshi Goto, “Human Detection Using Motion and Appearance based Feature”,978-1-4244-4657-5/09/$25.00 ©2009 IEEE, May 2009




S Chandrashekhar Reddy, P.V.N.Prasad, A. Jaya Laxmi

Paper Title:

Reliability Improvement of Distribution System: A Hybrid Approach Based on GA and NN

Abstract:   Due to high power demand, modern utilities are continuously planning the expansion of the electrical networks. One of the methods used for the expansion of electrical networks is connecting distributed generator (DG) in the distribution system. The main function of DG is to generate power based on the load condition or any fault occurs in the electrical network. By connecting DG in the distribution system, the power demand of the system can be satisfied and also it improves the reliability of the electrical network. The major problem in DG is, identifying the optimal location for fixing DG in the system and also computing the optimal number of DG to be connected in the system. By considering the abovementioned problem, here a hybrid technique is proposed, which includes genetic algorithm and neural network to identify the optimal number & location of DG to be connected in the system. The proposed method also computes the amount of power to be generated by each DG for various load conditions. By connecting DGs, the number of generators in the network increases and so that different generator states are possible for a particular load condition. From the possible generator states, the best state is selected based on some reliability parameters. Here, the reliability parameters that are considered for identifying the best generator states are loss of load probability (LOLP), loss of load expectation (LOLE), expected energy not supplied (EENS) and system expected outage cost (ECOST). The above reliability parameters are computed for different load conditions and also for the optimal number of DG identified using the proposed method. By using this method, the best generator state for different load conditions and also for different number of generators is computed. The result obtained shows the development in system reliability due to connecting optimal number of DG in the system.

   Reliability, ECOST, EENS, LOLP, LOLE, DG, Distribution system.


1.       Hadi Zayandehroodi, Azah Mohamed, Hussain Shareef and Marjan Mohammadjafari, "Impact of distributed generations on power system protection performance", International Journal of the Physical Sciences Vol. 6, No. 16, pp. 3999-4007, Aug 2011
2.       Sebastian Rios. M, Victor Vidal. P and David L. Kiguel, "Bus-Based Reliability Indices and Associated Costs in the Bulk Power System", IEEE Transactions on
Power Systems, Vol. 13, No. 3, pp. 719-724, Aug 1998

3.       A. A. Chowdhury, Sudhir Kumar Agarwal and Don O. Koval, "Reliability Modeling of Distributed Generationin Conventional Distribution SystemsPlanning and Analysis", IEEE Transactions on Industry Applications, Vol. 39, No. 5, pp. 1493-1498, Oct 2003.

4.       F. Gharedaghi, M. Deysi, H. Jamali, A khalili, "Investigation of Power Quality in Presence of Fuel Cell Based Distributed Generation", Australian Journal of Basic and Applied Sciences, Vol. 5, No. 10, pp. 1106-1111, 2011

5.       Akash T. Davda, M. D. Desai and B. R. Parekh, "Impact of Embedding Renewable Distributed Generation on Voltage Profile of Distribution System: A Case Study", ARPN Journal of Engineering and Applied Sciences, Vol. 6, No. 6, pp. 70-74, June 2011

6.       Moein Moeini-Aghtaie, Payman Dehghanian and Seyed Hamid Hosseini, "Optimal Distributed Generation Placement in a Restructured Environment via a Multi-Objective Optimization Approach", 16th Conference on Electrical Power Distribution Networks (EPDC), Iran, pp. 1-6, 2011

7.       R. Yousefian and H. Monsef, "DG-Allocation Based on Reliability Indices by Means of Monte Carlo Simulation and AHP", 10th International Conference on Environment and Electrical Engineering (EEEIC), Iran, pp. 1-4, 2011.

8.       Limbu, Tika R. and Saha, Tapan K., "Investigations of the impact of powerformer™ on composite power system reliability", Proceedings of the IEEE Power Engineering Society General Meeting, United States, pp. 406-413, 2005.

9.       Lingfeng Wang and Chanan Singh, "Adequacy Assessment of Power-generating Systems Including Wind Power Integration Based on Ant Colony System Algorithm", IEEE Power Tech, Lausanne, pp. 1629-1634, 2007.

10.     Saket R K, Bansal and R C, Singh G, “Generation capacity adequacy evaluation based on peak load consideration”, The South Pacific Journal of Natural Science Vol. 24 , pp. 38–44, 2006.

11.     Bindeshwar Singh, K.S. Verma, Deependra Singhand S.N. Singh, "A Novel Approach for Optimal Placement of Distributed Generation & FACTS Controllers In Power Systems: An Overview and Key Issues", International Journal of Reviews in Computing, Vol. 7, pp. 29-54, 2011.

12.     Fariba Gharedaghi, Hanieh Jamali, Mansoureh Deisi and Atefeh Khalili, "Investigation of a new islanding detection method for distributed power generation systems", International Journal of the Physical Sciences Vol. 6, No. 23, pp. 5540-5549, Oct 2011.

13.     Seyed Ali Mohammad Javadian and Maryam Massaeli, "Impact of Distributed Generation on Distribution System’s Reliability Considering Recloser-Fuse Miscoordination-A Practical Case Study", Indian Journal of Science and Technology, Vol. 4, No. 10, pp. 1279-1284, Oct 2011.

14.     Wenping Qin, Peng Wang, Xiaoqing Han, and Xinhui Du, "Reactive Power Aspects in Reliability Assessment of Power Systems", IEEE Transactions ON Power Systems, Vol. 26, No. 1, pp. 85-92, Feb 2011.

15.     Mohammad Mohammadi and M. Akbari Nasab, "PSO Based Multiobjective Approach for Optimal Sizing and Placement of Distributed Generation", Research Journal of Applied Sciences, Engineering and Technology, Vol. 2, No. 8, pp. 832-837, 2011

16.     Morteza heydari, Amin Hajizadeh and Mahdi Banejad, "Optimal Placement of Distributed Generation Resources", International Journal of Power System Operation and Energy Management, Vol. 1, Issue. 2, pp. 2231–4407, 2011

17.     Satish Kansal, B.B.R. Sai, Barjeev Tyagi and Vishal Kumar, "Optimal placement of distributed generation in distribution networks", International Journal of Engineering, Science and Technology, Vol. 3, No. 3, pp. 47-55, 2011.

18.     Mohammad Mohammadi and M. Akbari Nasab, "DG Placement with Considering Reliability Improvement and Power Loss Reduction with GA Method" Research Journal of Applied Sciences, Engineering and Technology, Vol. 3, No. 8, pp.  838-842, 2011

19.     Priyanka Paliwal and N.P. Patidar, "Distributed Generator Placement for Loss Reduction and Improvement in Reliability", World Academy of Science, Engineering and Technology, Vol. 69, pp. 809-813, 2010.

20.     H. Shayeghi, H. Hosseini, A. Shabani and M. Mahdavi, "GEP Considering Purchase Prices, Profits of IPPs and Reliability Criteria Using Hybrid GA and PSO", World Academy of Science, Engineering and Technology, Vol. 44, pp. 888-894, 2008.

21.     S. Chandrashekhar Reddy, P. V. N. Prasad, A. Jaya Laxmi, “Power Quality Improvement of Distribution System by Optimal Placement and Power Generation of DGs using GA and NN”, European Journal of Scientific Research, Vol.69, No.3, pp. 326-336, 2012

22.     Qing-Shan, Min XIE and Felix F.WU, “Ordinal Optimization Based Security Dispatching in Deregulated Power Systems”, Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, Shanghai, P.R.China, December 16-18, 2009, FrA 15.4.

23.     Samuel Raafat  Fahim, Walid Helmy, Hany M.Hasanien and M.A.L.Badr, “ Optimal Study of Distributed Generation Impact on Electrical Distribution Networks using GA and Generalized Reduced Gradient”, Recent Researches in Communications, Electrical & Computer Engineering, pp. 77-82, 2001, ISBN: 978-960-474-286-8.




Remica Aggarwal

Paper Title:

Selection of IT Personnel through Hybrid Multi-attribute AHP-FLP approach

Abstract:    The personnel evaluation and selection is an important problem that can considerably affect the future competitiveness and performance of an organization. This paper presents a comprehensive hierarchical structure for selecting and evaluating a right personnel and proposes a new approach called “Analytical Hierarchy Process Weighted Fuzzy Linear Programming Model (AHP-FLP)” for personnel selection based on multiple attributes or criteria. The weights of the various criteria, taken as local weights from a given judgment matrix, are calculated using Analytical Hierarchy Process (AHP) that are also considered as the weights of the fuzzy linear programming model. This new model is compared with the classical AHP method. The study concluded that the AHP-FLP method outperforms the AHP method for selection  of  personnel with respect to restricted selection criteria. An example demonstrates the feasibility of the presented framework. Drawing on a real case of an Indian company from IT industry, the approach has been used to analyze the selection criteria used in recruitment for different IT roles which differed significantly in professional skills required.

   Analytic Hierarchy Process, Decision making, Fuzzy linear programming.


1.        O.S Vaidya and S. Kumar, “Analytic hierarchy process: an overview of applications”, in European Journal of Operational Research , vol. 169(1) ,2006, pp  1 - 29.
2.        C. Kahraman, U. Cebeci and D.Ruan, “Multi-attribute comparison of catering service companies using fuzzy AHP: the case of Turkey”, in International Journal of Production Economics, vol. 87, 2004, pp 171-84.

3.        H.P. Fu, Y.C. Ho, R.C.Y Chen, T.H. Chang and P.H. Chien, “Factors affecting the adoption of electronic marketplaces”, in International Journal of Operations & Production Management  , vol. 26(12), (2006, pp 1301-24.

4.        T.L. Saaty, The Analytical Hierarchy Process McGraw-Hill, New York, NY, 1980.

5.        F.Y. Partovi, “Determining what to benchmark: an analytic hierarchy process approach”, in International Journal of Operations & Production Management ,  vol.14 (6) ,1994, pp 25-39.

6.        R.G. Bellman and L.A.  Zadeh , “Decision making in a fuzzy environment”, in Management Science vol. 17(2) ,1970, pp 141-64.

7.        H.J. Zimmermann, “Fuzzy programming and linear programming with several objective functions”, in Fuzzy Sets and Systems, vol.  1, 1978 ,pp 45-55.

8.        R.N. Tiwari, S. Dharmahr and J.R.Rao , “Fuzzy goal programming-an additive model”, in Fuzzy Sets and Systems vol. 24, 1987, pp 27-34.

9.        A. Amid, S.H. Ghodsypour  and  C. O’Brien, “Fuzzy multiobjective linear model for the supplier selection in a supply chain”, in International Journal of Production Economics , vol. 104, 2006, pp 394-407.

10.     Y.J. Lai and C.L. Hawang , Fuzzy Multiple Objective Decision Making, Methods and Applications, Springer, Berlin 1994.

11.     M. Sakawa, Fuzzy Sets and Interactive Multi-objective Optimization, Plenum Press, New York, NY 1993.

12.     T. Ravichandran and C.  Lertwongsatien , “Effect of information systems resources and capabilities on firm performance: a resource-based perspective”, in Journal of  Management  Information  System, vol.21(4), 2005 pp  237-276.

13.     M. Tarafdar and S.R. Gordon , “Understanding the influence of information systems competencies on process innovation: a resource-based view”, in  Journal of  Strategic  Information  System, vol. 16(4), 2007,pp 353-392.

14.     T.A. Byrd and D.E. Turner , “An exploratory analysis of the value of the skills of IT personnel: their relationship to IS infrastructure and competitive advantage”, in  Decision Sciences  vol. 32(1) , 2001, pp 21 - 54.

15.     L. Fink, “How do IT capabilities create strategic value? Toward greater integration of insights from reductionistic and holistic approaches” ,  European  Journal of  Information  System, vol.   20(1)  , 2011, pp 16-33.

16.     C.L. Noll and M. Wilkins, Critical skills of IS professionals: a model for curriculum development, in Journal of Information Technology Education , vol. 1(3) ,2002, pp 143-154.

17.     L. Fink and S. Neumann , “Gaining agility through IT personnel capabilities: the mediating role of IT infrastructure capabilities”, in  Journal of Associative  Information  System, vol. 8(8) ,2007, pp 440-462.

18.     M.J. Gallivan , D.P. Truex and L. Kvasny, “Changing patterns in IT skill sets 1988–2003: a content analysis of classified advertising data base”, Advertising and  Information  System  , vol. 35(3), 2004, pp 64-87.

19.     D.M.S Lee, E.M. Trauth and D. Farwell, “Critical skills and knowledge requirements of IS professionals: a joint academic/industry Investigation” ,in  MIS Quarterly vol. 19(3) ,1995, pp 313-340.

20.     G.D. Bhatt and V. Grover, “Types of information technology capabilities and their role in competitive advantage: an empirical study”, Journal of Management Information System, vol. 22(2) , 2005, pp  253 - 277.

21.     T. Goles, S. Hawk and K.M. Kaiser, “Information technology workforce skills: the software and IT services provider perspective”, in   Information System Frontiers , vol. 10(2) , 2008, pp 179 - 194.




Sujatha.B, Chandra Sekhar Reddy, P Kiran Kumar Reddy

Paper Title:

Texture Classification Using Texton Co-Occurrence Matrix Derived From Texture Orientation

Abstract:   The present paper derived a new co-occurrence matrix based on textons and texture orientation for rotation invariant texture classification of 2D images. The new co-occurrence matrix is called as Texton and Texture Orientation Co-occurrence Matrix (T&TO-CM). The Co-occurrence Matrix (CM) characterizes the relationship between the values of neighboring pixels, while the histogram based techniques have high indexing performance. If the CM is used to represent image features directly, then the dimension will be high and the performance is decreased. On the other hand, if histogram is used to represent image features, the spatial information will be lost. Texture Classification based on T&TO-CM, integrates color, texture and edge features of an image. The proposed T&TO-CM is used to describe the spatial correlation of textons and texture orientation for texture classification. T&TO-CM can capture the spatial distribution of edges, and it is an efficient texture descriptor for images with heavy textural presence. The proposed method is computationally attractive as it computes different features with limited number of selected pixels. The experimental results indicate the efficacy of the present method over the various other methods.

   Co-occurrence Matrix;Texton, Texture Orientation


1.       T.R. Reed, J.M.H. Du Buf, A review of recent texture segmentation and feature extraction techniques, CVGIP—Image Understanding 57 (3) (1993) 359–372.
2.       Tufis, Automated fabric inspection based on a structural texture analysis method, in: Recent Issues in Pattern Analysis and Recognition, Springer, Berlin (1989) 377–390.

3.       Bodnarova, M. Bennamoun, K.K. Kubik, Suitability analysis of techniques for Iaw detection in textiles using texture analysis, Pattern Anal. Appl. 3 (2000) 254–266.

4.       R. Haralick, K. Shanmugam, I. Dinstein, Texture features for image classi"cation, IEEE Trans. Syst. Man Cybernet. 3 (1973) 610–621.

5.       Lei Wang, Jun Liu, Texture classi"cation using multi-resolution markov random "eld models, Pattern Recognition Lett. 20 (2) (1999) 171–182.

6.       M. PietikSainen, T. Ojala, Z. Xu, Rotation-invariant texture classi"cation using feature distributions, Pattern Recognition 33 (2000) 43–52.

7.       A.K. Jain, F. Farrokhnia, Un-supervised texture segmentation using Gabor "lters, Pattern Recognition 24 (12) (1991) 1167–1186.

8.       T. Randen, J.H. HusHy, Multichannel "ltering for image texture segmentation, Opt. Eng. 33 (8) (1994) 2617–2625.

9.       Jing-Wein Wang, Chin-Hsing Chen, Wei-Ming Chien, Chih-Ming Tsai, Texture classi"cation using non-separable two-dimensional wavelets, Pattern Recognition Lett. 19 (13) (1998) 1225–1234.

10.     Vijaya Kumar V., Eswara Reddy B., Raju U.S.N., Chandra Sekharan K., "An innovative technique of texture classification and comparison  based on long linear patterns," Journal of computer science,vol.3(8),pp:633-638,2007. 

11.     Julesz B., “Texton gradients: The texton theory revisited,” Biological Cybernetics, vol.54, pp: 245-251, 1986.

12.     Julesz B., “Textons, The elements of texture perception and their interactions,” Nature 290, pp: 91-97, 1981.

13.     Guang-Hai Liu, Zuo-Yong Li, Lei Zhang, Yong Xu, "Image retrieval based on micro-structure descriptor," Pattern Recognition, vol. 44, pp:2123-2133, 2011.

14.     Arivazhagan S., Ganesan L., Priyal S.P, “Texture classification using gabor wavelets based rotation invariant features,” Pattern Recognition Letters, vol.27, pp:1976-1982, 2006.

15.     Simoncelli E.P., Freeman W.T, “The steerable pyramid: A flexible architecture for multi-scale derivative computation,” Proceedings of IEEE ICIP 13, pp:891-906,1995.

16.     Falkowski B. J.  and Perkowski M. A., “A family of all essential Radix addition/subtraction multipolarity transforms: Algorithms and interpretations in Boolean domain,” in Proc. 23rd IEEE Int. Symp. Circuits Systems, pp. 1596–1599, 1990.

17.     Haralick R.M., Shanmugan K. and Dinstein I., "Textural features for image classification," IEEE Trans. Sysr. Man. Cybern., vol. 3, pp:610-621, 1973.

18.     Wu J. and Duh W., “Feature extraction capability of some discrete transforms,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-36, pp: 1687, 1991.

19.     You J. and Cohen H. A., “Classification and segmentation of rotated and scaled textured images using texture tuned masks,” Pattern Recognit., vol. 26, pp. 245-258, 1993.

20.     Pichler O., Teuner A., and Hosticka B. J., “A comparison of texture feature extraction using adaptive Gabor filtering, pyramidal and tree structured wavelet transforms,” Pattern Recognit., vol. 29,  pp. 733–742,




Mohamed Bahaj, Abdellatif Soklabi, Ilias Cherti

Paper Title:

Load Balancing Management by Efficient Controlling Mobiles Agents

Abstract:    Load balancing is a computer networking methodology which allows the distribution of the workload across multiple computers or computing devices, such as central processing units, disk drives, or other resources, to reach optimal resource utilization, reduce response time, maximize throughput and circumvent overload. The Use of multiple computers with load balancing, instead of a single computer, may increase reliability through redundancy. Our contribution outlines the adaptation of the Shadow approach used to control mobiles agents for developing a load balancing management algorithm in distributed systems. This approach does not only distribute the loads on the nodes and collect its running result, but it also manages the tasks execution places during all the execution time. Thereby, we get a self-organized load balancing infrastructure.

   distributed computing, load balancing, mobiles agents.


1.       R. Jadhav, S. Kamlapur I. Priyadarshini, “Performance evaluation in distributed systems, using dynamic load balancing” in International Journal of Applied Information Systems (IJAIS), Foundation of Computer Science FCS, pages 36-41 February 2012.
2.       H.A. James, K.A. Hawick_and P.D. Coddington, “Scheduling Independent Tasks on Metacomputing Systems”, Distributed and High Performance Computing Group, 9 March 1999.

3.       N. Spanoudakis, P.  Moraitis, “Modular JADE Agents Design and Implementation using ASEME“ in IEEE International conference on web intelligence and intelligent agent technology, pages 221-228, 2010.

4.       P. Moraitis and N. Spanoudakis, “The Gaia2JADE Process for Multi-Agent Systems Development”, Applied Artificial Intelligence Journal 20(4-5), pages 251-273, 2006.

5.       J. Baumann, K. Rothermel, “The Shadow Approach, an Orphan Detection Protocol for Mobile Agent”, July 1998.

6.       A. Singh, “An Efficient Load Balancing Algorithm for Grid Computing using Mobile Agent” in Anand Singh / International Journal of Engineering Science and Technology (IJEST), pages 4744-4747, 6 June 2011.

7.       J. Stender, S. Kaiser, S. Albayrak, “Mobility-based Runtime Load Balancing in Multi-Agent Systems” 18th International Conference on Software Engineering and Knowledge Engineering, Reedwood City, CA, USA

8.       J. Chakravarti, G. Baumgartner, M. Lauria, “The Organic Grid, Self-Organizing Computation on a Peer-to-Peer Network” IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS,  2005.

9.       K. Rothermel, M. Schwehm, “MOBILE AGENTS”, in Encyclopedia for Computer Science and Technology, 1998.

10.     P. Sinha, “Distributed Operating Systems Concepts and Design”, IEEE Computer Society Press.

11.     G. Cabri, L. Leonardi, F. Zambonelli, “Weak and Strong Mobility in Mobile Agent Applications”.

12.     S. Fricke, K. Bsufka, J.Keiser, T. Schmidt, R. Sesseler, and S. Albayrak. “Agent based telematic services and telecom applications”. In communications of the ACM, 2001.

13.     N. Suri, J. Bradshaw, T. Groth, R. Breedy, A. Hill and R. Jeffers. “Strong mobility and fine-grained resource control in NOMADS” in Fourth international symposium on Mobile Agents, 2000.

14.     J. Baumann, F. Hohl, N. Radouniklis, K. Rothermel. “Communication Concepts for Mobile Agent Systems” in Mobile Agents springer-Verlag, pp. 123 - 135, 1997.

15.     J. Baumann, N. Radouniklis. “Agent Groups for Mobile Agent Systems“, in Distributed Applications and Interoperable Systems, 1997.

16.     F. Bellifemine, G. Caire, T. Trucco, G. Rimassa. “JADE PROGRAMMER’S GUIDE”, last update: 08-April-2010. JADE 4.0

17.     J. Cao, Y. Sun, X. Wang, S. Das, “Scalable Load Balancing on Distributed Web Servers Using Mobile Agents”.

18.     F. BOUZERAA,  “Agents Mobiles et Systèmes Distribués” 14 December 2009.

19.     A. Outtagarts, “Mobile Agent-based Applications: a Survey”, in International Journal of Computer Science and Network Security, VOL.9 No.11, November 2009.

20.     K. Chow, Y. Kwok, H. Jin, Kai Hwang “Comet: A Communication-Efficient Load Balancing Strategy for Multi-Agent Cluster Computing”




M Sripathy, K V Sharma, M Krishna

Paper Title:

Effect of Cyclic Compression Loading On Crushing Response of Polymer Based Composites Sandwich Panels

Abstract:  The objective of work was focused to investigate microstructure of polyurethane foam and cyclic crushing strength of its sandwich structure which made of sisal / coir / bamboo / glass fabrics as reinforcement with polyester resin to form composites skin.  The tested sandwich panels were constructed four type of FRP faceplates made of sisal / coir / bamboo / glass fiber reinforcements impregnated in polyester resin in four different material combinations. Each specimen subjected ten cyclic compression loading upto 40% maximum strain.  The results indicate that the foams initially harden after the first cycle and then soften in subsequent cyclic loading. The hysteresis loops tend to shrink and approach asymptotically to a steady state before failure both the foam and the skin.  The considered damage is in a form of through-width zone of crushed foam core accompanied by a residual crushing in the foam. It is shown that such damage causes a significant reduction of compressive strength. Glass/polyester and bamboo/polyester skin based sandwich structures have superior compressive strength. Coir /polyester based sandwich structure shows next to glass/polyester sandwich structures. 

   The hysteresis loops tend to shrink and approach asymptotically to a steady state before failure both the foam and the skin.


1.       D. Mohr, T. Wierzbicki, Crushing of soft-core sandwich profiles: experiments and Analysis, international Journal of Mechanical Sciences, vol.45 (2003), pp.253-271
2.       Zonghua Zhang, Shutian Liu, Zhiliang Tang, Crashworthiness investigation of kagome honeycomb sandwich cylindrical column under axial crushing loads,Thin- Walled Structures, Volume 48, Issue 1, January 2010, Pages 9-18

3.       Flavio de Andrade Silva, Nikhilesh Chawla, Romildo Dias de Toledo Filho, Tensile behavior of high performance natural (sisal) fibers, Composites Science and Technology, Volume 68, Issues 15-16, December 2008, Pages 3438-3443

4.       C. Alves, P.M.C. Ferrão, A.J. Silva, L.G. Reis, M. Freitas, L.B. Rodrigues, D.E.Alves, Ecodesign of automotive components making use of natural jute fiber composites, Journal of Cleaner Production, Volume 18, Issue 4, March 2010, Pages 313-327

5.       A. Awal, G. Cescutti, S.B. Ghosh, J. Müssig,  Interfacial studies of natural fibre/polypropylene composites using single fibre fragmentation test (SFFT),  Composites Part A: Applied Science and Manufacturing, Volume 42, Issue 1, January 2011, Pages 50-56

6.       S.V Joshi, L.T Drzal, A.K Mohanty, S Arora, Are natural fiber composites environmentally superior to glass fiber reinforced composites Composites Part A: Applied Science and Manufacturing, Volume 35, Issue 3, March 2004, Pages 371-376

7.       F.G. Torres, M.L. Cubillas, Study of the interfacial properties of natural fibre reinforced polyethylene. Polymer Testing, Volume 24, Issue 6, September 2005, Pages 694-698.

8.       Amin Ajdari, Hamid Nayeb-Hashemi, Ashkan Vaziri, Dynamic crushing and energy absorption of regular, irregular and functionally graded cellular structures, International Journal of Solids and Structures, Volume 48, Issues 3-4, February 2011, Pages 506-516.




G.Ajay, M.Suneel, K.Kiran Kumar, P.Siva Prasad

Paper Title:

Quality Evaluation of Rice Grains Using Morphological Methods

Abstract:   In this paper we present an automatic evaluation method for the determination of the quality of milled rice. Among the milled rice samples the quantity of broken kernels are determined with the help of shape descriptors, and geometric features. Grains are said to be broken kernels whose lengths are 75% of the grain size. This proposed method gives good results in evaluation of rice quality.

   Rice, Morphological Processing, Parameters, broken rice.


1.       Qing Yao, Jianhua Chen, Zexin Guan "Inspection of rice appearance quality using machine vision",Global Congress on Intelligent Systems. 2009.
2.       Siriluk Sansomboonsuk and Nitin Afzulpurkar, "The Appropriate Algorithms of Image analysis for Rice Kernel Quality Evalution",- 20th Conference of Mechanical Engineering Network of Thailand18-20 October 2006, Nakhon Ratchasima, Thailand

3.       Agustin, O.C., Byung-Joo Oh, "Automatic Milled Rice Quality Analysis", Future Generation Communication and Networking, 2008. FGCN '08. Second International Conference.

4.       Zhao-yan Liu, Fang Cheng,Yi-bin Ying, and Xiu-qin Rao,"Identification of rice seed varieties using neural network" -J Zhejiang Univ Sci B. 2005 November

5.       Sakai N, Yonekawa S, Matsuzaki A, “Two dimensional Image Analysis of the shape of Rice and its application to separating varieties”Journal of Food Engineering vol. 27, pp.397-407, 1996. Elsevier Science Ltd.

6.       Yadav, B. K., & Jindal, V. K. ‘Monitoring milling quality of rice by image analyses. Computers and Electronics in Agriculture, 33(1), 19–33.2001.

7.       Yanagihara, T. ‘Measurement of the visual characteristics of cooked rice using image analysis’. Nippon Shokuhin Kagaku Kaishi, 47(7), 516–522Cereal Chemistry, 75(5), 738–741.2001.

8        Dalen G.V.”Determination of the size rice distribution and percentage of broken kernels of using flatbed scanning and image analyses:”Food research International(37)51,2004 Elsevier




C. Saravanan, M. Surender

Paper Title:

Enhancing Efficiency of Huffman Coding using Lempel Ziv Coding for Image Compression

Abstract:   Compression is a technology for reducing the quantity of data used to represent any content without excessively reducing the quality of the picture. The need for an efficient technique for compression of images ever increasing because the raw images need large amounts of disk space seems to be a big disadvantage during transmission & storage. Compression is a technique that makes storing easier for large amount of data. It also reduces the number of bits required to store and transmit digital media. In this paper, a fast lossless compression scheme is presented and named as HL which consists of two stages. In the first stage, a Huffman coding is used to compress the image. In the second stage all Huffman code words are concatenated together and then compressed with Lempel Ziv coding. This technique is simple in implementation and utilizes less memory. A software algorithm has been developed and implemented to compress and decompress the given image using Huffman coding techniques in MATLAB software.

   Lossless image compression, Huffman coding, Lempel Ziv coding.


1.        http://www.TheLZWcompressionalgorithm.html


4.        J.Ziv and A.Lempel “A Universal Algorithm for Sequential data compression”, IEEE Transaction on Information theory, May 1977.

5.        Introduction to Data Compression, Khalid Sayood, Ed Fox  (Editor), March 2000.

6.        Kuo-Kun Tseng, JunMin Jiang, Jeng-Shyang Pan, Ling Ling  Tang, Chih-Yu Hsu, and Chih-Cheng Chen, “Enhanced Huffman Coding with Encryption for Wireless Data  Broadcasting System”, International Symposium on Computer, Consumer and Control, 2012.

7.        Mamta Sharma, “Compression Using Huffman Coding”, International Journal of Computer Science and Network Security, Vol.10, No.5, May 2010.

8.        C. Saravanan and R. Ponalagusamy, “Lossless Grey-scale Image Compression using Source Symbols Reduction and Huffman Coding”, International Journal of Image Processing (CSC Journals), Vol.3, Iss.5, pp.246-251, 2009.

9.        M.J.Weinberger, G.Seroussi, and G. Saprio. "LOCO-1 Lossless, Image Compression Algorithm: Principles and Standardization into JPEG-LS", IEEE trans. Image Processing, pp.1309 - 1324, August 2000.

10.     Martin DVORAK, Martin SLANINA, “Educational Video Codec”, 22nd International Conference Radioelektronika 2012,

11.     R.C. Gonzales, R.E. Woods, Digital Image Processing, pp. 525-626, Pearson Prentice Hall, Upper Saddle River, New Jersey, 2008.

12.     D. A. Huffman, “A method for the construction of minimum Redundancy codes,” in Proc. IRE, Sep. 1952, Vol. 40, pp. 1098–1101.

13.     G. Lakhani and V. Ayyagari, “Improved Huffman code tables for JPEG’s encoder,” IEEE Trans. Circuits Syst. Video Technol., Vol. 5, No. 6, pp. 562–564, Dec. 1995.

14.     Adina Arthur, V. Saravanan, “Efficient Medical Image Compression Technique for Telemedicine Considering Online and Offline Application”, International Conference on Computing, Communication, and Applications, 2012.

15.     Yu-Ting Pai, Fan-Chieh Cheng, Shu-Ping Lu, and Shanq-Jang Ruan, “Sub-Trees Modification of Huffman Coding for Stuffing Bits Reduction and Efficient NRZI Data Transmission”, IEEE  Transactions on Broadcasting, vol. 58, no. 2, June 2012.

16.     S.B. Choi and M.H. Lee, “High speed pattern matching for a fast Huffman decoder,” IEEE Trans. Consum. Electron., vol. 41, no. 1, pp. 97–103, Feb. 1995.

17.     Huang-Chih Kuo and Youn-Long Lin,”A Hybrid Algorithm for Effective Lossless Compression of Video Display Frames”, IEEE Transactions on Multimedia, vol.14, no.13, June 2012.




Zhenxing Luo

Paper Title:

Parameter Estimation in Wireless Sensor Networks Based on Decisions Transmitted over Rayleigh Fading Channels

Abstract:    In this paper, we present a distributed estimation method in wireless sensor networks (WSNs) based on decisions transmitted over Rayleigh fading channels. The fusion centre can uses either coherent receiver or non-coherent receiver to acquire decisions transmitted over Rayleigh fading channels. The estimation method using coherent receiver and the estimation method using non-coherent receiver are presented and the Cramer-Rao lower bounds (CRLBs) are derived. Simulation results showed that in ideal situations, the RMS errors given by the distributed estimation method were close to the CRLB. Moreover, simulation results highlighted the importance of the number of sensors, channel SNR, and accurate channel SNR information known to the fusion centre on estimation performance.

   Wireless sensor networks, maximum likelihood estimation, distributed estimation, Cramer-Rao lower bound, Rayleigh fading channel.


1.       D. Li, K. D. Wong, Y.H.Hu, and A. N. Sayeed, "Detection, Classification, and Tracking of Targets", IEEE Signal Processing Magazine, vol.19, no. 3, pp. 17-29, Mar. 2002.
2.       Z. X. Luo and T. C. Jannett, “Optimal Threshold for Locating Targets within a Surveillance Region Using a Binary Sensor Network”, in Proceedings of the International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CISSE 09), Dec. 2009.

3.       Z. X. Luo and T. C. Jannett, “A Multi-Objective Method to Balance Energy Consumption and Performance for Energy-Based Target Localization in Wireless Sensor Networks”, in Proc. of the 2012 IEEE Southeastcon, Orlando, FL, Mar. 2012.

4.       X. Sheng and Y. H. Hu, "Maximum Likelihood Multiple-Source Localization Using Acoustic Energy Measurements with Wireless Sensor Networks", IEEE Transactions on Signal Processing, vol.53, no.1, pp. 44-53, Jan. 2005.

5.       Z. X. Luo and T. C. Jannett, “Performance Comparison between Maximum Likelihood and Heuristic Weighted Average Estimation Methods for Energy-Based Target Localization in Wireless Sensor Networks”, in Proceedings of the 2012 IEEE Southeastcon, Orlando, FL, Mar. 2012.

6.       Z. X. Luo and T. C. Jannett, “Modelling Sensor Position Uncertainty for Robust Target Localization in Wireless Sensor Networks”, in Proceedings of the 2012 IEEE Radio and Wireless Symposium, Santa Clara, CA, Jan. 2012.

7.       Z. X. Luo and T. C. Jannett, “Energy-Based Target Localization in Multi-Hop Wireless Sensor Networks”, in Proceedings of the 2012 IEEE Radio and Wireless Symposium, Santa Clara, CA, Jan. 2012.

8.       Z. X. Luo, “A censoring and quantization scheme for energy-based target localization in wireless sensor networks,” Journal of Engineering and Technology 2012, no 2, pp. 69-74.

9.       Z. X. Luo, “Anti-attack and channel aware target localization in wireless sensor networks”, International Journal of Engineering and Advanced Technology, vol. 1, no.6, Aug. 2012.

10.     Z. X. Luo, “Robust energy-based target localization in wireless sensor networks in the presence of Byzantine attacks”, International Journal of Innovative Technology and exploring Engineering, vol. 1, no.3, Aug. 2012.

11.     Z. X. Luo, “A coding and decoding scheme for energy-based target localization in wireless sensor networks”, International Journal of Soft Computing and Engineering, vol. 2, no. 4, Sept. 2012.

12.     Z. X. Luo, “Distributed Estimation in Wireless Sensor Networks with Heterogeneous Sensors”, International Journal of Innovative Technology and Exploring Engineering, vol. 1, no. 4, Sept. 2012.

13.     R. X. Niu and P. K. Varshney, “Target Location Estimation in Sensor Networks with Quantized Data”, IEEE Transactions on Signal Processing, vol. 54, pp. 4519-4528, Dec. 2006.

14.     O. Ozdemir, R. X. Niu, and P. K. Varshney, "Channel Aware Target Localization with Quantized Data in Wireless Sensor Networks," IEEE Trans. Signal Process., vol. 57, pp. 1190-1202, 2009.

15.     A. Ribeiro, and G. B. Giannakis, “Bandwidth-constrained Distributed Estimation for Wireless Sensor Networks-part I: Gaussian case,” IEEE Trans. Signal
Process., vol. 54, no. 3, pp.1131-43, March 2006.

16.     A. Ribeiro, and G. B. Giannakis, “Bandwidth-constrained Distributed Estimation for Wireless Sensor Networks-part II: Unknown Probability Density Function,” IEEE Transactions on Signal Process., vol. 54, no. 7, pp. 2784-96, July 2006.

17.     C. Hao and P. K. Varshney, "Nonparametric One-Bit Quantizers for Distributed Estimation," IEEE Transactions on Signal Processing, vol. 58, pp. 3777-3787, 2010.

18.     C. Hao and P. K. Varshney, "Performance Limit for Distributed Estimation Systems With Identical One-Bit Quantizers," IEEE Transactions on Signal Processing, vol. 58, pp. 466-471, 2010.

19.     G. Liu, B. Xu, M. Zeng, and H. Chen, "Distributed estimation over binary symmetric channels in wireless sensor networks," IET Wireless Sensor Systems, vol. 1, pp. 105-109, 2011.




Zhenxing Luo

Paper Title:

Parameter Estimation in Wireless Sensor Networks with Normally Distributed Sensor Gains

Abstract:   Wireless sensor networks (WSN) have attracted significant attention recently. The distributed estimation problem is an important research topic in WSNs. In the distributed estimation problem, the fusion center estimates an unknown parameter based on information gathered from sensors. Usually, it is assumed that sensors have identical gains. However, this may not be true due to manufacture errors or environmental influence. In this paper, we assume sensor gains follow normal distribution and present a maximum likelihood estimation (MLE) approach for distributed estimation in WSNs with normally distributed sensor gains. Moreover, the Cramer-Rao lower bound (CRLB) corresponding to this MLE approach is also derived. Simulation results showed that the root square mean (RMS) estimation errors given by this MLE approach were close to the CRLB if the variance of the sensor gains is small. If the variance of the sensor gains was large, the RMS estimation errors were not close to the CRLB.

  Distributed estimation, maximum likelihood estimation, Gaussian distribution, wireless sensor networks.


1.       D. Li, K. D. Wong, Y.H.Hu, and A. N. Sayeed, "Detection, Classification, and Tracking of Targets", IEEE Signal Processing Magazine, vol.19, no. 3, pp. 17-29, Mar. 2002.
2.       Z. X. Luo and T. C. Jannett, “Optimal Threshold for Locating Targets within a Surveillance Region Using a Binary Sensor Network”, in Proceedings of the International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CISSE 09), Dec. 2009.

3.       Z. X. Luo and T. C. Jannett, “Energy-Based Target Localization in Multi-Hop Wireless Sensor Networks”, in Proceedings of the 2012 IEEE Radio and Wireless Symposium, Santa Clara, CA, Jan. 2012.

4.       Z. X. Luo and T. C. Jannett, “A Multi-Objective Method to Balance Energy Consumption and Performance for Energy-Based Target Localization in Wireless Sensor Networks”, in Proceedings of the 2012 IEEE Southeastcon, Orlando, FL, Mar. 2012.

5.       Z. X. Luo and T. C. Jannett, “Performance Comparison between Maximum Likelihood and Heuristic Weighted Average Estimation Methods for Energy-Based Target Localization in Wireless Sensor Networks”, in Proceedings of the 2012 IEEE Southeastcon, Orlando, FL, Mar. 2012.

6.       Z. X. Luo and T. C. Jannett, “Modeling Sensor Position Uncertainty for Robust Target Localization in Wireless Sensor Networks”, in Proceedings of the 2012 IEEE Radio and Wireless Symposium, Santa Clara, CA, Jan. 2012.

7.       Z. X. Luo, “A censoring and quantization scheme for energy-based target localization in wireless sensor networks”, Journal of Engineering and Technology, 2012, no 2, pp. 69-74.

8.       Z. X. Luo, “Anti-attack and channel aware target localization in wireless sensor networks”,  International Journal of Engineering and Advanced Technology, vol. 1, no.6, Aug. 2012.

9.       Z. X. Luo, “Robust energy-based target localization in wireless sensor networks in the presence of Byzantine attacks”, International Journal of Innovative Technology and exploring Engineering, vol. 1, no.3, Aug. 2012.

10.     Z. X. Luo, “A coding and decoding scheme for energy-based target localization in wireless sensor networks”, International Journal of Soft Computing and Engineering, vol. 2, no. 4, Sept. 2012.

11.     Z. X. Luo, “Distributed Estimation in Wireless Sensor Networks with Heterogeneous Sensors”, International Journal of Innovative Technology and Exploring Engineering, vol. 1, no. 4, Sept. 2012.

12.     Z. X. Luo, “A New Search Method for Distributed Estimation in Wireless Sensor Networks,” International Journal of Innovative Technology and Exploring Engineering, vol.1, no.4, Sept. 2012.

13.     X. Sheng and Y. H. Hu, "Maximum Likelihood Multiple-Source Localization Using Acoustic Energy Measurements with Wireless Sensor Networks", IEEE Transactions on Signal Processing, vol.53, no.1, pp. 44-53, Jan. 2005.

14.     R. X. Niu and P. K. Varshney, “Target Location Estimation in Sensor Networks with Quantized Data”, IEEE Transactions on Signal Processing, vol. 54, pp. 4519-4528, Dec. 2006.

15.     A. Ribeiro, and G. B. Giannakis, “Bandwidth-constrained Distributed Estimation for Wireless Sensor Networks-part I: Gaussian case,” IEEE Trans. Signal Process., vol. 54, no. 3, pp.1131-43, March 2006.

16.     A. Ribeiro, and G. B. Giannakis, “Bandwidth-constrained Distributed Estimation for Wireless Sensor Networks-part II: Unknown Probability Density Function,” IEEE Transactions on Signal Process., vol. 54, no. 7, pp. 2784-96, July 2006.

17.     G. Liu, B. Xu, M. Zeng, and H. Chen, "Distributed Estimation over Binary Symmetric Channels in Wireless Sensor Networks," IET Wireless Sensor Systems, vol. 1, pp. 105-109, 2011.

18.     A. Papoulis and S. U. Pillai, Probability, random variables, and stochastic processes. New York: McGraw-Hill, 4th edition, 2002.




R. L. Bhargavi, M.Merlin Moses, V.Karthikeyan and C.Karthikeyan

Paper Title:

Design of a High-Speed Matrix Multiplier Based on Balanced Word-Width Decomposition and Karatsuba Multiplication

Abstract:   This paper presents a flexible 2x2 matrix multiplier architecture. The architecture is based on word-width decomposition for flexible but high-speed operation. The elements in the matrices are successively decomposed so that a set of small multipliers and simple adders are used to generate partial results, which are combined to generate the final results. Balanced word-width decomposition scheme is discussed, which support 2’s complement inputs, and its overall functionality is verified and designed with a field-programmable gate array (FPGA). The architecture can be easily extended to a reconfigurable matrix multiplier. The objective is to propose a flexible and energy efficient matrix multiplier, which can be extended to reconfigurable high speed processing implementation, using word width decomposition technique. This technique is based on divide and conquers approach. The Karatsuba multiplication is proposed in this basic approach. This Karatsuba multiplication is an efficient procedure for multiplying large numbers, which gives high speed performance than the booth multiplier.

   Balanced word-width decomposition. Field-programmable gate array (FPGA) implementation, matrix multiplier, Reconfigurable architecture.


1.       K.Li,Y.Pan,and S.Q.Zheng,“Fast and processor efficient parallel matrix multiplication algorithms on a linear array with a reconfigurable pipelined bus system,”IEEE Trans.Parallel Distrib.Syst.,vol.9,no. 8,pp.705–720,Aug.1998.
2.       C.I.Brown and R.B.Yates,“VLSI architecture for sparse matrix mul-tiplication,” Electron.Lett.,vol.32,no.10,pp.891–893,May 1996.

3.       O.Mencer,M.Morf,and M.Flynn,“PAM-Blox: High performance FPGA design for adaptive computing,”in Proc.IEEE Symp.FPGAs Custom Computing Machines,1998,pp.167–174.

4.       A.Amira,A.Bouridane,and P.Milligan,“Accelerating matrix product on reconfigurable hardware for signal processing,”in Proc.11th Int. Conf.Field-Programmable Logic Appl.(FPL),2001,pp.101–111.

5.       J.Jang,S.Choi, and V.K.Prasanna, “Energy-efficient matrixmulti-plication on FPGAs,”in Proc.Int.Conf.Field Programmable Logic Appl.,2002,pp.534–544.

6.       V.K.Prasanna and Y.Tsai,“On synthesizing optimal family of linear systolic arrays for matrix multiplication,”IEEE Trans.Comput.,vol. 40,no.6,pp.770–774,Jun.1991.

7.       J.-W.Jang,S.Choi,and V.K.Prasanna,“Area and time efficient im-plementations of matrix multiplication on FPGAs,”in Proc.IEEE Int. Conf.Field Programmable Technol.,2002,pp.93–100.

8.       R.Lin,“Bit-matrix decomposition and dynamic reconfiguration:Uni-fied arithmetic processor architecture,design,and test,”in Proc.Re-configurable Arch.Workshop (RAW),2002,p.83.

9.       R.Lin,“Bit-matrix decomposition and dynamic reconfiguration: Uni-fied arithmetic processor architecture,design,and test,”in Proc.Re- configurableArch.Workshop (RAW),2002,p.83.

10.     R.Lin,“Reconfigurable parallel inner product processor architectures,” IEEE Trans. Very Large Scale Integr.(VLSI)Syst.,vol.9,no.2,pp. 261–272,Apr.2001.

11.     S.Choi,R.Scrofano, V.K.Prasanna, and J.-W.Jang,“Energy-effi-cient signal processing using FPGAs,”in Proc.ACM/SIGDA Int.Symp. Field-Programmable Gate
Arrays, 2003, pp.225–234.

12.     J.M.Rabaey, A.Chandrakasan and B.Nikolic ´,Digital Integrated Cir-cuits: A Design Persepective, 2nd ed.Englewood Cliffs,NJ: Pren-tice- Hall,2003.

13.     C.R.Baugh and B.A.Wooley,“A t wo’s complement parallel array multiplication algorithm, ”IEEE Trans.Comput., vol.C-22,no. 1–2, pp.1045–1047,Jan.193.




Jayasanthi Ranjith, NJR.Muniraj

Paper Title:

Novel Evolutionary Algorithm for ICA Processor for FPGA Implementation

Abstract:   Evolutionary programming (EP) has been applied to many numerical and combinatorial optimization problems in recent years. Independent component analysis (ICA) is a statistical signal processing technique for separation of mixed signals, voices and images. The need for evolutionary algorithm for ICA lies in the fact that it needs contrast function optimization which enables the estimation of the independent components. Independent component analysis (ICA) decomposes observed mixed random vectors into statistically independent variables. It aims at finding the underlying independent components in the mixture by searching a linear or nonlinear transformation. It is also more efficient when the cost function, which measures the independence of the components, is optimized. ICA algorithm for contrast function optimization is developed in VHDL .The use of low complexity evolutionary computation with additional operations of mutation and crossover resolves the permutation ambiguity to a large extent. This also ensures the convergence of the algorithm to a global optimum and VLSI implementation results in reduced complexity of algorithms. IEEE single-precision representation, which fits in thirty-two bits, is used for all the manipulations for covering large range of real values.

   ICA, Evolutionary optimization algorithm, FPGA , Statistical signal processing, VLSI


1.       Hyvarinen and E. Oja, “A fast fixed-point algorithm for independent component analysis,” Neural Comput., vol. 9, no. 7, pp. 1483–1492,Oct. 1997.
2.       Amit Acharyya and Koushik “hardware Efficient Fixed-Point VLSI Architecture for 2D Kurtotic FastICA”

3.       H. Du, H. Qi and X. Wang, “Comparative Study of VLSI Solutions to Independent Component Analysis”, IEEE Trans. Industrial Electronics, vol. 54, no. 1, February, 2007.

4.       K. K. Shyu, M. H. Lee, Y. T. Wu and P. L. Lee, “Implementation of Pipelined FastICA on FPGA for Real-Time Blind Source Separation”, IEEE Trans. Neural Networks, vol. 19, no. 6, pp. 958-970, June, 2008.

5.       Hyvarinen, “Fast and Robust Fixed-Point Algorithms for Independent Component Analysis”, IEEE Trans. Neural Networks, vol. 10, no. 3, May, 1999.

6.       E.Bingham and A. Hyvarinen, A Fast fixed-point algorithm for independent component analysis of complex valued signals, International Journal of Neural Systems, Vol. 10, No. 1, pp.1-8, February, 2000.

7.       Alan Paulo , Ana Maria ,”FPGA hardware design, simulation and synthesis for a Independent component analysis algorithm using system-level design software”

8.       Y.Tan and J.Wang, “Nonlinear Blind Source Separation Using Higher Order Statistics and a Genetic Algorithm”, IEEE Trans. On Evolutionary Computation, vol.5, No.6, pp.600-611, Dec. 2001.

9.       Xuehai Pan,” The Study on the Shuffled Frog Leap Algorithm and Its Applications”Advances in information Sciences and Service Sciences (AISS),Volume4, Number1, January 2012

10.     Xia Li, Jianping Luo,” An improved shuffled frog-leaping algorithm with external optimisation for  continuous optimization”, 2010 Elsevier

11.     Yamin Li and Wanming Chu”A New Non-Restoring Square Root Algorithm and Its VLSI Implementation” IEEE-2009

12.     N. Shirazi, A. Walters and P. Athanas, “Quantitative analysis of floating point arithmetic on FPGA based custom computing machines”in Proc. IEEE Symposium on FPGAs for Custom Computing Machines, 1995, pp. 155-162.

13.     Hongtao Du and Hairong Qi A Reconfigurable FPGA System for Parallel Independent Component Analysis, Hindawi Publishing Corporation EURASIP Journal on Embedded Systems Volume 2006, Article ID 23025, Pages 1–12

14.     Mohammadreza Farahani1, Saber Bayat Movahhed1 , Seyyed Farid Ghaderi,” A hybrid meta-heuristic optimization algorithm based on SFLA”,2nd International Conference on Engineering Optimization September 6 - 9, 2010, Lisbon, Portugal

15.     Xin Yao, Senior Member, IEEE, Yong Liu, Student Member, IEEE, and Guangming Lin,” Evolutionary Programming Made Faster”,IEEE transactions on evolutionary computation, VOL. 3, NO. 2, JULY 1999

16.     Emad Elbeltagi, Tarek Hegazy, and Donald Grierson,” Comparison among five evolutionary-based optimization algorithms”, Advanced Engineering Informatics, volume 19, issue 1 , 43-53, January 2005

17.     Jayasanthi Ranjith and Dr.N.J.R.Muniraj “Implementation of Optimized Floating Point Independent Component Analysis Processor on FPGA for EEG Separation,”Journal of Signal Processing Theory and Applications, (2012) 1: 36-43

18.     Jayasanthi Ranjith and Dr.N.J.R. Muniraj,” VLSI Implementation of Memory Efficient Single Bit Processor for Industrial Control Applications”, Int. J. of Recent Trends in Engineering and Technology, Vol. 4, No. 4, Nov 2010




Monica N. Agu

Paper Title:

Application of ICT in Agricultural Sector: Women’s Perspective

Abstract:   Agriculture is the mainstay of most third world economies and occupies a pivotal position in the development of these countries.  Despite the importance of agriculture, improvements in this sector have been uneven and, on the whole, disappointing.  In any farming system, it is important to recognize the various roles of women.  Many women experience a life that is a complex web of multi roles and multi-tasks which requires the average woman to conduct different things in a bid to fulfil her family needs.  Women in rural communities are extensively involved in arduous farm operations and agricultural activities, from planting to harvesting and other post harvesting operations.  These women have been using and managing natural resources, collecting food etc for their livelihood.  In Nigeria, women provide 60 – 80 percent of labour in agriculture through production, processing and marketing of food.  They assist on family farms and are farmers in their own right.  So the Nigerian women are in an important position to contribute to food supply.  This sector faces major challenges for enhancing production in a situation of dwindling natural resources necessary for production.  ICT plays an impotant role in addressing these challenges.  The paper analyzes the problems facing women in the agricultural sector-and suggests ways to solve these problems.  Furthermore the paper surveys the information needs of rural women and how ICT can be used to meet their information needs.

   Agriculture, information and communication technology, women.


1.        Blumberg, R. L.( 1992).  African women in agriculture: farmers, students, extension agents, chiefs. Winrock International Institute for Agricultural Development, Morrilton, AR, USA. Development Studies Paper. 43 pp.
2.        Blackden, Mark and Bhanu Chitra(1999) Gender Growth and Poverty Reduction. The World Bank Technical paper 428.

3.        Dunn, K. 1995. The busiest people in the world. Ceres, 27(4), 48-50

4.        GenARDIS,

5.        GrameenPhone ,

6.        Hilda Munyua (2000) Application of ICTs in Africa’s Agricultural Sector: A Gender Perspective: Gender and the Information

7.        IFAD (International Fund for Agricultural Development). 1989. Women: the roots of rural development. IFAD, Rome, Italy, 22pp.

8.        Lewis, Barbara(1984) The Impact of Development Policies on Women.In Hay and Stichter eds. African Women south of the sahara, New York:Longmans.

9.        Nidhi Tandon (2006).  ICTs to help Women Organic Farmers in the Caribbean.

10.     United Nations(2000) the World’s Women 2000. Trends and Statistics. United Nations: New York




Monica N. Agu

Paper Title:

Need to Empower Nigerian Children and Youths through Information Technology

Abstract:    Technology has become the driving force of change in the modern world.  It has altered our economic structures and ways we communicate.  Information is only one of our needs.  Email, satellites can not be sustituted for drugs; neither can they provide clean water.  Massive access to the Internet and ICT can accelerate awareness of these needs and will also facilitate development of solutions to tackle these needs effectively by empowering the youth of this country that are our future hopes.  The information highways is leaving the African youth  (Nigeria not excluded) poorer in ICT knowledge, skills and global reach.  Thousands of young people in Nigeria leave school with the hope of developing a career and sustainable life that often turns into an illusion.  The paper looks at the benefits of youth empowering, takes a review of youth empowerment initiatives in other developing countries and presents an approach of empowering Nigeria youths that are future leaders of tomorrow.  With this we will move from the realm of educational inadequacy to that of unlimited resources.

   Information Technology, Youth, Empowerment, Information and Communication Technology.


1.     Agu M. N. (2008). An Integrated Framework for Poverty Reduction Via IT Empowerment. An unpublished Ph.d Thesis.  Ebonyi State University.
2.     Ajialcom-Empowering Youth through Technology.
3.     Bernice Yeung (2008) Digital Equality: Empowering Underprivileged Youth in India with Information and Technology.

4.     Edalat Abbas (1999) Empowering the Youthh of our Homeland. Information Technology and the Internet for Education in Iran.

5.     Itir Akdogan (2007)Technology empowers youth, youth will empower Turkey. Common Ground News Service

6.     ILO,Global Employment Trends for youth(Geneve,2004).

7.     Information & Communications technology-Empowering Palestinian Children and Youth Through Digital Media. COMMUNICATIONAN...-51K

8.     Njideka Ugwuegbu (2002) Owerri Digital Village: A grassroots approach to empowering Nigerian youth and their communities, Digital Divide Network.




Mohamed Bahaj, Jamal Bakkas

Paper Title:

Automatic Conversion Method of Class Diagrams to Ontologies Maintaining Their Semantic Features

Abstract:    In this paper, we propose a new conversion’s method from UML class diagram to ontology in order to serve the Semantic Web. The ontology which results from the conversion is expressed in OWL / XML. This method allows us to preserve semantic of some feature's UML diagram such as inheritance, encapsulation, types of associations (composition, aggregation, or simple association), constraints of integrity, class identifier...etc.

   UML, ontology, mapping, OWL.


1.       G. Antoniou, F. van Harmelen “Web Ontology Language: OWL”. pages 76-92 Springer-verlag .2003
2.       D. L. McGuinness, F. van Harmelen,

3.       M. R. Jensen, T. H. Møller Torben, B. Pedersen “Converting XML Data to UML Diagrams For Conceptual Data Integration”. Data & Knowledge Eng., vol. 44, no. 3, pp. 323-346, 2003

4.       J. Fong, F. Pang, C. Bloor “Converting Relational Database into XML Document”. DEXA Workshop, pp 61-65. 2001

5.       N. GHERABI, K. ADDAKIRI, M. BAHAJ “Mapping relational database into OWL Structure with data semantic preservation”. CoRR abs/1205.5922. 2012

6.       J. Seidenberg, A. Rector “Web Ontology Segmentation: Analysis, Classification and Use”. IW3C 2006. ACM, 2006

7.       M. Arnoux, T. Despeyroux  “ Multi-représentation d’une ontologie : OWL, bases de données, systèmes de types et d’objets”. CoRR abs/1104.2982. 2011

8.       D. Gasevic, D. Djuric, V. Devedzic, V. Damjanovi “Converting UML to OWL ontologies”. In Proceedings of the 13 th International World Wide Web Conference, NY, USA, pp. 488-489. 2004

9.       M. Šeleng, M. Laclavík, Z. Balogh, L. Hluchý “RDB2Onto: Approach for creating semantic metadata from relational database data”. In INFORMATICS´ 2007: proceedings of the ninth international conference on informatic, Bratislava Slovak Society for Applied Cybernetics and Informatics, 113–116. 2007

10.     C. Nyulas, M. O’Connor, S. Tu “DataMaster – a Plug-in for Importing Schemas and Data from Relational Databases into Protégé” In Proceedings of 10 th International Protégé Conference, Budapest, Hungary, 2007

11.     J. Barrasa, Ó. Corcho, A. Gómez-Pérez “R2O, an Extensible and Semantically Based Database to ontology Mapping Language”. In Proceedings of the 2nd Workshop on Semantic Web and DatabasesSWDB2004Springer, p. 1069-1070, 2004




Oguike, O.E., Agu, M.N., Echezona, S.C.

Paper Title:

Modeling Variation of Waiting Time of Distributed Memory Heterogeneous Parallel Computer System Using Recursive Models

Abstract:    In a heterogeneous parallel computer system, the computational power of each of the processors differs from one another. Furthermore, with distributed memory, the capacity of the memory, which is distributed to each of the processors, differs from one another. Using queuing system to describe a distributed memory heterogeneous parallel computer system, each of the heterogeneous processors will have its own heterogeneous queue. The variation of waiting time of heterogeneous parallel computer system with distributed memory needs to be modeled because it will help designers of parallel computer system to determine the extent of variation of the waiting time. It will also help users to know when to realize minimum variation of the waiting time. This paper models the variation of the waiting time of distributed memory heterogeneous parallel computer system using recursive models. It also uses the statistical method of Z-Transform to verify and validate the recursive model.

   distributed memory, heterogeneous parallel computer, parallel computer system, queuing network, recursive models, variation, waiting time, Z-Transform.


1.        Henry H. Liu and Pat V. Crain, An Analytic Model for Predicting the Performance of SOA-Based Enterprise Software Applications, Proc. International Conference of Computer Measurement Group, (2004).
2.        S. Balsamo et al, A Review of Queueing Network Models with Finite Capacity Queues for Software Architecture Performance Prediction, (2002).

3.        Catalina M. Liado et al,  A Performance Model Web Service, Proc.  International Conference of Computer Measurement Group, (2005).

4.        Rosselio, J et al, A Web Service for Solving Queueing Network Models Using PMIF., (2005).

5.        Cathy H. Xia, Zhen Liu., Queueing systems with long-range dependent input process and subexponential service time.  Proc. ACM SIGMETRICS international conference on Measurement and modeling of computer systems,(2003).

6.        Shanti Subramanyam, Performance Modelling of a J2EE Application to meet Service Level s, Agreement, Proc. International Conference of Computer Measurement Group, (2005)

7.        Hamdy A. T.,. Operation Research: An Introduction, Prentice-Hall of  India, (1999).

8.        Ivan Stojmenovic; Recursive Algorithms in Computer Science Courses : Fibonacci Numbers and Binomial Coefficients; IEEE Transactions on Education; Vol. 48, No. 3

9.        Arjan J.C. van Gemund; Performance Modelling of Parallel Systems: An Introduction.

10.     Justyna Berlinska, The Statistical models of parallel applications, Annales UMCS Informatica, (2005).

11.     Arranchenkov, K.E., Vilchersky, N.O., Shevlyakor, G.L Priority  queueing with finite buffer size and randomized push-out; mechanism. Proc. of ACM SIGMETRICS international conference on measurement and modeling of computer systems.; (2003). 

12.     Abunday, B.D., and Khorram, E. The finite source queueing model for multiprogrammed computer systems with different CPU times and different I/O times.
Acta Cybern. 8, 4 , (1998)                                                                                                    

13.     J. Sztrik; Finite-Source Queueing Systems and their Applications: A Biliography;

14.     Trivedi K. Shridharbhai, Probability and Statistics with Reliability, Queuing and Computer Science Applications, John Wiley & Sons Inc., (2002).

15.     Per Brinch Hansen. Operating System Principles. Prentice-Hall of India Private Limited, (1990).

16.     J. Sztrika and T. Gál A recursive solution of a queueing model for a multi-terminal system subject to breakdowns; Performance Evaluation Volume 11, Issue 1, Published by Elsevier, (1990).

17.     Robert V. Hogg and Allen T. Craig; Introduction to Mathematical Statistics; Macmillan Publishing Co. Inc.; (1978).

18.     Andrea Clemantis, Angelo Corana; Modelling Performance of Heterogeneous Parallel Computer System; Journal of Parallel Computing, Volume 12, Issue 9, Elsevier; pages 1131-1145; (1999).

19.     E. Post, H.E. Goosen; Evaluating the Parallel Performance of a Heterogeneous System

20.     Beutler, F; Mean sojourn times in markov queuing network: Little’s formula revisited; IEEE Transaction on Information Theory; Volume 29, Issue 2, page 233-241; (2003).

21.     Ken Vastola;

22.     Xiaodong Zhang, Yong Yan; Modeling and Characterizing Parallel Computing Performance on Heterogeneous Network of workstations; Proceedings of the 7th IEEE Symposium on Parallel and Distributeed Processing (SPDP ’95) 1063-6374/95 $10.00 © 1995 IEEE

23.     O.E. Oguike et al; Modelling the Performance of Computer Intensive Applications of Parallel Computer System; Proc. Of IEEE 2nd International Conference on Computational Intelligence, Modeling and Simulation; (2010).

24.     O.E. Oguike et al; Evaluating the Performance of Parallel Computer System Using Recursive Models; Proc. Of IEEE 4th UKSim European Modeling Symposium; (2010).

25.     O.E. Oguike et al; Evaluating the Performance of Heterogeneous Distributed Memory Parallel Computer System Using Recursive Models; 2nd IEEE International Conference on Intelligent Systems, Modeling and Simulation; (2011).

26.     Leonard Kleinrock, Queueing Systems Volume 1 and 2, John Wiley & Sons, (1975).

27.     O.E. Oguike et al; Modelling Variation of a Performance Metric of Distributed Memory Heterogeneous Parallel Computer System, Using Recursive Models; In proc. of 3rd IEEE International Conference on Computational Intelligence Modeling and Simulation; (2011).

28.     Bernard P. Zeigler et al; Theory of Modelling and Simulation; Elsevier; (2000)

29.     Cor van Dijkum et al; Validation of Simulated Models; Siswo Publication 403, Amsterdam, (1999)




Karthikeyan.C, Karthikeyan.V, Jerin Sajeev.C.R, Merlin Moses.M

Paper Title:

Active Timing Based Approach for Tracking Anonymous Peer-to-peer Network in VoIP

Abstract:   Peer-to-peer VoIP calls are popular due to their low cost and convenience. When these calls are encrypted and anonymized the network becomes a secured one. Tracing of the anonymous VoIP call users are important and the traced information about them should be sent to the server to know how long the users are in communication. The key challenge in tracking encrypted VoIP calls across anonymous communication system is to identify the correlation between the VoIP flows of the caller and the callee. Since all the traffic of the peer-to-peer VoIP calls are encrypted, the best way to track anonymous VoIP calls across the internet is using the Active timing based correlation. It is done by embedding a unique watermark into the inter-packet timing domain. The analysis shows that it only takes several milliseconds time adjustment to make normal VoIP flows highly unique and the embedded delay value could be preserved across the low latency anonymizing network. In this proposal, tracking of anonymous VoIP calls across internet was successfully achieved by using active time based correlation method and the  results demonstrate that tracing of anonymous peer-to-peer VoIP calls on the internet is feasible and low latency anonymizing networks are susceptible to timing attacks.

   It is done by embedding a unique watermark into the inter-packet timing domain.


1.       A.Thamizharasi and M.Vanitha’ “Privacy and Packet Dispersion of Voice Applications in P2p Networks-VoIP”, International Journal of Computer Applications (0975 – 8887),Volume 43– No.12, April 2012
2.       Ge Zhang and Simone Fischer-Hubner, “Peer-to-Peer VoIP Communications Using Anonymisation Overlay Networks” , CMS 2010, LNCS 6109,  IFIP International Federation for Information Processing , pp. 130–141, 2010.

3.       Emanuel P. Freire, Artur Ziviani, and Ronaldo M. Salles, “Detecting VoIP Calls Hidden in Web Traffic”, IEEE transactions on network and service management, Vol. 5, No. 4, December 2008

4.       Shiping Chen, Xinyuan Wang, and Sushil Jajodia, George Mason University.  “On the Anonymity and Traceability of Peer-to-Peer VoIP Calls” IEEE Network, September/October 2006 page (32-37)

5.       Overlier, L., and Syverson. P. “Locating hidden servers”. In Proceedings of the 2006 IEEE Symposium on Security and Privacy (May 2006), IEEE CS.

6.       T. Kohno, A. Broido and K. Claffy. “Remote Physical Device Fingerprinting”. In Proceedings of the 2005 IEEE Symposium on Security and Privacy, IEEE, 2005.

7.       S. J. Murdoch and G. Danezis. “Low-Cost Traffic Analysis of Tor”. In Proceedings of the 2005 IEEE Symposium on Security and Privacy, IEEE, 2005.

8.       X. Wang, S. Chen, and S. Jajodia, “Tracking Anonymous Peer-to-Peer VOIP Calls on the Internet,” Proc. 12th ACM Conf. Comp. and Commun. Sec., Nov. 2005, Page. 81–91.

9.       S. A. Baset and H. Schulzrinne. An Analysis of the Skype Peer-to-Peer Internet Telephony Protocol. Columbia Technical Report CUCS-039-04, December 2004

10.     J. Li, M. Sung, J. Xu and L. Li. “Large Scale IP Trace back in High-Speed Internet: Practical Techniques and Theoretical Foundation”. In Proceedings of the 2004 IEEE Symposium on Security and Privacy, IEEE, 2004.

11.     G. Danezis, R. Dingledine, and N. Mathewson, Mixminion: “Design of a Type Anonymous Remailer Protocol,” Proc. IEEE Symp. Sec. and Privacy, May 2003, Page. 2–15.

12.     M. J. Freedman and R. Morris. “Tarzan: A Peer-to-Peer Anonymizing Network Layer”. In Proceedings of the 9th ACM Conference on Computer and Communications Security (CCS 2002), pages 193{206. ACM, November 2003

13.     Matthew Wright, Micah Adler, Brian Neil Levine, and Clay Shields. “Defending anonymous communication against passive logging attacks”. In Proceedings of the 2003 IEEE Symposium on Security and Privacy, May 2003.

14.     X. Wang and D. Reeves. “Robust Correlation of Encrypted Attack Traffic Through Stepping Stones by Manipulation of Interpacket Delays”. In Proceedings of the 10th ACM Conference on Computer and Communications Security (CCS 2003), pages 20-29. ACM, October 2003.

15.     Marc Rennhard and Bernhard Plattner. Introducing MorphMix: “Peer-to-Peer based Anonymous Internet Usage with Collusion Detection”. In Proceedings of the Workshop on Privacy in the Electronic Society (WPES 2002), Washington, DC, USA, November 2002.




M. Jabbari Ghadi, A. Baghramian

Paper Title:

A  New Heuristic Method for Solving Unit Commitment Problem in Competitive Environment

Abstract:   In a restructured power market, traditional scheduling of generating units needs modification. The classical unit commitment problem aims at minimizing the operation costs by satisfying the forecasted electricity load. However, under new structure, Generation companies (GENCOs) schedule their generators with an objective to maximize their own profit by relaxation of the demand fulfillment constraint and without any regard for system social profit, to match the competitive market. A Unit Commitment algorithm with capability of profit maximization plays a significant role in successful development bidding strategies of a competitive generator. In such an environment, power price turns into an important factor in decision process. In this paper the authors utilized a new heuristic technique called Imperialistic Competitive Algorithm (ICA) to exert Profit Based Unit Commitment (PBUC) problem. In fact, the presented approach assists GENCOs to make a decision, how to schedule generators in order to gain the maximum profit by selling adequate amounts of power in power market. The effectiveness of the proposed method to solve generation scheduling optimization problem in a day-ahead deregulated electricity market is validated on 10 generating unit systems available in the literature. Comparison of results obtained from simulation verifies the ability of proposed method.    

   Deregulation, Electricity Market, Profit Based Unit Commitment, Imperialistic Competitive Algorithm, Competitive Environment.


1.       S. Virmani, C. Adrian, K. Imhof, and S. Mukherjee, “Implementation of a Lagrangian relaxation based unit commitment problem,” IEEE Trans. Power Syst., vol. 4, no. 4, pp. 1373–1380, Nov. 1989.
2.       A. I. Cohen and M. Yoshimura, “A branch-and-bound algorithm for unit commitment,” IEEE Trans. Power App & Syst., vol. PAS-102, pp. 444–451, Feb. 1983.

3.       J. A. Muckstadt and R. C. Wilson, “An application of mixed-integer programming duality to scheduling thermal generating systems,” IEEE Trans. Power App & Syst., vol. PAS-87, pp. 1968–1978, Dec. 1968.

4.       G. B. Sheble, “Solution of the Unit Commitment Problem by the Method of Unit Periods,” IEEE Trans. Power Syst., vol. 5, no. 1, pp. 257–260, Feb. 1990.

5.       F. Zhuang and F. D. Galiana, “Unit Commitment by Simulated Annealing,” IEEE Trans. Power Syst., vol. 5, no. 1, pp. 311–317, 1990.

6.       T. O. Ting, M. V. C. Rao and C. K. Loo, “A novel approach for unit commitment problem via an effective hybrid particle swarm optimization,” IEEE Trans. Power Syst., vol. 21, no. 1, pp. 411–418, Feb. 2006.

7.       S. A. Kazarlis, A. G. Bakirtzis, and V. Petridis, “A genetic algorithm solution to the unit commitment problem,” IEEE Trans. Power Syst., vol. 11, no. 1, pp. 83–92, Feb. 1996.

8.       M. Moghimi Hadji and B. Vahidi, “A solution to the unit commitment problem using imperialistic competition algorithm,” IEEE Trans. Power Syst., vol. 27, no. 1, pp. 117–124, Feb. 2012.

9.       M. Shahidehpour and M. Marwali, Maintenance Scheduling in Restructured Power Systems. Norwell, MA: Kluwer, 2000.

10.     M. Shahidehpour, H. Yamin, and Z. Li, Market Operations in Electric Power Systems. New York: Wiley, 2002.

11.     T. Li and M. Shahidehpour,  “Price-Based Unit Commitment: A Case of Lagrangian Relaxation versus Mixed Integer Programming”, IEEE Trans. Power Syst., vol. 20, no. 4, pp. 2015 - 2025, Nov. 2005.

12.     W. Ongsakul and N. Petcharaks, “Unit commitment by enhanced adaptive Lagrangian relaxation,” IEEE Trans. Power Syst., vol.  19, pp. 620 - 628, Feb. 2004.

13.     C. W. Richter, Jr and G. B. Sheble, “A profit-based unit commitment GA for the competitive environment,” IEEE Trans. Power Syst., Vol. 15,  no. 2, pp. 715-721, May. 2000.

14.     C. Christopher Columbus, K. Chandrasekaran and Sishaj P. Simon, “Nodal ant colony optimization for solving profit based unit commitment problem for GENCOs," Applied Soft Computing., vol. 12, no. 1, pp. 145–160, Jan. 2012.

15.     C. Christopher Columbus and Sishaj P. Simon, “Profit based unit commitment: A parallel ABC approach using a workstation cluster,” Computers & Electrical Engineering., vol. 38, no. 3, pp. 724–745, May. 2012.

16.     T. A. A. Victoire and A. E. Jeyakumar, “Unit commitment by a tabu-search-based hybrid-optimisation technique,” in Proc.  IEE Conf. Generation, Transmission and Distribution, Jul. 2005, Vol. 152, no. 4.

17.     K. Chandram, N. Subrahmanyam and M. Sydulu, “New approach with Muller method for Profit Based Unit Commitment,” in Proc. IEE Power and Energy Society General Meeting, Jul. 2008, pp. 1–8.

18.     H. Y. Yamin and S. M. Shahidehpour, “Unit commitment using a hybrid model between Lagrangian relaxation and genetic algorithm in competitive electricity markets,” Electric Power Systems Research., vol. 68, no. 2, pp. 83–92, Feb. 2003.

19.     P. Attaviriyanupap, H. Kita, E. Tanaka, and J. Hasegawa, “A hybrid LR-EP for solving new profit-based UC problem under competitive environment,” IEEE Trans. Power Syst., vol. 18, no. 1, pp. 229-237, Feb. 2003.

20.     D. Janet Nahomi and V. Yuvaraju, “A new approach for Profit-Based Unit Commitment Using Lagrangian Relaxation combined with Particle Swarm Optimization algorithm,” Inte. J. of. Communications & Engineering., vol. 4, no. 4, pp. 159-166, Mar. 2012.          

21.     I. G. Damousis, A. G. Bakirtzis, and P. S. Dokopoulos, “A solution to the unit commitment problem using integer-coded genetic algorithm,” IEEE Trans. Power
Syst., vol. 19, no. 2, pp. 1165–1172, May 2004.  

22.     E. A. Gargari and C. Lucas, “Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition,” in Proc. IEEE Congr. Evolutionary Computation, Sep. 2007, pp. 4661 - 4667.             

23.     V. Khorani, F. Razavi, and V. R. Disfani, “A Mathematical Model for Urban Traffic and Traffic Optimization Using a Developed ICA Technique,” IEEE Trans. Intelligent Transportation Syst., vol. 12, no. 4, pp. 1024 - 1036, Dec. 2011.

24.     A. Khabbazi, E. Atashpaz-Gargari, and C. Lucas, “Imperialist competitive algorithm for minimum bit error rate beam-forming,” Inte. J. of. Bio-In-spired Computation (IJBIC), vol. 1, no. 1–2, pp. 125–133, 2009.




Lourthu Hepziba Mercy.M, Balamurugan.K, Vijayaraj.M

Paper Title:

Maximization of Lifetime and Reducing Power Consumption in Wireless Sensor Network Using Protocol

Abstract:    This paper is to avoid duplicate transmission, node reconfiguration and power consumption in Wireless Sensor Networks (WSN). Wireless sensor network requires robust and energy efficient communication protocols to minimize the energy consumption as much as possible. However, the lifetime of sensor network reduces due to the adverse impacts caused by radio irregularity and fading in multi-hop WSN. The scheme extends High Energy First (HEF) clustering algorithm and enables multi-hop transmissions among the clusters by incorporating the selection of cooperative sending and receiving nodes. The work proposed focuses to develop any node to act as cluster head (CH) instead of affected CH because we need to get a data from CH continuously. To reduce energy consumption, proposed scheme extends with the help of S-MAC layer to get the efficient energy saving. The performance of the proposed system is evaluated in terms of energy efficiency and reliability. Simulation results show that tremendous energy savings can be achieved by adopting hard network lifetime scheme among the clusters. Many routing protocols are developed, but among those protocols cluster based routing protocols are energy efficient, scalable and prolong the network lifetime .The network simulator 2 (NS2) is used to verify the proposed network lifetime predictability model, and the results show that the derived bounds of the predictability provide accurate estimations of the energy saving and network lifetime.

  cluster head selection, network lifetime, schedulability, timing constraints, wireless sensor networks, AODV.


1.       Bo-Chao Cheng, Hsi-HsunYeh, and Ping-Hai Hsu "Schedulability Analysis for Hard Network Lifetime Wireless Sensor Networks With High Energy First   Clustering" 2011.
2.       Victor, A. Khader, C. Rao and A. Mehta "Build an IEEE 802.15.4 Wireless Sensor Network for emergency response notification for indoor situations" 

3.       J. A. Stankovic and K. Ramamrithan, Eds., Tutorial on Hard Real-Time Systems. : IEEE Computer Society Press, 1988.

4.       B.-C. Cheng, A. Stoyenko, T. Marlowe, and S. Baruah, “LSTF: A new scheduling policy for complex real-time tasks in multiple processor systems,” Automatica, vol. 33, no. 5, pp. 921–926, May 1997.

5.       C. L. Liu and J. W. Layland, “Scheduling algorithms for multiprogramming in a hard-real time environment,” Journal of the Association forComputing Machinery, vol. 20, no. 1, pp. 46–61, January 1973.

6.       R. Cristescu and M. Vetterli, “On the optimal density for real-time data gathering of spatio-temporal processes in sensor networks,” in FourthInternational Conference on Information Processing in Sensor Networks (IPSN ’05), April 2005.

7.       T. He, J. A. Stankovic, C. Lu, and T. Abdelzaher, “SPEED: A stateless protocol for real-time communication in sensor networks,” in Proceedingsof the 23rd International Conference on Distributed ComputingSystems (ICDCS’03), May 2003.

8.       J. H. Chang and L. Tassiulas, “Routing for maximum system lifetime in wireless ad-hoc networks,” in Proc. 37. th. Annual Allerton Conf. on Communication, Control, and Computing, Monticello, IL, Sep. 1999.

9.       M. Qin and R. Zimmermann, “Studying upper bounds on sensor network lifetime by genetic clustering,” in IEEE International Conferenceon Distributed Computing in Sensor Systems (DCOSS), Marina del Rey, California, June 30-July, 1, 2005.

10.     M. Sirivianos, D.Westhoff, F. Armknecht, and J. Girao, “Non-manipulable aggregator node election protocols for wireless sensor networks,” in ICST WiOpt, 2007.

11.     T. He, P. A. Vicaire, T. Yan, L. Luo, L. Gu, G. Zhou, R. Stoleru, Q.Cao, J. A. Stankovic, and T. Abdelzaher, “Achieving real-time target tracking using wireless sensor networks,” in ACMTransactions on EmbeddedComputing Systems (TECS), 2007.

12.     J. Lichtenegger, G. Calabresi, and A. Petrocchi, “A near-real time oil slick monitoring demonstrator for the Mediterranean,” in XIX ISPRSCongress, Amsterdam, The Netherlands, 2000, pp. 193–200, XXXIII.

13.     R. L. Nord and B. C. Cheng, “Using RMA for evaluating design decisions,” in Second IEEE Workshop on Real-Time Applications, Washington, DC, July 1994.

14.     C.E. Perkins, E. Royer, and S.R. Das, "Ad hoc on demand distance vector (AODV) routing," Internet Draft, March 2000.

15.     H. Kaur and J. Baek, “A strategic deployment and cluster-header selection for wireless sensor networks,” IEEE Transactions on Consumer Electronics, vol. 55, no. 4, November 2009.

16.     K.Ramesh1 and Dr. K.Somasundaram2 DOI: 10.5121/ijcses.2011.2411 153"A comparative study of clusterheadselection algorithms in wireless sensor networks".International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.4, November 2011.

17.     H. Liu, P. Wan, C.-W. Yi, X. Jia, S. Makki, and P. Niki, “Maximal lifetime scheduling in sensor surveillance networks,” in IEEE Infocom, March 2005.

18.     R. Madan, C. Shuguang, S. Lall, and A. Goldsmith, “Cross-layer design for lifetime maximization in interference-limited wireless sensor networks,” in IEEE Infocom, March 2005.

19.     Hen-I Yang "wireless Sensor Network: the Challenges of Design and Programmability" Apr. 26, 2005.

20.     Yunxia Chen"On the Lifetime of Wireless Sensor          Networks", IEEE communications letters, vol. 9,    no. 11, november 2005.

21.     H. Chen, C. K. Tse, and J. Feng, “Impact of topology on performance and energy efficiency in wireless sensor networks for source extraction,” IEEE
Transactions on Parallel and Distributed Systems, vol. 20, no. 6, June 2009.

22.     R. L. Nord and B. C. Cheng, “Using RMA for evaluating design decisions,” in Second IEEE Workshop on Real-Time Applications,Washington, DC, July 1994.

23.     G. V. Crosby, N. Pissinou, and J. Gadze, “A framework for trust-based cluster head election in wireless sensor networks,” in DSSNS 2006 : Second IEEE Workshop on Dependability and Security in Sensor Networksand Systems : Proceedings, Columbia, Maryland, April 24-28, 2006, sponsored by IEEE, NASA, IEEE Computer Society.

24.     L.-C. Wang, C.-W. Wang, and C.-M. Liu, “Adaptive contention window-based cluster head election algorithms for wireless sensor networks,” in VTC-2005-Fall. 2005 IEEE 62nd, September 2005, vol. 3, pp. 1819–1823.

25.     W. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, “An application- specific protocol architecture for wireless microsensor networks,” IEEE Trans. on Communications, vol. 1, no. 4, pp. 660–670, Oct. 2002.

26.     S. Sivavakeesar and G. Pavlou, “Associativity-based Stable cluster formation in mobile ad hoc networks,” in Proceedings of IEEE Conference on Consumer Communications and Networking Conference (CCNC2005), January 2005, pp. 196–201, IEEE.

27.     O. Younis and S. Fahmy, “Distributed clustering in ad-hoc sensor networks: A hybrid, energy-efficient approach,” in In: Proceedings of the IEEE Conference on Computer Communications (INFOCOM), , Hong Kong, 2004.




Ugwuishiwu B. O., Obi O. F. and Ugwuishiwu, C. H.

Paper Title:

Information and Communication Technologies: Benefits and Challenges to the Environment

Abstract:    The issue of Information and Communication Technology (ICT) and the environment is a complex and multifaceted one. ICT can play both positive and negative roles in sustainable environment. This study shows the important linkages between ICTs, ICT-enabled innovation and the environment. It analyses the environmental impacts of ICTs in different stages of the life cycle and also as an enabling technology for mitigation of environmental impacts across all economic sectors. Direct environmental impacts were noted to be considerable in areas such as energy use, materials throughput and end-of-life treatment. ICT usage could also generate new activities and wastes with grave implications on efficient environmental resource management. The contribution of ICTs to systemic changes to achieve more sustainable environment was discussed. It was concluded that it is important that the environmental impacts of ICT products and operations be minimized through improved research and development, implementation of innovative ICT systems and government ICT policies.

   Benefits, Challenges, Environment, ICT, Wastes.


1.       Choi, B. C., Shin, H. Lee, S, Hur,S. (2006). Life    Cycle Assessment of a Personal Computer and its Effective Recycling Rate. The International Journal of Life Cycle Assessment, 11(2), pp. 122–128.
2.       Eugster, M., Hischier, R. and Duan, H. (2007). Key Environmental Impacts of the Chinese EEE-Industry. Report, EMPA Materials Science & Technology, St. Gallen.

3.       Fuhr, J. P. and Pociask, S. B. (2007). Broadband services: economic and environmental benefits. The American Consumer Institute.

4.       Goleniewski, L. (2006). Telecommunications essentials. The complete global source. Second Edition. Edited by K. W. Jarrett. Addison Wesley. 865 pp.

5.       Hilty, L. M. (2008), Information Technology and Sustainability. Essays on the Relationship between ICT and Sustainable Development, Books on Demand GmbH, Norderstedt.

6.       International Chamber of Commerce (ICC) (2010). ICTs and environmental sustainability. A discussion paper prepared by the ICC commission on E-business, IT and Telecoms. Document No. 373/494.

7.       Idowu, S. A. and Awodele, O. (2010). Information and Communication Technology (ICT) Revolution: Its Environmental Impact and Sustainable Development. International Journal on Computer Science and Engineering, Vol. 02, No.01S, 30 - 35.

8.       ITU (2006). World Telecommunication/ICT Development Report 2006. Measuring ICT for social and economic development. ITU. Geneva. 206 pp.

9.       ITU (2007). Trends in telecommunication reform. The Road to Next Generation Networks (NGN). Geneva. 238 pp.

10.     ITU (2008). ICTs for e-Environment- Guidelines for Developing Countries, with a Focus on Climate Change. ICT Applications and Cybersecurity Division, Policies and Strategies Department, ITU Telecommunication Development Sector. Geneva.

11.     OECD (2010). Greener and Smarter - ICTs, the Environment and Climate Change. Organization for Economic Co-Operation and Development, Paris.

12.     OECD (2008), OECD Environmental Outlook to 2030, OECD, Paris.

13.     Reed, B., Brown, J. F. and Loveland, T. R. (2002). Geographic data for environmental modeling and assessment. In Skidmore, A., Environmental modeling with GIS and remote sensing. Taylor and Francis. 268 pp.

14.     Sands P. (2003). Principles of International Environmental Law (2nd ed.). Cambridge University Press.

15.     Steinweg, T. and de Haan, E. (2007). Capacitating Electronics, SOMO – Centre for Research on Multinational Corporations, Amsterdam.

16.     Trotter, C. M., Leathwick, J. R. and Pairman, D. (2001). Spatial information for ecosystem classification, analysis, and forecasting. In Halls, P., Spatial information and the environment. Innovation in GIS 8. London and New York. Taylor and Francis. 284 pp.

17.     Williams, E. (2003). Environmental Impacts in the Production of Personal Computers. In Computers and the Environment. Understanding and managing their impacts, R. Kuehr and E. Williams (eds.), Kluwer/United Nations University, Dordrecht.

18.     World Econ. Forum Report (2009). Green Technology: Driving Economic and Environmental Benefits from ICT.


20.     Yi, L. and Thomas, H. R. (2007). A review of research on the environmental impact of e-Business and ICT. Environment International Volume 33, Issue 6, August 2007, Pages 841-849




Muhammad Tanveer, Amir Habib, Muhammad Bilal Khan

Paper Title:

The Inverted Double Heterojunction Organic Photovoltaic Devices using Electrospun TiO2 Nanofibers

Abstract:   The introduction of electrospun TiO2 nanofibers has improved the performance of inverted poly (3- hexylthiophene) (P3HT) and (6, 6)- phenyl-C61- butyric acid methyl ester (PCBM) solar cells by providing efficient charge generation and collection through double heterojunction. Electrospun TiO2 nanofibers increased the charge separation and collecting capability of the devices both from P3HT and PCBM by providing interfaces between P3HT-TiO2 and PCBM-TiO2 nanofibers. The resulting devices have reached to maximum power conversion efficiency (PCE) of 4.25±0.03% contributed by increased short circuit current (Jsc).

   Heterojunction, inverted, nanofibers, organic


1.        D. Carsten, D. Vladimir, "Polymer–fullerene bulk heterojunction solar cells", Reports on Progress in Physics, 2010, 73, 096401.
2.        C.N. Hoth, S.A. Choulis, P. Schilinsky, C.J. Brabec, "High Photovoltaic Performance of Inkjet Printed Polymer:Fullerene Blends", Adv. Mater., 2007, 19, 3973-3978.

3.        F.C. Krebs, M. Jørgensen, K. Norrman, O. Hagemann, J. Alstrup, T.D. Nielsen, J. Fyenbo, K. Larsen, J. Kristensen, "A complete process for production of flexible large area polymer solar cells entirely using screen printing--First public demonstration", Sol. Energy Mater. Sol. Cells, 2009, 93, 422-441.

4.        C. Girotto, B.P. Rand, J. Genoe, P. Heremans, "Exploring spray coating as a deposition technique for the fabrication of solution-processed solar cells", Sol. Energy Mater. Sol. Cells, 2009, 93, 454-458.

5.        G. Dennler, M.C. Scharber, C.J. Brabec, "Polymer-Fullerene Bulk-Heterojunction Solar Cells", Adv. Mater., 2009, 21, 1323-1338.

6.        L. Li, G. Lu, X. Yang, "Improving performance of polymer photovoltaic devices using an annealing-free approach via construction of ordered aggregates in solution", J. Mater. Chem., 2008, 18, 1984-1990.

7.        C.J. Brabec, J.R. Durrant, "Solution-Processed Organic Solar Cells", MRS BULLETIN, 2008, 33, 670-675.

8.        R. Søndergaard, M. Helgesen, M. Jørgensen, F.C. Krebs, "Fabrication of Polymer Solar Cells Using Aqueous Processing for All Layers Including the Metal Back
Electrode", Advanced Energy Materials, 2011, 1, 68-71.

9.        J.Y. Kim, K. Lee, N.E. Coates, D. Moses, T.-Q. Nguyen, M. Dante, A.J. Heeger, "Efficient Tandem Polymer Solar Cells Fabricated by All-Solution Processing", Science, 2007, 317, 222-225.

10.     R.G.-V. N. Espinosa, M. S. García-Cascales and A. Urbina, "Towards low-cost manufacturing of organic solar cells: multi-criteria assessment of fabrication technologies", International Conference on Renewable Energies and Power Quality (ICREPQ’10) Granada (Spain), 23th to 25th March, 2010, 2010.

11.     F.C. Krebs, H. Spanggard, T. Kjær, M. Biancardo, J. Alstrup, "Large area plastic solar cell modules", Materials Science and Engineering: B, 2007, 138, 106-111.

12.     M.S. White, D.C. Olson, S.E. Shaheen, N. Kopidakis, D.S. Ginley, "Inverted bulk-heterojunction organic photovoltaic device using a solution-derived ZnO underlayer", Appl. Phys. Lett., 2006, 89, 143517-143513.

13.     C. Waldauf, M. Morana, P. Denk, P. Schilinsky, K. Coakley, S.A. Choulis, C.J. Brabec, "Highly efficient inverted organic photovoltaics using solution based titanium oxide as electron selective contact", Appl. Phys. Lett., 2006, 89, 233517-233513.

14.     J.Y. Kim, S.H. Kim, H.H. Lee, K. Lee, W. Ma, X. Gong, A.J. Heeger, "New Architecture for High-Efficiency Polymer Photovoltaic Cells Using Solution-Based Titanium Oxide as an Optical Spacer", Adv. Mater., 2006, 18, 572-576.

15.     M.-S. Su, C.-Y. Kuo, M.-C. Yuan, U.S. Jeng, C.-J. Su, K.-H. Wei, "Improving Device Efficiency of Polymer/Fullerene Bulk Heterojunction Solar Cells Through Enhanced Crystallinity and Reduced Grain Boundaries Induced by Solvent Additives", Adv. Mater., 2011, 23, 3315-3319.

16.     M.K. Siddiki, J. Li, D. Galipeau, Q. Qiao, "A review of polymer multijunction solar cells", Energy Environ. Sci., 2010, 3, 867-883.

17.     J.H. Park, A.R. Carter, L.M. Mier, C.-Y. Kao, S.A.M. Lewis, R.P. Nandyala, Y. Min, A.J. Epstein, "Organic photovoltaic cells with nano-fabric heterojunction structure", Appl. Phys. Lett., 2012, 100, 073301-073304.

18.     Y. Zhou, M. Eck, C. Men, F. Rauscher, P. Niyamakom, S. Yilmaz, I. Dumsch, S. Allard, U. Scherf, M. Krüger, "Efficient polymer nanocrystal hybrid solar cells by improved nanocrystal composition", Sol. Energy Mater. Sol. Cells, 2011, 95, 3227-3232.

19.     J.J.M. Halls, R.H. Friend, "The photovoltaic effect in a poly(p-phenylenevinylene)/perylene heterojunction", Synth. Met., 1997, 85, 1307-1308.

20.     C.J. Brabec, A. Cravino, D. Meissner, N.S. Sariciftci, T. Fromherz, M.T. Rispens, L. Sanchez, J.C. Hummelen, "Origin of the Open Circuit Voltage of Plastic Solar Cells", Adv. Funct. Mater., 2001, 11, 374-380.

21.     C.-F. Lin, M. Zhang, S.-W. Liu, T.-L. Chiu, J.-H. Lee, "High Photoelectric Conversion Efficiency of Metal Phthalocyanine/Fullerene Heterojunction Photovoltaic Device", International Journal of Molecular Sciences, 2011, 12, 476-505.

22.     F. Silvestri, A. Marrocchi, "Acetylene-Based Materials in Organic Photovoltaics", International Journal of Molecular Sciences, 2010, 11, 1471-1508.

23.     S. Glenis, G. Horowitz, G. Tourillon, F. Garnier, "Electrochemically grown polythiophene and poly(3-methylthiophene) organic photovoltaic cells", Thin Solid Films, 1984, 111, 93-103.

24.     Y. Fang, S.-A. Chen, M.L. Chu, "Effect of side-chain length on rectification and photovoltaic characteristics of poly(3-alkylthiophene) Schottky barriers", Synth. Met., 1992, 52, 261-272.

25.     R.N. Marks, J.J.M. Halls, D.D.C. Bradley, R.H. Friend, A.B. Holmes, "The photovoltaic response in poly(p-phenylene vinylene) thin-film devices", J. Phys.: Condens. Matter, 1994, 6, 1379.

26.     C.W. Tang, "Two-layer organic photovoltaic cell", Appl. Phys. Lett., 1986, 48, 183-185.

27.     D. Vacar, E.S. Maniloff, D.W. McBranch, A.J. Heeger, "Charge-transfer range for photoexcitations in conjugated polymer/fullerene bilayers and blends", Phys. Rev. B, 1997, 56, 4573.

28.     J.J.M. Halls, C.A. Walsh, N.C. Greenham, E.A. Marseglia, R.H. Friend, S.C. Moratti, A.B. Holmes, "Efficient photodiodes from interpenetrating polymer networks", Nature, 1995, 376, 498-500.

29.     G. Yu, J. Gao, J.C. Hummelen, F. Wudl, A.J. Heeger, "Polymer Photovoltaic Cells: Enhanced Efficiencies via a Network of Internal Donor-Acceptor Heterojunctions", Science, 1995, 270, 1789-1791.

30.     S.E. Shaheen, R. Radspinner, N. Peyghambarian, G.E. Jabbour, "Fabrication of bulk heterojunction plastic solar cells by screen printing", Appl. Phys. Lett., 2001, 79, 2996-2998.

31.     Y. Zheng, J. Xue, "Organic Photovoltaic Cells Based on Molecular Donor-Acceptor Heterojunctions", Polymer Reviews, 2010, 50, 420 - 453.

32.     P. Peumans, S.R. Forrest, "Very-high-efficiency double-heterostructure copper phthalocyanine/C[sub 60] photovoltaic cells", Appl. Phys. Lett., 2001, 79, 126-128.

33.     S. Singh, B. Pandit, T.P. Basel, S. Li, D. Laird, Z.V. Vardeny, "Two-step charge photogeneration dynamics in polymer/fullerene blends for photovoltaic applications", Phys. Rev. B, 2012, 85, 205206.

34.     M. Al-Ibrahim, H.K. Roth, U. Zhokhavets, G. Gobsch, S. Sensfuss, "Flexible large area polymer solar cells based on poly(3-hexylthiophene)/fullerene", Sol. Energy Mater. Sol. Cells, 2005, 85, 13-20.

35.     A.C. Mayer, S.R. Scully, B.E. Hardin, M.W. Rowell, M.D. McGehee, "Polymer-based solar cells", Materials Today, 2007, 10, 28-33.

36.     G. Li, Y. Yao, H. Yang, V. Shrotriya, G. Yang, Y. Yang, "“Solvent Annealing” Effect in Polymer Solar Cells Based on Poly(3-hexylthiophene) and Methanofullerenes", Adv. Funct. Mater., 2007, 17, 1636-1644.

37.     C.J. Brabec, N.S. Sariciftci, J.C. Hummelen, "Plastic Solar Cells", Adv. Funct. Mater., 2001, 11, 15-26.

38.     S.E. Shaheen, C.J. Brabec, N.S. Sariciftci, F. Padinger, T. Fromherz, J.C. Hummelen, "2.5% efficient organic plastic solar cells", Appl. Phys. Lett., 2001, 78, 841-843.

39.     C. Schünemann, D. Wynands, L. Wilde, M.P. Hein, S. Pfützner, C. Elschner, K.-J. Eichhorn, K. Leo, M. Riede, "Phase separation analysis of bulk heterojunctions in small-molecule organic solar cells using zinc-phthalocyanine and C_{60}", Phys. Rev. B, 2012, 85, 245314.

40.     Z. Bao, A. Dodabalapur, A.J. Lovinger, "Soluble and processable regioregular poly (3hexylthiophene) for thin film fieldeffect transistor applications with high mobility", Appl. Phys. Lett., 1996, 69, 4108.

41.     A. Hayakawa, O. Yoshikawa, T. Fujieda, K. Uehara, S. Yoshikawa, "High performance polythiophene/fullerene bulk-heterojunction solar cell with a TiO[sub x] hole blocking layer", Appl. Phys. Lett., 2007, 90, 163517-163513.

42.     D.W. Zhao, P. Liu, X.W. Sun, S.T. Tan, L. Ke, A.K.K. Kyaw, "An inverted organic solar cell with an ultrathin Ca electron-transporting layer and MoO[sub 3] hole-transporting layer", Appl. Phys. Lett., 2009, 95, 153304-153303.

43.     J.-C. Wang, W.-T. Weng, M.-Y. Tsai, M.-K. Lee, S.-F. Horng, T.-P. Perng, C.-C. Kei, C.-C. Yu, H.-F. Meng, "Highly efficient flexible inverted organic solar cells using atomic layer deposited ZnO as electron selective layer", Journal of Materials Chemistry, 2010, 20, 862-866.

44.     K. Norrman, M.V. Madsen, S.A. Gevorgyan, F.C. Krebs, "Degradation Patterns in Water and Oxygen of an Inverted Polymer Solar Cell", J. Am. Chem. Soc., 2010, 132, 16883-16892.

45.     S.K. Hau, H.-L. Yip, A.K.Y. Jen, "A Review on the Development of the Inverted Polymer Solar Cell Architecture", Polymer Reviews, 2010, 50, 474 - 510.

46.     A.K.K. Kyaw, X.W. Sun, C.Y. Jiang, G.Q. Lo, D.W. Zhao, D.L. Kwong, "An inverted organic solar cell employing a sol-gel derived ZnO electron selective layer and thermal evaporated MoO[sub 3] hole selective layer", Appl. Phys. Lett., 2008, 93, 221107-221103.

47.     J. Gilot, I. Barbu, M.M. Wienk, R.A.J. Janssen, "The use of ZnO as optical spacer in polymer solar cells: Theoretical and experimental study", Appl. Phys. Lett., 2007, 91, 113520-113523.

48.     X. Zhan, D. Zhu, "Conjugated polymers for high-efficiency organic photovoltaics", Polymer Chemistry, 2010, 1, 409-419.

49.     K.M. Coakley, M.D. McGehee, "Conjugated Polymer Photovoltaic Cells", Chem. Mater., 2004, 16, 4533-4542.

50.     J.C.H.a.N.S.S. René A. J. Janssen, "Polymer–Fullerene Bulk Heterojunction Solar Cells", MRS Bulletin, 2005, 30, 33-36.

51.     P. Peumans, "Small molecular weight organic thin-film photodetectors and solar cells", J. Appl. Phys., 2003, 93, 3693.

52.     S.R. Forrest, "The Limits to Organic Photovoltaic Cell Efficiency", MRS Bulletin, 2005, 30, 28-32.

53.     J.H. Park, T.-W. Lee, B.-D. Chin, D.H. Wang, O.O. Park, "Roles of Interlayers in Efficient Organic Photovoltaic Devices", Macromol. Rapid Commun., 2010, 31, 2095-2108.

54.     Y. Vaynzof, D. Kabra, L. Zhao, P.K.H. Ho, A.T.S. Wee, R.H. Friend, "Improved photoinduced charge carriers separation in organic-inorganic hybrid photovoltaic devices", Appl. Phys. Lett., 2010, 97, 033309-033303.

55.     R. Steim, F.R. Kogler, C.J. Brabec, "Interface materials for organic solar cells", J. Mater. Chem., 2010, 20, 2499-2512.

56.     M. Tanveer, A. Habib, M.B. Khan, "Improved efficiency of organic/inorganic photovoltaic devices by electrospun ZnO nanofibers", Materials Science and Engineering: B, 2012, 177, 1144-1148.

57.     A. Kumar, R. Jose, K. Fujihara, J. Wang, S. Ramakrishna, "Structural and Optical Properties of Electrospun TiO2 Nanofibers", Chem. Mater., 2007, 19, 6536-6542.

58.     C.J. Brabec, V. Dyakonov, J. Parisi, N.S. Sariciftci, Organic photovoltaics: concepts and realization, Springer, 2003.

59.     C. Waldauf, M.C. Scharber, P. Schilinsky, J.A. Hauch, C.J. Brabec, "Physics of organic bulk heterojunction devices for photovoltaic applications", J. Appl. Phys., 2006, 99, 104503-104506.

60.     Y. Long, "Effects of metal electrode reflection and layer thicknesses on the performance of inverted organic solar cells", Sol. Energy Mater. Sol. Cells, 2010, 94, 744-749.

61.     J.C. Wang, W.T. Weng, M.Y. Tsai, M.K. Lee, S.F. Horng, T.P. Perng, C.C. Kei, C.C. Yu, H.F. Meng, "Highly efficient flexible inverted organic solar cells using atomic layer deposited ZnO as electron selective layer", J. Mater. Chem., 2010, 20, 862-866.




G.Banupriya, C.R.Jerinsajeev

Paper Title:

Optimal Image Upscaling Using Pixel Classification

Abstract:   Image magnification generally results in loss of image quality. Therefore image magnification requires interpolation to read between the pixels. Generally the enlarged images suffer from imperfect reconstructions, pixelization and jagged contours. The proposed system provides error-free high resolution for real images. The basic idea behind the system comprises two basic steps: Fast Curvature Based Interpolation (FCBI) which involves the filling of missing values after zooming and Iterative Curvature Based Interpolation (ICBI) which involves the modification of the filled values. The results obtained from the simulation shows that the proposed interpolation algorithm improves the quality of the image both subjectively and objectively compared to the previous conventional techniques.

   Image enhancement, image processing, Image magnification, interpolation, jagged contours, NEDI, FCBI, ICBI, nvidia CUDA.


1.       Andrea Giachetti and Nicola Asuni, “Real-Time Artifact-Free Image Upscaling”.IEEE Transactions on Image Processing, Vol.       20, No. 10, October 2011
2.       Battiato. S, Gallo. G, and Stanco. F, “A locally-adaptive zooming algorithm for digital images,”Image Visual Computing. , vol. 20, 2002, pp. 805–812.

3.       Chen. M. J, Huang. C. H, and Lee. W. L, “A fast edge-oriented algorithm for imageinterpolation,” Image Visual Computing. , vol. 23, 2005, pp.791–798.

4.       Fattal.,R,“Image upsampling via imposed edge statistics”,ACMTransactionsonGraph.,vol.26,no.3,2007,pp.95-1-95 8.

5.       Freeman. W. T, T. R. Jones, and  E. C. Pasztor,”Example-basedsuper-resolution,”IEEE computer Graphic application,vol.22,no.2,Mar./Apr. 2002,pp.56-65.

6.       Gilad Freeman and Raanan Fattal, “Image and video upscaling from Local self-Examples”,in proceedings of 12th International conference computer vision,2009,pp.349-356.

7.       Glasner.D,Bagon. S, and Irani. M,”Super-resolution from a single image,” in proceedings of 12th International conference on computer vision., 2009,pp.349-356.

8.       kim. K.I and Kwon. Y,”Example-based learning for single - image super- resolution,”in proceedings of 30th DAGM SymposiumonPatternRecognition,Berlin,Heidelberg,2008,pp.456-465

9.       Li. X and orchard. M. T,”New edge-directed interpolation,” IEEE Transaction on Image processing, vol. 10, no. 10,oct. 2001,pp. 1521-1527.

10.     Morse. B. S and schwartzwald.D,”Image magnification using level set reconstruction”, in proceedings of IEEE conference on Computer vision and pattern recognition ,2001,vol. 3,pp.333-340.

11.     Su.D and willis. P,”Image interpolation by pixel level data-dependent triangulation,” computer Graphics Forum,vol.23,2004,pp.189-201.

12.     Sun Jian, Z.B.Xu, and H.Y .shum,”Image super-resolution using gradient profile prior,”in proceedings of IEEE  on computer vision and pattern recognition(CVPR),2008,pp. 1-8





Paper Title:

Low Power and Area-Efficient Carry Select Adder

Abstract:    Carry Select Adder (CSLA) is one of the fastest adders used in many data-processing processors to perform fast arithmetic functions. From the structure of the CSLA, it is clear that there is scope for reducing the area and power consumption in the CSLA. This work uses a simple and efficient gate-level modification to significantly reduce the area and power of the CSLA. Based on this modification 8-, 16-, 32-, and 64-b square-root CSLA (SQRT CSLA) architecture have been developed and compared with the regular SQRT CSLA architecture. The proposed design has reduced area and power as compared with the regular SQRT CSLA with only a slight increase in the delay. This work evaluates the performance of the proposed designs in terms of delay, area, power, and their products by hand with logical effort and through custom design and layout in 0.18-m CMOS process technology. The results analysis shows that the proposed CSLA structure is better than the regular SQRT CSLA.

   Application-specific integrated circuit (ASIC), area efficient, CSLA, low power.


1.        O. J. Bedrij, “Carry-select adder,” IRE Trans. Electron. Comput., pp. 340–344, 1962.
2.        B. Ramkumar, H.M. Kittur, and P. M. Kannan, “ASIC implementation of modified faster carry save adder,” Eur. J. Sci. Res., vol. 42, no. 1, pp. 53–58, 2010.

3.        T. Y. Ceiang and M. J. Hsiao, “Carry-select adder using single ripple carry adder,” Electron. Lett., vol. 34, no. 22, pp. 2101–2103, Oct. 1998.

4.        Y. Kim and L.-S. Kim, “64-bit carry-select adder with reduced area,” Electron. Lett., vol. 37, no. 10, pp. 614–615, May 2001.

5.        J. M. Rabaey, Digtal Integrated Circuits—A Design Perspective. Upper Saddle River, NJ: Prentice-Hall, 2001.

6.        Y. He, C. H. Chang, and J. Gu, “An area efficient 64-bit square root carry-select adder for lowpower applications,” in Proc. IEEE Int. Symp. Circuits Syst., 2005, vol. 4, pp. 4082–4085.




Rashmi Agrawal, Mridula Batra

Paper Title:

A Detailed Study on Text Mining Techniques

Abstract:  Text Mining is an important step of Knowledge Discovery process. It is used to extract hidden information from not-structured or semi-structured data. This aspect is fundamental because most of the Web information is semi-structured due to the nested structure of HTML code, is linked and is redundant. Web Text Mining helps whole knowledge mining process in mining, extraction and integration of useful data, information and knowledge from Web page contents. Web Text Mining process able to discover knowledge in a distributed and heterogeneous multi-organization environment. In this paper, our basic focus is to study the concept of Text Mining and various techniques. Here, we are able to determine how to mine the Plain as well as Structured Text. It also describes the major ways in which text is mined when the input is plain natural language, rather than partially-structured Web documents.

   Plain, Structured, Text Mining, Web Documents.


1.        Agrawal, R. and Srikant, R. (1994)  “Fast algorithms for mining association rules.” Proc Int Conf on Very Large Databases VLDB-94, Santiago, Chile, pp. 487-499.
2.        Aone, C., Bennett, S.W., and Gorlinsky, J. (1996) “Multi-media fusion through application of machine learning and NLP.” Proc AAAI Symposium on Machine Learning in Information Access. Stanford, CA.

3.        Appelt, D.E. (1999) “Introduction to information extraction technology.” Tutorial, Int Joint Conf on Artificial Intelligence IJCAI’99. Morgan Kaufmann, San Mateo. Tutorial notes available at w w

4.        Apte, C., Damerau, F.J. and Weiss, S.M. (1994) “Automated learning of decision rules for text categorization.” ACM Trans Information Systems, Vol. 12, No. 3, pp. 233-251.

5.        Baeza-Yates, R. and Ribiero-Neto, B. (1999), Modern information retrieval. Addison Wesley Longman, Essex, England.

6.        Blum, A. and Mitchell, T. (1998) “Combining labeled and unlabeled data with co-training.” Proc Conf on Computational Learning Theory COLT-98. Madison, Wisconsin, pp. 92-100.

7.        Borko, H. and Bernier, C.L. (1975)  Abstracting concepts and methods. Academic Press, San Diego, California.

8.        Brill, E. (1992) “A simple rule-based part of speech tagger.” Proc Conf on Applied NaturalLanguage Processing ANLP-92. Trento, Italy, pp. 152-155.

9.        Brin, S. and Page, L. (1998) “The anatomy of a large-scale hypertextual Web search engine.” ProcWorld Wide Web Conference WWW-7. In Computer Networks and ISDN Systems, Vol. 30, No. 1-7, pp. 107-117.




Nalina.P, Muthukannan.K

Paper Title:

Survey on Image Segmentation Using Graph Based Methods

Abstract:   The main goal of this paper is to survey the high quality of image segmentation with improved speed and stability. In this paper to segment the image using three different graph based segmentation algorithms. These are Isoperimetric Segmentation Normalisd Cut Segmentation, and Spectral Segmentation.  Apply these algorithms in the image and find out segmentation result. Using the segmentation results the performance will be analyzed with speed and stability. To determine stability of image by adding the Additive Noise, Multiplicative Noise, Shot Noise

   Isoperimetric, Normalized Cut, Performance Evaluation, Spectral, Segmentation.


1.       Pedro F. Felzenszwalb and Daniel P. Huttenlocher. “Efficient     Graph-Based  Image Segmentation” International Journal of Computer   Vision, Volume 59: 167–181, Number 2, September  2004.
2.       Sudeep Sarkar and Padmanabhan Soundararajan, “Supervised learning of large perceptual organization: Graph spectral partitioning and learning automata”,. IEEE Trans. on Pat. Anal. and Mach. Int., vol. 22, no. 5, pp. 504.525, May 2000.

3.       L. Grady and E. L. Schwartz, “Isoperimetric partitioning: A new algorithm for graph partitioning,” SIAM Journal on Scientific Computing, vol. 27, no. 6, pp. 1844–1866, June 2006.

4.       Alex Pothen, Horst Simon, and Kang-Pu Liou, “Partitioning sparse matrices with eigenvectors of graphs”,. SIAM Journal of Matrix Analysis Applications, vol. 11, no. 3, pp. 430.452, 1990.

5.       Charles J. Alpert and Andrew B. Kahng, .Recent directions in net list partitioning: A survey,. Integration: The VLSI Journal, vol. 19,pp. 1.81, 1995.

6.       Y.C. Wei and C.K. Cheng, “Ratio cut partitioning for hierarchical designs,. IEEE Trans. on CAD, 1991.

7.       C. Hagen and A. Kahng, “New spectral methods for ratio cut partitioning and clustering” IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, vol. 11, no. 9, pp. 1074.1085, 1992.

8.       Mei Yeen Choong, Wei Leong Khong, et al. “Graph-based Image Segmentation using K-Means Clustering and Normalised Cuts” Fourth International Conference on Computational Intelligencce computer networks, 2012.

9.       Shi, J. and Malik, J “Normalized cuts and image segmentation” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 731–737 1997.

10.     Weiss,Y. “Segmentation using eigenvectors: A unifying view” In Proceedings of the International Conference on Computer Vision, pp. 975–982,  1999.

11.     Ratan, A.L., Maron, O., Grimson, W.E.L., and Lozano-Perez, T “A framework for learning query concepts in image  classification” In Proceedings of the IEEE Conference on Computer  Vision and Pattern Recognition, pp. 423–431, 1999.

12.     Urquhart, R  “Graph theoretical clustering based on limited neighborhood sets” In Proceedings of the IEEE Conference on Computer  Vision and Pattern Recognition pp. 173–187, 1982.

13.     Zahn, C.T “Graph-theoretic methods for detecting and describing gestalt clusters” IEEE Transactions on Computing, Vol. 20:68–86, 1971.

14.     J. Ning,L. Zhang, D. Zhang and C. Wu “Interactive Image Segmentation by Maximal Similarity based Region Merging” In Proceedings of the IEEE Conference on Computer  Vision and Pattern Recognition, vol. 43, pp. 445-456, Feb, 2010.

15.     Tranos Zuva, Oludayo O. Olugbara,, “Image Segmentation, Available   Techniques, Developments and Open Issues” Canadian Journal on Image Processing and Computer Vision Vol. 2, No. 3, March 2011

16.     Ming Zhang, Reda Alhajj, “Improving the Graph-Based Image Segmentation Method” Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'O6), 2006,  IEEE.

17.     L. Grady and E. L. Schwartz, “Isoperimetric graph partitioning for image segmentation,” IEEE Trans. on Pat. Anal. and Mach. Int., vol. 28, no. 3, pp. 469–475,March 2006.

18.     Yoram Gdalyahu, Daphne Weinshall, and Michael Werman, .Self-organization in vision: Stochastic clustering for image segmentation, perceptual grouping and image database organization,. IEEE Pattern Anal. and Mach. Int., vol. 23, no. 10, pp. 1053.1074, October 2001, 288.

19.     Bojan Mohar, “Isoperimetric inequalities, growth and the spectrum of graphs”,. Linear Algebra and its Applications, vol. 103, pp.119.131, 1988.

20.     Fan R. K. Chung, “Spectral Graph Theory”, Number 92 in Regional conference series in mathematics. American Mathematical Society, Providence, R.I., 1997.

21.     Jozef Dodziuk, “Difference equations, isoperimetric inequality and the transience of certain random walks”,. Transactions of the American Mathematical Society, vol. 284, pp. 787.794, 1984.

22.     Jozef Dodziuk and W. S Kendall, “Combinatorial Laplacians and the isoperimetric inequality”,. in From local times to global geometry, control and physics, K. D. Ellworthy, Ed., vol. 150 of Pitman Research Notes in Mathematics Series, pp. 68.74. Longman Scientific and Technical, 1986.

23.     Russell Merris, “Laplacian matrices of graphs: A survey”, Linear Algebra and its Applications, vol. 197,198, pp. 143.176, 1994.

24.     George Arfken and Hans-Jurgen Weber, Eds., “Mathematical Methods for Physicists”, Academic Press, 3rd edition, 1985.

25.     Norman Biggs, “Algebraic Graph Theory”, Number 67 in Cambridge Tracts in Mathematics. Cambridge University Press, 1974.

26.     David et al Martin, “a database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics”.

27.     Jefferey Cheeger, .A lower bound for the smallest eigenvalue  of the Laplacian,.in Problems in Analysis, R.C. Gunning, Ed., pp. 195.199. Princeton University Press, Princeton, NJ, 1970.

28.     Gene Golub and Charles Van Loan, Matrix Computations, The Johns Hopkins University Press, 3rd edition, 1996.

29.     Bruce Hendrickson and Robert Leland, .The Chaco user's guide,. Tech. Rep. SAND95-2344, Sandia National Laboratory, Albuquerque, NM, July 1995.




Smriti joshi, Anant Kr. Jaiswal, Pushpendra Kr. Tyagi

Paper Title:

A Novel Analysis of T Mac and S Mac Protocol for Wireless Sensor Networks Using Castalia

Abstract:    Wireless sensor networks have been kept evolving due to the advancements in various technologies like radio, battery and operating systems in sensor elements but mac protocols are still most important in wsn because the exact implementation of communication among sensors is derived by the mac protocols. Battery consumption, network lifetime, communication latency, packet collisions are some very important factors those depends on mac protocols used in a wireless sensor networks. T Mac and S Mac have been two landmark protocols in wireless sensor networks protocols because of their utility and ease of implementation along with simplicity.

   T Mac, protocol, S Mac, Castalia, Omnetpp, wsn.


2.        A. Varga, The OMNeT++ discrete event simulation system, in: European Simulation Multiconference (ESM’2001) (Prague, Czech Republic,


4.        Yuri Tselishchev, Athanassios Boulis, Lavy Libman, “Experiences and Lessons from Implementing a Wireless Sensor Network MAC Protocol in the Castalia Simulator,” submitted to IEEE Wireless Communications & Networking Conference 2010 (WCNC 2010), Sydney, Australia.

5.        Wei Ye, John Heidemann, Deborah Estrin “An Energy-Efficient MAC Protocol for Wireless Sensor Networks”, INFOCOM 2002. Twenty-First Annual Joint Conferences of the IEEE Computer and Communications Societies. Proceedings. IEEE.


7.        T. van Dam and K. Langendoen, An adaptive energy-efficient MAC protocol for wireless sensor networks, in: 1st ACM Conf. on Embedded Networked Sensor Systems (SenSys 2003), (Los Angeles, CA,November 2003)

8.        A. Varga. The OMNeT++ discrete event simulation system. In European Simulation Multiconference (ESM'2001), Prague, Czech Republic, June 2001.




Mukesh Kumar Jha, Debanjan Pakhira, Baisakhi Chakraborty

Paper Title:

Diabetes Detection and Care Applying CBR Techniques

Abstract:   Diabetes is a lifelong (chronic) disease increase at a rapid rate because of sedate life style, changes into urban culture, unhealthy foods and lacking of physical activity. It is an incurable chronic disease, but through true diabetes screening and advanced sugar monitoring can prevent risky complications. A little information, Precaution and absolute care plan can go a long way to dealing with diabetes. It is very hard to make an excellent care plan and maintaining healthy blood glucose level for patients and their health care providers. In this research work we proposed a case base decision support system for patients with diabetes. Case based reasoning is an artificial intelligence technique to detect diabetes and its type, its seriousness and giving the appropriate care plan. This system helps doctors and patients to check, analyze and repair solutions. A case consists of a problem description (e.g. symptoms) and a solution (e.g. a care plan and a therapy). Cases are stored in a database of cases called case bases. To solve an actual problem a notion of similarity is used to retrieve similar cases from case bases. The solutions of these found similar cases are used as starting points for solving the actual problems at hand. The system analyzes the symptoms of the patients and gives the exact types of diabetes, its seriousness level and the appropriate care plan for appropriate patients. If it is not found then system generates basic care plan by ontology. After that system modified that case and stored in its ever expanding database for future use. The learning process of CBR is retaining the modified solved case in the data base is gives a big scope to solve new problems in future.

   Case-Based Reasoning, Detection, Diagnosis, Ontology.


1.       Samaresh Deyashi, Debrup Banerjee, Baisakhi Chakraborty , D.Ghosh, Joyati Debnath, “Application Of CBR On Viral Fever Detection System(VFDS)”, 978-1-4577-0434-5/11/$26.00 ©2011 IEEE.
2.       Department of Health, "What is diabetes ", HT2006/service/QnA.aspx?FAQ)D=193.(accessed 04124/2010)

3.       I. Watson, F. Marir, "Case-Based reasoning: a review," The Knowledge Engineering Review, vol. 9, no. 4, 1994. pp. 355-381.

4.       T. R. Gruber, "Ontolingua: A translation approach to portable ontology specifications," Knowledge Acquisition, Vol. 5, No. 2, 1993, pp. 199-200.

5.       Jian-xun Chen, Shih-Li Su, Che-Ha Chang, “Diabetes care Decision Support System”, 2nd International Conference on Industrial and information Systems, 2010.

6.       Cindy Marling, Matthew Wiley and Razvan Bunescu, Jay Shubrook and Frank Schwartz, “Emerging Applications for Intelligent Diabetes Management”, Proceedings of the Twenty-Third Innovative Applications of Artificial Intelligence Conference.

7.       Cindy Marling, Jay Shubrook, And Frank Schwartz, “Toward Case-Based Reasoning for Diabetes Management: A Preliminary Clinical Study and Decision Support System Prototype”, Computational Intelligence, Volume 25, Number 3, 2009.

8.       Frank L. Schwartz, Jay H. Shubrook, and Cynthia R. Marling, “Use of Case-Based Reasoning to Enhance Intensive Management of Patients on Insulin Pump Therapy”, Journal of Diabetes Science and Technology Volume 2, Issue 4, July 2008.

9.       A. Bonzano, P. Cunningham, and C. Meckiff,  “ISAC: A CBR System for Decision Support in Air Traffic Control”, Advances in Case-Based       Reasoning, LNCS, Vol. 1168, , Springer Heidelberg, pp.44-57, 1996.

10.     Agnar Aamodt, Enric Plaza, “Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches”, AI Communications. IOS Press, Vol. 7: 1, pp. 39-59, 1994.

11.     Baisakhi Chakraborty, D.Ghosh, Ranjan Kumar Maji, Saswati Garnaik, Narayan Debnath,” Knowledge Management with Case-Based Reasoning Applied on Fire Emergency Handling”, 2012.









Sarvesh Tanwar, Prema K.V.

Paper Title:

Threats & Security Issues in Ad hoc network: A Survey Report

Abstract:    With the advancement in radio technologies like Bluetooth, IEEE 802.11, a new concept of networking has emerged; this is known as ad hoc networking where potential mobile users arrive within the range for communication. As network is becoming an increasingly important technology for both military and commercial distributed and group based applications, security is an essential requirement in mobile ad hoc network (MANETs). Compared to wired networks, MANETs are more vulnerable to security attacks due to the lack of a trusted centralized authority and limited resources. Attacks on ad hoc networks can be classified as passive and active attacks or internal attack and external attacks, the security services such as confidentiality, authenticity and data integrity are also necessary for both wired and wireless networks to protect basic applications. One main challenge in design of these networks is their vulnerability to security attacks. In this paper, we study the threats an ad hoc network faces and the security goals to be achieved.

   MANET, Security, IEEE802.11, vulnerability, authenticity, threats, ad hoc networks.


1.        T. Karygiannis and L. Owens, “Wireless Network Security, 802.11, Bluetooth and Handheld Devices,” NIST   Publication, p. 800(48), November 2002.
2.        S. A. Razak, S. M. Furnell, P. J. Brooke, Attacks  against Mobile Ad Hoc Networks Routing Protocols”

3.        Abhay Kumar Rai, Rajiv Ranjan Tewari & Saurabh Kant Upadhyay, “Different Types of Attacks on Integrated MANET-Internet Communication,” International Journal of Computer Science and Security (IJCSS) Volume: 4 Issue: 3.

4.        H. Deng, W. Li, Agrawal, D.P., “Routing security in wireless ad hoc networks”, Cincinnati Univ., OH, USA; IEEE Communications Magazine, Oct. 2002, Volume: 40, page(s): 70- 75, ISSN: 0163-6804.

5.        H.Deng, H.Li, and D.P.Ararwal, “Routing security in wireless Ad hoc networks”, IEEE Communication magazine. Vol. 40, No.10. Oct.2002.

6.        S. Murphy, “Routing Protocol Threat Analysis,” Internet Draft, draft-murphy-threat-00.txt, October 2002.

7.        Douceur, John: The Sybil Attack, 2002

8.        V. Gayraud and B. Tharon. Securing Wireless Ad Hoc Networks. ISS Master, MP 71 project, March 2003.

9.        K. Sanzgiri, D. Laflamme, B. Dahill, B. Levine, C. Shields and E. Royer. An Authenticated Routing for Secure Ad Hoc Networks. Journal on Selected Areas in  Communications special issue on Wireless Ad hoc Networks, March 2005.

10.     Pradip M. Jawandhiya et. al. / International Journal of Engineering Science and Technology Vol. 2(9), 2010, 4063-4071

11.     Charles P. Pfleeger, Shari Lawerence Pfleeger (2003), Security in Computing, Pearson Education, Singapore.

12.     Abhay Kumar Rai, Rajiv Ranjan Tewari & Saurabh Kant Upadhyay International Journal of Computer Science and Security (IJCSS) Volume (4): Issue (3)

13.     L. Zhou and Z. J. Haas. “Securing Ad Hoc Networks”. IEEE Network Magazine, Volume. 13, no. 6, Pages 24-30, December 1999.

14.     C. E. Perkins and E. M. Royer. "Ad Hoc On-Demand Distance Vector Routing". Proceedings of IEEE Workshop on Mobile Computing Systems and Applications, Pages 90-100, February 1999.




Manju, Ranjana Thalore, Jyoti, M.K Jha

Paper Title:

Performance Evaluation of Bellman-Ford, AODV, DSR and DYMO Protocols using QualNet in 1000m×1000m Terrain Area

Abstract:   Wireless sensor networks (WSNs) offer much promise for target tracking and environmental monitoring. While many WSN routing protocols have been proposed to date, most of these focus on the mobility of observers and assume that targets are fixed. In addition, WSNs often operate under strict energy constraints, and therefore reducing energy dissipation is also an important issue. In this paper we discuss various protocols like Bellman-Ford, Ad-Hoc on-Demand Routing (AODV), Dynamic Source Routing (DSR), Dynamic MANET On-demand Protocol (DYMO) and compare various parameters like Average End-to-End Delay (sec.), Residual Battery Capacity (mAhr), and Throughput (bits/sec.), Output Received at CBR Server.

   Wireless sensor networks, Routing Protocols, Energy efficiency, Qualnet 5.2.


1.        Kadivar, M., Shiri, M. E., & Dehghan, M. (2009). Distributed topology control algorithm based on one-and two-hop neighbors information for ad hoc networks. Computer Communications, 32(2), 368– 375.
2.        Dimokas, N., Katsaros, D., & Manolopoulos, Y. (2010). Energy-efficient distributed clustering in wireless sensor networks. Journal of Parallel and Distributed Computing, 70(4), 371–383.

3.        Song, C., Liu, M., & Cao, J. (2009). Maximizing network lifetime based on transmission range adjustment in wireless sensor networks. Computer Communications, 32(11), 1316–1325.

4.        Xu, Y., Hendemann, J., & Estrin, D. (2000). Adaptive energy-conserving routing for multihop ad hoc networks. Technical report TR-2000-527, USC/Information Sciences Institute.

5.        Li, J. P. (2008). A mobile ECG monitoring system with context collection. Master’s thesis, Dublin Institute of Technology.

6.        P. Hu, R. Robinson, M. Portmann, and J. Indulska, “Context-Aware Routing in Wireless Mesh Networks,” In Proceedings of the 2nd ACM International Workshop on Context-Awareness for Self-Managing Systems (CASEMANS), (Pervasive'08 Workshop), May 22, 2008, Sydney, Australia.

7.        Vicaire, P., et al. (2009). Achieving long-term surveillance in vigilNet. ACM Transactions on Sensor Netowkrs, 5(5), 626–648.

8.        P Kuosmanen – “Classification of Ad Hoc Routing Protocols” Finnish Defence Forces, Naval Academy, Finland, 2002 -

9.        Hadi Sargolzaey, Ayyoub Akbari Moghanjoughi and Sabira Khatun, - A Review and Comparison of Reliable Unicast Routing Protocols For Mobile Ad Hoc Networks, IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.1, pp. 186-196, January 2009.

10.     Beigh Bilal Maqbool, Prof.M.A.Peer (2010)  Classification of Current Routing Protocols for Ad Hoc Networks - A Review International Journal of Computer Applications (0975 – 8887) Volume 7– No.8.

11.     Charles Perkins and Elizabeth Royer. Ad hoc on demand distance vector (AODV) routing. aodv-02.txt, November 1998. IETF Internet Draft.

12.     D. Johnson and D. Maltz. Dynamic source routing in ad hoc wireless networks. In T. Imielinski and H. Korth, editors, Mobile computing. Kluwer Academic,1996.

13.     Parma Nand, Dr. S.C. Sharma, "Performance study of Broadcast based Mobile Adhoc Routing Protocols AODV, DSR and DYMO," International Journal of Security and Its Applications Vol. 5 No. 1, January, 2011.

14.     Scalable Network Technologies, “Qualnet simulator”, Software Package, 2003. http://

15.     T.V. P. Sundararajan, Dr. A. Shanmugam “Selfish avoidance routing protocol for Mobile Adhoc Network” International journal of wireless and Mobile Networks (IJWMN), Vol. 2, No. 2, may 2010.




Anil Saroliya, Upendra Mishra and Ajay Rana

Paper Title:

Secure transaction on the Peer to Peer based Virtual Network

Abstract:    Distributed Hash Tables or the DHT is a very crucial and attention seeking topic as far as the field of P2P network overlays is concerned; since the latter has set a new benchmark in the arena of file sharing. The use of DHTs in P2P network involves the cause of file searching within the network. The DHT protocol works by assigning a key to a single P2P function and finds the node or nodes associated to this key thereby completing the request for file search. Other functions involving the retrieval of information and its storage are facilitated by certain higher layers in the P2P network. Through the research made out in the paper, the goal is to find out various security issues related to the process and resolve them according to the routing protocols of the network. The Chord which is a DHT protocol has been taken as the target for research in this paper for certain reasons that will consequently be covered in the following.

   Structured P2P networks, distributed hash tables, routing, security, backtracking.


1.       Dehui Liu, Feng Chen, Gang Yini, HuaiMin Wangl, Peng Zoul: LSB-Chord:Load Balancing in DHT based P2P systems under Churn. In:Proc. IEEE ICCSIT’10, Changsha, China (2010)
2.       Heinbockel, W., and Kwon, M.: Phyllo: A peer-to-peer overlay security framework.  The First Workshop on Secure Network Protocols (NPSec), Boston, MA (2005)

3.       Ratnasamy, S., Francis, P., Handley, M., Karp, R., Shenker, S.: A scalable content addressable network. In: Proc. ACM SIGCOMM’01, San Diego, CA (2001)

4.       Stoica, I., Morris, R., Karger, D., Kaashoek, M.F., Balakrishnan, H.: Chord: A scalable peer-to-peer lookup service for Internet applications. In: Proc. ACM SIGCOMM’01, San Diego, California (2001)

5.       Wallach, D.: A survey of peer-to-peer security issues, International Symposium on Software Security, Tokyo, Japan (2002)

6.       Gurari, Eitan, Backtracking algorithms "CIS 680: Data Structures: Chapter 19: Backtracking Algorithms" (1999)

7.       Mariem Thaalbi, Nabil Tabbane, Tarek Bejaoui, Ahmed Meddahi: Enhanced Backtracking Chord protocol for mobile Ad hoc networks. In: Proc. IEEE ICCIT’12, Ariana, Tunisia (2012)




V.Selvi, R.Umarani

Paper Title:

Comparative Study of GA and ABC for Job Scheduling

Abstract:  In the field of computer science and operation’s research, Artificial Bee Colony (ABC) is an optimization algorithm relatively new swarm intelligence technique based on behaviour of honey bee swarm and Meta heuristic. It is successfully applied to various paths mostly continuous optimization problems. Swarm intelligence systems are typically made up of a population of simple agents or boids interacting locally with one another and with their environment. The job scheduling problem is the problem of assigning the jobs in the system in a manner that will optimize the overall performance of the application, while assuring the correctness of the result. ABC algorithm, is proposed in this paper, for solving the job scheduling problem with the criterion to decrease the maximum completion time. In this paper, modifications to the ABC algorithm is based on Genetic Algorithm (GA) crossover and mutation operators. Such modifications applied to the creation of new candidate solutions improved performance of the algorithm.

   Artificial Bee Colony, Genetic algorithm, Job scheduling.


1.       Dror G. Feitelson, Larry Rudolph and Uwe Schwiegelshohn, "Parallel Job Scheduling -A Status Report", In Proeedings of the Conference on JSSPP, pp.1-16, 2004.
2.       Ivan Rodero, Francesc Guim and Julita Corbalan, "Evaluation of Coordinated Grid Scheduling Strategies", In Proceedings of 11th IEEE International Conference on High Performance Computing and Communications, Seoul, pp. 1-10, 2009.

3.       Oliner, Sahoo, Moreira, Gupta and Sivasubramaniam, "Fault-aware Job Scheduling for BlueGene/L Systems", In Proceedings of 18th International Parallel and Distributed Processing Symposium, 2004.

4.       Grudenic and Bogunovi, "Computer Cluster Scheduling Algorithm Based on Time Bounded Dynamic Programming", In Proceedings of the 34th International Convention on MIPRO, 2011, Opatija, pp. 722-726, 2011 

5.       Abdelrahman Elleithy, Syed S. Rizvi and Khaled M. Elleithy, "Optimization and Job Scheduling in Heterogeneous Networks ", International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering, 2008

6.       Zhang, Franke, Moreira and Sivasubramaniam, "A Comparative Analysis of Space- and Time-Sharing Techniques for Parallel Job Scheduling in Large Scale Parallel Systems", pp. 1-33, 2008.

7.       Surekha and Sumathi, "Solution to the Job Shop Scheduling Problem using Hybrid Genetic Swarm Optimization Based on (λ, 1)-Interval  Fuzzy Processing Time", European Journal of Scientific Research, Vol. 64, No. 2, pp. 168-188, 2011.

8.       Bin Cai, Shilong Wang and Haibo Hu, "Hybrid Artificial Immune System for Job Shop Scheduling Problem", World Academy of Science, Engineering and Technology, Vol. 59, No. 18, pp. 81-86, 2011.

9.       Mohammad Akhshabi, Mostafa Akhshabi and Javad Khalatbari, "Parallel Genetic Algorithm to Solving Job Shop Scheduling Problem", Journal of Applied Sciences Research, Vol. 1, No. 10, pp. 1484-1489, 2011.

10.     Manish Gupta, Govind sharma, "An Efficient Modified Artificial Bee Colony Algorithm for Job Scheduling Problem", International Journal of Soft Computing and Engineering (IJSCE), Vol. 1, No. 6, pp. 291-296, January 2012.

11.     Hadi Mokhtari, "Adapting a Heuristic Oriented Methodology for Achieving Minimum Number of Late Jobs with Identical Processing Machines", Research Journal of Applied Sciences, Engineering and Technology, Vol. 4, No. 3, pp. 245-248, 2012.

12.         Elnaz ZM, Amir MR, Mohammad R, Feizi D (2008). Job Scheduling in Multiprocessor Architecture Using Genetic Algorithm. Proc. IEEE, pp. 248-250.

13.    Thanushkodi K, Deeba K (2009). An Evolutionary Approach for Job Scheduling in a Multiprocessor Architecture. CiiT Int. J. Artif. Intell. Syst. Mach. Learn., 1(4).

14.    Tung-Kuan L, Jinn- Tsong T, Jyh-Hong C (2005). Improved genetic algorithm for the job-shop scheduling problem. International Journal Advanced Manufacture Technology (Spiringer), pp. 1021-1029.




CH.Appala Narayana, D.V.N. Ananth, K.D. Syam Prasad, CH. Saibabu, S.Sai Kiran, T. Papi Naidu

Paper Title:

Application of STATCOM for Transient Stability Improvement and Performance Enhancement for a Wind Turbine Based Induction Generator

Abstract:    Voltage stability is a key issue to achieve the uninterrupted operation of wind farms equipped with squirrel cage induction generators (SCIG) during grid faults. A Static Synchronous Compensator (STATCOM) is applied to a power network which includes a SCIG driven by a wind turbine, for steady state voltage regulation and transient voltage stability support. The STATCOM is controlled by using PQ controller technique with voltage regulation as basic scenario. The system is implemented using MATLAB/ SIMULINK. Results illustrate that the STATCOM improves the transient voltage stability and therefore helps the wind turbine generator system to remain in service during grid faults. The time to reach steady state torque and speed without using vector control or direct torque control can also be achieved by using this STATCOM control technique.

Keywords:   STATCOM, PQ control theory, induction machine, PWM.


1.        Sannino, “Global power systems for sustainable development,” in IEEE General Meeting, Denver, CO, Jun. 2004.
2.        K.S Hook, Y. Liu, and S. Atcitty, “Mitigation of the wind generation integration related power quality issues by energy storage,” EPQU J., vol. XII, no. 2, 2006.

3.        R. Billinton and Y. Gao, “Energy conversion system models for adequacy assessment of generating systems incorporating wind energy,” IEEE Trans. on E. Conv., vol. 23, no. 1, pp. 163–169, 2008,Multistate.

4.        D. Tziouvaras, “Relay Performance during Major System Disturbances,” in  Proc. Protective Relay Engineers, 2007. 60th Annual Conference, College Station, TX, 27-29 March 2007, pp. 251-270.

5.        J. Manel, “Power electronic system for grid integration of renewable energy source: A survey,” IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1002–1014, 2006, Carrasco.

6.        M. Tsili and S. Papathanassiou, “A review of grid code technology requirements for wind turbine,” Proc. IET Renew.power gen., vol. 3,pp. 308–332, 2009.

7.        S. Heier, Grid Integration of Wind Energy Conversions. Hoboken, NJ: Wiley, 2007, pp. 256–259.

8.        J. J. Gutierrez, J. Ruiz, L. Leturiondo, and A. Lazkano, “Flicker measurement system for wind turbine certification,” IEEE Trans. InstrumMeas., vol. 58, no. 2, pp. 375–382, Feb. 2009.

9.        Indian Wind Grid Code Draft report on, Jul. 2009, pp. 15–18, C-NET.

10.     P. Kundur, Power System Stability and Control, McGraw Hill, 1994.

11.     Charles Mozina, “Power Plant Protection and Control Strategies for Blackout Avoidance,” in Proc. IEEE PES Advanced Metering, Protection, Control, Communication, and Distributed Resources Conference, March 14-17, 2006, pp. 200-218. 

12.     W. Elmore, Protective Relaying Theory and Applications, CRC Press, 2nd Edition, 2004.




CH.AppalaNarayana, D.V.N.Ananth, T. PapiNaidu, B. Santosh Kumar, S. Saikiran, I. Prasanna Kumar, Y.Naveen Kumar, K.V.Ramana

Paper Title:

Application of STATCOM and CROWBAR for Transient Stability Improvement and Performance Enhancement for A Wind Turbine Based Doubly Fed Induction Generator

Abstract:   This paper presents a robust control of Doubly Fed Induction Generator (DFIG) wind turbine in a sample power system. DFIG consists of a common induction generator with slip ring and a partial scale power electronic converter. Indirect field-oriented controller is applied to rotor side converter for active power control and voltage regulation of wind turbine. On grid side PQ control scheme is applied. Wind turbine and its control units are described in details and also for STATCOM control. All power system components are simulated in MATLAB/ SIMULINK software. For studying the performance of controller, different abnormal conditions are applied even the worst case. Simulation results prove that the performance of STATCOM and DFIG control schemes as improving power quality and stability of wind turbine.

   STATCOM, PQ control theory, induction machine, PWM, crowbar, rotor side controller, grid side controller, DFIG, wind turbine.


1.        Sannino, “Global power systems for sustainable development,” in IEEE General Meeting, Denver, CO, Jun. 2004.
2.        K.S Hook, Y. Liu, and S. Atcitty, “Mitigation of the wind generation integration related power quality issues by energy storage,” EPQU J., vol. XII, no. 2, 2006.

3.        R. Billinton and Y. Gao, “Energy conversion system models for adequacy assessment of generating systems incorporating wind energy,” IEEE Trans. on E. Conv., vol. 23, no. 1, pp. 163–169, 2008,Multistate.

4.        D. Tziouvaras, “Relay Performance during Major System Disturbances,” in  Proc. Protective Relay Engineers, 2007. 60th Annual Conference, College Station, TX, 27-29 March 2007, pp. 251-270.

5.        J. Manel, “Power electronic system for grid integration of renewable energy source: A survey,” IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1002–1014, 2006, Carrasco.

6.        M. Tsili and S. Papathanassiou, “A review of grid code technology requirements for wind turbine,” Proc. IET Renew.power gen., vol. 3,pp. 308–332, 2009.

7.        S. Heier, Grid Integration of Wind Energy Conversions. Hoboken, NJ: Wiley, 2007, pp. 256–259.

8.        J. J. Gutierrez, J. Ruiz, L. Leturiondo, and A. Lazkano, “Flicker measurement system for wind turbine certification,” IEEE Trans. InstrumMeas., vol. 58, no. 2, pp. 375–382, Feb. 2009.

9.        Indian Wind Grid Code Draft report on, Jul. 2009, pp. 15–18, C-NET.

10.     P. Kundur, Power System Stability and Control, McGraw Hill, 1994.

11.     Charles Mozina, “Power Plant Protection and Control Strategies for Blackout Avoidance,” in Proc. IEEE PES Advanced Metering, Protection, Control, Communication, and Distributed Resources Conference, March 14-17, 2006, pp. 200-218. 

12.     W. Elmore, Protective Relaying Theory and Applications, CRC Press, 2nd Edition, 2004.

13.     Holdsworth, L., X.G. Wu, J.B. Ekanayake and N. Jenkins, 2003. Comparison of fixed speed  and doubly-fed induction wind turbines during power system disturbances. IEE Proc. Gener. Transm. Distrib., 150 (3): 343-352.

14.     W. Zhang, P. Zhou, and Y. He, “Analysis of the by-pass resistance of an active crowbar for doubly-fed induction generator based wind turbines under grid faults,” in Proc. Int. Conf. Electr. Mach. Syst. (ICEMS), Oct.,2008, pp. 2316–2321.

15.     G. Pannell, D. Atkinson, and B. Zahawi, “Minimum-threshold crowbar for a fault-ride-through grid-code-compliant dfig wind turbine,” IEEE Trans. Energy Convers., vol. 25, no. 3, pp. 750–759, Sep. 2010.

16.     J. Morren and S. de Haan, “Short-circuit current of wind turbines with doubly fed induction generator,” IEEE Trans. Energy Convers., vol. 22, no. 1, pp. 174–180, Mar. 2007.

17.     J. Yang, J. Fletcher, and J. O’Reilly, “A series-dynamic-resistor-based converter protection scheme for doubly-fed induction generator during various fault conditions,” IEEE Trans. Energy Convers., vol. 25, no. 2, pp. 422–432, Jun. 2010.

18.     A. Causebrook, D. Atkinson, and A. Jack, “Fault ride-through of large wind farms using series dynamic braking resistors (march 2007),” IEEE Trans. Power Syst., vol. 22, no. 3, pp. 966–975, Aug. 2007.

19.     P. Flannery and G. Venkataramanan, “Unbalanced voltage sag ride-through of a doubly fed induction generator wind turbine with series grid-side converter,” IEEE Trans. Ind. Appl., vol. 45, no. 5, pp. 1879–1887, Sep./Oct. 2009.

20.     J. Liang,W. Qiao, and R. Harley, “Direct transient control of wind turbine driven dfig for low voltage ride-through,” in Proc. IEEE Power Electron. Mach. Wind Appl. (PEMWA), Jun. 2009, pp. 1–7.




T. D. Dongale, S. R. Ghatage, R. R. Mudholkar

Paper Title:

Application Philosophy of Fuzzy Regression

Abstract:   The uncertainties and its prediction normally tend to be complex phenomena. The randomness and fuzziness are two kinds of uncertainties possible in real time. The randomness deals with the general uncertainties whereas; the fuzzy logic addresses the linguistic uncertainties. The fuzzy logic and its allied field deal with the every part of uncertainties in fuzzy way. For a situation where, complex predictions are to tackle then statistical regression methodology is used from many years. The next step in this scenario for dealing with uncertainties is the ‘Fuzzy Regression’. This paper presents the elementary theory of fuzzy regression and the philosophy behind its potential application.

   Fuzzy Logic, Fuzzy Regression, Uncertainties, Computational Intelligence


1.       R. R. Mudholkar, Transformer Design A Fuzzy Approach, Ph.D. thesis, Shivaji University Kolhapur, 2003
2.       Fathi M. and Lambrecht M., EBFLATSY: A fuzzy logic system to calculate and optimize parameters of electron beam welding machine.  Fuzzy Sets and Systems, 1995, pp.3-13.

3.       Kevin, Designing With Fuzzy Logic. IEEE Spectrum, 1990, pp.42-44.

4.       Cheok K. C., Kobayashi K., Scaccia S. and Scaccia G., Fuzzy Logic-Based Smart Automatic Windshield Wiper. IEEE Control Systems, 1996, pp.28-34.

5.       Kosko B. and Isaka S., Fuzzy Logic. Scientific American, 1993, pp. 62-67.

6.       Ibrahim A.M., Introduction to Applied Fuzzy Electronics. Prentice-Hall, New Jersey, 1996, pp.1-96.

7.       Cox E., The Fuzzy Systems Handbook-A Practitioners Guide to building, using and maintaining Fuzzy Systems. AP-Professional, Boston, 1998, pp.45-469.

8.       Terano T. Asai K and Sugeno M., Applied Fuzzy Systems. AP Professional. New York, 1994.

9.       Journal of Technology, IED, Fuzzy Logic Widens Its Appeal To Industrial Controls. IEEE Control systems, 1994, pp.73-78.

10.     Freska C., Linguistic description of human judgments in expert systems and in soft science. 1982, pp.279-305.

11.     Larsen P.M., Industrial Applications of Fuzzy Logic Control, 1981, pp.335-342. 

12.     Sugeno M., Murofushi T., Mori T., Tatematsu T. and Tanaka, J., Fuzzy algorithmic control of a model car by oral instructions. Fuzzy Sets and Systems, 32, 1989, pp. 207-219.

13.     Driankov D., Time for Some Fuzzy Thinking. Internal Report Siemens A.G., Munich. 1990.

14.     Cox E.D., Integrating Fuzzy Logic with Neural Networks. AI Expert, 1992, pp.40-45.

15.     Dubois D., Prade H. and Yager R.R., (eds.), Readings in Fuzzy Sets for Intelligent Systems. Morgan Kuafmann Publishers, Inc. SM, California. 1997

16.     Zimmermann H.J., Fuzzy Set Theory and its Applications. Academic Publishers, Boston, 1996.

17.     Rosma Mohd Dom et al, An Adaptive Fuzzy Regression Model for the Prediction of Dichotomous Response Variables, Fifth International Conference on Computational Science and Applications, IEEE, 2007

18.     M. Nasiri et al., “Comparison of Statistical Regression, Fuzzy Regression and Artificial Neural Network Modeling Methodologies in Polyester Dyeing”, Proceedings of 2005 International Conference for modeling, control and automation, 2005.

19.     S. Dreiseitl and O. Machado, “Logistic Regression and Artificial Neural Network Classification Models: a Methodology Review”, Journal of Biomedical Informatics, 35, 2003, pp352- 359.

20.     F. Shapiro, “Fuzzy Regression Models”, ARC, 2005

21.     J.V. Tu, “Advantages and Disadvantages of Using Artificial Neural Networks Versus Logistic Regression for Predicting Medical Outcomes”, J Clin Epidemol, Vol 49, No 11, 1996, pp.1225-1231

22.     Rosma M. Dom et al, A Learning System Prediction Method Using Fuzzy Regression, Proceedings of the International Multi Conference of Engineers and Computer Scientists, Vol- I,IMECS- 2008, Hong Kong.

23.     H. Tanaka, S. Uejima, and K. Asai, “Linear Regression Analysis with Fuzzy Model”, IEEE Transactions on Systems, Man and Cybernetics, Vol. 12, No 6, 1982, pp 903 – 907.

24.     J. Miles and M. Shevlin, “Applying Regression and Correlation. A guide for Students and Researchers”. SAGE Publication Ltd., 2001

25.     Amory Bisserier et al, A revisited approach to linear fuzzy regression using trapezoidal fuzzy Intervals, Information Sciences, 180, 2010, pp. 3653–3673.

26.     Hsiao-Fan Wang, Ruey-ChynTsaur, Insight of a fuzzy regression model, Fuzzy Sets and Systems 112, 2000, pp. 355-369.

27.     D.T. Redden, W.H. Woodall, Further examination of fuzzy linear regression, Fuzzy Sets and Systems, 79, 1996, pp. 203-211

28.     M. Sakawa, H. Yano, Multi objective fuzzy linear regression analysis for fuzzy input output data, Fuzzy Sets and Systems 47, 1992, pp.173 -181.

29.     G. Peters, Fuzzy linear regression with fuzzy intervals, Fuzzy Sets and Systems 63. 1994, pp. 45 -55.

30.     W. Pedrycz, D.A. Savic, Evaluation of fuzzy regression models, Fuzzy Sets and Systems, 39, 1991, pp. 51-63.

31.     K.J. Kim, H. Moskowitz, M. Koksalan, Fuzzy versus statistical linear regression, European J. Oper. Res. 92, 1996, pp.417-434

32.     Yun-Hsi O. Chang, Bilal M. Ayyub, Fuzzy regression methods- a comparative assessment, Fuzzy Sets and Systems 119, 2001, pp. 187-203.

33.     Dongale, T. D., Kulkarni, T. G., & Mudholkar, R. R. Fuzzy Modelling of Voltage Standing Wave Ratio using Fuzzy Regression Method., International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, Volume 2, Issue 6, 2012, pp. 21-26.




Sanjivani Shantaiya, Kesari Verma, Kamal Mehta

Paper Title:

Study and Analysis of Methods of Object Detection in Video

Abstract:  Object detection is generally performed in the context of higher-level applications that require the location and/or shape of the object in every frame. In the recent years various object detection methods have been proposed over by many researchers and both the apprentice and the proficient can be confused about their benefits and restrictions. In order to overcome this problem, this paper presents an analysis of some important methods and presents innovative classification based on time, memory requirements and accuracy. Results of Such an analysis can efficiently guide the researcher to select the most suitable method for a given application in a proper way. This research paper includes various approaches that have been used mostly by different researchers for object detection.

   frame difference, approximate median, mixture of Gaussian.


1.        Alok K. Watve, Dr. Shamik Sural “Object tracking in video scenes”, seminar at IIT Kharagpur, 1998
2.        Yiwei Wang and John F. Doherty, Robert E. Van Dyck “Moving Object  Tracking in Video,” 1999

3.        Dong Kwon Park, Ho Seok Yoon and Chee Sun Won, “fast object tracking in digital video”, IEEE Transactions on Consumer Electronics,     Vol. 46, No. 3, AUGUST 2000

4.        Shanik Tiwari, Deepa Kumari, Deepika Gupta, Raina,”  Enhanced Military Security Via Robot Vision Implementation Using Moving Object Detection and Classification Methods  “,IOSR Journal of Engineering (IOSRJEN), Vol. 2 Issue 1, Jan.2012, pp.162-165

5.        Robert Bodor, Bennett Jackson, Nikolaos Papanikolopoulos,” Vision-Based Human Tracking and Activity Recognition”,2000

6.        Paul Viola,Michael Jones,Daniel Snow,” Detecting Pedestrians Using Patterns of Motion and Appearance “,Proceedings of the International   Conference on Computer Vision (ICCV),October 13, 2003, Nice, France.

7.        Alper Yilmaz, Omar Javed, Mubarak Shah,” Object Tracking: A Survey “ACM Comput. Surv. 38, 4, Article 13 (Dec. 2006), 45 pages. doi = 10.1145/1177352.1177355

8.        Lan Wu,” Multiview Hockey Tracking with Trajectory Smoothing and Camera Selection “,2005

9.        Massimo Piccardi, “Background subtraction techniques: a review “,2004 IEEE International Conference on Systems, Man and Cybernetics 0-7803-8566-7/04/$20.00 @ 2004 IEEE

10.     Arnab Roy, Sanket Shinde and Kyoung-Don Kang,” An Approach for Efficient Real Time Moving Object Detection “,2009

11.     Sivabalakrishnan.M and Dr.D.Manjula,” An Efficient Foreground Detection Algorithm for Visual Surveillance System “IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.5, May 2009

12.     Shireen Y. Elhabian*, Khaled M. El-Sayed* and Sumaya H. Ahmed,” Moving Object Detection in Spatial Domain using Background Removal Techniques - State-of-Art” Recent Patents on Computer Science 2008, 1, 32-54 , 1874- 4796 /08 $100.00+.00 © 2008 Bentham Science Publishers Ltd.

13.     Young Min Kim,” Object Tracking in a Video Sequence”,2007

14.     Sourabh Khire & Jochen Teizer,” Object Detection and Tracking “,2008

15.     Prof. William H. Press,”Gaussian Mixture Models and EM Methods”, The University of Texas at Austin, CS 395T, Spring 2008, Prof. William H. Press

16.     Jiyan Pan, ,BoHu, and Jian Qiu Zhang,” Robust and Accurate Object Tracking Under Various Types of Occlusions “,IEEE Transactions On Circuits And
Systems For Video Technology, Vol. 18, No. 2, February 2008

17.     Vibha L, Chetana Hegde, P Deepa Shenoy, Venugopal K R, L M Patnaik,” Dynamic Object Detection, Tracking and Counting in Video Streams for Multimedia Mining “,IAENG International Journal of Computer Science,35:3,IJCS_35_3_16,21 august 2008

18.     S. Saravanakumar, A. Vadivel and C.G. Saneem Ahmed,” Human object tracking in video sequences “ICTACT Journal On Image And Video Processing, August 2011, Volume: 02, Issue: 01




D. David NeelsPon Kumar, Praveen David, S.Rimlon Shibi, K.Arun Kumar

Paper Title:

Security Enhancement for Mobile WiMAX Network

Abstract:    Security in wireless networks has traditionally been considered to be an issue to be addressed at the higher layers of the network.IEEE 802.16, known as WiMAX, is at the top of communication technology drive because it is gaining a great position in the next generation of wireless networks. Due to the evolution of new technologies wireless is not secured as like others networking technologies. A lot of security concerns are needed to secure a wireless network.Secure communication can only be provided after successful authentication and a robust security network association is established. By keeping in mind the importance of security, the WiMAX working groups has designed several security mechanisms to provide protection against unauthorized access and threats, but still facing a lot of challenging situations. WiMAX security architecture deals with all of the basic wireless security requirements like authentication, authorization, access control, data integrity, confidentiality and privacy.This paper examines the threats which are associated with MAC layer and physical layer of WiMAX and also proposes some enhancements to the existing model for improving the performance of the encryption algorithm and proposes some techniques in the existing model to enhance its functionality and capability.

   WiMAX, Authentication, Authorization, Access control, Data integrity, Confidentiality


1.        Dr. S.A.M Rizvi, Neeta Wadhwa, Dr. Syed ZeeshanHussain, “Performance Analysis of AES and TwoFish Encryption Schemes”, Commn Sys & Network Tech's, IEEE, 2011.
2.        C. Xenakis, N. Laoutaris, L. Merakos and I. Stavrakakis, “A generic characterization of the overheads imposed by IPsec and associated cryptographic algorithms”, Elsevier Computer Networks, 2006.

3.        Alex Biryukov, Dmitry Khovratovich, IvicaNikolic. “Distinguisher and Related-Key Attack on the Full AES-256”, University of Luxembourg, 2009.

4.        TrungNguyen,Prof. Raj Jain, “A survey of WiMAX security threats”, jain/cse571 09/ftp/wimax2/index.html, 2010.

5.        Bruce Schneier, John Kelsey, Doug Whiting, David Wagner, Chris Hall, Niels Ferguson, “Twofish: A 128-Bit Block Cipher”, Counterpane Systems, 2000.

6.        AamerNadeem, Dr M. YounusJaved, “A Performance Comparison of Data Encryption Algorithms”, IEEE 2005.

7.        D. S. Abdul. Elminaam, H. M. Abdul Kader, M. M. Hadhoud, “Performance Evaluation of Symmetric Encryption Algorithms”, Communications of the IBIMA, Vol 8, 2009.

8.        Rakesh Kumar Jha, DrUpena D Dalal, “A Journey on WiMAX and its Security Issues”, IJCSIT, Vol. 1 (4) , 2010.

9.        Mathieu Lacage, “Experimentation with ns-3”, Trilogy Summer School, 27th august 2009.

10.     “\ ns-3 Tutorial” Release ns-3.12, 2011.

11.     Elias Weingrtner, Hendrikvom Lehn and Klaus Wehrle, “A performance comparison of recent network simulators”, RWTH Aachen University, 2009.

12.     NaganandDoraswamy, Dan Harkins, “IPSec: The New Security Standard for the Internet, Intranets, and Virtual Private Networks”, Prentice Hall PTR, 2003.

13.     Víctor A. Villagra, “Security Architecture for the Internet Protocol: IPSEC”, DIT-UPM, 2002.

14.     Ibikunle F.A., Jamshedhasan, “Security Issues in Mobile WiMAX (802.16e)”, Mobile WiMAX Symposium, pp. 117 – 122, 2009.

15.     E. B. Fernandez and M. VanHilst, ‘‘An overview of WiMAX security,’’ in WiMAX Standards and Security, M. Ilyas, Ed. Boca Raton, FL: CRC Press, 2008, pp. 197–204.

16.     Andrey Bogdanov, Dmitry Khovratovich, and Christian Rechberger, “Biclique Cryptanalysis of the Full AES”, in Crypto 2011 Cryptology conference in Santa Barbara, California.

17.     RFC1321 - The MD5 Message-Digest Algorithm rfc1321.html.

18.     Rivest, R., "The MD4 Message Digest Algorithm", RFC 1320, MIT and RSA Data     Security, Inc., April 1992.




Pradip P.Patel, Sameena Zafar

Paper Title:

New E-Shape Rectangular Antenna Using the Square and Giuseppe Peano Fractals for Ultra Wide Band Application

Abstract:   In this paper, a compact design and construction of microstrip Ultra Wide Band (UWB) antenna is proposed. The proposed antenna has the capability of operating between 3.2 GHz to 10 GHz. The antenna parameter in frequency domain analysis has been investigated to show its capability as an effective radiating element. The fractal antenna is preferred due to small size, light weight and easy installation. A fractal micro strip antenna is used for Ultra Wide Band application in this paper provides a simple and efficient method for obtaining the compactness. A New E-Shape Rectangular fractal antenna is designed for Ultra Wide Band. It should be in compactness and less weight is the major point for designing an antenna. This antenna is providing better efficiency.



Keywords:   Component, New E-Shape Rectangular fractal antenna, Giuseppe peano fractal.


1.       Pramendra Tilanthe and P. C. Sharma, “Design of a single layer multiband microstrip square ring antenna” IEEE, Applied Electromagnetic Conference (AEMC), year: 2009, PP: 1– 4.
2.       Duixian Liu and Brian Gaucher, “A New multiband Antenna for WLAN/Cellular Applications”, Vehicular Technology Conference, 2004;VTC2004-Fall; IEEE 60th, Year: 2004, Vol: 1, PP: 243 – 246.

3.       C. Puente, J. Romeu, R. Pous, A. Cardama, “On the behavior of the Sierpinski multiband antenna,”IEEE Trans. Antennas Propagat., vol. 46, pp. 517-524, Apr. 1998

4.       D. H. Werner, S. Ganguly, "An overview of Fractal Antenna Engineering Research", IEEEAntennas and Propagation Magazine, vol. 45, pp.38-57, 2003.

5.       D. H. Werner and R. Mittra, Frontiers in Electromagnetics  Piscataway,NJ: IEEE Press, 2000, pp. 48–81.

6.       C. P. Baliarda, J. Romeu, and A. Cardama, “The kochmonopole: A small fractal antenna,” IEEE Trans Antennas Propagate., vol. 48, no.11, Nov. 2000.

7.       T. Mustafa Khalid, “Combined fractal dipole wire antenna,” in Proc.2nd Int.ITG Conf. Antenna, Munich,Germany, Mar. 2007, pp.76–180.




P.Vamsi Krishna, D.Yugandhar

Paper Title:

An Enhanced Railway Transport System using FPGA through GPS & GSM

Abstract:  Travel time information is a vital component of many intelligent transportation systems (ITS) applications. In recent years, the number of passengers travels in train & number of trains in India has increased tremendously. Due to the increase in number of trains the train times may be delayed and the passengers have to wait at railway stations.  A desirable strategy to deal with such issues is to provide better service (comfort, convenience and so on) the notification of location of time through GSM. One such application provides accurate information about train arrivals to passengers, leading to reduced waiting times at railway stations. This needs a real-time data collection technique, a quick and reliable data and informing the passengers regarding the same. The scope of this proposed system is to use global positioning system data collected from trains in the city in India, to show the location.  The system consists of three modules: Vehicle section Module, BASE Station section Module, User mobile section Module. Equipped with PC and GSM modem, BASE Station Module sends the initialization information containing the train number to Vehicle section Module using SMS. The microcontroller based vehicle section Module consisting mainly of a GPS receiver and GSM modem then starts transmitting its location to BASE Station Module. BASE Station Module equipped with a microcontroller unit and GSM modems interfaced to PCs is designed to keep track record of every train, processes user request  about  a particular train location out of BASE Station and updates trains location at stations. GPS Module is installed at every station  and consists of a GSM modem, memory unit and dot matrix display all interfaced to a microcontroller. This module receives trains location information coming towards that station from BASE Station module and displays the information on a dot matrix display. The performance of the proposed system is found to be promising and expected to be valuable in the development of advanced public transportation systems (APTS) in India. The work presented here is one of the first attempts at real-time short-term prediction of arrival time for ITS applications in India.

   GPS;GSM; Intelligent transportation systems;Base Station Module; Vehicle section Module; User mobile  section Module; rush statistical analysis


1.        P & D Department Punjab and Dainichi Consultants Inc., “Urbantransport policy study for five cities of PunjabProvinc,” Nov 2008.
2.        Available [online]:

3.        Available [online]:

4.        Available [online]:

5.        M. A. Mazidi, J. C. Mazidi, R. D. Mckinaly, The 8051Microcontroller and Embedded Systems, PearsonEducation, 2006.

6.        Available [online]:




Mihir Gandhi, Jwalant Baria

Paper Title:

SQL INJECTION Attacks in Web Application

Abstract:   Databases are the first target of the attackers in Web Application  Once your ID and PASSWORD are out there may be several misuse of it. These paper  discuss about Advance SQL Injection (ASQLIA)  first of all it identifies which type of attacks according to that prevention measures are suggested  .Some New features are added to it Web Crawling ,Web Services and  Advance SQL Injection (ASQLA)which  will emphases  more Security of Web Application. In short enhancing database security with the aspect of web developer is main aim of my paper.

   Cybercrime, hash function, encryption algorithm.SQL Injection, Tautology, SQLIA, Blind injection, piggy backing, PSIAW.


1.        By1Prasant Singh Yadav, 2 Dr pankajYadav, 3Dr. K.P.Yadav “A Modern Mechanism to Avoid SQL Injection Attacks in Web Applications”,IJRREST: International
Journal of Research Review in Engineering Science and Technology ,Volume-1 Issue-1, June 2012.

2.        By MayankNamdev *, FehreenHasan, GauravShrivastav “Review of SQL Injection Attack and Proposed Method for Detection and Prevention of SQLIA”Volume 2, Issue 7, July 2012.

3.        By AtefehTajpour ,Suhaimi Ibrahim, Mohammad SharifiWeb Application Security by SQL Injection DetectionTools.IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 2, No 3, March 2012




Nidhi Saxena, Vipul Saxena, Neelesh Dubey, Pragya Mishra

Paper Title:

HAND GEOMETRY: A New Method for Biometric Recognition

Abstract:   This research method demonstrates a study about personal verification and identification using hand geometry. Hand geometry used in this research consists of the lengths and widths of fingers and the width of a palm. Users can place their hands freely without the need for pegs to fix the hand placement. In this method, six dierent distance functions were tested and compared. Test data obtained were from different users. Among the six dierent distance functions, S1 gives the best results in both verification and identification.

   Biometric, Hand geometry, Recognition, Identification


1.        K. Jain, A. Ross, and S.Prabhakar,“An Introduction to Biometric Recognition,” IEEE Transactions on circuits and Systems for Video Technology, Special Issue on Image- and Video-Based Biometrics, Vol. 14, No. 1, pp. 4-20, Jan. 2004.
2.        John Chirillo, and Scott Blaul,Implementing Biometric Security, John Wiley & Sons, Apr. 2003.

3.        R. Sanchez-Reillo, C. Sanchez-Avila, and A.Gonzalez- Marcos,“Biometric Identification Through Hand Geometry Measurements,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 10, pp. 1168-1171, 2000

4.        Alexandra L.N. Wong and Pengcheng Shi,“Peg  Free Hand Geometry Recognition Using Hierarchical Geometry and Shape Matching,” IAPR Workshop on Machine Vision Applications, Nara, Japan, pp. 281- 284, Dec. 2002.

5.        Linda G. Shapiro, and George C. Stockman, Computer Vision, Prentice Hall, Jan. 2001.

6.        Sezgin, M., Sankur, B., “Survey over image thresholding techniques and quantitative performance evaluation”, Journal of Electronic Imaging, 13 (1), 2004, pp.146–156.

7.        A. K. Jain and N. Duta,“Deformable Matching of Hand Shapes for Verfication,” IEEE International Conference on Image Processing, pp. 857- 861, Oct.1999.

8.        R.Sanchez-Reillo,“Hand Geometry Pattern Recognition Through Gaussian Mixture Modeling,” 15th, International Conference on Pattern Recognition,  Vol.  2, pp. 937-940, Sep. 2000

9.        Öden, A. Erçil, and B. Büke, “Combining implicit polynomials and geometric features for hand recognition,” Pattern Recognit. Lett.,Vol. 24, 2003, pp. 2145–2152.

10.     D.P.Sidlauskas, “3D hand profile identification apparatus", US Patent No.4736203, 1988.

11.     Y. A. Kumar, and A. K.  Jain,“Personal verification using palmprint and hand geometry biometric”, in  Proc.  4th Int. Conf. Audio Video-Based Biometric Person Authentication, Guildford, U.K., Jun. 9–11, 2003, pp.668–678.

12.     C. Han, H.L. Cheng,C. L. Lin, and K C.Fan, “Personal authentication using palm printfeatures,” Pattern Recognit., Vol. 36, 2003, pp. 371–381.

13.     N. Otsu,“A Threshold Selection Method From Gray-scale Histogram,” IEEE Transaction Syst., Man, Cybern., Vol. 8, pp. 62-66, 1978.

14.     Otsu, N., “A threshold selection method from gray-level histograms”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-9 (1), 1979, pp. 62–66.




Roop Singh Takur, E.Ramkumar

Paper Title:

Embedded Systems and Robotics that Improving Security Model with 2D and 3D of Face -Recognition Access Control System Using Neural Networks

Abstract:   Recognition and Sensor monitor Control are the basic tasks performed by Artificial Neural Networks.  In this paper we present new technology for Security reasons. That is Robots and Embedded systems Using Camera inside the devices. By using sensor controls we can captures the photos and videos of the crimes and terrorists activities performing by human beings. Embedded Systems devices such as LED TV, Car, Air conditioners where we are using in Airports and Markets, Bus stations, Railway stations so on. Especially in public places. By using these method crimes will be reduced greater Extent. Some countries are using robots for security purpose and in other countries we are using embedded systems for Entertainments, announcements, air, travelling purpose. They are using embedded applications inside. I am going to show by keeping cameras inside into that we can also performs Face recognition in 2D and 3D.Here in this paper using Back propagation algorithm is used to detect the face in proper manner and right direction without any errors and transferred images into memories in micro controller chip.

  Artificial Neural Networks, Back propagation, 3D-Model-Face recognition, Robots, Embedded systems.


1.       Recognition of Human Face by Face Recognition System using 3D Bayan Ali Saad Al-Ghamdi* Sumayyah Redhwan Allaam* Yanbu University College, Saudi Arabia.
2.       Robust face recognition using posterior union model based neural networks J. Lin1,2 J. Ming2 D. Crookes2

3.       High-speed face recognition using self-adaptive radial basis function neural networks Jamuna Kanta Sing Æ Sweta Thakur Æ Dipak Kumar Basu Æ Mita Nasipuri Æ Mahantapas Kundu

4.       Face Recognition: A Literature Review A. S. Tolba, A.H. El-Baz, and A.A. El-Harby

5.       Face Recognition under Occlusions and Variant Expressions with Partial Similarity Xiaoyang Tan, Member Songcan Chen, Zhi-Hua Zhou, Senior Member, Jun Liu

6.       Film Colorization, Using Artificial Neural Networks and Laws Filters Mohammad Reza Lavvafi Department Computer, Islamic Azad University of Mahallat Mahallat, Arak, Iran S. Amirhassan Monadjemi and Payman Moallem Department of Computer  Engineering, Department of Electrical Engineering Faculty of Engineering, University of Isfahan

7.       Reconstruction and recognition of face and digit images using autoencoders Chun Chet Tan • C. Eswaran

8.       A Neural Network-Based Intelligent Image Target Identification Method And Its Performance Analysis  Xiaofang Li 1, Yanhong Sun1,Ming Tang2, Xijun Yan1, Yanping Kang1 1College of Computer and Information Engineering Hohai University Nanjing,China Dept. of Information Technology Communication University of China, Nanjing

9.       Image Compression using Multilayer Feed Forward Artificial Neural Network and DCT Fatima B. Ibrahim Information and communication engineering, Baghdad University, Baghdad, Iraq

10.     Cerebrovascular Accident Attack Classification Using Multilayer Feed Forward Artificial Neural Network with Back Propagation Error 1Olatubosun Olabode and 2Bola Titilayo Olabode 1Department of Computer Science, Federal Universityof Technology, Akure, Nigeria 2Department of Mathematical Sciences, Olabodebola Federal University of Technology, Akure, Nigeria

11.     Appearance-based face detection with artificial neural networks 1 Ioanna-Ourania Stathopoulou and George A. Tsihrintzis University of Piraeus, Department of Informatics, Piraeus 185 34, Greece




P. Ajith, B. Tejaswi, M.S.S.Sai

Paper Title:

Rule Mining Framework for Students Performance Evaluation

Abstract:    Academic Data Mining used many techniques such as Decision Trees, Neural Networks, Naïve Bayes, K- Nearest neighbor, and many others. Using these techniques many kinds of knowledge can be discovered such as association rules, classifications and clustering. The discovered knowledge can be used for prediction and analysis purposes of student patterns. Prior approaches used decision tree classifications optimized with ID3 algorithms to obtain such patterns. Among sets of items in transaction databases, Association Rules aims at discovering implicative tendencies that can be valuable information for the decision-maker which is absent in tree based classifications. So we propose a new interactive approach to prune and filter discovered rules. First, we propose to integrate user knowledge in the post processing task. Second, we propose a Rule Schema formalism extending the specifications to obtain association rules from knowledge base. Furthermore, an interactive framework is designed to assist the user throughout the analyzing task. Applying our new approach to discover the likelihood of students deviations / requiring special attention is organized and efficient providing more insight by considering more information. Compared to tree based classifications the results are better to understand and can be applied to real time use. An implementation of the proposed system validates our claim.

   Association Rules, Knowledge base, Prediction, and Rule Schema.


1.        Al-Radaideh, Q., Al-Shawakfa, E. and Al-Najjar, M. (2006) ‘Mining Student Data Using Decision Trees’, The 2006 International Arab Conference on Information Technology (ACIT'2006) – Conference Proceedings.
2.        Ayesha, S. , Mustafa, T. , Sattar, A. and Khan, I. (2010) ‘Data Mining Model for Higher Education System’, European Journal of Scientific Research, vol. 43, no. 1, pp. 24-29.

3.        Baradwaj, B. and Pal, S. (2011) ‘Mining Educational Data to Analyze Student s’ Performance’, International Journal of Advanced Computer Science and Applications, vol. 2, no. 6, pp. 63-69.

4.        Chandra, E. and Nandhini, K. (2010) ‘Knowledge Mining from Student Data’, European Journal of Scientific Research, vol. 47, no. 1, pp. 156-163.

5.        El-Halees, A. (2008) ‘Mining Students Data to Analyze Learning Behavior: A Case Study’, The 2008 international Arab Conference of Information Technology (ACIT2008) – Conference Proceedings, University of Sfax, Tunisia, Dec 15- 18.

6.        Romero, C. and Ventura, S. (2007) ‘Educational data Mining: A Survey from 1995 to 2005’, Expert Systems with Applications (33), pp. 135-146.

7.        Shannaq, B. , Rafael, Y. and Alexandro, V. (2010) ‘Student Relationship in Higher Education Using Data Mining Techniques’, Global Journal of Computer Science and Technology, vol. 10, no. 11, pp. 54-59.

8.        S. T. Hijazi, and R. S. M. M. Naqvi, “Factors affecting students performance: A Case of Private Colleges”, Bangladesh e-Journal of Sociology, Vol. 3, No. 1, 2006.

9.        U. K. Pandey, and S. Pal, “A Data mining view on class room teaching language”, (IJCSI) International Journal of Computer Science Issue, Vol. 8, Issue 2, pp. 277-282, ISSN:1694-0814, 2011.

10.     Shaeela Ayesha, Tasleem Mustafa, Ahsan Raza Sattar, M. Inayat Khan, “Data mining model for higher education system”, Europen Journal of Scientific Research, Vol.43, No.1, pp.24-29, 2010.




T. Kalai Chelvi, P.Rangarajan

Paper Title:

Criterion based Two Dimensional Protein Folding Using Extended GA

Abstract:   In the dynamite field of biological and protein research, the protein fold recognition for long pattern protein sequences is a great confrontation for many years. With that consideration, this paper contributes to the protein folding research field and presents a novel procedure for mapping appropriate protein structure to its correct 2D fold by a concrete model using swarm intelligence. Moreover, the model incorporates Extended Genetic Algorithm (EGA) with concealed Markov model (CMM) for effectively folding the protein sequences that are having long chain lengths. The protein sequences are preprocessed, classified and then, analyzed with some parameters (criterion) such as fitness, similarity and sequence gaps for optimal formation of protein structures. Fitness correlation is evaluated for the determination of bonding strength of molecules, thereby involves in efficient fold recognition task. Experimental results have shown that the proposed method is more adept in 2D protein folding and outperforms the existing algorithms.

   classification, CMM, criterion analysis, EGA, protein folding, sequence gaps


1.       Yudong Zhang, Lenan Wu, Yuankai Huo and Shuihua Wang, “Chaotic Clonal Genetic Algorithm for Protein folding model,” In the Proceedings of International Conference on Computer Application and System Modeling, 2010, Vol. 3, pp. 120-124.
2.       Yudong Zhang and Lenan Wu, “Bacterial Chemotaxis Optimization for Protein Folding Model,” In the Proceedings of Fifth International Conference on Natural Computation, 2009, Vol. 4, pp. 159-162.

3.       Md. Tamjidul Hoque, Madhu Chetty and Laurence S Dooley, “A New Guided Genetic Algorithm for 2D Hydrophobic-Hydrophilic Model to Predict Protein Folding,” In the Proceedings of Congress on Evolutionary Computation, 2005, Vol. 1, pp. 259-266.

4.       D. Bouchaffra and J. Tan, “Protein Fold Recognition using a Structural Hidden Markov Model,” In the Proceedings of 18th International Conference on Pattern Recognition, 2006, Vol. 3, pp. 186-189.

5.       Piotr Berman and Bhaskar DasGupta, “The Inverse Protein Folding Problem on 2D and 3D Lattices,” Journal on Discrete Applied Mathematics, 2007, Vol. 155, Issue. 6-7, pp. 719-732.

6.       Guang Song and Nancy M. Amato, “A Motion Planning Approach to Folding: From Paper Craft to Protein Folding,” IEEE Transactions On Robotics And Automation, 2004, Vol. 20, Issue. 1, pp. 60-71.

7.       Md Tamjidul Hoque, Madhu Chetty, Andrew Lewis, and Abdul Sattar, “Twin-Removal in Genetic Algorithms for Protein Structure Prediction using Low Resolution Model,” IEEE/ACM Transactions On Computational Biology And Bioinformatics, 2011, Vol. 8, Issue. 1, pp. 234-245.

8.       R. F. Mansour, “Applying an Evolutionary Algorithm for Protein Structure Prediction,” American Journal of Bioinformatics Research, 2011, Vol. 1, Issue. 1, pp. 18-23.

9.       Yudong Zhang, LenanWu, “Artificial Bee Colony for Two Dimensional Protein Folding,” Advances in Electrical Engineering Systems, Vol. 1, Issue. 1, pp. 19-23.

10.     Md Tamjidul Hoque, Madhu Chetty and Abdul Sattur, “Protein folding prediction in 3D FCC HP lattice model using genetic algorithm,” In the Proceedings of IEEE Conference on Evolutionary Computation, 2007, pp. 4138 – 4145.

11.     Trent Higgs, Bela Stantic, Md Tamjidul Hoque, and Abdul Sattar, “Hydrophobic-Hydrophilic Forces and their Effects on Protein Structural Similarity,” Supplementary Proceedings [of the] Third IAPR International Conference on Pattern Recognition in Bioinformatics, 2008.

12.     Md Tamjidul Hoque, Madhu Chetty, Andrew Lewis, Abdul Sattar and Vicky M Avery, “DFS generated pathways in GA crossover for protein structure prediction,” In ScienceDirect Journal of Neurocomputing, 2010, Vol. 73, Issue. 13-15, pp. 2308-2316.

13.     Heitor Silv´erio Lopes, “Evolutionary Algorithms for the Protein Folding Problem: A Review and Current Trends,” Journal on Computational Intelligence in Biomedicine and Bioinformatics, 2008, Vol. 151, pp. 297-315.

14.     Luca Bortolussi, Alessandro Dal Palu, Agostino Dovier and Federico Fogolari, “Protein Folding Simulation in CCP,” In the Proceedings of 20th International Conference on Logical Programming, 2004, Vol. 20, pp. 1-19.

15.     Benhui CHEN, Long LI and Jinglu HU, “A Novel EDAs Based Method for HP Model Protein Folding,” IEEE Congress on Evolutionary Computation, 2009, pp. 309-315.

16.     Swagatam Das, Ajith Abraham and Amit Konar, “Swarm Intelligence Algorithms in Bioinformatics,” Journal of Computational Intelligence in Bioinformatics, 2008, Vol. 94, pp. 113-147.

17.     Jan Kubelka, James Hofrichter and William A Eaton, “The protein folding ‘speed limit’,” Journal of Current Opinion in Structural Biology, 2004, Vol. 14, Issue. 1, February 2004, pp. 76-88.

18.     Arvind Ramanathan and Christopher J. Langmead, “Dynamic Invariants in Protein Folding Pathways Revealed by Tensor Analysis,” In the Proceedings of 8th Annual International Conference on Computational Systems Bioinformatics, 2009.

19.     Tamjidul Hoque, Madhu Chetty and Abdul Sattar, “Extended HP Model for Protein Structure Prediction,” Journal of Computational Biology, 2009, Vol. 16, Issue. 1, Pp. 85–103.

20.     Shawna Thomas Nancy M. Amato, “Parallel Protein Folding with STAPL,” Journal of Concurrency and Computation: Practice and Experience, 2005, Vol. 17, Issue. 14.

21.     Torsten Thalheim, Daniel Merkle, Martin Middendorf, “A Hybrid Population based ACO Algorithm for Protein Folding,” Proceedings of the International MultiConference of Engineers and Computer Scientists, 2008, Vol. 1, pp. 19-21.

22.     Julia Hockenmaier, Aravind K. Joshi and Ken A. Dill, “Routes Are Trees: The Parsing Perspective on Protein Folding,” Journal of Proteins: Structure, Function, and Bioinformatics, 2006, Vol. 66, Issue. 1, pp. 1-15.

23.     L. Lo Conte, B. Ailey, T. hubbard, S. Brenner, A. G. Murzin, and C. Chothia, “Scop: a structural classification of proteins database,” Journal of Nucleic Acids Research, 2000, Vol. 28, pp. 257-259.




Tahere Panahi, Saideh Naderi, Tahere Heidari, Elham Zeidabadi nejad , Peiman Keshavarzian

Paper Title:

New Ternary Logic Subtractor Using Carbon Nanotube Field-Effect Transistors

Abstract:   In this paper, we present a new Ternary logic Subtractor (TLS) that is implemented by CNTFET. In addition, we investigate the design of two Novel subtractors based on the proposed TLS. Ternary results are better than the Binary ones. Results show large decrements in delay time. Further, the second presented circuit with its Simulation results has demonstrated significant development in speed, area and power consumption. In the past extensive design techniques, Multiple-Valued Logic (MVL) circuits (especially ternary logic inverters) have been proposed by CMOS Technology. Here, the new TLS based on CNTFETs is presented, and wide simulation results have been done by HSPICE.

   CNTFET, Subtractor, Multiple-Valued Logic.


1.        P.C. Balla, A. Antoniou, 1984. “Low power dissipation MOS ternary logic family”. In Solid-State Circuits, IEEE Journal of, Vol. 19,Issue 5:739 – 749.
2.        J. Appenzeller, 2008. “Carbon Nanotubes for High-Performance Electronics—Progress and Prospect”. Proceedings of the IEEE, Volume 96, Issue 2: 201 - 211.

3.        A. Rahman, J. Guo, S. Datta, M.S. Lundstrom,2003. “Theory of ballistic nano transistors”. Electron Devices, IEEE Transactions on, vol. 50,no. 10: 1853 - 1864.

4.        H. Hashempour, F. Lombardi,2008. “Device Model for Ballistic CNFETs Using the First Conducting Band”. on IEEE Design &Test of Computers, Vol 25, Issue 2:178-186.

5.        Synthesis, Structure Properties and Application, M.Dresselhaus, G. Dresselhaus, Ph. Avouris,2001.“Carbon Nanotubes”. Springer-Verlag, Berlin.

6.        Philip G. Collins et al, 2001. “Study of carbon nanotube field effect transistor performance based on changes in gate parameters”.Phys. Rev. Lett. 86: 3128.

7.        K. Maehashi, H. Ozaki, Y. Ohno, K. Inoue, K. Matsumoto, S. Seki, and S. Tagawa,2007. “Formation of single quantum dot in single-walled carbon nanotube channel using focused-ion-beam technique”. Appl. Phys. Lett.,vol. 90: 023103.

8.        M. Bockrath, D. Cobden, P. McEuen, N. Chopra, A. Zettl, A. Thess, and R. Smalley,1997.“Single-electron transport in ropes of carbon nanotubes”. Science, vol. 275: 1922-1925.

9.        Y. Ohno, Y. Asai, K. Maehashi, K. Inoue, and K. Matsumoto,2009.“Roomtemperature-operating carbon nanotube single-hole transistors with significantly small gate and tunnel  capacitances”. Appl. Phys. Lett., vol.94: 053112.

10.     S. Iwasaki, M. Maeda, T. Kamimura, Y. Ohno, K. Maehashi, and K. Matsumoto,2008. “Room-temperature carbon nanotube single-electron transistors fabricated using defect-induced plasma process”. Jpn. J. Appl. Phys., vol. 47: 2036-2039.

11.     T. Rueckes, K. Kim, E. Joselevich, G.Y. Tseng, C.-L.Cheung, C.M. Lieber,2000. "Carbon nanotube based nonvolatile random access memory for molecular computing". Science, vol. 289: 94-97.

12.     R. Martel, V. Derycke, J. Appenzeller, S. Wind, Ph. Avouris,2002. "Carbon Nanotube Field-Effect Transistors and Logic Circuits", in Proc. DAC 2002:94-98, June 10-14, New Orleans, Lousiana, USA.

13.     A. Bachtold, P. Hadley, T. Nakanishi, C. Dekker,2001."Logic Circuits with Carbon Nanotube Transistors,"Science, vol. 294, no. 9 : 1317-1320.

14.     Hurst S.L,1984. “Multiple-valued logic—Its status and its future”. IEEE Trans. Comput., vol. C-33,no. 12: 1160–1179.

15.     M.Mukaidono,1986. “Regular ternary logic functions—Ternary logic functions suitable for treating ambiguity”. IEEE Trans. Comput., vol. C-35, no. 2:179–183.

16.     T. Araki, H. Tatsumi, M. Mukaidono, and F. Yamamoto,1998. “Minimization of incompletely specified regular ternary logic functions and its application to fuzzy switching functions”. in Proc. IEEE Int. Multiple-Valued Logic: 289–296.

17.     Raychowdhury A, Roy K, 2005 .“Carbon-Nanotube-Based Voltage-Mode Multiple-Valued Logic Design” IEEE Trans. Nanotechol., vol 4, no.2: 168-179.

18.     Raychowdhury A, Roy K,2007. “Carbon Nanotube Electronics: Design of High-Performance High-Performance and Low-Power Digital Circuits”. IEEE Trans. Circuits Syst. I, Reg. Papers, vol.54, no.11: 2391-2401.

19.     Y. Yasuda, Y. Tokuda, S. Taima, K. Pak, T. Nakamura, and A. Yoshida,1986.“Realization of quaternary logic circuits by n-channel MOS devices”. IEEE J. Solid-State Circuits, vol. 21, no. 1: 162–168.

20.     Timarchi S, Navi K,2009.“ Arithmetic Circuits of Redundant STU-RNS”. IEEE Trans, Instrum. Meas., vol 58, no 9: 2959- 2968, DOI: 10.1109/TIM.2016793.

21.     Sheng Lin, Yong-Bin Kim and Fabrizio Lombardi, 2011.” A Novel CNTFET-Based Ternary Logic Gate Design”. Department of Electrical and Computer Engineering IEEE Transaction on Nanotechnology, vol. 10, NO. 2.

22.     Stanford University  CNFET  Model website,

23.     Jie Deng, H.-S.P. Wong,2007.“A Compact SPICE Model for Carbon-Nanotube Field-Effect Transistors Including Non idealities and Its Application—Part I: Model of the Intrinsic Channel Region”. in Electron Devices, IEEE Journal of, Volume 54, Issue 12: 3186 – 3194.

24.     Jie Deng, H.-S.P. Wong,2007. “A Compact SPICE Model for Carbon-Nanotube Field-Effect Transistors Including Non idealities and Its Application—Part II: Full Device Model and Circuit Performance Benchmarking”. In Electron Devices, IEEE Journal of, Volume 54, Issue 12: 3195 – 3205.

25.     Y. Li, W. Kim, Y, Zhang, M, Rolandi, D. Wang,2001. “Growth of Single-Walled Carbon Nanotubes from Discrete Catalytic Nanoparticles of Various Sizes”. J. Phys. Chem., Vol. 105:11 424.

26.     Y. Ohno, S. Kishimoto, T. Mizutani, T. Okazaki, H. Shinohara,2004 .“Chirality assignment of individual single-walled carbon nanotubes in carbon nanotube field-effect transistors by micro photo current spectroscopy”. Applied Physics Letters, Vol. 84, no.8.




Om Prakash Sharma, M. K. Ghose, Krishna Bikram Shah, Benoy Kumar Thakur

Paper Title:

Recent Trends and Tools for Feature Extraction in OCR Technology

Abstract:  This paper presents a recent trends and tools used for feature extraction that helps in efficient classification of the handwritten alphabets. Numerous models of feature extraction have been defined by different researchers in their respective dissertation. It is found that the use of Euler Number in addition to zoning increases the speed and the accuracy of the classifier as it reduces the search space by dividing the character set into three groups.

   Handwritten Character Recognition, Feature Extraction, Zoning, Euler Number, Classification.


1.       J Pradeep, E Shrinivasan and S.Himavathi, “Diagonal Based Feature Extraction for Handwritten Alphabets Recognition System Using Neural Network”, International Journal of Computer Science & Information Technology (IJCSIT), vol . 3, No 1, Feb 2011.                                                                                             
2.       M. Alata — M. Al-Shabi,  “ Text Detection And Character Recognition Using Fuzzy Image Processing”, Journal of Electrical Engineering, vol. 57, no. 5, 2006, 258–267

3.       R. Plamondon and S. N. Srihari, “On-line and off- line handwritten character recognition: A comprehensive  survey,”IEEE. Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 63-84, 2000.

4.       N. Arica and F. Yarman-Vural, “An Overview of Character Recognition Focused on Off-line Handwriting”, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2001, 31(2), pp. 216 - 233.

5.       U. Bhattacharya, and B. B. Chaudhuri, “Handwritten numeral databases of Indian scripts and multistage recognition    of mixed numerals,” IEEE Transaction on Pattern analysis and machine intelligence, vol.31, No.3, pp.444-457, 2009.

6.       U. Pal, T. Wakabayashi and F. Kimura, “Handwritten numeral recognition of six popular scripts,” Ninth International conference on Document Analysis and Recognition ICDAR 07, Vol.2, pp.749-753, 2007.

7.       Devinder Singh and Baljit Singh Khehra, “Digit Recognition System Using Back Propagation Neural Network”, International Journal of Computer Science and Communication Vol. 2, No. 1, January-June 2011, pp. 197-205

8.       VENTZAS, DIMITRIOS 1, NTOGAS, NIKOLAOS , “A BINARIZATION ALGORITHM FOR HISTORICAL MANUSCRIPTS”,   12th  WSEAS  International conference on Communications, Heraklion, Greece, July 23-25, 2008.  

9.       Bindu Philip, R. D. Sudhaker Samuel and C. R. Venugopal,  Member, IACSIT, “A Novel Segmentation Technique for Printed Malayalam Characters”,  International Journal of Computer and Electrical Engineering, Vol. 2, No. 4, August, 2010 1793-8163 Printed Malayalam Characters.

10.     Anil Kumar Jain and Torfinn Taxt, “Feature extraction Methods for Character Recognition- A Survey”, Pattern  Recognition, Vol.29, No.4, pp. 641-662, 1996.

11.     S.V. Rajashekararadhya, and P.VanajaRanjan, “Efficient zone based feature extraction algorithm for handwritten numeral recognition of four popular south-Indian scripts,” Journal of Theoretical and Applied Information Technology,  JATIT vol.4, no.12, pp.1171-1181, 2008.

12.     Anita Pal & Dayashankar Singh, “ Handwritten English Character Recognition Using Neural Network”, International Journal of Computer Science & Communication”, Vol. 1, No.2, July-December 2010, pp. 141-144

13.     G. Vamvakas, B. Gatos, I. Pratikakis, N. Stamatopoulos, A. Roniotis, S.J. Perantonis, "Hybrid Off-Line OCR for Isolated Handwritten Greek Characters", The Fourth IASTED International Conference on Signal Processing, Pattern Recognition and Applications (SPPRA’07), pp. 197-202, Innsbruck, Austria, February 2007.

14.     G. Vamvakas, B. Gatos, S. Petridis and N. Stamatopoulos, ''An Efficient Feature Extraction and Dimensionality Reduction Scheme for Isolated Greek Handwritten Character Recognition'', Proceedings of the 9th International Conference on Document Analysis and Recognition, Curitiba, Brazil, 2007, pp. 1073-1077.

15.     Dinesh Acharya U, N V Subba Reddy and Krishnamurthy, “Isolated handwritten Kannada numeral recognition using structural feature and K-means cluster,” IISN-2007, pp-125 -129.

16.     M Arijit Bishnu, Bhargab B. Bhattacharya, Malay K. Kundu b C.A. Murthy, Tinku Acharya, “A pipeline architecture for computing the Euler number of a binary image”, Journal of Systems Architecture 51 (2005) 470–487.

17.     Om Prakash Sharma, M. K. Ghose, Krishna Bikram Shah, “An Improved Zone Based Hybrid Feature Extraction Model using Euler Number”, Innternationa Journal on Soft Computing and Engineering (IJSCE’12), ISSN 2231-2307, Volume -II, Issue- II, Article no-96, pp. 154-158.

18.     Bishnu Chaulagain, Brizika Bantawa Rai, Sharad Kumar Raya, “Final Report on Nepali Optical Character Recognition NepaliOCR”, Submitted On July 29, 2009.

19.     R. J. Vidmar. (1992, August). On the use of atmospheric plasmas as electromagnetic reflectors. IEEE Trans. Plasma Sci. [Online]. 21(3). pp. 876—880.  Available:




Kapil Bhagchandani, Yatendra Mohan Sharma

Paper Title:

Exploration of VANET Mobility Models with New Cluster Based Routing Protocol

Abstract:   Vehicular ad-hoc network (VANET) is high dynamic wheeled networks in which moving vehicles that can move in any direction at varying speed are behave as network nodes and router for data exchange. The frequently changes in topology and mobility pattern in VANET pose many unique networking research challenges which make crucial the designing of an new suit of efficient routing protocol for VANET. Recently, several approaches proposed by some authors in order to overcome the problem of discovering and maintaining the efficient and effective route for the data transmission over the wireless network but still there is scope of modernization. In this paper we are presenting cluster based routing approach for VANET and compare their performances with existing routing protocols. This new routing approach will have an aim of increasing the overall network throughput and minimize end to end delay. This paper, considering the mobility models like: random way point mobility model and group mobility model. Simulation studies are conducted using NS2. 

   VANET, Clustering, Routing, Mobility, AODV, DSR.


1.        Hang Dok, Huirong Fu, Ruben Echevarria, and Hesiri Weerasinghe, “Privacy Issues of Vehicular Ad-Hoc Networks”, International Journal of Future Generation Communication and Networking Vol. 3, No. 1, March , 2010
2.        Yun-Wei Lin, Yuh-Shyan Chen, And Sing-Ling Lee, “ Routing Protocols in Vehicular Ad Hoc Networks: A Survey and Future Perspectives”, JISE-2009-1

3.        Xiong Wei , Li Qing-Quan, “Performance Evaluation Of Data Disseminations For Vehicular Ad Hoc Networks In Highway Scenarios”, The International Archives of
the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B1. Beijing 2008

4.        D.Rajini Girinath, S.Selvan, “A Novel Cluster based Routing Algorithm for Hybrid Mobility Model in VANET” International Journal of Computer Applications (0975 - 8887) Volume 1 – No. 15, 2010

5.        Martin Koubek, Olivia Brickley, Susan Rea, Dirk Pesch, “ Application Driven Routing for Vehicular Ad Hoc Networks – A Necessity”, ISVCS 2008, July 22 - 24, 2008, Dublin, Ireland. ISBN 978-963-9799-27-1

6.        J. Broch, D.A. Maltz, D.B. Johnson, Y-C Hu, and J.Jetcheva, “A Performance Comparison of multi-hop wireless ad hoc network routing protocols,” in ACM MOBICOM’98, Oct. 1998.

7.        D. Johnson and D. Maltz, “Dynamic source routing in ad hoc wireless networks,” Mobile Computing, 1996.

8.        C.E. Perkins and E.M. Royer, “Ad hoc on demand distance vector (AODV) routing,” in 2nd IEEE Workshop on Mobile Computing Systems and Applications, Feb. 1999.

9.        James Bernsen, D. Mnivannan, “Unicast routing protocols for vehicular ad hoc networks: A critical comparison and classification”, in journal of Pervasive and Mobile Computing 5 (2009) 1-18

10.     Jagadeesh Kakarla, S Siva Sathya, B Govinda Laxmi, Ramesh Babu B.” A Survey on Routing Protocols and its Issues in VANET” International Journal of Computer Applications (0975 – 8887) Volume 28– No.4, August 2011

11.     Uma Nagaraj, Dr. M. U. Kharat, Poonam Dhamal “Study of Various Routing Protocols in VANET” IJCST Vol. 2, Issue 4, Oct . - Dec. 2011

12.     Rakesh Kumar, Mayank Dave “ A Comparative Study of Various Routing Protocols in VANET” IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 4, No 1, July 2011

13.     Yatendra Mohan Sharma, Dr. Saurabh Mukherjee “ A Contemporary Proportional Exploration of Numerous Routing Protocol in VANET” International Journal of Computer Applications (0975 – 8887) Volume 50– No.21, July 2012

14.     Zhan Haawei and Zhou Yun.Comparison and analysis AODV and OLSR Routing Protocols in Ad Hoc Network, 2008, IEEE.

15.     D. Johnson, B.D.A. Maltz, and Y.C.Hu, “The Dynamic Source Routing Protocol for Mobile Ad Hoc Networks (DSR)”, draft-ietf-manet-dsr-10.txt, 2004.

16.     C.E.Perkins and E. M. Royer. Ad-Hoc On Demand Distance Vector Routing, Proceedings of the 2nd IEEE Workshop on Mobile Computing Systems and Applications (WMCSA), pp. 90-100, 1999.

17.     J. Blum,"Mobility management in IVC networks," 2003.

18.     R. A. Santos, "Performance evaluation of routing protocols in vehicular ad hoc networks," 2005.

19.     Tao Song,” A Cluster-Based Directional Routing Protocol in VANET”.

20.     Ajay Kumar, Ashwani Kumar Singla “Performance   evaluation of Manet routing protocols on the basis of tcp traffic pattern” International Journal of Information Technology Convergence and Services (IJITCS) Vol.1, No.5, October 2011

21.     S H Manjula, C N Abhilash, Shaila K, K R Venugopal, L M Patnaik, "Performance of AODV Routing Protocol using group and entity Mobility Models in Wireless Sensor Networks," Proceedings of the International Multi Conference of Engineers and Computer Scientists (IMECS 2008), vol. 2, 19-21 March 2008, Hong Kong, pp. 1212-1217




Manas Kumar Parai, Banasree Das, Gautam Das

Paper Title:

An Overview of Microcontroller Unit: From Proper Selection to Specific Application

Abstract:    It is very difficult to choose a particular Microcontroller for specific application. Success or failure of any project largely depends on proper selection of the Microcontroller Unit. In this paper a brief overview of the unit is described as far as the right selection for particular application is concerned. So many manufactures are producing microcontroller in bulk amount. Comparison is based on products of few leading manufactures. System requirements, availability, performance, size, power dissipation, flexibility, Reliability, Maintainability, Environmental constraints, software support, correctness, safety, Cost, manufacturer’s history and track records are the vital factors to be considered whenever a system is to be implemented using a microcontroller which is the heart of the device. In this paper several factors are focused and follow up of those factors leads to success of the project.

   Assembler, Compiler, Debugger, IDE, In-System-Programming, Microcontroller, On-Chip ROM.


1.        M. A. Mazidi, J.G. Mazidi, R.. D. Mckinlay, “The 8051 Microcontroller and Embedded Systems: using Assembly and C”, Pearson Education, Inc., 2nd edition 1999.
2.        Takawira F., Dawoud D.S., “Selecting the Right Microcontroller Unit”,  Zimb. J. Sci. Technol., Vol. 4, no. 1, p 39-46

3.        Gonzales D.R., “Understanding the Key Architectural Features of a Microcontroller” , Available at :

4.        Gajski, Daniel D., Principles of Digital Design. Englewood Cliffs, NJ: Prentice-Hall, 1997.

5.        “Selecting the Microcontroller Unit”,  Freescale Semiconductor, Application Note Available at:

6.        Technical Guide to Microcontroller Selection, "Microcontrollers: options and trends in today’s applications", Wednesday, January 25, 2006 Available at website:

7.        Types of Microcontroller: Application note available at:

8.        Selection of a microcontroller:

9.        “Comparison of Microcontroller”, Documentation available at

10.     “List of Microcontrollers”, Software documentation – Available: “”

11.     “Software documentation – Available:”, November, 2009.




Alpa K. Oza

Paper Title:

Data Visualization for University Research Papers

Abstract:   Quite many publications are being published either in form of Theses, essays or Research papers at various levels of scientists, research scholars or Ph.D students. This is a big jargon. They are required to be segregated under various Topics. Topic modeling is a set of tool that provides a solution. Topic modeling discovers a hidden thematic structure in collection of documents. Topic models are high level statistical tools. A user must scrutinize numerical distribution to understand and explore their results. Latent Dirichlet Allocation LDA has been used to generate automatically topics of text corpora and also to subdivide the corpus words among those topics. Topic models also fall in the same line of functioning. This model (topic model) has proven remarkably powerful for information retrieval tasks. Information visualization technologies when used in conjunction with data mining and text analyses tools can be of great value for various types of tasks. For this reason various visualizations have been designed. Quite laborious work has been done and still being labored at various levels of scholars. Here our aim is to present a brief description to the topical method of visualization under data mining.

   Topic Models, Text Visualization, Visual analysis, Text, Statistical model


1.        J. Chuang, C. D. Manning, and J. Heer, “Interpertation and Trust: Designing model-driven visualizations for text analysis”, In CHI, 2012.
2.        J. Chuang, C. D. Manning, and J. Heer, “Termite: Visualization Techniques for assessing textual topic models”, In ACM, 2012.

3.        Allison J. B. Chaney and D. M. Blei. “Visualizing Topic Models”, In AAAI, 2012.

4.        Y. Chen, L. Wang, M. Dong and J. Hua. “Exemplar-based Visualization of Large Document Corpus”, 2009. . IEEE Transactions on Visualization and Computer Graphics 15(6): 1161-1168.

5.        N. Cao, J. Sun, Y-R. Lin, D. Gotz, S. Liu and H. Ou. “FacetAtlas: Multifaceted Visualization for Rich Text Corpora”, 2010. IEEE Transactions on Visualization and Computer Graphics 16(6): 1172-1181.

6.        D. M. Blei, A. Y. Ng and M. I. Jordan. “Latent Dirichlet Allocation”, J Machine Learning Research, 3:993-1022, 2003.

7.        L. Alsumait, D. Barbara, J. Gentle and C. Domeniconi. “Topic Significant ranking of LDA generative models”, In ECML, 2009.

8.        J. Chang, J. Bod-Graber, C. Wang, S. Gerrish and D. M. Blei. “Reading tes leaves: How Humans interpret topic models”, In NIPS, pages 288-296, 2009.




N. Gwangwava, S. Mhlanga and W. Goriwondo

Paper Title:

Implementation Of A Computerized Balanced Scorecard (BSC) System In A Manufacturing Organisation In Zimbabwe

Abstract:   This modern era’s high technological improvements present manufacturers and other organizations with a plethora of Management Information Systems (MISs) which makes them face challenges when choosing a corporate information system. High initial investment in setting up the information systems make it very difficult for companies to adopt new systems as they come into market before realizing a reasonable return from the previous system. In line with these concerns, a methodology for building a Balanced Scorecard module as a strategic management platform that can be integrated smoothly into already existing information system such as MRP/ERP is presented. The paper uses a case study of a manufacturing company based in Zimbabwe. Various manufacturing based metrics are reviewed with the main intent of showing how these can be tracked in a computerized platform. Sample data extracted from the production system is used to test the built system. The paper shows a methodology for software design, setting up and adopting a BSC system. The proposed approach is used to design a computerized BSC system for the case study company, which incorporates a BSC dashboard for the four main perspectives derived from various operational metrics.

   Balanced Scorecard (BSC), Metrics, MRP/ERP, Management Information System (MIS).


1.       K. J. Fernandes, V. Raja and A. Whalley, Lessons from Implementing the balances scorecard in a small and medium size manufacturing organisation, Technovation, 26, 2006, pp 623-634
2.       L. Garvin, A. Henrik and C. Ian, Balanced scorecard implementation in SMEs: reflection in literature and practice. 2GC working paper, 2006, 2GC Limited.

3.       R. S. Kaplan and D. P. Norton, Translating strategy into action: the balanced scorecard, Harvard Business School press, 2000.

4.       M. Torbacka and W. Torbacki, BSC methodology for determining strategy of manufacturing enterprises of SME sector, Journal of Achievements in
Materials and Manufacturing Engineering, Vol 23 Issue 2, 2007, pp 99-102.

5.       M. S. Seyedhosseini and A. Soloukdar, Modelling for World Class Manufacturing at Iran Khodro Company: A dynamic system approach, American Journal of Scientific Research, Issue 26 (2011), pp.48-58.- available:

6.       CIMA Technical Briefing, Developing and Promoting Strategy, 2002, CIMA Publishing.

7.       A. Grobler, An Exploratory System Dynamics model of strategic capabilities in Manufacturing, Journal of Manufacturing Technology Management, 21(6): 2010, pp 651-669.

8.       P R. Niven, Balanced Scorecard Step-By-Step: Maximizing Performance and Maintaining Results, Second Edition, 2006, Wiley.

9.       R. S. Kaplan, and D. P. Norton, Strategy Maps: Converting Intangible Assets into Tangible Outcomes. Harvard Business School Press, 2004.




Le Hoang Son, Nguyen Dinh Hoa

Paper Title:

A Novel Stochastic-Based Algorithm for Terrain Splitting Optimization Problem

Abstract:    This paper deals with the problem of displaying large Digital Elevation Model data in 3D GIS. Current approaches relate to the splitting algorithms by 2D Polygonal Vector Data such as Particle Swarm Optimization (PSO-TSA) and Genetic Algorithm (GA-TSA). We will, herein, present another method based on stochastic optimization for the considered problem. It also employs some ideas of Wife-Selection scenario and Stick Procedure. The new method allows us to quickly find the optimal saving threshold. The comparison with the state-of-the-art method will be made to verify the efficiency of the proposed method.

   Digital Elevation Model, Geographic Information Systems, Stochastic Optimization, Terrain Splitting.


1.       Anderson, H. L., “Metropolis, Monte Carlo and the MANIAC”, Los Alamos Science, vol. 14, 1986, pp. 96-108.
2.       Holland, J. H., “Adaptation in natural and artificial system”. Ann Arbor: The University of Michigan Press, 1975.

3.       Kennedy, J., Eberhart, R. C., “Particle swarm optimization”, In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, 1995, pp. 1942-1948.

4.       Son, L. H., Thong, P. H., Linh, N. D., Hoa, N. D., Cuong, T. C., “Some Results of 3D Terrain Splitting By 2D Polygonal Vector Data”, International Journal of Machine Learning and Computing, vol.1, no. 3, 2011, pp. 253-262.

5.       Son, L. H., Thong, P. H., Linh, N. D., Cuong, T. C., Hoa, N. D., “Developing JSG Framework and Applications in COMGIS Project”, International Journal of Computer Information Systems and Industrial Management Applications, vol. 3, 2011, pp. 108-118.

6.       Thien, N. D., Son. L. H., Lanzi, P. L., Thong, P. H., ”Heuristic Optimization Algorithms For Terrain Splitting and Mapping Problem”, International Journal of Engineering and Technology, vol. 3, no. 4, 2011, pp. 376-383.




T.Revathi,  P.Sumathi

Paper Title:

Distributed Data Mining based on Random Projection with Optimal Communication

Abstract:   Distributed data mining discovers hidden useful information from data sources distributed among several sites. Privacy of participating sites becomes great concern and sensitive information pertaining to the individual sites needs high protection when data mining occurs among several sites. Different approaches for mining data securely in a distributed environment have been proposed but in the existing approaches, collusion among the participating sites may reveal sensitive information about other participating sites and they suffer from the intended purposes of maintaining privacy of the individual participating sites, reducing computational complexity and minimizing communication overhead. The proposed method finds global frequent itemsets in a distributed environment with minimal communication among sites and ensures higher degree of privacy with randomized site selection. The experimental analysis shows that proposed method generates global frequent itemsets among colluded sites without affecting mining performance and confirms optimal communication among sites.

   Distributed data mining, privacy, secure multiparty computation, frequent itemsets.


1.        J.P. Bigus.(1996),"Data Mining with Neural Networks", New York: McGraw- Hill,
2.        A survey of Knowledge Discovery and Data Mining process models The    Review, Vol. 21:1-   2006, Cambridge University Press   Printed in the United Kingdom.

3.        Sellappan, P., Chua, S.L.: “Model-based Healthcare Decision Support System”, Proc. Of Int. Conf. on Information Technology in Asia CITA’05, 45-50, Kuching, Sarawak, Malaysia, 2005

4.        Atallah, M., Bertino, E., Elmagarmid, A., Ibrahim, M. & Verykios, V.S. (1999). Disclosure limitation of sensitive rules. In Proceedings of the IEEE Knowledge and Data Exchange Workshop (KDEX'99). IEEE Computer Society, 45-52.

5.        Burnett.A, Winters.K, and Dowling.T, (2002). A Java implementation of an elliptic curve Cryptosystem-Java programming and practices. In Proceedings of the inaugural conference on the Principles and Practice of programming.

6.        Cheung, D., Ng, V., Fu, A. & Fu, Y.(1996). Efficient Mining of Association Rules in Distributed Databases. IEEE Transactions on Knowledge and Data Engineering. 8(6), 911-922.

7.        Clifton, C. (2001) Secure Multiparty Computation Problems and Their Applications: A Review and Open Problems. In Proceedings of the Workshop on New Security Paradigms, Cloudcroft, New Mexico.

8.        Clifton, C., Kantarcioglu, M. & Vaidya, J.(2004). Defining privacy for data mining. Book Chapter Data Mining,   Next generation challenges and future directions.

9.        Mehmed, K.: “Data mining: Concepts, Models, Methods and Algorithms”, New Jersey: John Wiley, 2003.

10.     Mohd, H., Mohamed, S. H. S.: “Acceptance Model of Electronic Medical Record”, Journal of Advancing    Information and Management Studies. 2(1), 75-92, 2005m]. Volume(issue), paging if given.




Amitha P L, Geethu Joy, Geethu S Pillai, Tharakrishnan L, Soman K P

Paper Title:

Innovative Use of What if Analysis for Visualization

Abstract:    The novelty of this paper is aimed at simplifying the tasks in Spreadsheet using a feature called what if analysis, which fires up the performance of the task we are working on. The use of spreadsheet helped us save time, perform many operations such as sorting, searching, classifying and comparing easily and to solve a problem without any programming knowledge. What if or sensitivity analysis is one of the most powerful and valuable concepts in Spreadsheet, the potential of which is not well exploited. The advantage of what if analysis is that if we show one computation in excel,  the remaining part of the process will be computed by its own for a given range of variable values. It can be used to solve many problems other than the conventional managerial applications. Two Dimensional function evaluation and graphing, creating Pascal triangles, enumerating Pythagorean triplets in a given range, error function evaluation are some of the real applications. These types of applications can be exploited to enhance computational thinking of children in high schools. Computational thinking brings about a neoteric approach in problem-solving and model simulation.

   Computational Thinking, Excel Computation, What if Analysis.


1.       Aravind H, C Rajgopal, Soman K P, “A Simple Approach to Clustering in Excel”, International Journal of Computer Applications, Volume 11- No.7, December 2010.
2.       Soman K P, Manu Unni V G, Praveen Krishnan, Sowmya V,” Enhancing Computational Thinking with Spreadsheet and Fractal Geometry: Part 1”, International Journal of Computer Applications 55(14):1-8, October 2012.

3.       K P Soman, Manu Unni V G, Praveen Krishnan, Sowmya V,” Enhancing Computational Thinking with Spreadsheet and Fractal Geometry: Part 2- Root-finding using Newton Method and Creation of Newton Fractals", International Journal of Computer Applications, 55(14):9- 15, October 2012.

4.       K P Soman, Manu Unni V G, Praveen Krishnan, Sowmya V,” Enhancing Computational Thinking with Spreadsheet and Fractal Geometry: Part 3- Mandelbrot and Julia Set ", International Journal of Computer Applications, 55(14):16- 23, October 2012.

5.       K P Soman, Manu Unni V G, Praveen Krishnan, Sowmya V,” Enhancing Computational Thinking with Spreadsheet and Fractal Geometry: Part 4- Plant Growth modeling and Space Filling Curves ", International Journal of Computer Applications, 55(14):24- 29, October 2012.

6.       Anand R, Pinchu Prabha, Sikha O K, Suchithra M, Sukanya P, Sowmya V, Soman K P ,” Visualization Of OFDM Using Microsoft Excel Spreadsheet In Linear Algebra Perspective”, International Conference on Advances Computing and Communication(ICACC),pg-58-64,Aug 2012.

7.       J M Wing,” Computational Thinking” , CACM viewpoint, vol. 49  no. 3, March 2006, pp. 33-35.

8.       Ozar, Mirac,” Spreadsheets in Education”, Hacettepe Journal of Education, No.13, pp.81-83.

9.       S A OKE,”Spreadsheet Applications in Engineering Education: A Review”, International. Journal of Engineering Education. Vol. 20, No. 6, pp. 893-901, 2004.

10.     Paul Cornell, “Synthetic Beginning Excel What-If Data Analysis Tools: Getting Started with Goal Seek, Data Tables, Scenarios, and Solver” Apress; 1 edition, December 13, 2005.

11.     K P Soman, Sachin Kumar S, Soumya V, Shajeesh K U,” Computational Thinking with Spreadsheet: Convolution, High-Precision Computing and Filtering of Signals and Images”, International Journal of Computer Applications 60(19):1-7, December 2012.

12.     Committee for the Workshops on Computational Thinking; National Research Council, “Report of a Workshop Pedagogical Aspects of Computational Thinking”, Washington DC, 2011.

13.     Pinchu Prabha, Sikha O K, Suchithra M, Sukanya P, Sowmya V, Soman K P” Computation Of Continuous Wavelet Transform Using Microsoft Excel Spreadsheet ", International Conference on Advances in Computing and Communication(ICACC),pg-73-77, Aug 2012.

14.     Indukala P K, Lakshmi K, Sowmya V, Soman K P,” Implementation Of L1 Magic And One Bit Compressed Sensing Based On Linear Programming Using Excel", International Conference on Advances in Computing and Communication(ICACC),pg-69-72,Aug 2012.




Hari Prasada Rao Pydi, Balamurugan Adhithan, A.Syed Bava Bakrudeen

Paper Title:

Microstructure Exploration of the Aluminum-Tungsten Carbide Composite with different Manufacturing circumstances

Abstract:   In the last decade, as demand for high quality materials are increased, the development of lightweight aluminum (Al) also increased especially in aerospace and automotive industries. It has been well known that Al based metal matrix composites (MMCs) offers a very low thermal expansion coefficient, high specific strengths, wear and heat resistance as compared to conventional Al alloys. In order to combine all these properties, MMCs have become a very attractive method for various industrial applications. The interest in Tungsten Carbide (WC) as reinforcements for aluminum (Al) has been growing considerably. Efforts have been largely focused on investigating their contribution to the enhancement of the mechanical performance of the composites. The uniform dispersion of Tungsten Carbide in the Al matrix has been identified as being critical to the pursuit of enhanced properties. In this present research paper emphasis, the effect of Tungsten Carbide content on the Physical properties of the composites like SEM, XRD was investigated.  The improvement of physical properties for composites of Al/WC has been compared with pure aluminum.

   MMC, SEM, Tungsten Carbide, XR.D.


1.        V.K.”Cemented carbide cutting tools”,Advances in powder Technology. Ed.D.Y.Chin, ASM, 253-287, 1981
2.        V. H. Hartmann, F. Ebert, O. Bretschneider, Z. Anorg, chem., (1931), 198, 116.

3.        V. Constantin, L. Scheed and J. Masounava, Journal of Tribology, Transactions of the ASME, 788/ Vol. 121, October 1999.

4.        A.Wang & H.J.Rack, “Transition wear behaviour of SiC-particulate and SiCwhisker reinforced 7091 Al metal matrix composites”, Journal of Material Science and Engineering A, Vol. 147, 1991, pp 211-224.

5.        L.Cao, Y.Wang, C.K.Yao: “The wear properties of an SiC whisker reinforced aluminum composite”. Journal of wear, Vol. 140, Issue-2, Nov 1990, pp. 273-277.

6.        D. Siva Prasad1 ,Dr. A. Rama Krishna2 ,”Fabrication and Characterization of A356.2-Rice Husk Ash Composite using Stir casting technique” D. Siva Prasad et al. / International Journal of Engineering Science and Technology Vol. 2(12), 2010, pp7603-7608




Fayçal Messaoudi, Mimoun Moussaoui, Ahmed Bouchboua, Aziz Derouich

Paper Title:

Modeling Approach to a Learner Based on Ontology

Abstract:    A new generation of advanced systems of learning  has to integrate new educational approach giving to the learner an active role to learn and build his knowledge and so allowing to integrate a vision more centered on the learner . The systems adaptive hypermedia in the field of distance education (e-learning) propose solutions of these problems. The objective of these systems is to adapt the presentation of the knowledge and to help the learner to navigate through the graph consisted by all the pages and the links.

   Modeling teaching, ontology, model of the domain, CEHL.


1.       P. Dillenbourg et A. Self. “A framework for learner  Modelling. Interactive Learning Environments, ” vol. 2, nº 2 pp. 111-137, 1992.
2.       P. TCHOUNIKINE. “Quelques éléments sur la conception et l'ingénierie des EIAH,” pp 233-245, 2002.

3.       K. Höök, J. Karlgren, A. Wærn, “A Glass Box Approach to Adaptive Hypermedia, User Modeling and User-Adapted Interaction, ” pp. 175-184, 1996.

4.       G. Fischer, “User Modeling in Human-Computer Interaction, UserModeling and User-Adapted Interaction, ” Vol. 11, pp. 65-86, 2001.

5.       C. Piombo, “Modélisation probabiliste du style d’apprentissage et application à l’adaptation de contenus pédagogiques indexés par une ontologie,” Thèse de Doctorat, Institut National Polytechnique de Toulouse, Université de Toulouse, France, 2007.

6.       A. Derouich, M. Karim, E. K. Hachem, “Automatic treatment of the learner’s productions,” International Journal of Computer Science and Network Security, Vol. 9, No. 12, pp. 96-100, 2009.

7.       M. Trella, R. Conejo, D. Bueno, E. Guzmn, “An autonomous component architecture to develop WWW-ITS, Proceedings of the Workshops on Adaptive Systems for Web-Based Education, ” Malaga, 2002.

8.       M. Laroussi, “Conception et réalisation d’un système didactique  hypermédia adaptatif :” CAMELEON, Thèse de Doctorat, Université Manouba, Tunisie, 2001.

9.       G. Webb, M. Pazzani, D. Billsus, “Machine Learning for User Modeling, User Modeling and User-Adapted Interaction,” Vol.11, pp. 19-29, 2001.

10.     R. Sison and M. Shimura, “Student modeling and machine learning,” IJAIED International Journal of Artificial Intelligence in Education, pp. 128 -158, 1998.

11.     A. Behaz & all, “Approche de modélisation d’un apprenant à base d’ontologie pour un hypermédia adaptatif pédagogique,” Vol.12, pp.3-4.

12.     C. R. Todd, Myers-Briggs Type Indicator. The Skeptic's Dictionary. (Consulté Octobre 2008).

13.     V. Psyché, O. Mendes, J.Bourdeau, “Apport de l’ingénierie ontologique aux environnements de formation à distance,” Vol 10, 2003.

14.     PROTÉGÉ was developed by Stanford Center for Biomedical Informatics Research. Protege 3.4.8 released!. 12 Jan 2012




Jyoti Mahajan, Simmi Dutta

Paper Title:

COREAN: A proposed Model for Predicting Effort Estimation having Reuse

Abstract:    The estimation accuracy has been focused in various formal estimation models in recent research initiatives. The formal estimation models were developed to measure lines of code and function points in the software projects but most of them failed to improve accuracy in estimation. The concept of reusability in software development in estimating effort using artificial neural network is focused in this paper. Incorporation of reusability metrics in COCOMO II may yield better results. In COCOMO II it is very difficult to find the values of size parameters. A new model called COREAN has been proposed  in this paper for better effort estimation accuracy and reliability. The proposed model has focused on two components of COCOMO II. First, instead of using RUSE cost driver, three new reuse cost drivers are introduced. Second, In order to reduce the project cost, three cost drivers such as PEXE, AEXE, LTEX are combined into single cost driver Personnel Experience (PLEX). Finally, this proposed model accuracy is more improved with the help of Enhanced RPROP algorithm and simulated annealing optimization technique.

   Effort Estimation, Software Reuse, COCOMO II, Artificial Neural Network, Simulated Annealing.


1.       K. Molokken-Ostvold and M. Jorgensen, “A Review of Surveys on Software Effort Estimation,” Proc. 2003 ACM-IEEE Intternational Symposium on Empirical Software Eng, pp. 220-230, 2003.
2.       Saleem Basha and Dhavachelvan P, “Analysis of Empirical Software Effort Estimation Models”, International Journal of Computer Science and Information Security, Vol. 7, No. 3, 2010

3.       Chao-Jung Hsu, Nancy Urbina Rodas, Chin-Yu Huang and Kuan-Li Peng “A Study of Improving the Accuracy of Software Effort Estimation Using Linearly Weighted Combinations”, 34th Annual IEEE Computer Software & Application Conference Workshops, 2010

4.       B. Boehm, B. Clark, E. Horowitz, C. Westland, R. Madachy, and R. Selby, “Cost Models for Future Software Life Cycle Processes: COCOMO 2. 0,” Annals of Software Engineering: Special Volume on Software Process and Product Measurement, Science Publishers, vol. 1, pp. 45-60, 1995.

5.       Boehm, B. COCOMO II Model Definition Manual. Center for Software Engineering, University of Southern California. 1997.

6.       Balda, D,. M. and D. A. Gustafson, "Cost Estimation Models for the Reuse and Prototype Software Development Life-Cycles", ACM Sigsoft Software Engineering Notes, 15 (3), pp. 4250, 1990.

7.       A.Windsor Brown url:, 1999

8.       Barry Boehm, A.Winsor Brown,, 1999

9.       Sunita Chulani, Barry Boehm "Modeling Software Defect Introduction Removal: COQUALMO (COnstructive QUALity MOdel)", Technical Report USC-CSE-99-510, 1998

10.     C.Abst, B.Boehm, E.Clark. “COCOTS: A COTS Software Integration Lifecycle Cost Model - Model Overview and Preliminary Data Collection Findings", Technical report USC-CSE-2000-501, USC Center for Software Engineering, 2000.

11.     Heiat A, “Comparison of artificial neural network and regression models for estimating software development effort,” Journal of Information and Software Technology, Volume 44, Issue 15, Pages 911-922, 2002.

12.     Wittig, G., Finnie, G., “Estimating software development effort with connectionist models”, Information and Software Technology, 39 (7), 469–476, 1997

13.     Karunanithi, N., D. Whitely, Y. K. Malaiya, “Using neural networks in reliability prediction”, IEEE Software, pp. 53-59, 1992.

14.     Tadayon, N., “Neural network approach for software cost estimation,” International Conference on Information Technology: Coding and Computing (ITCC 2005), Volume: 2, on page(s): 815- 818, 2005.

15.     Dawson, C.W., “A neural network approach to software projects effort estimation,” Transaction: Information and Communication Technologies, Volume 16, pages 9, 1996.

16.     Lionel C. Briand, Sandro Morasca, and Victor R. Basili, “An Operational Process for Goal-Driven Definition of Measures”, IEEE Transactions On Software Engineering, Vol. 28, No. 12, 2002.

17.     V. Basili, "Software Modeling and Measurement: The Goal/Question/Metric Paradigm" University of Maryland, Department of Computer Science, Tech. Rep. CS-TR-2956, 1992.

18.     L. Briand, K. El Emam, S. Morasca, “Theoretical and Empirical Validation of Software Product Measures”, Technical Report ISERN-95-03, Fraunhofer Institute for Experimental Software Engineering, Germany, 1995.

19.     Murat Ayyıldız, Oya Kalıpsız, and Sırma Yavuz, “A Metric-Set and Model Suggestion for Better Software Project Cost Estimation”, World Academy of Science, Engineering and Technology, 2006.

20.     G. Poels, G. Dedene, DISTANCE: A Framework for Software Measure Construction, Reserch Report 9937, Dep. of Applied Economics, Katholieke Universiteit Leuven, 1999.

21.     Jyoti Mahajan, Devanand, “Reusability in Effort Estimation model based on Artificial Neural Network for Predicting Effort in Software Development”, Research
Cell : An International Journal of Engineering Sciences, vol. 4, 2011.

22.     Mitat Uysal, “Estimation of the Effort Component of the Software Projects Using Simulated Annealing Algorithm”, World Academy of Science, Engineering and Technology, 2008




Garima Bhardwaj, Vasudha Vashishtha

Paper Title:

Energy – Efficient MAC Protocol (EE-MAC Protocol)

Abstract:   Because of the difficulty in recharging or replacing the batteries of each node in a Wireless Sensor Network, the energy efficiency of the system is a major issue in the area of network design. Other critical parameters such as delay, adaptability to traffic conditions, scalability, system fairness, and throughput and bandwidth utilization are mostly dealt as secondary objectives. Some sensor network applications adopt IEEE 802.11-like MAC protocol, which is however, not a good solution for sensor network applications because it suffers from energy inefficiency problem. The adaptive Sensor-MAC (S-MAC) proposes enhanced schemes such as periodic sleep and overhearing avoidance to provide a better choice for different sensor network applications. In this research paper we propose an energy efficient MAC (EE-MAC) protocol, which is based on adaptive S-MAC with added transmission power control techniques. The main contribution of our work is to introduce a controlled power transmission of RTS, CTS, DATA and ACK frames according to the adaptive S-MAC protocol. We simulate our proposed protocol i.e., EE-MAC protocol using ns-2.33 simulator for two parameters energy consumption and throughput, for determining the behavior of the proposed protocol. The simulation results show that our proposed EE-MAC protocol performs better than adaptive S-MAC protocol in terms of energy consumption and throughput.

   IEEE802.11, S-Mac Protocol, Transmission Power Control, Wireless Sensor Network


1.       J. Yick, B. Mukherjee, and D. Ghosal, “Wireless sensor network survey,” Copyright ©   2008 Elsevier, pp. 2292-2330.
2.       C. Shiva Ram Murthy, and B. S. Manoj, “Ad-hoc Wireless Networks:       Architectures and Protocols,” Copyright © 2004 by Pearson education, Inc.

3.       S. S. Kulkarni, “TDMA Services for Sensor Networks,” Proc. 24th Int’l. Conf.      Distrib. Comp. Sys. Wksps., Mar. 2004, pp. 604-09.

4.       Wei Ye, John Heidmann, “Medium Access Control in Wireless Sensor Networks” USC/ISI TECHNICAL REPORT ISI-TR-580, OCTOBER 2003.

5.       I. Demirkol, C. Ersoy, F. Alagoz. “MAC protocols for Wireless Sensor Networks:       A Survey” IEEE communication magazine, pages 115-121, April 2006.
6.       Koen Langendoen, “Medium access control in wireless sensor networks,” Vol. II: Practice and Standards edited by H. Wu and Y. Pan, published by Nova Science     Publishers © 2007.

7.       P. Naik, K. Sivalingam, “A Survey of MAC Protocols for Wireless Sensor       Networks,” in: C. Raghavendra, K. Sivalingam, T. Znati (Eds.), Wireless Sensor Networks, Kluwer Academic Publishers, 2004.

8.       W. Ye, J. Heidemann, and D. Estrin, “An energy efficient MAC protocol for Wireless Sensor Networks,” in Proceedings of the 21st International Annual Joint Conference of the IEEE Computer and Communication Societies (INFOCOM 2002), New York, USA, June 2002, pp. 1567-1576.

9.       IEEE 802.11, Wireless LAN media access controls (MAC) and physical layer (PHY) specifications, 1999.

10.     W. Ye, J. Heidemann, and D. Estrin, “Medium Access Control with Coordinated Adaptive Sleeping for Wireless Sensor Networks,” in IEEE/ACM Transactions on Networking, June 2004, pp. 493-506.

11.     Jian Xiao, and Fengqi Yu, “An Efficient Transmission Power Control Algorithm in Wireless Sensor Networks,” in Proceedings of, 2007, pp. 2767-2770.

12.     H. W. Tseng, S. H. Yang, P. Y. Chuang, E. H. Kuang Wu and G. H. Chen, “An Energy Consumption Analytic Model for A Wireless Sensor MAC Protocol”, in Proceedings of the IEEE Vehicular Technology Conference (VTC’2004), September 2004, pp. 4533-4537.

13.     T. Ezzedine, M. Miladi, and R. Bouallegue, “An Energy-Latency-Efficient MAC Protocol for Wireless Sensor Networks,” in Proceedings of International Journal of Electronics, Communications and Computer Engineering, January 2009, pp. 30-35.

14.     J. Gomez and A.T. Campbell, M. Naghshineh, and C. Bisdikian, “Conserving Transmission Power in Wireless Ad Hoc Networks,” in ICNP’01, November 2001.

15.     S. Agarwal, S. Krishnamurthy, R. H. Katz, and S. K. Dao, “Distributed Power Control in Ad-hoc Wireless Networks,” in PIMRC, 2001.




Priyanka Sharma, Urvashi Mutreja

Paper Title:

Analysis of Satellite Images using Artificial Neural Network

Abstract:   Data from Remote Sensing Satellites are used for various applications of resources survey and management. For collection and analysis of remotely sensed data, Artificial Neural Network(ANN) have become a popular tool. As we know Remotely Sensed images are major sources of data & information which is used in various fields such as Environmental impact analysis, Forest survey, rural to urban change detection(Urban Planning), Mineral Prospecting etc. Although many neural network based Methods has been developed for image classification but some issues still remain to be fixed. Digital interpretation (quantitative analysis) is one of the main approaches for extracting information from remotely sensed image. ‘Classification’ is one of the most common digital technique used as information extraction method from remotely sensed data. In pattern recognition two techniques are used which are supervised classification & unsupervised classification. Supervised Classification is done using Supervised Learning technique according to which the networks know the target and changes accordingly to get the required output corresponding to the input sample data. Already a lot of work has been done in the field of supervised classification. This paper examines remotely sensed data analysis with neural network and unsupervised classification method of ANN for classification of satellite images.

   ANN, LVQ, Satellite Images, SOFM, SOM.


1.        “The ART of adaptive pattern recognizing neural Network,”IEEE Comput. Mag., pp. 77-88, Mar. 1988.
2.        AB. Yegnarayana, “Artificial Neural Networks”, Prentice Hall of India Pvt. Ltd, New Delhi, 1999.

3.        Rafael C. Gonzalez, Richard E.Woods.  Digital Image Processing, Pearson Education, Inc Second Edition.

4.        Hyo S. Chae, Seong J. Kim, Jeong A Rye , “ A Classification of MultiSpectral Landsat TM Data using  Principal Component Analysis and ANN”, 1997, IEEE.

5.        Neil R. Euliano and Jose C. Principe. Self- Spatio Temporal Self Organising Feature Map. Computational NeuroEngineering Laboratory, Department of Electrical Engineering, University of Florida, Gainesville, FL 32611

6.        Wikipedia ‘Article on Satellites’.

7.        Archana Mangal, Pratistha Mathur and Rekha Govil. Trend Analysis in satellite Imagery Using SOFM. Apaji Institute of Mathematics & Computer Technology, Banasthali Vidhyapith, Rajasthan, India.




Saurabh Kumar Gaur, S.K.Tyagi, Pushpender Singh

Paper Title:

“VANET” System for Vehicular Security Applications

Abstract:  Today the vehicular security and passenger safety is an alarming issue in the field of automobile industry. The technocrats of the companies are very much varied about this issue. Ad hoc network (VANETs) is becoming the mostsuitable solution for this purpose. It provides vehicle to vehicle connectivity. A vehicular Ad hoc network (VANETs) can be used as an alert system. By this we get the alert about the traffic jam. It helps to create balance in traffic load to reduce travelling time. This system is also useful to broadcast emergency signal to the driver of the vehicle behind the accident. It also helps to send message to ambulance and traffic police in the case of traffic emergency.  In this paper, we take the position that VANETs would indeed turn out to be the networking platform that would support the future vehicular applications. We analyze the critical factors in deciding the networking framework over which the future vehicular Applications would be deployed. A reactive research effort is needed for making VANETs a reality in the near future.

   LPGs, VANETs, VANETs architecture, V2V.


1.        Z. Li, Z. Wang, and C. Chigan, “Security of Vehicular Ad Hoc Networks in Intelligent Transportation Systems,” in Wireless Technologies for Intelligent Transportation Systems, Nova Science Publishers, 2009 (in press)
2.        Vehicular ad-hoc Network: Wikipedia, the free encyclopedia.

3.        “State of the art and research challenges for VANETs” Jakub Jakubiak, Yevgeni Koucheryavy

4.        A. Stampoulis and Z. Chai, “A survey of security in vehicular    networks.

5.        “Secure VEhicular COMmunications,”

6.        “Enhancing location privacy in wireless lan through disposable     interface identifiers- a quantitative analysis,” pp. 315–325, 2005.

7.        K. Lee, S.-H. Lee, R. Cheung, U. Lee, and M. Gerla, “First experience with cartorrent in a real vehicular ad hoc network testbed,” in 2007 Mobile Networking for Vehicular Environments, 2007, pp. 109–114.

8.        Noncoperative Content Distribution In Mobile Infostations Networks- wing Ho, Yuen Roy D.Yates Siun-chuon Mau

9.        Comparative Study of Data Dissemination Models of VANETs” Praveen Shankar, LIftode, Mobiquitous 2006.

10.     M. Raya, P. Papadimitratos, and J.-P. Hubaux, “Securing vehicular communications.”


12.     Rosslin Robles and Maricel O. Balitanas,” A Review on Strategies to Optimize and Enhance the performance of WLAN and Wireless Networks” ,


14.     ArunkumarTangavelu “Location Identification and Vehicle Tracking using VANET (VETRAC)” IEEE-ICSN 2007, Feb 22-24, 2007pp 112-116

15.     Tamer Nadeem, Pravin Shankar, Liviu Iftode “A Comparative Study of Data Dissemination Models for VANETs”




Mukta, Balwinder Singh Surjan

Paper Title:

Grid Stability of Interconnected System with Fuzzy-logic controller & HVDC in Deregulated Environment

Abstract:   This paper investigates the effects of integral controller, fuzzy controller,HVDC on an interconnected two area power system in a deregulated environment. The system is simulated using Matlab-Simulink along with controllers. The frequency deviation responses are studied using Matlab-simulink. Robustness of the controller is thus checked and we get a new proposed system with better results i.e. lesser deviation for reliable and quality power supply.

   Frequency Control, Interconnected Power System, Integral Controller, Fuzzy logic Controller,HVDC, Deregulated Environment, Wind Turbine Generator.


1.       Yogendra Arya, Narender Kumar, Hitesh Dutt Mathur, “Automatic Generation Control in Multi Area Interconnected Power System by using HVDC Links”  IJPEDS( International Journal of Power Electronics and Drive System) Vol.2,No.1, March 2012, pp.67~75, ISSN :2088-8694.
2.       I.J. Nagrath and D.P. Kothari, “Modern Power System Analysis” 3rd Edition, Sixteenth reprint 2009, Tata Mc-Graw Hill publication.


4.       Glenn V. Hicks, “An Investigation of Automatic Generation Control for an Isolated Power System” Master Thesis of Mernorial University of Newfoundland, September’97.

5.       Manuel A. Matos, “The Fuzzy Power Flow Revisited”, IEEE Transactions on Power Systems, Vol.23, No.1, February 2008.

6.       Timothy J. Ross, “Fuzzy Logic with Engineering Applications” Second Edition, Wiley Publication.

7.       “Basic Process Controllers” by


9.       Jorris Soens, Johan Driesen, Ronnie Belman, “Equilvalent Transfer Function for a Variable Speed Wind Turbine in a Power System Dynamic Simulations” Paper by Electrotechnical Department ESAT-ELECTA, Heverlee.


11.     A.R.Abhyanker,S.A.Khaparde, “Introduction to deregulation in power industry”,Report by Indian Institute of Technology, Mumbai

12. For MATLAB help with simulink.

13.     Peter Meisen, “ Cross-Border Interconnections on Every Continent” Report by Global Energy Network Institute (GENI), June 2010. 

14.     Ngamroo, “A Stabilization of frequency oscillation in a parallel ac-dc interconnected power system via an HVDC Link,” Science Asia, vol. 28, pp. 173-180, 2002

15.     R. Thottungal, P. Anbalagan, T. Mohanaprakash, A. Sureshkumar and G.V. Prabhu, “Frequency stabilisation in multi area system using HVDC link,” in Proc. of IEEE International Conference on Industrial Technology, 15-17, December, 2006. pp. 590-595.

16.     K.R.Padiyar, “HVDC Power Transmission Systems”, New Age International(P) Limited Publishers, Second Edition,2012.




Sonal Dubey, R.K. Pandey, S.S. Gautam

Paper Title:

Literature Review on Fuzzy Expert System in Agriculture

Abstract:    Agriculture constitutes the backbone of the Indian economy. Farmer need advance expert knowledge to take decision during land preparation, sowing, fertilizer management, irrigation management, integrated pest management, storage etc. for higher crop production. Expert systems are being used in agriculture which assists the farmers to make right decisions. Expert systems for pest management and crop protection constitute a very significant class of agricultural expert systems. Knowledge of entomology, plant pathology, nematology, weeds and nutritional disorders and various number of techniques used  are included in integrated pest management and crop protection.Uncertainty is confronted during time of sowing, weed management, diagnosis of insect, disease and nutritional disorders, storage, marketing of the produce etc. This uncertainty is compounded by the fact that many agricultural decision- making activities are often vague or based on intuition. Fuzzy logic is used to handle imprecision, vagueness and insufficient knowledge. Fuzzy logic lets expert systems perform optimally with uncertain or ambiguous data and knowledge. Fuzzy expert systems use fuzzy logic instead of classical Boolean logic. They are oriented towards numerical processing The paper presents a review of various fuzzy expert systems in agriculture over the last two decades.

   Agriculture, Integrated Pest management, fuzzy expert system, rules.


1.       Chen, C.L. and Chen, W.C. 1994. “Fuzzy Controller Design by Using Neural Network Techniques”. IEEE Transactions on Fuzzy Systems. 2(3):235-244.
2.       Fadzilah Siraj & Nureize Arbaiy(2006), “ Integrated Pest Management System Using Fuzzy Expert System”, www.

3.       G. Delgado, V. Aranda, J. Calero, M. Sánchez-Marañón, J. M. Serrano, D. Sánchez and M. A. Vila “Building a fuzzy logic information network and a decision-support system for olive cultivation in Andalusia” Spanish Journal of Agricultural Research 2008 6(2), 252-263.

4.       Harvinder S. Saini, Raj Kamal and A. N. Sharma (2002), “Web Based Fuzzy Expert System For Integrated Pest Management in Soyabean”, International journal Of Information technology, Vol 8, No. 2, 2002.

5.       Nureize Arbaiy, Azizul Azhar Ramli, Zurinah Suradi,Mustafa Mat Deris “Pest Activity Prognosis in the Rice Field” ,

6.       Philomine Roseline,Clarence J. M Tauro, N. Ganesan ” Design and Development of Fuzzy Expert System for Integrated Disease Management in Finger Millets “ International Journal of Computer Applications (0975 – 8887) Volume 56– No.1, October 2012


8.       S. Abdullah1, A. A. Bakar, N. Mustafa, M. Yusuf, S. Abdullah and A.R Hamdan” Fuzzy Knowledge Modelling for Image-based Paddy Disease Diagnosis Expert System” Proceedings of the International Conference on Electrical Engineering and Informatics Institut Teknologi Bandung, Indonesia June 17-19, 2007

9.       Savita Kolhe, Raj Kamal, Harvinder S. Saini and G.K. Gupta , “A web-based intelligent disease-diagnosis system using a new fuzzy-logic based approach for drawing the inferences in crops”, Journal of Computers and Electronics  in Agriculture, year 2011, volume 76, pp 16–27

10.     Virparia P.V. A Web Based Fuzzy Expert System For Insect Pest Management In Groundnut Crop 'Prajna' - Journal Of Pure & Applied Sciences, 15 (2007) 36-41

11.     Zadeh, L.A. (1965): Fuzzy sets, Information and Control 8(3):338–353.

12.     Ajith Abraham “Rule-based Expert Systems” Handbook of Measuring System Design, edited by Peter H. Sydenham and Richard Thorn  2005 John Wiley & Sons, Ltd.




Rinky D. Patel, Dheeraj Kumar Singh

Paper Title:

Credit Card Fraud Detection & Prevention of Fraud Using Genetic Algorithm

Abstract:    Companies and institutions move parts of their business, or the entire business, towards online services providing e-commerce, information and communication services for the purpose of allowing their customers better efficiency and accessibility. Payment card fraud has become a serious problem throughout the world. Companies and institutions loose huge amounts annually due to fraud and fraudsters continuously seek new ways to commit illegal actions. In this we will try to detect fraudulent transaction through the with the genetic algorithm. Genetic algorithm are used for making the decision about the network topology, number of hidden layers, number of nodes that will be used in the design of neural network for our problem of credit card fraud detection.

   Credit cards; Credit card fraud detection; Artificial neural networks; Genetic algorithm.


1.        Ganesh Kumar.Nune, and P.Vasanth Sena, and T.P.Shekhar, “Novel Artificial Neural Networks and Logistic Approach for Detecting Credit Card Deceit”, In IJCSMR Vol 1 Issue 3 October 2012  ISSN 2278-733X October 2012.
2.        Raghavendra Patidar, and Lokesh Sharma, “Credit Card Fraud Detection Using Neural Network”, In IJSCE ISSN: 2231-2307, Volume-1, Issue-NCAI2011, June 2011.

3.        Khyati Chaudhary, and Jyoti Yadav, and Bhawna Mallick, “A review of Fraud Detection Techniques: Credit Card”, In IJCA Volume 45– No.1, May 2012.

4.        K.RamaKalyani, and D.UmaDevi, “ Fraud Detection of Credit Card Payment System by Genetic Algorithm”, In IJSER Volume 3, Issue 7, July 2012.

5.        Mehzabin Shaikh and Mrs. Gyankamal J. Chhajed, “ Review on Financial Forecasting using Neural Network and Data Mining Technique”,In Global Journals Inc. Volume 12 Issue 11, 2012.

6.        Alejandro Correa,  Banco Colpatria, Andres Gonzalez, Banco Colpatria, camilo Ladino, Banco Colpatria, “Genetic Algorithm Optimization for Selecting the Best Architecture of a Multi-Layer Perceptron Neural Network: A Credit Scoring Case” ,SAS Global Forum,  2011.

7.        Khyati Chaudhary, Bhawna Mallick, “Exploration of Data mining techniques in Fraud Detection: Credit Card”, In IJECSE  ISSN 277-1956/V1N3-1765-1771.

8.        Sushmita Mitra, Sankar K. Pal, Pabitra Mitra, “Data Mining in Soft Computing Framework: A Survey”, IEEE Transactions On Neural Networks, VOL. 13, NO. 1, January 2002.

9.        S.Benson Edwin Raj, A. Annie Portia, ―Analysis on Credit Card Fraud Detection Methods, IEEE International Conference on Computer, Communication and Electrical Technology, IEEE March 2011.

10.     Genetic algorithms for credit card fraud detection by Daniel Garner, IEEE Transactions May 2011.




R.Harikumar, T.Vijayakumar

Paper Title:

A Comparison of Elman and Radial Basis Function (RBF) Neural Networks in Optimization of Fuzzy outputs for Epilepsy Risk Levels Classification from EEG Signals

Abstract:   In this paper; we investigate the optimization of fuzzy outputs in the classification of epilepsy risk levels from EEG (Electroencephalogram) signals using two categories (Recurrent &Non Recurrent) of neural networks. The fuzzy techniques are applied as a first level classifier to classify the risk levels of epilepsy based on extracted parameters like energy, variance, peaks, sharp and spike waves, duration, events and covariance from the EEG signals of the patient.  Elman neural network (with error Back propagation training) & Radial Basis Function (RBF) neural network are identified as post classifiers on the classified data to obtain the optimized risk level that characterizes the patient’s epilepsy risk level. The Performance Index (PI) and Quality Value (QV) are calculated for the above methods. A group of ten patients with known epilepsy findings are used in this study. High PI such as 97.87 %, and 98.92% was obtained at QV’s of 23.31, and 23.98 in Elman and RBF neural network optimization when compared to the value of 40% and 6.25 through fuzzy techniques respectively. We find that the RBF (Non Recurrent) neural network out performs Elman Network in optimizing the epilepsy risk levels.

   EEG Signals, Epilepsy Risk Levels,Fuzzy Logic, RBF, Elman Neural Networks, Back propagation.


1.       Leon D.Iasemidis etal., Adaptive Epileptic  SeizurePrediction System, IEEE Transactions on Biomedical Engineering, May 2003,50(5): 616-627.
2.       K P Adlassnig, Fuzzy Set Theory in Medical diagnosis, IEEE Transactions on Systems Man Cybernetics, March 1986,16: 260-265.

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

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

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

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

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

8.       C B Gupta and Vijay Gupta, An Introduction to Statistical Methods, 22nd Ed., Vikas Publishing House Lt., 2001.

9.       R.Harikumar and B.Sabarish Narayanan, Fuzzy Techniques for Classification of Epilepsy risk level from EEG Signals, Proceedings of IEEE Tencon – 2003, 14-17 October 2003,Bangalore, India, 209-213.

10.     Mark van Gils, Signal processing in prolonged EEG recordings during intensive care, IEEE EMB Magazine  November/December 1997,16(6): 56-63.

11.     R.Harikumar, Dr.(Mrs). R.Sukanesh, P.A. Bharthi, Genetic Algorithm Optimization of Fuzzy outputs for Classification of Epilepsy Risk Levels from EEG signals,I.E . India Journal of Interdisciplinary  panels, May 2005, 86(1):9-17. 

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

13.     Pamela McCauley-Bell and Adedeji B.Badiru, Fuzzy Modeling and Analytic Hierarchy Processing to Quantify  Risk levels Associated with Occupational Injuries- Part I: The Development of Fuzzy- Linguistic Risk Levels,   IEEE Transaction on Fuzzy Systems, 1996,4 ( 2): 124-31.

14.     Joel.J etal, Detection of seizure precursors from depth EEG using a sign periodogram transform, IEEE Transactions on Bio Medical Engineering, April 2004,51 (4):449-458.

15.     S.Vitabile etal.,Daily peak temperature forecasting with Elman neural networks, Proceedings of   IEEE2004, 2765-2769.

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

17.     Drazen.S.etal., Estimation of difficult –to- Measure process variables using neural networks, Proceedings of IEEE MELECON  2004,May 12-15, 2004, Dubrovnik, Croatia, 387-390.

18.     Moreno.L. etal., Brain maturation estimation using neural classifier, IEEE Transaction of Bio Medical Engineering ,,April 1995,42(2):428-432.

19.     Arassenko.L,Y.U.Khan,M.R.G.Holt,Identification of inter-ictal spikes in the EEG using neural network analysis,IEE Proceedings –Science Measurement Technology, November 1998,145(6):270-278.

20.     Hwang et al., Recognition of Unconstrained Handwritten Numerals by A Radial Basis Function Network Classifier,Pattern Recognition Letters,   1997,18:657-664.

21.     H.Demuth and M..Beale, Neural network tool box: User’s guide, Version 3.0 the math works, Inc., Natick,MA, 1998.

22.     G.Fung etal, Fault Detection In Inkjet Printers Using Neural Networks, Proceedings of IEEE SMC, 2002.

23.     Guoqiang Peter Zhang , Neural Networks for Classification: A Survey ,IEEE Transactions on Systems  Man  Cybernetics- Part C: Applications and Reviews, November 2000,30(4): 451-462.

24.     Jonathan lee etal., A Neural Network Approach to Cloud Classification, IEEE Transactions on Geosciences and Remote Sensing, September 1990,28 (5): 846-855.

25.     S.Haykin, Neural networks a Comprehensive     Foundation, Prentice- Hall Inc. 2nd  Ed. 1999.

26.     Meng Joo Er,Shiqian Wu,Juwei Lu, Face Recognition with Radial Basis Function  (RBF) Neural Network, IEEE Transactions on Neural Networks, May 2002,13 (3): 697-710.

27.       Mu-chun Su, Chien –Hsing Chou, A modified version of the k-means clustering algorithm with a distance based on cluster symmetry, IEEE Transactions on Pattern Analysis and Machine Intelligence June 2001, 23 (6): 674-680.

28.     Masaaki Tsujitani, Takashi Koshimizu, Neural Discriminant Analysis, IEEE Transactions on Neural Networks, November 2000,11 (6):1394-1401.

29.     Rangaraj M. Rangayyan, Bio- Medical Signal Analysis A Case Study Approach, IEEE Press-John Wiley   &sons Inc  New York 2002.

30.     Yuan-chu Cheng, Wei-Min Qi,WeiYou Cai, Dynamic Properties of Elman And  Modified Elman Neural Network,Proceedings of IEEE, First International Conference on Machine Learning And Cybernatics, Beijing, 2002,637-640.

31.     K.Sriramamurty  and B.Yegnannarayana, Combining Evidence from Residual Phase and MFCC Features    for Speaker Recognition, IEEE Signal Processing Letters,  January 2006,13 (1): 52-55.




Devesh D. Nawgaje, Rajendra D. Kanphade

Paper Title:

Hardware Implementation of Genetic Algorithm for Ovarian Cancer Image Segmentation

Abstract:   Imaging plays an important role in the diagnosis and treatment of ovarian cancer. An accurate segmentation is critical, especially when the ovarian tumor morphological changes remain subtle, irregular and difficult to assess by clinical examination. Traditionally, segmentation is performed manually in clinical environment that is operator dependent and very tedious and time consuming labor intensive work. In this paper genetic algorithm for selecting the optimal threshold in image segmentation is proposed. In the computational process, the GA adjusts crossover probability and mutation probability automatically according to the variance between the target and background. Moreover, the complete algorithm is implemented using Digital Signal Processor TMS320C6713 which decreases the run time greatly.

   Genetic algorithm, Ovarian Cancer, Digital Signal Processor, Segmentation.


1.       American Cancer Society. Cancer Facts and Figures 2011. Atlanta, GA: American Cancer Society; 2011.
2.       Jermal A, Murray T, Samuels A, Ghafoor A, Ward E, Thun MJ. Cancer statistic 2003. CA: Cancer Journal for Clinician 2003;13:5—26.

3.       W. Kenong, D. Gauthier and M. D. Levine: “Live Cell Image Segmentation”, IEEE Transactions on Biomedical Engineering, vol.42, no. 1, Jun. 1995.

4.       J. Sijbers, P. Scheunders, M. Verhoye, A. Van der Linden, D. Van Dyck, E. Raman: “Watershed-based segmentation of 3D MR data for volume quatization”, Magnetic Resonance Imaging, vol. 15, no. 6, pp 679-688, 1997.

5.       C. Chin-Hsing, J. Lee, J. Wang and C. W. Mao: “Color image segmentation for bladder cancer diagnosis”, Mathl. Comput. Modeling, vol. 27, no. 2, pp. 103-120, 1998.

6.       Rodríguez, R., Alarcón, T., Wong, R. and Sanchez, L.: “Color segmentation applied to study of the angiogenesis. Part I”, Journal of Intelligent and Robotic System, Vol. 34, No.1, May 2002.

7.       P. Schmid: “Segmentation of digitized dermatoscopic images by two-dimensional color clustering”, IEEE Trans. Med. Imag., vol. 18, no. 2, Feb., 1999.

8.       J.E. Koss, F. D. Newman, T. K. Johnson and D. L. Kirch: “Abdominal organ segmentation using texture transforms and a hopfield neural network”, IEEE Trans.
Med. Imag., vol. 18, no. 7, July, 1999.

9.       Payel Ghosh, M. Mitchess, “ Automatic segmentation of the prostate using a genetic algorithm for prostate cancer treatment planning”, IEEE proceeding of ICMLA 2010.

10.     Liu Jianli, Zuo Baoqi, “The segmentation of skin cancer image based on genetic neural network”, IEEE proceeding of CSIE 2008.

11.     Texas Instruments, TMS320C6713, Floating-Point Digital Signal Processors, Data Sheet, Dallas, TX, 2002.




Abhay Mundra, Poonam Tomar, Deepak Kulhare

Paper Title:

Rapid Update in Frequent Pattern form Large Dynamic Database to Increase Scalability

Abstract:    Association rule mining is a popular data mining technique which gives us valuable relationships among different items in a dataset. In dynamic databases, new transactions are appended as time advances. This may introduce new association rules and some existing association rules would become invalid [1]-[2]. Thus, the maintenance of association rules for dynamic databases is an important problem. Several incremental algorithms, is proposed to deal with this problem. In this paper we proposed algorithm RUPF (Rapid Update in Frequent Pattern). This algorithm reduces a number of times to scan the database (old and new) to generate frequent pattern. As a result, the algorithm has execution time faster than that of previous Algorithms. This paper also conducts experiments to show the performance of the proposed algorithm. The result shows that the proposed algorithm has a good performance.

   Association rule, frequent pattern for Dynamic maintenance, incremental algorithms.


1.       Rahman, Mohammad.M AL-Widyan “Reduce Scanning Time Incremental Algorithm (RSTIA) of Association rules” Academic Research International2, September Volume 1, Issue 2, September 2011 University, Amman, JORDAN
2.       N.L. Sarda N. V. Srinivas “an Adaptive Algorithm for Incremental Mining of Association Rules “Computer Science and Engineering Indian Institute of Technology Bombay Mumbai.

3.       Siddharth Shah, N. C. Chauhan, S. D. Bhander “Incremental Mining of Association Rules: A Survey “International Journal of Computer Science and Information Technologies, Vol. 3 , 2012,

4.       Wei-Guang Teng and Ming-Syan Chen “Incremental Mining on Association Rules” Department of Electrical Engineering National Taiwan University Taipei, Taiwan.

5.       Animesh Tripathy, Subhalaxmi Das “An Association Rule Based Algorithmic Approach to Mine Frequent Pattern in Spatial Database System” International Journal of Computer Science & Communication Vol. 1, No. 2, July-December 2010,

6.       Sandhya Rani Jetti, Sujatha D “Mining Frequent Item Sets from incremental database: A single pass approach “International Journal of Scientific & Engineering Research, Volume 2, Issue 12, December-2011,

7.       Chelliah Balasubramanian*, Karuppaswamy ”A mining method for tracking changes in temporal association rules from an encoded database” International Journal on Computer Science and Engineering Vol.1(1), 2009, 

8.       Nibedita Panigrahi “An Efficient Algorithm for Mining Of frequent items using incremental model” Konark Institute of Science and Technology International Journal of Computer Science & Informatics, Volume-1,

9.       Wuzhou Dong, Juan Yi, Haitao He, and Jiadong Ren “An incremental algorithm for frequent pattern mining based on bit-sequence (IJACT)” Volume3, Number9, October 2011

10.     Ahmed Taha1, Mohamed Taha1, Hamed Nassar2, and Tarek F. Gharib3 “DARM: Decrement Association Rules Mining “Journal of Intelligent Learning Systems and Applications,

11.     Romans Tumasonis, Gintautas Dzemyda “A probabilistic    algorithm for mining frequent sequences “

12.     Jia-Dong Ren and Xiao-Lei Zhou Yanshan “A New Incremental Updating Algorithm for Mining Sequential Patterns “University, , China  Journal of Computer Science 2

13.     F.A. Dafa-Alla, Ho Sun Shon and Khalid E.K.  Saeed “IMTAR: Incremental Mining of General Temporal Association Rules “ Journal of Information Processing Systems, Vol.6, No.2, June 2010

14.     Vasile Parvan 2, Timisoara, Romania “A Comparative Study of Association Rules Mining Algorithms”Computer & Software Engineering Department, Politehnica University of Timisoara, Bd.




Namita Jain and Neeraj Jain

Paper Title:

Study of Energy Efficient Time Synchronization Algorithm for the development of Smart Wireless Sensor Network

Abstract:   Time Synchronization algorithm guarantees the connectivity, coverage, reliability and security of networking operations for a maximized period of time. We propose energy efficient time synchronization algorithm for deployment of Underwater Wireless Sensor Network (UWSN) for the purpose of monitoring phenomenon of our interest in the coverage region. This paper describes a prototype of a synchronization protocol which is suitable for UWSN considering the effects of both propagation delay and movement. In the algorithm, no time synchronization is necessary if the time stamps of the received data packets are within the tolerance. In this context, the network underwater neither performs global time synchronizations frequently nor periodically and it reduces the time used to synchronize clocks among sensor nodes.

   Algorithm, Protocols, Time Synchronization, Underwater Wireless sensor Network (UWSN).


1.       Li Liu, 2010, “Time Synchronization of Underwater Wireless Sensor Networks”, Smart Wireless Sensor Networks.
2.       Romer, K., 2001, “Time synchronization in ad hoc networks”, in: Proceedings of ACM Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc’01), pp. 173–182.

3.       Elson, J., Girod L., and Estrin D., 2002, “Fine¬grained network time synchronization using  reference  broadcasts”,  in:  Proceedings  of  Fifth  Symposium  on  Operating Systems Design and Implementation (OSDI 2002), 36, pp. 147–163.

4.       Gautam Gopal, Sharma Teek, 2011, “A Comparative Study of Time Synchronization Protocols in Wireless Sensor Network” in International Journal of Applied Engineering Research Dindigul Volume 1, No 4, 2011.

5.       Su,  W.,  and  Akyildiz,  I.F.,  2005,  "Time¬diffusion  synchronization  protocol  for wireless sensor networks", Networking, IEEE/ACM Transactions on, 13(2), pp. 384¬ 397.

6.       Mingxia Xu, Minjian Zhao, and   Shiju Li, 2005, "Lightweight and energy efficient time synchronization for sensor network," in proceedings of International Conference on Wireless Communications, Networking and Mobile Computing, 2005, 2, pp. 947¬ 950.

7.       Lamport,L. & Melliar-Smith, P.(1985). Synchronizing clocks in the presence of faults. Journal of the Association for Computing Machinery, Vol. 32, No. 1,(1985) 52-78,ISSN 0004-5411.

8.       Mar’oti, M. ; Kusy, B. ; Simon, G. & L’edeczi, A. (2004). The flooding time synchronization protocol, Proceedings of Sensys 2004,pp. 39-49, ISBN 1-58113-879-2,Baltimore, MD, USA, November 2004, ACM Press, New York, NY, USA.




G. Balasubramanian, S. Singaravelu

Paper Title:

Fuzzy Controller for StandAlone Hybrid PV-Wind Generation Systems

Abstract:    This paper proposes a fuzzy logic based voltage controller for hybrid generation scheme using solar and wind energy for the stand alone applications. The fuzzy logic controller is designed to vary the duty-cycle of the DC-DC converter automatically such that to maintain the load voltage constant. The hybrid scheme inherently adapts to the changes in wind speed or load on generator. A dynamic and steady-state mathematical model and simulations for the entire scheme is presented. The model is implemented in the MATLAB/Simulink platform. Results from the simulations and laboratory tests bring out the suitability of the proposed hybrid scheme in remote areas.

   DC-DC converter, Fuzzy logic, Induction generator, PV array, Single-phase and Wind energy.


1.       S.Meenakshi, K.Rajambal, C.Chellamuthu, and S.Elangovan, “Intelligent Controller for Stand-Alone Hybrid Generation System”, Power India Conference IEEE, pp 8-15, 2006.
2.       Ashraf A.Ahmed, Li Ran, Jim Bumby, “Simulation and control of a Hybrid PV-Wind System”, Power Electronics Machines & Drives, PEMD 4th IET conference, pp 421-425, 2008.

3.       Meenakshmisundaram Arutchelvi, Samuel Arul Daniel, “Grid Connected Hybrid Dispersed Power Generators Based on PV Array And Wind Driven  Induction Generator”, Journal of Electrical Engineering , Vol., 60, pp 313-320, 2009.

4.       T.Ahmed, N. Katsumi, N.Mutsuo, “Advanced control of PWM converter with variable-speed induction generator”, IET Electr.Power Appl, pp 239-247, 2007.

5.       S.Bhim, K.Gaurav kumar, “Solid state voltage and frequency controller for a stand- alone wind power generating system”,IEEE Trans.Power Electron, pp 1170-1177, 2008.

6.       B.Venkatesa perumal, K.Jayanta, “Voltage and frequency controller of a stand- alone brushless wind electric generation using generalized impedance controller” IEEE Trans. Energy Convers, pp  632-641, 2008.

7.       S.Bhim, K.Gaurav kumar, “Voltage and frequency controller for three phase four wire autonomous wind energy conversion system”, IEEE Trans.Energy Convers,pp 509-518, 2008.

8.       M.G.Villalva, J.R.Gazol, and E.R.Filho, “Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays", IEEE trans. on Power Electronics, vol.24, no.5, pp.1198-1208, 2009.

9.       H. Patel, and V. Agarwal, “MATLAB based modeling to study the effects of partial shading on PV array characteristics”, IEEE trans. on energy conv., vol. 23, no.1, pp. 302-310, 2008.

10.     Bhim singh, L.B. Shilpakar, S.S Murthy, A.K. Tiwari,    “Improved steady state and transient performance with optimum excitation of single phase self-excited induction generator”, Electric machines and power system, 2000.

11.     A.Karthikeyan, C.Nagamani, G.Saravana Illango, A.Sreenivasulu, “Hybrid, open-loop excitation system for a wind turbine –driven stand-alone induction generator”, IET Renewable Power Generation, Vol.5, no.2,pp.184-193,2011.

12.     Timothy and Ross J, Fuzzy logic with engineering applications, McGraw hill international editions, Electrical engineering series, New York, 1997.




Supriya Jana, Bipadtaran Sinhamahapatra, Sudeshna Dey, Arnab Das, Bipa Datta, Moumita Mukherjee, Santosh Kumar Chowdhury, Samiran Chatterjee

Paper Title:

Single Layer Monopole Hexagonal Microstrip Patch Antenna for Satellite Television

Abstract:   A single layer monopole hexagonal patch antenna is thoroughly simulated in this paper.  Resonant frequency has been reduced drastically by cutting two equal slots which are the combinations of two triangular and one rectangular slot at the upper right and lower left corner and middle point symmetrical Y-junction slot located from the conventional microstrip patch antenna.  It is shown that the simulated results are in acceptable agreement. More importantly, it is also shown that the differentially-driven microstrip antenna has higher gain of simulated 3.19 dBi at 9.12GHz and -0.62 dBi at 13.71GHz and beam width of simulated 162.910 at 9.12GHz and 64.470at 13.71GHz of the single-ended microstrip antenna. Compared to a conventional microstrip patch antenna, simulated antenna size has been reduced by 56.55% with an increased frequency ratio.

   Compact, Patch, Slot, Resonant frequency, Bandwidth.


1.        I.Sarkar, P.P.Sarkar, S.K.Chowdhury “A New Compact Printed Antenna for Mobile Communication”, 2009 Loughborough   Antennas& Propagation Conference, 16-17 November 2009, pp 109-112.
2.        S. Chatterjee, U. Chakraborty, I.Sarkar, S. K. Chowdhury, and P.P.Sarkar, “A Compact Microstrip Antenna for Mobile Communication”, IEEE annual conference. Paper ID: 510

3.        J.-W. Wu, H.-M. Hsiao, J.-H. Lu and S.-H. Chang, “Dual broadband design of rectangular slot antenna for 2.4 and 5 GHz wireless communication”, IEE Electron. Lett. Vol.  40 No. 23, 11th November 2004.

4.        U. Chakraborty, S. Chatterjee, S. K. Chowdhury, and P. P. Sarkar, "A comact microstrip patch antenna for wireless communication," Progress In Electromagnetics Research C, Vol. 18, 211-220, 2011

5.        Rohit K. Raj, Monoj Joseph, C.K. Anandan, K. Vasudevan, P. Mohanan, “ A New Compact Microstrip-Fed Dual-Band Coplaner Antenna for WLAN Applications”, IEEE Trans. Antennas Propag., Vol. 54, No. 12, December 2006, pp  3755-3762.

6.        Zhijun Zhang, Magdy F. Iskander, Jean-Christophe Langer, and     Jim Mathews, “Dual-Band WLAN Dipole Antenna Using an Internal Matching Circuit”, IEEE Trans. Antennas and Propag.,VOL. 53, NO. 5, May 2005, pp 1813-1818.

7.        J. -Y. Jan and L. -C. Tseng, “ Small planar monopole Antenna with a shorted parasitic inverted-L wire for Wireless communications in the 2.4, 5.2 and 5.8 GHz. bands” , IEEE Trans. Antennas and Propag., VOL. 52, NO. 7, July 2004, pp -1903-1905.

8.        Samiran Chatterjee, Joydeep Paul, Kalyanbrata Ghosh, P. P. Sarkar and S. K. Chowdhury “A Printed Patch Antenna for Mobile Communication”, Convergence of Optics and Electronics conference, 2011, Paper ID: 15, pp 102-107

9.        C. A. Balanis, “Advanced Engineering Electromagnetics”, John Wiley & Sons., New York, 1989.

10.     Bipa Datta, Arnab Das, Samiran Chatterjee, Bipadtaran Sinhamahapatra, Supriya Jana, Moumita Mukherjee, Santosh Kumar Chowdhury,  “Design of Compact Patch Antenna for Multi-Band Microwave Communication”, National Conference on Sustainable Development through Innovative Research in Science and Technology (Extended Abstracts), Paper ID: 115, pp 155, 2012

11.     Arnab Das, Bipa Datta, Samiran Chatterjee, Bipadtaran Sinhamahapatra, Supriya Jana, Moumita Mukherjee, Santosh Kumar Chowdhury, "Multi-Band Microstrip Slotted Patch Antenna for Application in Microwave Communication," International Journal of Science and Advanced Technology, (ISSN 2221-8386), Vol. 2, Issue-9, 91-95, September 2012.

12.     Zeland Software Inc. IE3D: MoM-Based EM Simulator.  Web: