S. No

Volume-1 Issue-3, August 2012, ISSN:  2277-3878 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Suvarna Pathade, Vinaya Gohokar

Paper Title:

Real-Time Detection of Driver Fatigue using DIP

Abstract:  A Drowsy Driver Detection System has been developed, based on eye features detection algorithm and using a computer vision based concepts. The system uses a small monochrome security camera that points directly towards the driver’s face and monitors the driver’s eye and head movement in order to detect fatigue. The system deals with using information obtained for the binary version of the image to find the edges of the face, which narrows the area of where the eyes may exist.  Once the face area is found, the eyes are found by computing the horizontal averages in the area. Once the accurate eyes are located, measuring the distances between the intensity changes in the eye area determine whether the eyes are open or closed. If the eyes are found closed for 5 consecutive frames, the system draws the conclusion that the driver is falling asleep and issues a warning signal.  The system is also able to detect when the eyes cannot be found, and works under reasonable lighting   conditions.

Keywords:   Drowsiness detection, drowsy driving, sleeps deprivation, opening and Closing.

References:
1.           Qiang Ji, Zhiwei Zhu, and Peilin Lan “Real-Time Non intrusive  Monitoring   and Prediction of Driver Fatigue” IEEE Trans on Vehicular Technology Vol 53, pp no.4, July2004.                                                                                                                                          
2.           Chuu-Hai Fan,Wen-Bing Horng, Chih-YuanChen,Yi Chang,“Driver Fatigue Detection Based on Eye Tracking and Dynamic Template Matching”Proceedings of the 2004, IEEE International Conference on  Nclvorkinp,Sensing & Control Taipei, Taiwan, pp 21-22, March 2004.
3.           R. Thilak Kumar,S. Kumar Rajaand A.G. Ramakrishn  “Eye  Detection  using color CuesAnd projection functions “IEEE pp 7803-7622, 2002
4.           Zhiwei Zhu, Qiang Ji, “Robust Real-Time Eye Detection and tracking Under Variable Lighting Conditions and Various Face Orientations”
5.           Kun Peng, Liming Chen, Su Ruan, Georgy Kukharev “A Robust Alogorithm Detection on Gray Intensity Face without Spectacles” JCS&T Vol.5, pp 3
6.           S. Asteriadis, N. Nikolaidis, A. Hajdu, I. Pitas”A Novel Eye Detection Algorithm Utilizing EdgeRelated Geometrical Information14thEuropean Signal Processing Conference (EUSIPCO 2006), Florence ,Italy, pp 4-8, Sep 2006.
7.           D.Sidibe,P.Montesinos, S.Janaqi” A simple and efficient eye detection method in color Images” Author  manuscript publishe in"InternationalConference  Image andVision Computing New Zealand 2006, Nouvelle-Zélande (2006)"
8.           Shifeng Hu, Zuhua Fang, Jie Tang, Hongbing Xu, Ying Sun “Research of Driver Eye Features Detection Algorithm Based on Open CV”
9.           Rafael C. Gonzalez ”Digital Image Processing”
10.        Andreas Koschan  Mongi Abidi “Digital Color Image Processing”

1-6

2.

Authors:

Iyengar Bhargava K., Bodhankar Ninad, Singh Satyam

Paper Title:

Environmental Impact Analysis Study of Gare Sector- III Coal Block, Mand-Raigarh Coalfield

Abstract:   One of most important of world concern demanding international communication and cooperation is that of environment. More and more people are becoming aware of the urgent need to understand the effects of man actions in and on his environment and to control these actions, so as to preserve the ecological relationships necessary for his present and future survival. The Mining industry is a boon at one hand a curse in another. Boon because we get all comforts of life by making money out of mining and curse because the atmosphere gets polluted. The pollution may be in the air, water, or land, one way is not to have any mining activity and that mean we have to go to the Stone Age and the other alternative is to fight back by analyzing the impact.

Keywords:    Atmosphere, environment, ecological, impact, mining, pollution, survival

References:
1.        Qiang Ji, Zhiwei Zhu, and Peilin Lan “Real-Time Non intrusive   Monitoring   and Prediction of Driver Fatigue” IEEE Trans on Vehicular Technology Vol 53, pp no.4, July2004.                                                                                                                                         
2.        Chuu-Hai Fan,Wen-Bing Horng, Chih-YuanChen,Yi Chang,“Driver Fatigue Detection Based on Eye Tracking and Dynamic Template Matching”Proceedings of the 2004, IEEE International Conference on  Nclvorkinp,Sensing & Control Taipei, Taiwan, pp 21-22, March 2004.
3.        R. Thilak Kumar, S. Kumar Raja and A.G. Ramakrishn “Eye  Detection  using color CuesAnd projection functions “IEEE pp 7803-7622, 2002
4.        Zhiwei Zhu, Qiang Ji, “Robust Real-Time Eye Detection and tracking Under Variable Lighting Conditions and Various Face Orientations”
5.        Kun Peng, Liming Chen, Su Ruan, Georgy Kukharev “A Robust Alogorithm Detection on Gray Intensity Face without Spectacles” JCS&T Vol.5, pp 3
6.        S. Asteriadis, N. Nikolaidis, A. Hajdu, I. Pitas”A Novel Eye Detection Algorithm Utilizing EdgeRelated Geometrical Information14thEuropean Signal Processing Conference (EUSIPCO 2006), Florence ,Italy, pp 4-8, Sep 2006.
7.        D.Sidibe,P.Montesinos, S.Janaqi” A simple and efficient eye detection method in color Images” Author  manuscript publishe in"InternationalConference  Image andVision Computing New Zealand 2006, Nouvelle-Zélande (2006)"
8.        Shifeng Hu, Zuhua Fang, Jie Tang, Hongbing Xu, Ying Sun “Research of Driver Eye Features Detection Algorithm Based on Open CV”
9.        Rafael C. Gonzalez ”Digital Image Processing”
10.     Andreas Koschan  Mongi Abidi “Digital Color Image Processing”

7-9

3.

Authors:

Vijaya Kumar.S, D.V.Ashok Kumar, Ch.Sai Babu

Paper Title:

Neural Network Controller for Enhancement of Uninterruptible Power Supply Inverter

Abstract: Uninterruptible Power Supplies (UPS) are emergency power sources, which have widespread applications in critical equipments, such as computers, automated process controllers and hospital instruments. With rapid growth in the use of high efficiency power converters, more and more electrical loads are nonlinear and generate harmonics. It is a big challenge for a UPS to maintain a high-quality sinusoidal output voltage under a nonlinear loading condition. The conventional methods employ multi-loop control strategies to perform same task. In conventional methods more inputs cannot be given to the controller, though it accounts for better performance under nonlinear conditions, it will increase the complexity of the system. Whereas a neural network controller can accommodate more inputs and learn from data. Neural Networks (NNs) have been employed in many applications in recent years. A neural network is an interconnection of a number of artificial neurons that simulate a biological brain system. It has the ability to approximate nonlinear functions and can achieve higher degree of fault tolerance. NNs have been successfully introduced into power electronics circuits and application of NNs for harmonic elimination of Pulse Width Modulation (PWM) inverters, where a NN replaced a large and memory demanding look-up table to generate the switching angles of a PWM inverter for a given modulation index. This paper aims to study the behavior of UPS inverter under nonlinear loading condition. A neural network based controller is designed and tested for performance enhancement.

Keywords:    Neural networks, Pulse width modulation, Uninterruptible Power Supply.

References:
1.        Salman Mohagheghi, Ronald G. Harley, Thomas G. Habetler  and Deepak Divan, ”Condition Monitoring of Power Electronic Circuits Using Artificial  Neural Networks”, IEEE Transactions on power electronics, vol. 24, no. 10, October 2009.
2.        Yiping Dong*, Yang Wang, Zhen Lin, and Takahiro Watanabe,” High Performance and Low Latency Mapping for Neural Network into Network on Chip Architecture”, IEEE Transactions on power electronics, vol. 24, no. 10, October 2009.
3.        Orlando Soares, Henrique Gonçalves, António Martins and Adriano Carvalho,” Neural Networks Based Power Flow Control of the Doubly Fed Induction Generator”,IEEE 2009.
4.        Xinggui Wang, Bing Xu ,Lei Ding,” Simulation Study on A Single Neuron PID Control System of DC/DC Converters”, Workshop on Power Electronics and Intelligent Transportation System, 2008.
5.        Wenxin Liu, Li Liu, David A. Cartes and Xin Wang,” Neural Network Based Controller Design for Three-Phase PWM AC/DC Voltage Source Converters”, International Joint Conference on Neural Networks,2008.
6.        Yi-Chin Fang and Bo-Wen Wu,” Prediction of the Thermal Imaging Minimum Resolvable (Circle) Temperature Difference with Neural Network Application”, IEEE  Transactions on pattern analyis and machine intelligence, Vol. 30, No. 12,Dec. 2008.
7.        Xiao Sun and Martin H.L “Analog Implementation Of a Neural Network Controller for UPS Inverter Applications”, IEEE Trans. Power Electron., vol.17, pp. 305-313, May 2002.
8.        Buso S., S. Fasolo, and P. Mattavelli, “Uninterruptible power supply multiloop control employing digital predictive voltage and current regulators,” Proc. IEEE APEC’01, pp. 907–913, 2001.
9.        M. J. Ryan, W. E. Brumsickle, and R. D. Lorenz, “Control topology options for single-phase UPS inverter,” IEEE Trans. Ind. Applicat., vol.33, pp. 493–501, Mar./Apr. 1997.
10.     Abdel-Rahim.N.M. and J. E. Quaicoe, “Analysis and design of a multiple feedback loop control strategy for single-phase voltage-source UPS inverters,” IEEE Trans. Power Electron., vol. 11, pp. 532–541, July 1996.
11.     F. Kamran, R. G. Harley, B. Burton, and T. G. Habetler, “An on-line trained neural network with an adaptive learning rate for a wide range of power electronic applications,” in Proc. IEEE-PESC’96, 1996, pp. 1499–1505.
12.     Bose.B.K, “Expert system, fuzzy logic, and neural network applications in power electronics and motion control,” Proc. IEEE, vol. 82, pp. 1303–1323, Aug. 1994.
13.     K. J. Hunt, D. Sbarbaro, R. Zbikowski, and P. J. Gawthrop, “Neural networks for control system—A survey,” Automatica, vol. 28, no. 6, pp. 1083–1112, 1992.
14.     Buhl.M.R and R. D. Lorenz, “Design and implementation of neural networks for digital current regulation of inverter drives,” in Proc. IEEE-IAS Annu. Meeting,
1991, pp. 415–423.
15.     T. Haneyoshi, A. Kawamura, and R. G. Hoft, “Waveform compensation of PWM inverter with cyclic fluctuating loads,” in Proc. IEEE IAS Annu. Meeting, Denver, CO, 1986, pp. 744–751.

10-16

4.

Authors:

M. V. Dalvi, Vinay Patil, R. S. Bindu

Paper Title:

FEA Based Strength Analysis of Weld Joint for Curved Plates (Overlap) Specially for Designing Pressure Vessel Skirt Support

Abstract:  Weld joints form an important part of pressure vessels, they are highly essential for structural integrity of the system. Typical welds are done on flat surfaces and their strengths are well catalogued for reference. If a lap joint is required for longitudinal plates, the reference for taking overlap length is available. When a lap joint is required for curved plates, no reference is available for it. The objective of the project is to form certain set of guidelines or set of formulations which will serve as a guideline for overlap length in lap joint of curve plates. Analysis type for this will be Structural Non Linear Finite Element Analysis i.e. by using ANSYS 12.0 Workbench for modeling and ANSYS 12.0 for Structural Non Linear Analysis. No of Estimated Analysis conducted is 53 for three different cases of analysis. We have studied by analysis and experimentation of 03 cases, for which we required to conduct 53 analyses. After this analysis, experimentation results and their comparison we have to make the conclusion or forming certain set formulations which will serve as a guideline for welds of curve plates with an overlap.

Keywords:     Lap Joint Design, Overlaping angle, Ultimate Tensile Strength of Weld Joint.

References:
1.        Duane K. Miller, Sc.D., P.E. Designing Welded Lap Joints, Practical Ideas for the Design Professional
2.        D.T. Thao, J.W. Jeonga, I.S. Kim, J.W.H.J. Kim predicted Lap-Joint bead geometry in GMA welding process
3.        AWS D1.1/D1.1M:2006 An American National Standard focused on the Maximum Weld Size in Lap Joints
4.        Prof. S. R. Satish Kumar & Prof. A. R. Santha Kumar from IIT Madras.Design consideration for welding.
5.        International Journal of Pressure Vessels and Piping, Volume 78, Issue 9, September 2001, Pages (591-597) by X.Y. Li, T. Partanen, T. Nykänen, T. Björk
6.        Jaesong Kim, Kyungmin Lee, Boyoung Lee Estimation of fatigue life for a lap joint.
7.        R.D.S.G. Campilho a, M.F.S.F.deMoura a, J.J.M.S.Domingues Numerical prediction on the tensile residuals strength of wled joint
8.        Welding operations I- OD1651 - LESSON 1/TASK 1
9.        S. ZHANG investigated the Fatigue strength of laser beam-welded lap joints.
10.     Design of lap joint for longitudinal plates, Guil Environment systems GES, USA (Company Document)

17-23

5.

Authors:

Ramya.S.Kumar, N.Kumaresan

Paper Title:

Systemwide Safety and Reliability for Intelligent Intersections in Hybrid Systems

Abstract: There is an increasing interest in the automation of driving tasks highway traffic management. We address intelligent intersections design problems, where traffic lights are removed. Cars exercise an interaction of centralized and distributed decision making to negotiate the intersection through. Intelligent intersections are a representative of complex hybrid systems, where the challenge is to design a tractable distributed algorithm that guarantee safety and provide better performance. The architecture should allow distributed freedom of action to cars yet should watch against worst-case behavior of other cars to ensure collision avoidance.

Keywords:   intelligent intersections, vehicle safety, traffic control, networked control systems.

References:
1.           R. Girard, J. A. Misener, J. B. de Sousa,  and J. K. Hedrick, “A control architecture for integrated cooperative cruise control and collision warning systems,” in Proc. 40th IEEE Conf. Decision Control, 2001, pp. 1491–1496.
2.           Tomlin, G. Pappas, and S. Sastry, “Conflict resolution of air traffic management: A study in multiagent hybrid systems,” IEEE Trans. Autom. Control, vol. 43, no. 4, pp. 509–521, Apr. 1998.
3.           Tomlin, J. Lygeros, and S. Sastry, “A game-theoretic approach to controller design for hybrid systems,” Proc. IEEE, vol. 88, no. 7, pp. 949–970, Jul. 2000.
4.           Feron, E. Frazzoli, and M. A. Dahleh “A maneuver-based hybrid control architecture for autonomous vehicle motion planning,” in Software Enabled Control:
Information Technology for Dynamical Systems, G. Balas and T. Samad, Eds. Piscataway, NJ: IEEE Press, 2003.
5.           K. Antonsson, K. Grote, and Y. Zhang,  “A new threat assessment measure for collision avoidance systems,” in Proc. IEEE Intell. Transp. Syst. Conf., Toronto, ON, Canada, 2006, pp. 968–975.
6.           E. S. Prassas, R. P. Roess, and W. R. McShane, Traffic Engineering. Englewood Cliffs, NJ: Prentice-Hall, 2004.
7.           Leitmann and J. Skowronski, “Avoidance control,” J. Optim. Theory Appl., vol. 23, no. 4, pp. 581–591, Dec. 1977.
8.           Kowshik, “Provable systemwide safety in intelligent intersections,” M.S. thesis, Univ. Illinois, Urbana-Champaign, Urbana, IL, 2008.
9.           J. K. Kuchar and L. C. Yang, “A review of conflict detection and resolution modeling methods,” IEEE Trans. Intell. Transp. Syst., vol. 1, no. 4, pp. 179–189, Dec. 2000.
10.        K. Akuzawa, M. Sato, and Y. Fujita, “Radar brake system,” in Proc. Annu.Meeting ITS America, 1995, vol. 1, pp. 95–101.
11.        M. K. Powell and J. S. Adelstein, “Report and order: Amend rules regarding dedicated short-range communication services and rules for mobile service for dedicated short-range communications for intelligent transportation services,” U.S. Fed. Commun. Comm., Washington, DC, Dec. 2003.
12.        P. Varaiya, “Smart cars on smart roads: Problems of control,” IEEE Trans. Autom. Control, vol. 38, no. 2, pp. 195–207, Feb. 1993.

24-27

6.

Authors:

M.Nagendramma, K.Subba Reddy

Paper Title:

Behavior Prediction Via Social Dimensions Extraction

Abstract: Online social networks play an important role in everyday life for many people. Social media has reshaped the way in which people interact with each other. The rapid development of participatory web and social networking sites like YouTube, Twitter, and Face book also brings about many data mining opportunities and novel challenges. In particular, given information about some individuals, how can we infer the behavior of unobserved individuals in the same network? A social-dimension-based approach has been shown effective in addressing the heterogeneity of connections presented in social media. However, the networks in social media are normally of colossal size, involving hundreds of thousands of actors. The scale of these networks entails scalable learning of models for collective behavior prediction. To address the scalability issue, we propose an edge-centric clustering scheme to extract sparse social dimensions. With sparse social dimensions, the proposed approach can efficiently handle networks of millions of actors while demonstrating a comparable prediction performance to other non-scalable methods.

Keywords:    Classification with network Data, Collective Behavior, community detection, social dimensions.  

References:
1.       L. Tang and H. Liu, "Toward predicting collective behavior via social dimension Extraction," IEEE Intelligent Systems, vol. 25, pp. 19-25, 2010.
2.       "Relational learning via latent social dimensions," in KDD ’09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining.  New York, NY,
3.       USA: ACM, 2009, pp. 817-826.
4.       M. Newman, "Finding community structure in networks using the eigenvectors of matrices," Physical Review E (Statistical, Nonlinear, and Soft Matter Physics), vol. 74, no. 3, 2006. [Online].          Available: http://dx.doi.org/10.1103/PhysRevE.74.036104
5.       L. Tang and H. Liu, "Scalable learning of collective behavior based on sparse social dimensions,        “in CIKM ’09: Proceeding of the 18th ACM conference on Information and knowledge  manage ment New York, NY, USA: ACM, 2009, pp. 1107-1116.
6.       P. Single and M. Richardson, "Yes, there is a correlation: - from social networks to personal behavior on the web," in WWW ’08: Proceeding of the 17th international conference on World Wide Web. New York,NY, USA: ACM, 2008, pp. 655-664.
7.       M. McPherson, L. Smith-Lavin, and J. M. Cook, "Birds of a feather: Homophily in social networks," Annual Review of Sociology, vol. 27, pp. 415-444, 2001.  ACM, 2005, pp. 1371-1374.
8.       S. A. Macskassy and F. Provost, "Classification in networked data: A toolkit and a univariate case study,"
9.       X.  Zhu,  Z.  Ghahramani,  and  J.  Lafferty,  "Semi-supervised learning using Gaussian fields and harmonic functions," in ICML, 2003.

28-32

7.

Authors:

K Y Patil, D S Chavan

Paper Title:

Study of Wind Power Generation Using Slip Ring Induction Generator

Abstract:  Wind energy is now firmly established as a mature technology for electricity generation. There are different types of generators that can be used for wind energy generation, among which Slip ring Induction generator proves to be more advantageous. To analyze application of Slip ring Induction generator for wind power generation, an experimental model is developed and results are studied. As power generation from natural sources is the need today and variable speed wind energy is ample in amount in India, it is necessary to study more beneficial options for wind energy generating techniques. From this need a model is developed by using Slip ring Induction generator which is a type of Asynchronous generator.

Keywords:  Wind energy, Slip Ring Induction Generator

References:
1.       IEEE/PES Transmission and Distribution Conference paper 2005.
2.       PhD Thesis on power quality of wind turbine by AKE LARSSON
3.       IEEE Canadian Review – Spring
4.       Wind farm models and control strategies by Poul Sørensen, Anca D. Hansen, Florin Iov, Frede Blaabjerg
5.       Wind book on Wind Power in Power Systems by Thomas Ackermann
6.       Conceptual survey of Generators and Power Electronics for Wind Turbines;   2001
7.       IEEE power & energy magazine 1540 7977/03/$17.00©2003 IEEEE
8.       Emerging Practices in developing wind power for the clean Development Management by jyoti painuly
9.       Design and economics of reactive power control in distribution substation by  Khin Trar Trar Soe
10.    Paper on Wind Power generation technology
11.    Paper on Effects on major power quality issue due to incoming induction generators in power system (APRN journal of Engineering and science)

33-36

8.

Authors:

M.Balasubba Reddy, Y.P.Obulesh, S.Sivanaga Raju

Paper Title:

An IPM-APSO based hybrid Method for Multiple Objective Minimizations using TCPS

Abstract: This paper presents an Interior Pont Method (IPM) and variant of Particle Swarm Optimization (APSO) based hybrid method to solve optimal power flow in power system incorporating Flexible AC Transmission Systems (FACTS) such as Thyristor Controlled Phase Shifter (TCPS) for minimization of multiple objectives. The proposed IPM-APSO algorithm identifies the optimal values of generator active-power output and the adjustment of reactive power control devices. The proposed optimization process with IPM-APSO is presented with case study example using IEEE 30-bus test system to demonstrate its applicability. The results are presented to show the feasibility and potential of this new approach.

Keywords:   Optimal power flow, Adaptive Particle Swarm Optimization, Flexible AC Transmission, and TCPS

References:
1.        O. Alsac, and B. Sttot, “Optimal power flow with steady-state security”, IEEE Trans. on Power Systems, Vol. 93, 1974, pp. 745-751.
2.        N. Srinivasan, K. S. Prakaesa, and S. S. Venkata, “On-line computation of phase shifter distribution factors and line load alleviation,” IEEE Trans. on Power Systems, vol. PAS-104, no. 7, pp. 1656–1662, 1985.
3.        B. Stott and E. Hobson, “Power system security control calculations using linear programming—Part I,” IEEE Trans. On Power Systems, vol. PAS-97, pp. 1713–1719, 1978.
4.        R. Baldick and E. Kahn, “Contract paths, phase shifters, and efficient electricity trade,” IEEE Trans. on Power Systems, vol. 12, no. 2, pp.749–755, 1997.
5.        M. R. Iravani and D. Maratukulam, “Review of semiconductor controlled (static) phase shifters for power system applications,” IEEE Trans. on Power Systems, vol. 9, no. 4, pp. 1833–1839, 1994.
6.        T. K. P. Medicherla, R. Billinton, and M. S. Sachdev, “Generation rescheduling and load shedding to alleviate line overloads— Analysis,” IEEE Trans. on Power Systems, vol. PAS-98, pp. 1876–1884, 1979.
7.        D. Papalexopoulos, C. F. Imparato, and F. F. Wu, “Large scale optimal power flow: Effects of initialization decoupling and discretization,” IEEE Trans. on Power Systems, vol. 4, pp. 748–759, 1989.
8.        O. Alsac and B. Sttot, “Optimal power flow with steady-state security,” IEEE Trans. on Power System, vol. 93, pp. 745–751, 1974.
9.        J. Z. Zhu and G. Y. Xu, “A new real power economic dispatch method with security,” Electric Power Systems Research, vol. 25, no. 1, pp. 9–15, 1992.
10.     S. Granville, J. C. O. Mello, and A. C. G. Melo, “Application of interior point methods to power flow unsolvability,” IEEE Trans. on Power System, vol. 11, pp. 1096
1103, 1996.
11.     J. A. Momoh, L. G. Dias, S. X. Guo, and R. A. Adapa, “Economic operation and planning of multi-area interconnected power system,” IEEE Trans. on Power System, vol. 10, pp. 1044–1051, 1995.
12.     Lai LL,Ma JT. Improved genetic algorithms for optimal power flow solutions under both normal and contingent operation states.Int J Elec Pwr Syst 1997;19(5):287-92.
13.     Yuryevich J,Wong KP.Evolutionary programming based optimal power flow algorithm. IEEE Trans Pwr Syst 1999;14(4):1245-50.
14.     [14] Fogel DB.Evolutionary computation toward a new philosophy of machine intelligence.New York:IEEE Press, 1995.
15.     Kennedy J.The particle swarm: social adaptation of knowledge.Proc 1997 IEEE Int Conf Evol Comput ICEC,97,Indianapolis,IN,USA 1997:303-8.
16.     Angeline P.Evolutionary optimization versus particle swarm optimization:philosophy and performance differences.Proc 7th Annu Conf Evol Prog 1998:601-10.
17.     Shi Y,Eberhart R.Parameter selection in particle swarm optimization.Proc 7th Annu Conf Evol Prog 1998:591-600.
18.     Ozean E,Mohan C.Analysis of a simple particle swarm optimization system.Intel Engng Syst Artif Neural Networks 1998;8:253-8.
19.     N.Acharya, A.Sody-Yome, N.Mithulananthan, Facts about flexible ac Transmission systems(FACTS) controllers: Practical installations and benefits, in: Australasian Universities Power Engineering Conference (AUPEC),Australia, September 25- 28,2005,pp.533-538
20.     M.R. Iravani, and D. Maratukulam, “Review of semiconductor-controlled (static) phase shifters for power system applications,” IEEE Trans. on Power Systems, Vo1.9, No.4, 1994, pp 1833-1839
21.     J.A. Momoh, J.Z. Zhu, and J.L. Dolce, “Optimal load shedding study of space station power system using generalized Lagrangian multipliers,” Proceedings of 1998 Large Engineering Systems Conference on Power Engineering, LESCOPE’98, Canada, 7-10 June, 1998.
22.     T.K.P. Medicherla, R. Billinton, and M.S. Sachdev, “Generation rescheduling and load shedding to alleviate line overloads - analysis,” IEEE Trans. on Power Systems, Vol.PAS-98, 1979, pp. 1876-1884.
23.     N. Srinivasan, K.S. Prakaesa, and S.S. Venkata, "On-line computation of phase shifter distribution factors and line load alleviation," IEEE Trans. on Power Systems, Vol.PAS-104, No.7, 1985, pp 1656-1662
24.     B. Stott, and Eric Hobson, "Power system security control calculations using linear programming, Part I," IEEE Trans. on Power Systems, Vol. PAS-97, 1978, pp 1713-1719.
25.     Overbye T. and DeMarco C., “Voltage Security Enhancement Using Energy Based Sensitivities”, IEEE Trans. On Power Systems, Vol. 6, No. 3, August 1991, pp. 1196-1202.
26.     Kim S., Song T., et al, “Development of Voltage Stability Constrained Optimal Power Flow (VSCOPF)”, Power Engineering Society Summer Meetings, 2001 IEEE, Vol. 3, 15-19 July 2001, pp. 1664-1669.
27.     Tuan T.Q., et. al., “Emergency Load Shedding to Avoid Risks of Voltage Instability Using Indicator”, IEEE Trans. On Power Systems, Vol.9, No. 1, February 1994, pp. 341-347.
28.     Kessel P. and Glavtisch H., “Estimating the Voltage Stability of a Power System”, IEEE Trans. on Power Delivery, Vol.1, No.3, 1986, pp. 346-354.
29.     Kennedy J, Eberhart R. Swarm intelligence. San Mateo, CA: Morgan Kaufmann; 2001.
30.     Kim S., Song T., et al, “Development of Voltage Stability Constrained Optimal Power Flow (VSCOPF)”, Power Engineering Society Summer Meetings, 2001 IEEE, Vol. 3, 15-19 July 2001, pp. 1664-1669.

37-43

9.

Authors:

Shwetanshu Maan, Sunita Tiwari

Paper Title:

Route Rating and Centrality in Zone Routing Protocol for MANET

Abstract:  Routing is an important challenge in wireless adhoc networks. Mobile Adhoc networks are infrastructure less and are characterized by multi-hop wireless connectivity and changing network topology. They are self organized networks. Role of routing protocols becomes even more significant because of the dynamically changing topology of MANET. A routing protocol is required whenever a packet needs to be transfer to a destination via number of nodes. Since the mid 90s MANETs are popular research topic due to growth of laptops and wifi wireless networking. Various protocols are evaluated to make MANETs reliable. Most prominent categories are proactive, reactive and hybrid. Proactive and Reactive both have some disadvantages therefore we use hybrid routing protocols to enhance performance of adhoc networks. In this work we aim to compare the performance of proactive (DSDV), reactive (DSR) and hybrid (ZRP and modified ZRP) routing protocols and also aim to propose an enhanced form of ZRP to uplift its performance.

Keywords:    Mobile Adhoc Networks (MANETs), Routing Protocols: Reactive, Proactive, Hybrid, Route Rating.

References:
1.       Sree Ranga Raju and Dr. Jitendrnath Mungara, “ZRP versus AODV and DSR : A Comprehensive Study on ZRP Performance” 2010 International Journal of Computer Applications (0975-8887) Volume 1,  No. 12.
2.       Amandeep Verma, “A Study of Performance Comparisons of Simulated Ad hoc Network Routing Protocols” Int.J. Comp.Tech.Appl. , Vol 2 (3), 565-569.
3.       Oussama Souihli, Mounir Frikha and Mahmoud Ben Hamouda, “ Load-Balancing in MANET shortest-path routing protocols” www.elsevier.com/locate/adhoc.
4.       Kavita Pandey and Abhishek Swaroop, “ A Comprehensive Performance Analysis of Proactive, Reactive and Hybrid MANETs Routing Protocols” IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 6, No. 3, November 2011.
5.       Manijeh Keshtgary and Vahide BaBaiyan, “Performance Evaluation of Reactive, Proactive and Hybrid Protocols in MANET” International Journal on Computer Science and Engineering(IJCSE).
6.       Singh Rajeshwar, Singh Dharmendra K. and Kumar Lalan (2011), “Performance Evaluation of DSR and DSDV Routing Protocols for Wireless Adhoc Networks” International Journal Advanced Networking and Applications, Volume: 02, Issue 04, pp: 732-737.
7.       G.Vijaya Kumar, Y.Vasudeva and Dr. M.Nagendra, “ Current Research Work on Routing Protocols for MANET: A Literature Survey” International Journal on Computer Science and Engineering Vol. 02, No. 03, 2010, 706-713.
8.       Shah Samyak, Khandre Amit, Shirole Mahesh and Bhole Girish, “Performance Evaluation of Adhoc Routing Protocols using NS2 Simulation” Proceedings of International Conference on Mobile and Pervasive Computing” pp: 167-171.
9.       Kapang Lego, Pranav Kumar Singh, Dipankar Sutradhar. “Comparative Study of Adhoc Routing Protocols AODV, DSR and DSDV in Mobile Adhoc Networks”,  International Journal of Computer Science and Engineering,  Vol.1,  No. 4 364-371 2011.
10.    S. A. Ade, P.A.Tijare , “Performance Comparison of AODV, DSDV, OLSR and DSR Routing Protocols in Mobile Ad Hoc Networks”, International Journal of Information Technology and Knowledge Management July-December 2010, Volume 2, No. 2, pp. 545-548.
11.    Hsu J., Bhatia S., Takai M., Bagrodia R. and Acriche M.J. “Performance of Mobile Adhoc Networking Routing Protocols in Realistic Scenarios” , Proceedings of IEEE Conference on Military Communications, Vol. 2, pp. 1268-1273.
12.    Tyagi S S and Chauhan R K (2010). “Performance Analysis of Proactive and Reactive Routing Protocols for Adhoc Networks”, International Journal of Computer Applications, Vol. 1, No. 14, pp. 27-30.
13.    Singla Vikas, Singla Rakesh and Kumar Ajay (2001). “Performance Evaluation and Simulation of Mobile Adhoc Network Routing Protocols”, International Journal of Engineering and Information Technology, Vol. 1, No.1.

44-48

10.

Authors:

Akbar Saleem. Mohammad, Jitendranath Mungara

Paper Title:

XLP: An Integrated Protocol for Efficient and Reliable Communication in Wireless Sensor Networks

Abstract:   Now-a-days, vast majority of the existing solutions are based on the classical layered protocol approach, which leads to significant overhead. Severe energy constraints of battery-powered sensor nodes necessitate energy-efficient communication in Wireless Sensor Networks (WSNs).In this paper, a cross-layer protocol (XLP) is introduced, to achieve congestion control, routing between nodes, and medium access control in a cross-layer fashion. XLP is the first protocol that integrates functionalities of all layers from PHY to Transport into a cross-layer protocol. The design principle of XLP protocol is based on the initiative determination concept, which enables receiver-based contention, initiative-based forwarding, congestion control, and duty cycle operation to realize efficient and reliable communication in WSNs. This concept constitutes the core part of the XLP,A cross-layer analytical framework is developed to investigate the performance of the XLP. XLP improves the communication performance and outperforms the traditional layered protocol architectures in terms of both network performance and implementation complexity

Keywords:  XLP protocol, congestion control, routing between nodes, medium access control, WSN’s

References:
1.    “Cross Layer design for low Power Wireless sensor networks” –by MS.Sajjad Ahmad Madani, June 27 ,2008  at Vietnam University Of Technology-
2.    The Impact of Imperfect Scheduling on Cross-Layer congestion Control in Wireless Sensor networks”- by Xiaojun Lin, Member, IEEE.
3.    “ESRT- Event to Sink reliable Transport in Wireless sensor Networks”-by Yogesh Sankara subramaniam, Ozgur B.Akan, June, 2003 at Georgia Institute Of Technology.
4.    “A Survey of Void Handling Techniques for Geographic Routing in Wireless Networks,” by D. Chen and P.K. Varshney IEEE Comm. Surveys and Tutorials, vol. 9, no. 1, pp. 50-67, 2007.
5.    “To Layer or Not to Layer: Balancing Transport and Physical Layers in Wireless Multihop Networks,” by M. Chiang, Proc. IEEE INFOCOM, vol. 4, pp. 2525-2536, Mar. 2004.

49-54

11.

Authors:

Y. Sandeep kumar, R.K. Suresh, B.Jayachandraiah

Paper Title:

Optimization of design based on Fillet radius and tooth width to minimize the stresses on the Spur Gear with FE Analysis

Abstract:   Gears are one of the most critical components in mechanical power transmission system. Spur gears are mostly used in the applications varying from domestic items to heavy engineering applications. The contact stress and tooth stresses due to transmission depends on some parameters. In this paper the effect of tip radius, tooth width is considered and how the contact stress results vary with these parameters is studied. The Gear design is optimized based on FE analysis. The stresses were calculated using the Lewis equation and then compared with the FE model. The Bending stresses in the tooth root and at mating region were examined using 3D FE mode.

Keywords:  GEAR FE MODEL, FILLET RADIUS, 3D FEM

References:
1.       Kahraman, A, Kharazi, A.A, Umrani, M, A “Deformable body dynamic analysis of planetary gears with thin rims”- Journal of sound and vibration 262(2003) 752-768
2.       Jinliang Zhang, Zongde Fang, Xuemei Cao, Xiaozheong Deng, “The modified pitch cone design of the hypoid gear: Manufacture, stress analysis and experiment tests”- Mechanism and Machine Theory 42(2007) 147-158
3.       Lin, Tengjiao, Ou, H, Li, Runfang, “A finite element method for 3D static and dynamic contact/impact analysis of gear drives”- Computer Methods in Applied Mechanics and Engineering, Volume 196, issue 9-12 (February 1, 2007), p. 1716-1728
4.       K.Mao, “gear tooth contact analysis and its application in the reduction of fatigue wear”262(2007) 1281-1288
5.       Chabert, G.T Dang Tran ,R.Mathis “ An Evaluation of Stresses and Deflection of Spur Gear tooth under strain”-Journal of Engineering for Indusry,1974,pp 85-93
6.       Wilcox, L.E, Gear tooyh stresses-Machine Design, No 23, 1978, pp88-92
7.       F. K. Choy, M. J. Braun, V. Polyshchuk, J. J. Zakrajsek,  D. P.Townsend, and R.F. Handschuh, “Analytical and experimental vibration analysis of a faulty gear
system”-1994 Fall Technical Workshop, the American Gear Manufacturers Association, St. Louis, Missouri, October 24-26, 1994
8.       Robert Basan, Marina Franulovic, and Bozidar Krizan. “Numerical model and procedure for determination of stresses in spur gear teeth flanks”-XII International Conference on Mechanical Engineering-Nov 2008.
9.       M.S.Hebbal, V.B. math, B.G. Sheeparamatti- “A Study on reducing the root fillet stress in spur gear using Internal Stress Relieving Feature of Different Shapes”- International Journal of Recent Trends in Engineering, Vol.1, no.5, may 2009.
10.    Hsiang H. Lin, Professor, Department of Mechanical Engineering. The University of Memphis. “ Compact Design for non-standard spur gear” Journal Of Mechanical, aerospace aand Industrial Engineering-Volume2.
11.    Prof.Dr Marunic. G-University of Rijeka – Rijeka,Croatia-“ Tooth root stress modifiying factors of webbed gears”
12.    Vera nikolic, Cemal Dolicanin, Dejan Dimitrijevic – The state University of Novi Pazar, Serbia. “ Dynamic model for the stress and strain state analysis of a spur gear transmission.” Journal of Mechanical Engineering.

55-58

12.

Authors:

D.J.R.Kiran Kumar, Nalini Kotnana

Paper Title:

Design and Implementation of Portable Health Monitoring system using PSoC Mixed Signal Array chip

Abstract:  Health monitoring systems become a hot topic and important research field today. Research on health monitoring were developed for many applications such as military, home care unit, hospital, sports training and emergency monitoring system. In this work, a portable real-time wireless health monitoring system is implemented using Programmable System on Chip (PSoC) and developed. The developed acquisition system is used for remote monitoring of patients’ temperature, heart rate and oxygen saturation in blood i.e. pulse oximetry, pH level of blood, ECG. This system allows the physician able to understand patient's scenario on the computer screen by wireless module. Here low cost, low power consumption and flexible network topology ZigBee wireless module is used to sense the remote patient data. All sensor data are transferred within a group of ZigBee wireless module. The goal is to demonstrate the possibilities offered by system-on-chip programmable devices in specific processing systems, where the costs make the use of specific integrated circuits unaffordable. The sensor unit consists of (1) temperature sensor; (2)two types of LEDs and photodiode packed in Velcro strip that is facing to a patient’s fingertip for pulse oximetry and heart beat; (3) three color LED with LDR for pH level;(4) ECG; (5) Microcontroller unit for interfacing with wireless module, processing all biomedical sensor data sending to base PC; PSoC circuits built by Cypress Microsystems  which represents a new concept in embedded systems design that replaces multiple traditional MCU-based system components with one, low cost single-chip programmable device. PSoC designer tool will be used for implementing the application and building the software.

Keywords:    temperature sensor, heartbeat, pulse oximetry, pH level, ECG, PSoC

References:
1.       Elder care Thailand association, http://eldercarethailand.com.
2.       http://en.wikipedia.org/wiki/1-Wire. R. E.
3.       Cady, Fredrick. Software and Hardware Engineering Free scale HCS12. Oxford University Press. 2008. http://www.freescale.com.
4.       http://www.oximetry.org/pulseox/principles.htm.
5.       http://www.howequipmentworks.com/physics/respi_measurements/oxygen/oximeter/pulse_oximeter.html.
6.       C.Otto, Milenkovic, A., Sanders, C., Jovanov, E., "System architecture of a wireless body area sensor network for ubiquitous.Cypress Microsystems.
7.       PSoC User Module Data Sheets.
8.       T.Martin,E.Jovanov,D.Raskovic,Issuesinwearablecomputingformedicalmonitoringapplications:acasestudyofawearableECGmonitoringdevice,in:
9.       InternationalSymposiumonWearableComputersISWC2000,Atlanta,October2000.
10.    “What is PSoC,”David Tomanek, DAT Consulting Cons, Pasadena, Applied Electronics (AE), 2010 International Conference on California, USA.
11.    Bronzino, Joseph D. (Editor). The Biomedical Engineering Handbook. Boca Raton: CRC Press, Inc., 1995.
12.    B.B. Flick & R. Orglmeister, A Portable Microsystem-Based Telemetric Pressure & Temperature Measurement Unit, IEEE Tr on Biomedical Engg., 47(1), 2000, 12-16.
13.    Arif C.,“Embedded Cardiac Rhythm Analysis and Wireless Transmission (Wi-CARE),” MS Thesis, School of Computing and Software Engineering, Southern Polytechnic State University, Marietta, Georgia, USA, 2004.
14.    Congestion-Aware, Loss-Resilient Bio-Monitoring Sensor Networking for Mobile Health Applications Fei Hu, Yang Xiao, Senior Member, IEEE, and Qi Hao, Member, IEEE, IEEE Journal on selected areas in communications, vol. 27, no. 4, may 2009.

59-65

13.

Authors:

Namita Malhotra, Shefali Pruthi

Paper Title:

An Efficient Software Quality Models for Safety and Resilience

Abstract:   Software Quality Engineering is an emerging discipline that is concerned with improving the approach to software quality. The goal is to propose a quality model suitable for such a purpose through the comparative evaluation of existing quality models and their respective support for Software Quality Engineering. Cost, schedule and quality are highly correlated factors in software development, and difficult to increase the quality without increasing either cost or schedule or both for the software under development. 

Keywords:   schedule and quality are highly correlated factors in software development,

References:
1.        Rafa E. Al-Qutaish, PhD Al Ain University of Science and Technology – Abu   Dhabi Campus, PO Box: 112612, Abu Dhabi, UAE Quality Models in Software Engineering Literature: An Analytical and Comparative Study Journal of American Science, 2010.
2.        Bhansali. P.V., 2005. Software safety: Current  status and future directions. ACM SIGSOFT Software Eng. Notes, 30: 3.
3.        Ralph R. Young, The Requirements Engineering Handbook, Artech House, 2004.
4.        Dunn, W.,2003. Designing Safety Critical Computer  Systems. IEEE-Computer, 36: 40-46. DOI: 10.1109/MC.2003.1244533.
5.        John C.Knight, “Safety Critical Systems: Challenges and directions” Proceedings of the 24th International Conference on Software Engineering (ICSE), Orlando, Florida, 2002.
6.        John McDermid, “Software Hazard and Safety Analysis”. Book chapter in Formal Techniques in Real-Time and Fault Tolerant systems, page 23-24, Springer Link Book Series, 2002.
7.        Lutz, R.R., 2000. Software engineering for safety A roadmap. Proceedings of    the Conference on The Future of Software Engineering Limerick, June 04-11, Ireland, pp: 213-226.
8.        Kitchenham, B. and Pfleeger, S.L., "Software  quality: the elusive target [special issues section]", IEEE Software, no. 1, pp. 12-21, 1996.
9.        Leveson, N., 1995. Software : System Safety and Computers.1st Edn., Addison-Wesley Publishing Company, Reading, Massachusetts.
10.     Grady, R. B., Practical software metrics for project management and process    improvement, Prentice Hall, 1992.
11.     Boehm, B. W., Brown, J. R., Kaspar, H., Lipow, M., McLeod, G., and Merritt, M., Characteristics of Software Quality, North Holland, 1978.
12.     Boehm, Barry W., Brown, J. R, and Lipow, M.Quantitative evaluation of software quality, International Conference on Software Engineering, Proceedings of the 2nd international conference on Software engineering, 1976.

66-70

14.

Authors:

A. Chenchu Deepa, B.Jayachandraiah

Paper Title:

CFD Analysis for Estimation of Efficiency of Low Pressure Steam Turbine

Abstract: The performance of steam turbine blade is related to many factors. One of the important factors is the degradation and change in turbine blade profile after many hours of operation. This leads to increased in flow losses and hence reduction in overall turbine efficiency. The performance of turbine blade can be predicted and improved by using Computational Fluid Dynamics (CFD). CFD is the art of numerically solving the governing fluid flow equations in order to obtain the descriptions of the complete flow-field of interest. With the availability of computer power and efficient numerical algorithm, CFD is becoming a very important tool for engineers in improving the performance of component involving fluid flows. The reason is that CFD effectively replace the needs to perform expensive experimental measurement and testing of new design and prototype. To  develop better performing blades, it is essential  to  identify  the  losses  generating mechanism  and  study  their  influence  and effects on performance. This paper outlines design considerations and the estimation of efficiency of LP Steam Turbine using CFD, thus aiding in optimizing the design and helps in integrating CFD into the design process itself. The CFD results are in concurrence with the analytical values.

Keywords:    CFD, steam turbine, Performance

References:
1.     Zamri, M.Y., “ An Improved Treatment of Two-Dimensional Two-Phase Flows of Steam by a Runge-Kutta Method”, Ph.D. Thesis, Department of Manufacturing and Mechanical Engineering, The University of Birmingham, U.K., 1997
2.     Bakhtar, F., So, K.S., “ A Study of Nucleating Flow of Steam in a Cascade of  Supersonic Blading by the Time-Marching Method”, International Journal oh Heat and Fluid Flow, Vol. 12, pp: 52-64., 1991
3.     Deckers, M., Simon, V., Scheuerer, G., “The Application of CFD to Advanced Steam Turbine Design”, International Journal of Computer Applications Technology, 1997.
4.     Bakhtar, F., Shojaee-Fard, M.H., Siraj, M. A., “An Experimental Facility for Studies of Nucleating and Wet Steam Flows in Turbine Blading”, IMechE Paper No. C423/003, 1991.
5.     Edwin Krämer, Hans Huber and Dr.Brendon Scarlin, ABB Power Generation, Low-pressure steam turbine retrofits.
6.     Paul Albert, Steam Turbine Thermal Evaluation and Assessment.

71-74

15.

Authors:

Rajib Baran Roy, Md. Ruhul Amin

Paper Title:

Design and Construction of Single Phase Cycloconverter

Abstract:  The variable frequency has always been of great importance in the industrial world. The generating station generates electricity of the frequency of 50 Hz which is not always applicable for some electrical appliances. Some electrical devices need variable frequency ranging from one tenth to one third of the supply frequency. Some examples are induction motors used in AC traction, aircraft power supplies, mobile power supplies and others. Therefore the variable frequency generation becomes necessary for meeting the ever growing demand of industrial application.  The cyclo-converter is such a device which generates variable frequency. The development of the semiconductor devices has made it possible to control the frequency of the cycloconverter according to the requirement and deliver a large amount of controlled power with the help of semiconductor switching devices like thyristors and others. The aim of this project is to design and construct a single phase cyclo-converter circuit which could generate variable frequency. The proper generation of the blanking and gate pulses of the switching devices and synchronizing them with the input signal is the most important thing in designing a cyclo-converter circuit which becomes easier due to the availability of the integrated circuit(IC). The use of 555 timer and operational amplifier ICs has simplified the generation of blanking and triggering signals. Moreover the synchronization of these signals with the input signal is performed by means of the comparator circuit where the operational amplifier IC is used. Due to the cost constraint, a transformer of secondary rating of 9V and 800mA is considered for designing the cycloconverter which delivers a power of about 7 W. This similar circuit can be used for large scale output power after some modifications in the control circuit. The cycloconverter circuit is designed as a prototype and the aspects of the commercial cycloconverter are not considered.

Keywords:     cycloconverter,intergrated cicuit synchronization, variable frequency generation, prototype

References:
1.        M. H. Rashid, Power Electronics Circuits, Devices and Application 6th edition, Copy right 2009, Prentice Hall, Inc Upper Saddle River, NJ.
2.        T. J. Malony, Modern Industrial Electronics, 5th Edition, 2008,  The McGraw Hill-Companies Inc. USA
3.        V.K Mehta and R. Mehta, Principles of Electronics(Multicolor Illustrative Edition),Copy right-2004,2003,2002, S. Chand and Company Ltd, New Delhi.
4.        T. L. Floyd, Electronics Fundamentals: Circuits, Devices and Applications ,7th Edition, 2008, 5.        J. Millman and Taub, Pulse, digital and switching wave forms, 4th Edition, 2008, The McGraw Hill-Companies Inc. USA.
6.        P. Malvino, Electronics Principles, 6th Edition, Copyright 1999, The McGraw Hill-Companies, Inc., USA.
7.        W. Stanley, Operational Amplifiers With Liner Integrated Circuits, 6th Edition, 2009, Prentice-Hall, Inc Upper Saddle River, NJ.
8.        M.K.Bagde, S.P.Singh and Kamal Singh, Elements of Electronics, 4th Edition, Copyright-2002, 2000, 1998, Rajendra Ravindra Printers (Pvt) Ltd, Ram Nagar, New Delhi.
9.        R. F. Coughlin and F. F. Driscoll, Operational Amplifiers and Linear Integrated Circuits5th Edition), Copy right-2001, 1998, 1991 by Prentice-Hall, Inc Simon and Schuster Viacom Company, Upper Saddle River, NJ.
10.     G. Hunter,  A milll motor inching drive using a three pulse cycloconverter with double integral phase control, 1998, http://www.pdfgeni.com/book/cycloconverter-pdf.html.
11.     S. Badri, J.Cook, M. Halpin and M. Nelms, Modelling and Analysis of Cycloconverter operation, Auburn University, Auburn, AL, Zip 36832.
12.     Highly flexible drive solutions and flexible power demands, Simens VIA Metal Technologies, http://www.industry.siemens.com/metals-mining/en/Electrics_Automation/motors_and_drives/cycloconverter.htm , dated24/10/09.

75-82

16.

Authors:

Swajeeth Pilot. Panchangam, V. N. A. Naikan

Paper Title:

Failure Analysis Methods for Reliability Improvement of Electronic Sensors

Abstract:   This paper has documented the common failure modes of electronic sensors. The effects of failure modes are studied in detail and these are classified based on their criticality and probability of occurrence. Methods for taking corrective actions for eliminating the occurrence of various failure modes are also proposed. The paper also addresses FRACAS method and its effectiveness for reliability studies of sensors based on the real failure modes observed in practice. It is understood that the designer has an important role in elimination of the failure modes at the design stage itself. This is expected to result in reliability growth of sensor systems used in many critical systems such as space applications, nuclear power plants, and chemical industries etc.

Keywords:  Sensor reliability, FMEA, FRACAS, reliability growth analysis, sensor failure modes.

References:
1.        David G. Edwardsi, “Testing for MOS IC Failure Modes,” IEEE Trans.Reliability. VoL. R-3152, No. 1, April 1982.
2.        Christopher. J. Price, David R. Pugh, Myra S. Wilson, and Neal Snooke,  “The Falme System: Automating Electrical failure Modes & Effect Analysis (FMEA),” IEEE Proceedings Annual Reliability and Maintanability symposium, 1995.    
3.        Gerard. C. M. Meijer, Senior Member, IEEE, Guijie Wang, and Fabiano Fruett, “Temperature Sensors and Voltage References Implemented in CMOS Technology,” IEEE Sensors Journal, Vol.1, No.3, October 2001.
4.        O. Tohyama, M. Kohashi, K. Yamamoto, H. Itoh, "A fiber-optic pressure sensor for ultra-thin catheders,” Sensors and Actuators A54, 622, 1996.
5.        Nur A. Touba and Edward J. McCluskey, “Test Point Insertion for Non-Feedback Bridging Faults,” Technical report, Centre for reliability testing, Stanford university, August 1996.
6.        Liu xiaoyu, Shao Jiang, Wang Yun, and Zeng Chenhui, “Research on Failure Modes and Mechanisms of Integrated Circuits,” IEEE  Prognostics & System Health Management Conference, 2011.
7.        Charles E. Ebeling, “Reliability and Maintainability Engineering,” McGraw-Hill International Editions, 1997.
8.        IEC 61014 Programmes for Reliability Growth.
9.        Loll Valter, “Optimizing the number of failure modes for design analysis based on physics of failures,” IEEE Trans., 2008.
10.     “Benchmarking commerial reliability practices,” Reliability Analysis Centre, www.dtic.mil.

83-87

17.

Authors:

Sarita Choudhary, Kriti Sachdeva

Paper Title:

Discovering a Secure Path in MANET by Avoiding Black/Gray Holes

Abstract:   Mobile ad hoc networks (MANET) are widely used in places where there is little or no infrastructure. A number of people with mobile devices may connect together to form a large group. Later on they may split into smaller groups. This dynamically changing network topology of MANETs makes it vulnerable for a wide range of attack. In this paper we propose a complete protocol for detection & removal of networking Black/Gray Holes by using OPNET network simulator 14.5; it is the latest version of simulation software.  Basically, OPNET allows you to build a network with a range of simulated "real-life" equipment, so different configuration options can be tested. And considering two different networks with 15nodes and 35 nodes in network and evaluating a security attack against MANET as a network, different statistics or performance metrics Packet loss, Packet delivery ratio and Average end to end delay has been used

Keywords:  -Mobile Ad-hoc Networks, Black Holes, Gray Holes, Routing, AODV, Routing Table .

References:
1.     Security Issues in Mobile Ad Hoc Networks- A Survey” Wenjia Li and Anupam Joshi, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County.
2.     Hongmei Deng, Wei Li, and Dharma P. Agrawal, ”Routing Security in Wireless Ad Hoc Network”, IEEE Communications Magzine, vol. 40, pp. 70-75, 2002.
3.     Sanjay Ramaswamy, Huirong Fu, Manohar Sreekantaradhya, John Dixon, and Kendall Nygard, ”Prevention of Cooperative Black Hole Attack in Wireless Ad Hoc Networks”. In Proceedings of 2003 International Conference on Wireless Networks (ICWN’03), Las Vegas, Nevada, USA, pp. 570-575.
4.     Sukla Banerjee “Detection/Removal of Cooperative Black and Gray Hole Attack in Mobile Ad-Hoc Networks” Proceedings of the World Congress on Engineering and Computer Science 2008 WCECS 2008, October 22 - 24, 2008, San Francisco, USA
5.     Piyush Agrawal, R. K. Ghosh, Sajal K. Das, Cooperative Black and Gray Hole Attacks in Mobile Ad Hoc Networks In Proceedings of the 2nd international conference on Ubiquitous information management and communication, Pages 310-314, Suwon, Korea, 2008.
6.     Sudath Indrasinghe, Rubem Pereira, John Haggerty,“Conflict Free Address Allocation Mechanism for Mobile Ad Hoc Networks”, 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW'07)
7.     Mansoor Mohsin and Ravi Prakash,”IP Address Assignment in a mobile ad hoc network”, The University of Texas at Dallas Richardson, TX Kaixin Xu, Xiaoyan Hong, Mario Gerla Computer Science Department at UCLA, Los Angeles, CA 90095 project under contract N00014-01-C-0016
8.     Poongothai T. and Jayarajan K., “A non-cooperative game approach for intrusion detection in Mobile Adhoc networks”, International Conference of Computing, Communication and Networking (ICCC), 18-20 Dec 2008, St. Thomas, VI, pp 1-4
9.     Bo Sun,Yong Guan,Jian Chen,Udo , “Detecting Black-hole Attack in Mobile Ad Hoc Network” , The institute of Electrical Engineers, Printed and published by IEEE, 2003.
10.  Satoshi Kurosawa, Hidehisa Nakayama, Nei Kato, Abbas Jamalipour, and Yoshiaki Nemoto, “Detecting Black hole Attack on AODV-based Mobile Ad Hoc Networks by Dynamic Learning Method”, International Journal of Network Security, Vol.5, issue 3, Nov 2007, pp 338–346.
11.  Chang Wu Yu, Tung-Kuang Wu, Rei Heng Cheng, and Shun Chao Chang,“A Distributed and Cooperative Black Hole Node Detection and Elimination Mechanism for Ad Hoc Network” , Springer-Verlag Berlin Heidelberg, 2007.
12.  Payal N. Raj and Prashant B. Swadas, “DPRAODV: A dynamic learning system against black hole attack in AODV based MANET”, International Journal of Computer Science Issues (IJCSI), Volume 2, Number 3, 2009, pp 54-59
13.  S. Ramaswamy, H. Fu, M. Sreekantaradhya, J. Dixon, and K. Nygard, “Prevention of cooperative black hole attack in wireless ad hoc networks,” International Conference (ICWN’03), Las Vegas, Nevada, USA, 2003, pp 570-575
14.  Mohammad Al-Shurman, Seong-Moo Yoon and Seungjin Park, “Black Hole Attack in Mobile Ad Hoc Networks”, ACM Southeast Regional Conference , Proceedings of the 42nd  annual Southeast regional conference, 2004, pp 96-97
15.  Chang Wu Yu, Tung-Kuang, Wu, Rei Heng, Cheng and Shun Chao Chang, “A Distributed and Cooperative Black Hole Node Detection and Elimination Mechanism for Ad Hoc Networks”, PAKDD 2007 International Workshop, May 2007, Nanjing, China, pp 538–549
16.  Satoshi Kurosawa, Hidehisa Nakayama, Nei Kato, Abbas Jamalipour, and Yoshiaki Nemoto, “Detecting Blackhole Attack on AODV-based Mobile Ad Hoc Networks by Dynamic Learning Method”, International Journal of Network Security, Volume 5, Number 3, 2007, pp 338–346
17.  Hongmei Deng, Wei Li, and Dharma P. Agrawal, “Routing Security in Wireless Ad Hoc Network”, IEEE Communications Magazine, Volume 40, Number 10, 2002, pp 70-75
18.  Latha Tamilselvan and V Sankaranarayanan, “Prevention of Black hole Attack in MANET”, Journal of networks, Volume 3, Number 5, 2008, pp 13-20
19.  Bo Sun Yong, Guan Jian Chen and Udo W. Pooch, “Detecting Black-hole Attack in Mobile Ad Hoc Networks”, The Institution of Electrical Engineers (IEE) ,Volume 5, Number 6, 2003, pp 490-495
20.  Marc Greis, “Tutorial for the Network Simulator”,
21.  http:// www.isi.edu/nsnam/ns/tutorial/index.html.
22.  http://en.wikipedia.org/wiki/Personal_area_network, 25 July 2005.
23.  T. Franklin, “Wireless Local Area Networks”, Technical Report http://www.jisc.ac.uk/uploaded_documents/WirelessLANTechRep.pdf. 25 July 2005.
24.  J. Reynold, “Going Wi-Fi”, Chapter 6, The Wi-Fi Standards Spelled out, Pg. 77.
25.  http://certifications.wi-fi.org/wbcs_certified_products.php 25 July 2005.
26.  P. Misra,. “Routing Protocols for Ad Hoc Mobile Wireless Networks”,http://www.cse.wustl.edu/~jain/cis788-99/adhoc_routing/index.html, 14 May 2006.
27.  P. Yau and C. J. Mitchell, “Security Vulnerabilities in Adhoc Network”.
28.  G. Vigna, S. Gwalani and K. Srinivasan, “An Intrusion Detection Tool for AODV-Based Ad hoc Wireless Networks”, Proc. of the 20th Annual Computer Security Applications Conference (ACSAC’04).
29.  P. Ning and K. Sun, “How to Misuse AODV: A Case Study of Insider Attacks Against Mobile Ad-Hoc Routing Protocols”, Proc.of the 2003 IEEE Workshop on Information Assurance United States Military Academy, West Point, NY., June 2003. Computer Science, 2008

88-93

18.

Authors:

Kartik Sharma, Gianetan Singh Sekhon

Paper Title:

Design and Development of Ball Catching Robotic Arm

Abstract: This paper presents a ball catching robotic arm system with 3DOF assembled from the commercially available parts. Other than the previous research work a mechatronically complex system were designed. We have designed a very simple arm which is able to move for catching the ball when it has to move. The given robotic arm system is low cost implementation compare to the previous one. In this system, a single camera system is use to perceive the trajectory of the ball, the system detects the ball in each frame with the help of a fast mean shift algorithm. It calculates the shift of the mean of the identified color intensity and according to that it sends the control commands over the serial port to the robotic arm via ZigBee. The basic objective to catch the flying object at the expected location. This catcher robotic arm can catch the ball thrown to it from 5-6 meter with an average success rate of 70-75%.

Keywords:  - Ball Catching Robotic Arm; Robotics; Robotic Vision

References:
1.        T. Koivo and A. J. Koivo,” Catchability of a Moving Object by a Robot”, IEEE/ICMC Proceedings of International Conference on Man and Cybernetics Systems, pp 2911- 2916 Vol. 3 2005.
2.        Christian Smith and Henrik I Christensen,” Constructing a High Performance Robot from Commercially Available Parts” IEEE/RAS Robotics and Automation Magazine, Vol 16:4, pp. 75-83, Dec 2009.
3.        U. Frese et al.,” Off-the-Shelf Vision for a Robotic Ball Catcher”, IEEE/RSJ International Conference on  Intelligent Robots and Systems, pp 1623- 1629 vol.3, 2001
4.        B. Hove and J. Slotine, “Experiments in robotic catching,” in Proceedings of the 1991 American Control Conference, 1991, pp. 380–385.
5.        W. Hong and J. Slotine, “Experiments in hand-eye coordination using active vision,” in Proceedings of the Fourth International Symposium on Experimental
Robotics, ISER95, 1995.
6.        M. Riley and C. G. Atkeson, “Robot catching: Towards engaging human-humanoid interaction,” Autonomous Robots, vol. 12, pp. 119– 128, 2002.
7.        C. Smith and H. I. Christensen, “Using COTS to construct a high performance robot arm,” in Proc. of the IEEE Intern. Conf. on Robotics and Automation, 2007.
8.        G. Batz, et al., “Dynamic manipulation: Nonprehensile ball catching” in Proc. of the IEEE Mediterranean Conf. on Control and Automation, 2010.
9.        K. Nishiwaki at al.  “The humanoid saika that catches a thrown ball,” in Proceedings of the IEEE International Workshop on Robot and Human Communication, 1997, pp. 94–99.
10.     Bradski, G.R.et al. “Computer Vision Face Tracking For Use in a Perceptual User Interface”, Microcomputer Research Lab, Santa Clara, CA, Intel Corporation, 1998.
11.     Cheng. Y, “Mean shift, mode seeking, and clustering”. IEEE Transactions on Pattern Analysis and Machine Intelligence, pp 790-799 vol 7, 1995.
12.     Fukunaga, K. & Hostetler, L. (1975). The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on
Information Theory, pp 32–40 vol 21, 1975
13.     R. O. Duda and P. E. Hart, “Use of the Hough transformation to detect lines and curves in pictures,” Comm. of the ACM, vol. 15, , pp. 11–15, 1972.
14.     C. Kimme et al, “Finding circles by an array of accumulators,” Comm. of the ACM,vol. 18, pp. 120–122, 1975.
15.     H. Yuen et al. “A comparative study of Hough transform methods for circle finding,” in Proc. of the Alvey Vision Conf., 1989.
16.     Oliver Birbach et al.,” Real-time Perception for Catching a Flying Ball with a Mobile   Humanoid” (ICRA), 2011 IEEE International Conference on Robotics and Automation, pp 5955- 5962, May 2011

94-99

19.

Authors:

Priyanka Sharma, Anu Aggarwal

Paper Title:

Modified Dynamic Algorithm of Data Clustering Using Fuzzy C Mean Algorithm

Abstract:  Clustering is a division of data into group of similar objects. Each group, called a cluster, consists of objects that are similar between them selves and dissimilar compared to objects of other group. Dynamic-means is a widely used clustering method. While there are considerable research efforts to characterize the key features of K-means clustering, further investigation is needed to reveal whether the optimal number of clusters can be found. This paper presents a modified Dynamic-means algorithm with the intension of improving cluster quality.In dynamic mean algorithm each data elements can be a member of one and only one cluster at a time. The proposed works apply Fuzzy c means algorithm over dynamic-means algorithm to improve the membership grade i.e. each data element can show their membership in each and every clusters.

Keywords:  clustering, dynamic mean clustering and fuzzy c mean clustering.

References:
1.        Ahamad Shafeeq and Hareesha”Dynamic clustering of data with modified K-mean algorithm”, 2012.
2.        Ran Vijay Singh and M.P.S Bhatia , “Data Clustering with Modified K-means Algorithm”, IEEE International Conference on Recent Trends in Information Technology, ICRTIT 2011, pp 717-721.
3.        Shi Na., Liu Xumin, Guan Yon , "Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm", Third International Symposium on Intelligent Information Technology and Security Informatics(IITSI), pp.63-67, 2-4 April 2010.
4.        Grigorios F. Tztzis and Aristidis C. Likas, “The Global Kernel k-Means Algorithm for Clustering in Feature Space”, IEEE Trans. On Neural Networks, Vol. 20, No. 7, July 2009, pp. 1181-1194.
5.        Wei Li, “Modified K-means clustering algorithm”, IEEE computer society Congress on Image and Signal Processing, 2008, pp. 618-621.
6.        Mohamad Alata, Mohamad Molhim and Abdullah Ramini”Produce and optimization of Fuzzy C mean clustering algorithm using GA”, 2008.
7.        David Altman, Efficient Fuzzy Clustering of Multi-spectral Images, FUZZ-IEEE , 1999
8.        Vicenc Torra, 2004” Fuzzy C-means For fuzzy hierarchical clustering”.

100-103

20.

Authors:

Barun Mazumdar

Paper Title:

A Compact Microstrip antenna for X band Application

Abstract:   In the recent years the development in communication systems requires the development of low cost, minimal weight, low profile antennas that are capable of maintaining high performance over a wide spectrum of frequencies. This technological trend has focused much effort on the design of a Microstrip patch antenna. A single feed compact Circular microstrip antenna is presented in this paper. L slits are introduced at the right edge of the patch to reduce the resonant frequency. For the proposed antenna resonant frequency obtained at 11.4 GHz with -38.2 dB return loss &. 21GHz Bandwidth which is suitable for X band application.

Keywords:   Compact, Conventional, patch, slit.

References:
1.     Jui-Han Lu “Broadband Dual-Frequency Operation of Circular Patch   Antennas and Arrays with a Pair of L shaped Slots,” IEEE Transactions on Antenna and propagation, Vol.51, pp 1018-1023 No.5, May 2003.
2.     Jui - Han Lu “Bandwidth Enhancement Design of Single layer Slotted Circular Microstrip Antennas,” IEEE Transactions on Antenna and propagation, Vol.51, pp 1126-1129 No.5, May 2003.
3.     Y. J. Sung and Y.S Kim “Circular Polarized Microstrip Patch Antenna for Broadband and Dual Band Operation ”Electronics letters 29th April 2004, Vol.40 no.9
4.     Ho, M.-H. Hsu, C.-I.G.Dept. of Electron. Eng., Nat. Changhua Univ. of Educ., Changhua City, Taiwan, Circular-waveguide-fed microstrip patch antennas, IEEE transaction Issue Date : 27 Oct. 2005  Volume : 41 , Issue:22
5.     Boyon Kim “Novel Single-Feed Circular Microstrip Antenna with Reconfigurable Polarization Capability,” IEEE Transaction on Antenna and Propagation, Vol. 56, No.3 March 2008
6.     Kin-Lu Wong, Compact and Broadband Microstrip Antennas, Jon Wiley & Sons, Inc.,2002
7.     S. Bhunia, M.-K. Pain, S. Biswas, D. Sarkar, P. P. Sarkar, and B. Gupta, “Investigations on Microstrip Patch Antennas with Different slots and Feeding Points” , Microwave and Optical Technology Letters, VOL 50, NO. 11, November 2008 pp   2754-2758.
8.     J.-S. Kuo and K.-L. Wong, “A compact microstrip antenna with meandering slots in the ground plane,” Microwave and Optical Technology Letters, vol. 29, no. 2, pp. 95–97, 2001.
9.     C.A.Balanis, “Advanced Engineering Electromagnetics”, John Wiley & Sons., New York, 1989.
10.  Zeland Software Inc. IE3D: MoM-Based EM Simulator.
11.  Web: http://www.zeland.com

104-106

21.

Authors:

Prateek Sharma, Kapil Kumar, Ajay Kumar Singh

Paper Title:

Trapping Parallel Port to Operate 220V Appliances

Abstract:  With advancement of technology things are becoming simpler and easier for us. Automation is the use of Technology to reduce Human work. Automatic systems are being preferred over manual system. Internet controlling offers a new approach to control electric appliances from a remote terminal, using the Internet, Bluetooth and Local Area Network connection. This system is accomplished by personal computers, parallel port, local area network connection, internet connection, mobile phone and Bluetooth device. The system is designed to control home appliances ‘on/off’, to regulate their output power. The prototype of this system was tested and it responded successfully, which verifies the feasibility of this system’s theory and concept. In this paper we have tried to show automatic control of a home appliances as a result of which power can be saved to some extent.

Keywords: Home Automation, Remote Access, Blue jacking Parallel Port.

References:
1.       Marriam Butt, Mamoona Khanam, Aihab Khan, Malik Sikandar Hayat Khiyal,  “Controlling Home Appliances Remotely Through Voice Command ,” International Journal of Advanced Computer Science and Application, pp. 35-39, Nov. 2011.
2.       Z. Ahmed, “Home Automation,” 9th National Research Conferrence on Managemant and Computer Sciences, SZABIST Institute of Science and Techonology, Pakistan, pp. 1–3, 2009.
3.       A.Alheraish, “Design and Implementation of Home Automation System,” IEEE Trans. on Consumer Electronics, vol. 50, no. 4, pp. 1087-1092, Nov. 2004.
4.       Inderpreet Kaur, “Microcontroller Based Home Automation System with Security,” International Journal of Advanced Computer Science and Applications, vol. 1, no. 6, pp.60-65, Dec. 2010
5.       Erik C. W. de Jong, J. A. Ferreira and Pavol Bauer “Toward the Next Level of PCB Usage in Power Electronic Converters,” IEEE Trans. on Power Electronics,” vol. 23, no. 6,  pp. 3151-3163, Nov. 2008.
6.       S.Schneider, J. Swanson, Peng-Yung Woo, “Remote Telephone Control System,” IEEE Trans. on Consumer Electronics, vol. 43, no. 2, pp. 103-111, May 1997.
7.       R. Al-Ali, M. AL-Rousan, “Java-Based Home Automation System,” IEEE Trans. on Consumer Electronics, vol. 50, no. 2, pp. 498-504, May 2004
8.       Bisikian, “An overview of the Bluetooth Wireless Technology,” IEEE Communications Magazine, vol. 39,  pp. 86-94, Dec. 2001.
9.       Muralidhar Medidi and Jonathan Campbell, “Energy-Efficient Bounded-diameter Tree Scatternet for Bluetooth PANs,” IEEE Conference on Local Computer Networks, pp. 268 – 275, Nov. 2005.
10.    H. Kanma, N. Wakabayashi, R. Kanazawa, H. Ito, “Home Appliance Control System over Bluetooth with a Cellular Phone,” IEEE Trans. on Consumer Electronics, vol. 49, no. 4, pp. 1049-1053, Nov. 2003.
11.    Yashvant Kanitkar, “Let us C,” Second Edition, BPB Publication, ISBN 81-7029-534-3, 1995.

107-110

22.

Authors:

Shikha Garg, Gianetan Singh Sekhon

Paper Title:

Shape Analysis and Recognition Based on Oversegmentation Technique

Abstract:   Shape recognition plays an important role in machine vision applications. This paper represents a modified shape recognition algorithm to predict the shapes of different objects using oversegmentation technique. This algorithm predicts the different shapes of objects based on two parameters corners, and the dimensions (length, breadth) of a particular object in a prediction table. Obviously, the boundary of shape is an important property in shape representation and description. In proposed method, the boundaries of various objects are extracted using some morphological operations. Then the feature extraction process is then applied on the detected shapes. Finally for shape recognition we will match the features of the current object with the preloaded features in the database or we can say training set for recognition. Also profiling of the algorithm is done to measure the execution time. With the help of the mathematical relation it can predict the accuracy of algorithm execution.

Keywords:  geometrical shapes, morphological operations, oversegmentation, shape recognition.

References:
1.        S.Thilagamani, N.Shanthi, “A Novel Recursive Clustering Algorithm for Image algorithm,” European Journal of Scientific Research, ISSN 1450-216X, Vol.52, No.3, 2011.
2.        Ehsan Moomivand, “A Modified Structural Method for Shape Recognition,” IEEE Symposium on Industrial Electronics and Applications (ISIEA2011), September 25-28, Langkawi, Malaysia, 2011.
3.        Jon Almaz´an, Alicia Forn´ Third es, Ernest Valveny, “A Non-Rigid Feature Extraction Method for Shape Recognition,” International Conference on Document Analysis and Recognition, Spain, 2011.
4.        Kosorl Thourn and Yuttana Kitjaidure, “Multi-View Shape Recognition Based on Principal Component Analysis,” International Conference on Advanced Computer Control Department of Electronics, Bangkok, 10520, 2008.
5.        Sen Wang, Yang Wang, Miao Jin, Xian feng David Gu, and Dimitris Samaras, “Conformal Geometry and Its Applications on 3D Shape Matching, Recognition, and Stitching,” IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 29, NO. 7, JULY 2007.6.        Ruixia Song , Zhaoxia Zhao, Yanan Yanan Li,
Qiaoxia   Zhang, Xi Chen, “The Method of Shape Recognition Based on V-system,” Fifth International Conference on Frontier of Computer Science and Technology, China, 100144,2010.
7.        Tudor Barb, “Automatic Unsupervised Shape Recognition Technique using Moment Invariants,” unpublished, 2011.
8.        S. W.Chen, S. T. Tung, and C. Y. Fang, “Extended Attributed String Matching for Shape Recognition,” COMPUTER VISION AND IMAGE UNDERSTANDING, Vol. 70, No. 1, April, pp. 36–50, 1998.
9.        Sajjad Baloch, “Object Recognition Through Topo-Geometric Shape Models Using Error-Tolerant Subgraph Isomorphism,” IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 5, 1191, 2010.
10.     Suhas G. Salve, “Shape Matching and Object Recognition Using Shape Context”, IEEE 978-1-4244-5540-9, 2010.
11.     Donggang Yu1, Jesse S. Jin1, Suhuai Luo1, Wei Lai, Mira Park1 and Tuan D. Pham,” Shape Analysis and Recognition Based on Skeleton and Morphological Structure,” Seventh International Conference on Computer Graphics, Imaging and Visualization, 2010.

111-115

23.

Authors:

Suresh Kumar Pittala, Swajeeth Pilot. Panchangam, A. Jhansi Rani

Paper Title:

Reliability Prediction for Low Power Adiabatic Logic Families

Abstract: This paper focuses on predicting reliability of low power adiabatic logic families. Reliability logic diagram for each logic family is briefly discussed. Power dissipation is an important aspect of digital computing systems because of the increasing demand for portable electrical digital systems. Unlike conventional CMOS logic circuits, adiabatic circuits recover and reuse circuit energy that would otherwise be dissipated as heat and thus improve the portability of system. Development of adiabatic logic as an approach to reduce the energy dissipation of the digital circuits has became a major focus of interest over the last one decade. In this paper, we performed simulations at the schematic level using a standard 0.18 µm CMOS technology. The performance and power dissipation of the logic styles are evaluated for a maximum frequency of operation of 100MHz.

Keywords: Reliability prediction, Adiabatic system, low power digital system, CMOS logic circuits, Power dissipation.

References:
1.        Y. Moon, and D. Jeong, “ An efficient adiabatic charge – recovery logic,” IEEE Trans. on solid state circuits, Vol. 31, No. 4, pp. 514-522, April 1996.
2.        V. G. Oklobdzija, D. Maksimovic, and F. Lin, “Pass- transistor adiabatic logic using single power-clock supply,” IEEE Trans. on circuits and systems-11: analog and digital signal processing, Vol. 44, No. 10, pp. 842-846, 1997.
3.        Pierrs Savaq et. al. 2001, "Dual-band multi-mode power amplifier module  Using a third generation HBT technology", IEEE GaAS IC Symposium technical digest. 2001, pp. 71-74.
4.        C. Long, J. Xiong, and L. He. 2004, "On optimal physical synthesis of sleep transistors," in Proc. ISPD, pp. 156-161.
5.        Thylen, L., 2006, “A moores law for photonics,” IEEE symposium on. Bio photonics, Nanophotonics, and Meta materilas, pp.256-263.
6.        Muhammad Arsalan and Maitham Shams, “An Investigation into Transistor-Based Adiabatic Logic Styles,” IEEE Trans. on Analog techniques, pp.1- 4, 2004.
7.        William J. Kerscher, and Flint, “Failure- Time Distribution of Electronic Components,” Proceedings Annual  Reliability and Maintainability Symposium. 1988.
8.        Charles E. Ebeling, “Reliability and Maintainability Engineering,” McGraw-Hill International Editions, 1997.

116-121

24.

Authors:

Davesh Singh Som, Dhananjaya Singh

Paper Title:

Performance Analysis and Simulation of AODV, DSR and TORA Routing Protocols in MANETs

Abstract:  Mobile ad hoc network (MANET) is an autonomous system of mobile nodes connected by wireless links. Each node operates as an end system and also as a router to forward packets. The nodes are free to move about and organize themselves into a network. These nodes change position frequently. They can be studied formally as graphs in which the set of edges varies in time. The main method for evaluating the performance of MANETs is simulation. In this paper work an attempt has been made to compare the performance of three on-demand routing protocols for MANETs:- Ad hoc On Demand Distance Vector (AODV), Dynamic Source Routing (DSR) protocols, and Temporally Ordered Routing Algorithm (TORA) with  respect  to  the  three  performance metrics: average End-to-End delay, throughput and packet delivery ratio. The performance differentials are analyzed using varying number of nodes. These simulations are carried out using the ns-2 network simulator. The results presented in this work illustrate the importance in carefully evaluating and implementing routing protocols in an adhoc environment.

Keywords:  AODV, DSR, MANETs, Route Reply, Route Request, Throughput. TORA.

References:
1.        Amit N. Thakare, Mrs. M. Y. Joshi, “Performance Analysis of AODV & DSR Routing Protocol in Mobile Ad hoc Networks” IJCA Special Issue on “Mobile Ad-hoc Networks” MANETs 2010, pp. 211-218
2.        Krishna Gorantala, “Routing Protocols in Mobile Ad-hoc Networks”, Umea University, Sweden, June-2006.
3.        Geetha Jayakumar and Gopinath Ganapathy, "Performance Comparison of Mobile Ad-hoc Network Routing Protocol”, International Journal of Computer Science and Network Security (IJCSNS), VOL.7 No.11, pp.77-84 November 2007.
4.        Elizabeth M. Royer and Chai-Keong Toh,”A review of current  routing protocols for ad hoc mobile wireless networks”, Technical report, University of California and Georgia Institute of Technology, USA, 1999.pp.46-55
5.        Anuj K. Gupta, Dr. Harsh Sadawarti, Dr. Anil K. Verma, “Performance analysis of AODV, DSR & TORA Routing Protocols” IACSIT International Journal of Engineering and Technology Vol.2, No.2, April 2010
6.        Baruch Awerbuch and Amitabh Mishra, “Dynamic Source Routing (DSR) Protocol”, johns hopkins university,US.
7.        C.Y. Chong and S.P. Kumar, “Sensor Networks: Evolution, Opportunities, and Challenges,” Proc. IEEE, vol. 91, no. 8, pp.1247-1256, Aug. 2003.
8.        Liu, Z.; Kwiatkowska, M.Z; Constantinou, C. A Biologically Inspired QOS Routing Algorithm for Mobile Ad Hoc Networks. In 19th International Conference on Advanced Information Networking and Applications, 2005; pp. 426–431.
9.        Luke Klein-Berndt, “A Quick Guide to AODV Routing”, National Institute of Standards and Technology, US.
10.     Abdul Hadi Abd Rahman and Zuriati Ahmad Zukarnain, “Performance Comparison of AODV, DSDV and I-DSDV Routing Protocols in Mobile Ad Hoc Networks”, European Journal of Scientific Research, ISSN 450-216X Vol.31 No.4, pp.566-576, 2009.
11.     Padmini Misra, “Routing Protocols for Ad Hoc Mobile Wireless Networks”
12.     Larry L. Peterson and Bruce S. Davie “Computer Networks –A Systems Approach”, San Francisco, Morgan Kaufmann Publishers, fifth edition
13.     M. Zonoozi and P. Dassanayake, “User mobility modeling and characterization of mobility patterns”, IEEE Journal on Selected Areas in Communications, pp.1239-1252, September 1997.
14.     Baruch Awerbuch and Amitabh Mishra, “Ad hoc On Demand Distance Vector (AODV) Routing Protocol”, johns Hopkins university, US.

122-127

25.

Authors:

Preeti Markan, Balwinder Singh

Paper Title:

Object Based Real Time Lossless Video Compression – A Review

Abstract:   This paper describes video compression in real time. The aim is to achieve higher compression ratio in lossless compression. Efficient compression is achieved by separating the moving objects from stationary background and compactly representing their shape, motion, and the content. Video compression techniques are used to make efficient use of the available bandwidth. Lossless means that the output from the decompressor is bit-for-bit identical with the original input to the compressor. The decompressed video should be completely identical to original. In addition to providing improved coding efficiency in real time the technique provides the ability to selectively encode, decode, and manipulate individual objects in a video stream. The technique used results in video coding that a high compression ratio can be obtained without any loss in data in real time.

Keywords:   Compression Ratio, Motion Detection, Video Compression.

References:
1.        Raj Talluri, Member, IEEE, Karen Oehler, Member, IEEE, Thomas Bannon, Jonathan D. Courtney, Member, IEEE, Arnab Das, and Judy Liao “A Robust, Scalable, Object-Based Video Compression Technique for Very Low Bit-Rate Coding”, IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 7, NO. 1,
FEBRUARY 1997.
2.        G.Suresh, P.Epsiba, Dr.M.Rajaram, Dr.S.N.Sivanandam “A LOW COMPLEX SCALABLE SPATIAL ADJACENCY ACC-DCT BASED VIDEO COMPRESSION METHOD”, 2010 Second International conference on Computing, Communication and Networking Technologies.
3.        Tarek Ouni, Walid Ayedi, Mohamed Abid “New low complexity DCT based video compression Method”, 2009 IEEE.
4.        K.Uma, P.Geetha palanisamy, P.Geetha poornachandran “Comparison of Image Compression using GA, ACO and PSO techniques” , IEEE-International Conference on Recent Trends in Information Technology, ICRTIT 2011.
5.        Tzong-Jer Chen, Keh-Shih Chuang “A Pseudo Lossless Image Compression Method”, 2010 3rd International Congress on Image and Signal Processing.
6.        Nasir D. Memon, Khalid Sayood “Asymmetric Lossless Image Compression”, 2011IEEE
7.        S.L. Lahudkar and R.K. Prasad “Real Time Video Compression  implemented using adaptive block transfer/ motion compensation for lower bit rates” Journal of Engineering Research and Studies E-ISSN0976-7916.

128-130

26.

Authors:

Anil Kumar Neelapala, Mehar Niranjan Pakki

Paper Title:

Analyzing the Severity of the Diabetic Retinopathy and Its Preventive Measures by Maintaining Database Using Gui In Matlab

Abstract: Diabetic-related eye disease is a major cause of blindness in the world. It is a complication of diabetes which can also affect various parts of the body. When the small blood vessels have a high level of glucose in the retina, the vision will be blurred and can cause blindness eventually, which is known as diabetic retinopathy. Regular screening is essential to detect the early stages of diabetic retinopathy for timely treatment and to avoid further deterioration of vision. This project aims to detect the presence of abnormalities in the retina such as the structure of blood vessels, micro aneurysms and exudates using image processing techniques by automating the detection of Diabetic retinopathy (DR). This Process is achieved by the fundus images using morphological processing techniques to extract features such as blood vessels, micro aneurysms and exudates and then we calculate the area of each extracted feature. Depending on the area of each feature we classify the severity of the disease. Then finally by knowing the severity of the disease corresponding treatment measures can be analyzed. In addition to this, well established database have been developed regarding the disease analysis of patients which is implemented using GUI in MATLAB. It will surely help to reduce the risk and increase efficiency for ophthalmologists.

Keywords: Diabetic Retinopathy, Exudates, Fundus Camera, Micro-aneurysms, Morphological Operations, Segmentation.

References:
1.     R Acharya, C M Lim, E Y K Ng, C Chee and T Tamura. ‘Computer- based detection of diabetes retinopathy stages using digital fundus images’.
2.     Singapore Association of the Visually Handicapped. http://www.savh.org.sg/info_cec_diseases.php.
3.     What is Diabetic Retinopathy?  http://www.news-medical.net/health/ What-is-Diabetic-Retinopathy.aspx.
4.     Diabetic Retinopathy. http://www.hoptechno.com/book45.htm.James L. Kinyoun, Donald C. Martin, Wilfred Y. Fujimoto, Donna L. Leonetti. Opthalmoscopy Versus Fundus Photographs for Detecting and Grading Diabetic Retinopathy.
5.     Salvatelli A., Bizai G., Barbosa G.Drozdowicz and Delrieux (2007), ‘A comparative analysis of pre-processing techniques in colour retinal images’, Journal of Physics: Conference series 90.
6.     Andrea Anzalone, Federico Bizzari, Mauro Parodi, Marco Storace (2008), ‘A modular supervised algorithm for vessel segmentation in red-free retinal images’, Computers in Biology and Medicine, Vol. 38, pp. 913-922.
7.     Daniel Welfer, Jacob Schacanski, Cleyson M.K., Melissa M.D.P., Laura W.B.L., Diane Ruschel Marinho (2010), ‘Segmentation of the optic disc in color eye fundus images using an adaptive morphological approach’, Journal on Computers in Biology and Medicine”, Vol. 40, pp. 124-137.
8.     Cemal Kose, Ugur Sevik, Okyay Gencalioglu (2008), ‘Automatic segmentation of age-related macular degeneration in retinal fundus images’, Computers in Biology and Medicine,Vol.38, pp. 611-619.
9.     Dietrich Paulus and Serge Chastel and Tobias Feldmann (2005), ‘Vessel segmentation in retinal images’, Proceedings of SPIE, Vol. 5746, No.696.
10.  Ana Maria Mendonca and Aurelio Campilho (2006), ‘Segmentation of Retinal Blood Vessels by Combining the Detection of centerlines and Morphological Reconstruction’, IEEE Transaction on Medical Imaging, Vol. 25, No. 9, pp. 1200-1213.
11.  Jagadish Nayak, Subbanna Bhat (2008), ‘Automated identification of diabetic retinopathy stages using digital fundus images’, Journal of medical systems, Vol.32, pp. 107-115.
12.  Akara Sopharak, Bunyarit Uyyanonvara, Sarah Barman, Thomas H.Williamson (2008), ‘Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods’, Computerized Medical Imaging and Graphics, Vol. 32, pp. 720-727.
13.  ANOVA test for severity of disease. http://afni.nimh.nih.gov/pub/dist/ HOWTO/howto/ht05_group/html/background_ANOVA.shtml
14.  ‘Save’ function in matlab. 
15.  http://www.mathworks.in/help/techdoc/ref/save.html
16.  ‘Load’ function in matlab.http://www.mathworks.in/help/techdoc/ref/load.html
17.  ‘strvcat’ function in matlab. http://www.mathworks.in/help/techdoc/ref/strvcat.html
18.  Graphical User Interfaces (GUIs) Matlab, http://www.mathworks.com

131-136

27.

Authors:

Sonali Surawdhaniwar, Ritesh Diwan

Paper Title:

An Improved Approach of Perturb and Observe Method Over Other Maximum Power Point Tracking Methods

Abstract:     Maximum power point trackers (MPPTs) participate in photovoltaic (PV) power systems for the reason that they maximize the power output from a PV system for a given set of conditions, and therefore maximize the array efficiency. Thus, an MPPT can minimize the overall system cost. MPPTs find and sustain action at the maximum power point, using an MPPT algorithm. Many such algorithms have been proposed. However, one particular algorithm, the perturb-and-observe (P&O) method, claimed by many in the literature to be inferior to others, continues to be by far the most widely used method in viable PV MPPTs. Part of the reason for this is that the published comparisons between methods do not include an experimental comparison between multiple algorithms with all algorithms optimized and a standardized MPPT hardware. This paper provides such a comparison. MPPT algorithm performance is quantified through the MPPT efficiency. In this work, results are obtained for three optimized algorithms. It is found that the P&O method, when properly optimized, can have MPPT efficiencies well in excess of 97%, and is highly competitive against other MPPT algorithms

Keywords:  Maximum Power Point Tracking, MPPT efficiency, Power Electronics

References:
1.        Maximum Power Point Tracking, MPPT efficiency, Power ElectronicsRobert C.N. Pilawa-Podgurski, Nathan A. Pallo, Walker R. Chan, David J. Perreault, Ivan L. Celanovic, Low-Power Maximum Power Point Tracker with Digital Control for Thermophotovoltaic Generators 978-1-4244-4783-1/10/$25.00 ©2010 IEEE.
2.        Greg Smestad and Patrick Hamill, Concentration of solar radiation by white backed photovoltaic panels, APPLIED OPTICS / Vol. 23, No. 23 / 1 December 1984.
3.        D. P. Hohm and M. E. Ropp,Comparative Study of Maximum Power Point Tracking Algorithms, Published online 22 November 2002 Received 12 February 2002
Copyright # 2002 John Wiley & Sons, Ltd. Revised 2 June 2002
4.        Nicola Femia, Member, IEEE, Giovanni Petrone, Giovanni Spagnuolo, Member, Optimization of Perturb and Observe Maximum Power Point Tracking Method IEEE, and Massimo Vitelli IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 20, NO. 4, JULY 2005.
5.        C. Liu, B. Wu and R. Cheung Department of Electrical & Computer Engineering, Ryerson University, Toronto, Ontario, Canada M5B 2K3, ADVANCED ALGORITHM FOR MPPT CONTROL OF PHOTOVOLTAIC SYSTEMS, Canadian Solar Buildings Conference Montreal, August 20-24, 2004.
6.        M.Lokanadham and K.Vijaya Bhaskar, Incremental Conductance Based Maximum Power Point Tracking (MPPT) for Photovoltaic System, International Journal of Engineering Research and Applications (IJERA), Vol. 2, Issue 2,Mar-Apr 2012, pp.1420-1424.
7.        Jae Ho Lee , HyunSu Bae and Bo Hyung Cho Seoul National University School of Electrical Engineering and Computer Science, Advanced Incremental Conductance MPPTcAlgorithm with a Variable Step Size, 1-4244-0121-6/06/$20.00 ©2006 IEEE.
8.        Ratna Ika Putri and M. Rifa’I Maximum Power Point Tracking Control for PhotovoltaicSystem Using Neural Fuzzy, International Journal of Computer and Electrical Engineering, Vol.4, No.1, February 2012.
9.        Steven L. Brunton, Clarence W. Rowley, Sanjeev R. Kulkarni, Fellow, IEEE, and Charles Clarkson, Maximum Power Point Tracking for Photovoltaic Optimization Using Ripple-Based Extremum Seeking Control, IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 25, NO. 10, OCTOBER 2010
10.     Johan H. R. Enslin, Senior Member, IEEE, Mario S. Wolf, Dani¨el B. Snyman, and Wernher Swiegers, Integrated Photovoltaic Maximum Power Point Tracking Converter, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 44, NO. 6, DECEMBER 1997
11.     Milan Ilic and Dragan Maksimovic, Senior Member, IEEE, Interleaved Zero-Current-Transition Buck Converter, IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, VOL. 43, NO. 6, NOVEMBER /DECEMBER 2007
12.     Keyue M. Smedley, Member, IEEE, and Slobodan Cuk, Senior Member, IEEE, One-Cycle Control of Switching Converters, IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 10, NO. 6, NOVEMBER 1995
13.     Tamer T.N. Khatib National University of Malaysia, Department of Electrical Electronic & System Engineering Bangi 43600, Selangor, Malaysia, A New ControllerScheme for Photovoltaics Power Generation Systems, European Journal of Scientific Research ISSN 1450-216X Vol.33 No.3 (2009), pp.515-524©EuroJournalsPublishing,Inc.2009.
14.     Yungtaek Jang, Senior Member, IEEE, Milan M. Jovanovic´, Fellow, IEEE, Kung-Hui Fang, and Yu-Ming Chang, High-Power-Factor Soft-Switched Boost Converter, IEEE TRANSACTIONS ON POWER ELECTRONICS, VOL. 21, NO. 1, JANUARY 2006
15.     Domingos S´avio Lyrio Simonetti, Member, IEEE, Javier Sebasti´an, Member, IEEE, and Javier Uceda, Senior Member, IEEE, The Discontinuous Conduction Mode Sepic and´ Cuk Power Factor Preregulators: Analysis and Design, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 44, NO. 5, OCTOBER 1997.
16.     Dezso Sera, Student Member, IEEE, Remus Teodorescu, Senior Member, IEEE, Jochen Hantschel, and Michael Knoll, Optimized Maximum Power Point Tracker for Fast-Changing Environmental Conditions, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 55, NO. 7, JULY 2008.
17.     ROBERTO FARANDA, SONIA LEVA Department of Energy Politecnico di Milano Piazza Leonardo da Vinci, 32 – 20133 Milano ITALY, Energy comparison of MPPT techniques for PV Systems, WSEAS TRANSACTIONS on POWER SYSTEMS ISSN: 1790-5060 Issue 6, Volume 3, June 2008

137-144

28.

Authors:

Sonal Sharma, Preeti Tuli

Paper Title:

Study and Analysis of Image Reconstruction Techniques for Fraud and Tamper Detection in Authenticity Verification

Abstract:  Image reconstruction is a very important research problem with respect to information security. Digital image reconstruction is a robust means by which the underlying images can be revealed and analyzed for any fraud.  One of the principal problems in image forensics and security systems is determining if a particular image is authentic or not. This can be a crucial task when images are used as basic evidence to influence judgment like, for example, in a court of law. To carry out such forensic analysis, various technological instruments have been developed in the literature. In this paper the problem of detecting if an image has been forged is investigated. To detect such tampering, a novel methodology based on image reconstruction is proposed. Such a method allows understanding with high reliability whether an attack has occurred.

Keywords:   Gradient, Poisson equation, Region of interest (ROI), Digital image forensics, Authenticity verification, Image reconstruction from projections

References:
1.        . Lyu and H. Farid, “How realistic is photorealistic?,”  IEEE Transactions on Signal Processing, vol. 53, no. 2, pp. 845–850, 2005.
2.        H. Farid, “Photo fakery and forensics,” Advances in Computers, vol. 77, pp. 1–55, 2009.
3.        J. A. Redi, W. Taktak, and J. L. Dugelay, “Digital image forensics: a booklet for beginners,” Multimedia Tools and Applications, vol. 51, no. 1, pp. 133–162, 2011.
4.        J. Cox, M. L. Miller, and J. A. Bloom, Digital watermarking. San Francisco, CA: Morgan Kaufmann, 2002.
5.        M. Barni and F. Bartolini, Watermarking Systems Engineering: Enabling Digital Assets Security and Other Applications. Marcel Dekker, 2004.
6.        H. Farid, “A survey of image forgery detection,” IEEE Signal Processing Magazine, vol. 2, no. 26, pp. 16–25, 2009.
7.        Popescu and H. Farid, “Statistical tools for digital forensics,” in Proc of Int.’l Workshop on Information Hiding, Toronto, Canada, 2005.
8.        Swaminathan, M. Wu, and K. Liu, “Digital image forensics via intrinsic fingerprints,” IEEE Transactions on Information Forensics and Security, vol. 3, no. 1, pp. 101–117, 2008.
9.        M. Chen, J. Fridrich, M. Goljan, and J. Lukas, “Determining image origin and integrity using sensor noise,” IEEE Transactions on Information Forensics and Security, vol. 3, no. 1, pp. 74–90, 2008.
10.     N. Khanna, G. T.-C. Chiu, J. P. Allebach, and E. J. Delp, “Forensic techniques for classifying scanner, computer generated and digital camera images,” in Proc. of IEEE ICASSP, Las Vegas, USA, 2008.
11.     R. Caldelli, I. Amerini, and F. Picchioni, “A DFT-based analysis to discern between camera and scanned images,” International Journal of Digital Crime and Forensics, vol. 2, no. 1, pp. 21–29, 2010.
12.     Luc Vincent, “Morphological Grayscale Reconstruction in Image Analysis: Applications and Efficient Algorithms” IEEE Transactions on Image Processing, vol. 2, no. 2, 1993.
13.     Di Zang and G. Sommer, “Phase Based Image Reconstruction in the Monogenic Scale Space” DAGM-Symposium, 2004.
14.     S. Leng, T. Zhuang, B. Nett and Guang-Hong Chen, “Exact fan-beam image reconstruction algorithm for truncated projection data acquired from an asymmetric half-size detector” Phys. Med. Biol. 50 (2005) 1805–1820.
15.     L. Kesidis, N. Papamarkos, “Exact image reconstruction from a limited number of projections” J. Vis. Commun. Image R. 19 (2008) 285–298.
16.     P. Weinzaepfel, H. Jegou, P. Perez “Reconstructing an image from its local descriptors, ” Computer Vision and Pattern Recognition (2011).
17.     R. Fatta, D. Lischinski, M. Werman “Gradient domain high dynamic range compression” ACM Transactions on Graphics 2002;21(3):249-256. 
18.     W. Press, S. Teukolsky, W. Vetterling, B. Flannery “Numerical Recipes in C: The Art of Scientific Computing” Cambridge University Press; 1992.
19.     R. Raskar, K. Tan, R. Feris , J. Yu, M. Turk  “Non-photorealistic camera: depth edge detection and stylized rendering using multi-flash imaging” ACM Transactions
on Graphics 2004;23(3):679-688.
20.     Agrawal, R. Raskar, S. K Nayar , Y. Li, “ Removing flash artifacts using gradient analysis” ACM Transactions on Graphics 2005;24(3):828-835.
21.     J. Shen, X. Jin,  C. Zhou, Charlie C. L. Wang, “Gradient based image completion by solving the Poisson equation,” PCM’05 Proceedings of  the 6th Pacific-Rim conference on Advances in Multimedia Information Processing – Volume Part I 257-268

145-148

29.

Authors:

Ashish Raj, Akanksha Deo, Manoj Kumar Bandil

Paper Title:

Canvassing Various Techniques for Removal of Biological Artifact in EEG

Abstract:  EEG is an important tool for diagnosis, monitoring and managing various nervous disorder .It is a neurophysiologic measurement of the electric activity of bioelectric potential of brain. The electrical activity of brain changes in accordance with various parameters inside & outside environment. To study human physiology with respect to EEG, bioelelectric potential of brains is recorded with help of electrodes. These raw signals are firstly processed with help of mathematical tools in order to make them more and more informative. The informative signal thus calculated from recording is known as ERP (event related potential). These ERP data are very specific and it changes with every physiological & biological change in human body. The analysis of ERP has got a wide range of clinical importance. It serves as a base for diagnosis and detection of various diseases. ERP are also helpful in designing various emotion sensor interfaces. But there are certain artifacts which are present in raw EEG recording. These artifacts make the ERP contaminated and it introduces inconsistency in the output. Thus it is necessary to eliminate these artifacts from the EEG. The ERP generated from artifacts free EEG are most suitable for versatile researches and efficient diagnosis. The clinical information thus obtained is of considerable importance in identifying different pathologies. Artifacts in EEG signals arise due to two types of factors; Biological factors and External factors. The Biological factors are caused by EOG (Elecro-oculogram), ECG (Electrocardiogram), EMG (Electromyogram) and Respiratory (PNG).The External factors are caused due to line-interference, leads and electrodes. These noises have an adverse effect on EEG signals and act as a hindrance to obtain clear cut information from EEG signals. This is a paper scrutinizing different methods for removing artifacts with illustrating characteristics of a good informative EEG signal.

Keywords: EEG; EMG; ECG; ocular artifacts; muscular artifacts; spike detection; Wavelet transform; Neural network.

References:
1.    ERP lecture, Dr .John J. Curtin, University of Wisconsin-Madison.
2.    Ian Daly, Floriana Pichiorri, Josef Faller, Vera Kaiser, Alex Krielinger, Reinhold Scherer and Gernot M¨uller-Putz,” What does clean EEG look like”,EMBC 2012.
3.    James N. Knight Department of Computer Science Colorado State University Fort Collins, Fall 2003,”Signal Fraction Analysis and Artifact Removal.”
4.    R. Romo Vázquez, H. Vélez-Pérez , R. Ranta , V. Louis Dorr , D. Maquin , L. Maillard,” Blind source separation, wavelet denoising and discriminant analysis for EEG artefacts and noise cancelling”, Biomedical Signal Processing and Control volume-7.
5.    Janett Walters-Williams & Yan Li” Performance Comparison of Known ICA Algorithms to a Wavelet-ICA Merger”.
6.    G.geetha, Dr.S.N.Geethalakshm,” EEG De-noising using SURE Thresholding based on Wavelet Transforms”, International Journal of Computer Applications, Volume 24– No.6, June 2011
7.    A Garcés Correa, E Laciar, H D Patiño, M E Valentinuzzi,” Artifact removal from EEG signals using adaptive filters in cascade”. 16th Argentine Bioengineering Congress and the 5th Conference of Clinical Engineering.
8.    Rafal Ksiezyk, Katarzyna Blinowska, Piotr Durka,” Neural Networks with Wavelet Preprocessing in EEG Artifact Recognition”

149-154

30.

Authors:

Shilpa Dhanjibhai Serasiya, Neeraj Chaudhary

Paper Title:

Simulation of Various Classifications Results using WEKA

Abstract:   In this paper, we focused on the construction of class association rules and classification model. In knowledge discovery process association rule mining and classification are two important techniques of data mining and widely used in various fields. In order to mine only rules that can be used for prediction, we modified the well known association rule mining algorithm - Apriori to handle user-defined input constraints. The paper tries to explain the basics of class association rule mining and classification through WEKA. This article presents how problems of classification and prediction can be solved using class association rules. In the simulation on WEKA, we have used selected classification techniques to propose the appropriate result from our training dataset. Thus, by using the simulated results, we suggest the classification using association rules.

Keywords:  Association rule, class association rules, classification, Data mining.

References:
1.        R. Agrawal and R. Srikant, “Fast Algorithm for Mining Association Rules”, Proceedings of the 20th International Conference on Very Large Data Bases (VLDB-94), Morgan Kaufman Publishers, Santiago de Chile, Chile, September 1994, pp. 487-499.
2.        P. R. Pal, R. C. Jain, CAARMSAD: “Combinatorial Approach of Association Rule Mining for Sparsely Associated Databases”.  Journal of Computer Science, Tamilnadu India, Vol. 2, No 5, pp 717, July 2008.
3.        J. Han, J. Pei, and Y. Yin, “Mining frequent patterns without candidate generation”, proceedings of international conference on management of data (ACMSIGMOD’00), pp 1-12, Dallas, TX, May 2000.
4.        R. Bayardo, “Brute-force mining of high-confidence classification rules”,Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining (KDD-97), AAAI Press, Newport Beach, CA, United States, August 1997, pp. 123-126.
5.        J. Quinlan, C4.5, “Programs for machine learning”, San Mateo, CA: Morgan Kaufmann, 1993.
6.        K. Ali, S. Manganaris, and R. Srikant, “Partial Classification using Association Rules”, Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining (KDD-97), AAAI Press, Newport Beach, CA, United States, August 1997, pp.115-118.
7.        B. Liu, W. Hsu, and Y. Ma, “Integrating Classification and Association Rule Mining”, Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD-98), AAAI Press, New York City, NY, United States, 1998, pp. 80-86.
8.        Jiawei Han, Jian Pei, and Yiwen Yin. Mining frequent patterns without candidate generation. SIGMOD Record (ACM Special Interest Group on Management of Data), 29(2):1{?, 2000.
9.        Tom Johnsten and Vijay V. Raghavan, “Impact of Decision- Region Based Classification Mining Algorithms On Database Security”, supported in part by a grant from the U.S. Department of Energy.
10.     Hui Yin1,2, Fengjuan Cheng2, Chunjie Zhou1, “An Efficient SFL-Based Classification Rule Mining Algorithm”, Proceedings of 2008 IEEE International Symposium on IT in Medicine and Education, pp. 969 – 972.
11.     Xianneng Li, Shingo Mabu, Huiyu Zhou, Kaoru Shimada and Kotaro Hirasawa, “Analysis of Various Interestingness Measures in Classification Rule Mining for Traffic Prediction”, SICE Annual Conference 2010 ,August 18-21, 2010, pp. 1969 – 1974.
12.     Bing Liu, Wynne Hsu, and Yiming Ma. Integrating classi_cation and association rule mining. In Knowledge Discovery and Data Mining Integrating, pages 80-86, 1998.

155-162

31.

Authors:

Ayan Mitra, Budhaditya Pyne

Paper Title:

Measuring Synchronization for coupled systems using Visibility Graph Similarity

Abstract: Synchronization is defined as interdependencies among two or more time series. Recent advances on information theory and non-linear dynamical systems has allowed us to investigate different types of synchronization measures on different time series data such Electroencephalogram (EEG), Magnetoencephalogram (MEG) and other non-stationary signals.However,these kind of statistical interdependencies are also prominently observed in the coupled chaotic systems occurring in nature. In most coupled systems the internal variants and the interdependencies among their subsystems are not accessible. Therefore, to measure the statistical interdependencies among the coupled systems, different non-linear approaches has been adopted thateffectively determines the amount of synchronization between the dynamical systems under investigation. In this paper the recently proposed synchronization measurement performance of the Visibility Graph Similarity (VGS)[10],[11] is computed for two coupled identical Hénon map, two non-identical coupled rössler and Lorenz system over the entire time domain & also compared against linear correlation to estimate the superiority of the method.

Keywords:   Coupled model systems, Dynamic systems, Nonlinear system, Synchronization.

References:
1.           Y. Tang, Q. Miao, J.A. Fang, Synchronization of stochastic delayed chaotic neuralnetworks with Markovian switching and application in communication, Int. J.Neural Syst. 19 (1) (2009) 43–56.
2.           J.A. White, C.C. Chow, J. Ritt, C. Soto-Trevino, N. Kopell, Synchronization andoscillatory dynamics in heterogeneous, mutually inhibited neurons, J. Comput. Neurosci. 5 (1) (1998) 5–16.
3.           H. V.S. Chakravarthy, N. Gupte, S. Yogesh, A. Salhotra, Chaotic synchronizationusing a network of neural oscillators, Int. J. Neural Syst. 18 (2) (2008) 157–164.
4.           L.B. Good, S. Sabesan, S.T. Marsh, K.S. Tsakalis, L.D. Iasemidis, Control ofsynchronization of brain dynamics leads to control of epileptic seizures inrodents, Int. J. Neural Syst. 19 (3) (2009) 173–196.
5.           C.J. Stam, B.W. van Dijk, Physica D 163 (2002) 236.
6.           R.Q. Quiroga, J. Arnhold, P. Grassberger, Phys. Rev. E 61 (2000) 5142.
7.           T. Kreuz, F. Mormann, R.G. Andrzejak, A. Kraskov, K. Lehnertz, P. Grassberger, Measuring synchronization in coupled model systems: a comparison of different approaches, Physica D 225 (1) (2007) 29–42.
8.           T. Montez, K. Linkenkaer-Hansen, B.W. van Dijk, C.J. Stam, Synchronization likelihood with explicit time–frequency priors, NeuroImage 33 (4) (2006) 1117–1125.
9.           J. Bhattacharya, E. Pereda, H. Petsche, Effective detection of coupling in short and noisy bivariate data, IEEE Trans. Syst. Man Cybern. 33 (1) (2003) 85–95.
10.        M. Ahmadlou, H. Adeli, A. Adeli, New diagnostic EEG markers of the Alzheimer’s disease using visibility graph, J. Neural Transm. 117 (9) (2010) 1099–1109.
11.        MehranAhmadlou, HojjatAdeli, Visibility graph similarity: A new measure of generalized synchronization in coupled dynamic systems, Physica D: Nonlinear
Phenomena, Volume 241, Issue 4, p. 326-332.
12.        J.G. Semmler, M.A. Nordstrom, A comparison of cross-correlation and surface EMG techniques used to quantify motor unit synchronization in humans, J. Neurosci. Methods 90 (1) (1999) 47–55.
13.        Kittel, J. Parisi, K. Pyragas, Generalized synchronization of chaos in electronic circuit experiments, Physica D 112 (3–4) (1998) 459–471.
14.        X. Zhao, Z. Li, S. Li, Synchronization of a chaotic finance system, Appl. Math. Comput. 217 (3) (2011) 6031–6039.
15.        Bohn, J. García-Ojalvo, Synchronization of coupled biological oscillators under spatially heterogeneous environmental forcing, J. Theoret. Biol. 250 (1) (2008) 37
47.
16.        J. Theiler, Phys. Rev. A 34 (1986) 2427.
17.        T. Kreuz, R.G. Andrzejak, F. Mormann, A. Kraskov, H. St¨ogbauer, C.E. Elger, K. Lehnertz, P. Grassberger, Phys. Rev. E 69 (2004) 061915
18.        Stam, C.J., van Dijk, B.W., 2002. Synchronization likelihood: an unbiased measure of generalized synchronization in multivariate data sets. Physica D 163, 236–241.

163-168

32.

Authors:

R. Sarojini, Ch.Rambabu

Paper Title:

Design and Implementation of DSSS-CDMA Transmitter and Receiver for Reconfigurable Links Using FPGA

Abstract:  Direct sequence spread Spectrum (DSSS), is also called as direct sequence code division multiplexing (DS-CDMA). In direct sequence spread spectrum, the stream of information to be transmitted is divided into small pieces, each of which is allocated across to a frequency channel across the spectrum. A data signal at the point of transmission is combined with a higher data-rate bit sequence (also called chipping code) that divides the data according to a spreading ratio. The redundant chipping code helps the signal resist interference and also enables the original data to be recovered if data bits are damaged during the transmission. Direct sequence contrasts with the other spread spectrum process, known as frequency hopping spread spectrum. Frequency hopping code division multiple access (FH-CDMA), in which a broad slice of the bandwidth spectrum is divided into many possible broadcast frequencies. In general, frequency-hopping devices use, less power and are cheaper, but the performance of DS-CDMA systems is usually better and more reliable. In this project direct sequence spread spectrum principle based code division multiple access (CDMA) transmitter and receiver is implemented in VHDL for FPGA. The transmitter module mainly consists of data generator, programmable chip sequence generator (PN sequence generator), direct digital frequency synthesizer (DDFS), BPSK modulator blocks. The receiver modular mainly consists of BPSK demodulator, programmable chip sequence generator (PN sequence generator), matched filters, threshold detector blocks. Modelsim 6.2(MXE) tool will be used for functional and logic verification at each block. The Xilinx synthesis technology (XST) of Xilinx ISE 9.2i tool will be used for synthesis of transmitter and receiver on FPGA Spartan 3E.

Keywords:    CDMA, DSSS, BPSK, PN code, DDFS.

References:
1.     B. Sreedevi, V. Vijaya, CH. Kranthi Rekh, Rama Valupadasu, B. RamaRao Chunduri, “FPGA implementation of  DSSS-CDMA transmitter and receiver for Adhoc Networks.” IEEE Symposium on computers and informatics 2011.
2.     Shahraki, A.S.; Nabavi, A, “Implementation of GSM and IS-95 equalizers on a reconfigurable architecture for software radio systems, Circuits and Systems for communications 2008, Publication Year: 2008, Page(s): 336 – 339.3.     M.Habib Ullah, Akhmad Unggul Priantoro, M.Jasim Uddin, “Design and Construction of Direct
Sequence Spread Spectrum CDMA Transmitter and Receiver” Proceedings of 11th International Conference on Computer and Information Technology (ICCIT 2008).
4.     Jeffrey G.Andrews, “Interference Cancellation for Cellular Systems: A Contemporary Overview”, IEEE Wireless Communications Magazine, Apr. 2005.
5.     Ashwin Sampath, Joseph S. Kaufman, , Muralidharan S. Kodialam, , and Kenneth C. Budka,, “Performance Analysis of Call Assignment and Carrier Packing Schemes for TDMA Systems”, IEEE Transactions On Vehicular Technology, vol. 52, no. 6, November 2003
6.     R. Derryberry, S. Gray, “Transmit Diversity in 3G CDMA Systems”. IEEE Communication Magazine, pp 68-75, April 2002.
7.     David Tse , “Multiuser Diversity in Wireless Networks.”, Stanford University, April, 2001.
8.     S. Bisada H. Baraka A. Abdelwahab and M. El Sherif “A Software Radio Architecture for CDMA IS-95-based Dual Mode Mobile Terminals”, 43rd IEEE Mdwest Symp. on Cmuts and Systems, Lansing MI, Aug 8-1 1, 2000
9.     Gina Hooper and Alan Sicher, “Advanced TDMA Digital AMPS Mobile Data and Messaging Capabilities” Ericsson Inc., 1996 IEEE
10.  S.Moshavi, “Multi -user Detection for DS-CDMA Communications”, IEEE Communications Magazine, Vol. 34 No. 10, pp. 124-36, Oct. 1996.

169-174

33.

Authors:

Abhijit A. Rajguru, S.S. Apte

Paper Title:

A Comparative Performance Analysis of Load Balancing Algorithms in Distributed System using Qualitative Parameters

Abstract:   A distributed system can be viewed as a collection of computing and communication resources shared by active users.  In this environment, a number of workstations or computers are linked through a communication network to form a large loosely coupled distributed computing system. When the demand for computing power increases, the load balancing problem becomes important. The problem of task scheduling and load balancing in distributed system are most important and challenging area of research in computer engineering. Task Scheduling and load balancing in distributed system has an important role in overall system performance.  Task scheduling in distributed system can be defined as allocating processes to processor so that total execution time will be minimized, utilization of processors will be optimized. Load balancing is the process of improving the performance of system through a redistribution of load among processor. In this paper we present the performance analysis of various load balancing algorithms based on different parameters, considering two load balancing approaches static and dynamic. The analysis indicates that static and dynamic both types of algorithm have some advantages as well as disadvantages. The main purpose of this paper is to help in design of new algorithms in future by studying existing static and dynamic algorithms.

Keywords: Load balancing, static load balancing, dynamic load balancing, comparative parameters

References:
1.    Sandeep Sharma, Sarabjit Singh, and Meenakshi Sharma, “Performance Analysis of Load Balancing Algorithms”, World Academy of Science, Engineering and Technology, 2008.
2.    Hisao Kameda, El-Zoghdy Said Fathyy and  Inhwan Ryuz Jie Lix, “A Performance Comparison of Dynamic vs. Static Load Balancing Policies in a Mainframe { Personal Computer Network Model”, Proceedings of the 39th IEEE Conference on Decision and Control, 2000.
3.    Daniel Grosua, Anthony T. and Chronopoulosb,”Non-cooperative load balancing in distributed systems”, Elsevier, Journal of Parallel and Distributed Computing, 2005.
4.    M. Nikravan and M. H. Kashani, “A Genetic Algorithm for Process Scheduling in Distributed Operating Systems Considering Load balancing”, Proceedings 21st European Conference on Modelling and Simulation (ECMS), 2007.
5.    Hendra Rahmawan, Yudi Satria Gondokaryono, “The Simulation of Static Load Balancing Algorithms”,
6.    2009 International Conference on Electrical Engineering and Informatics, Malaysia.
7.    Sandeep Sharma, Sarabjit Singh, and Meenakshi Sharma, “Performance Analysis of Load Balancing Algorithms”, academy of science, engineering and technology,
issue 38, February 2008, pp. 269-272.
8.    S. Malik, “Dynamic Load Balancing in a Network of Workstation”, 95.515 Research Report, 19 November,2000.[8] Ali M. Alakeel, A Guide to Dynamic Load Balancing in Distributed Computer Systems, IJCSNSInternational Journal of Computer Science and Network Security, VOL.10 No.6, June 2010.
9.    William Leinberger, George Karypis, Vipin Kumar, "Load Balancing Across Near Homogeneous Multi-Resource Servers", 0-7695-0556- 2/00, 2000 IEEE.

175-179

34.

Authors:

C.Yaminika, M.Vijayalaxmi

Paper Title:

Image Coding For Aestentically Acceptable Distortion Using Depth Blurring

Abstract:    Realistic simulation of distance blurring, with the desirable properties of aiming to mimic occlusion effects as occur in natural blurring, and of being able to handle any number of blurring and occlusion levels with the same order of computational complexity will help in compressing the image. Image compression may be lossy or lossless. Lossless compression is preferred for archival purposes and often for medical imaging, technical drawings, clip art, or comics. This is because lossy compression methods, especially when used at low bit rates, introduce compression artifacts. Lossy methods are especially suitable for natural images such as photographs in applications where minor (sometimes imperceptible) loss of fidelity is acceptable to achieve a substantial reduction in bit rate. The lossy compression that produces imperceptible differences may be called visually lossless. the concept of depth-based blurring to achieve an aesthetically acceptable distortion when reducing the bitrate in image coding is proposed which is vital in lossless image compression. Depth-based blurring reduces high-frequency components by mimicking the limited depth of field effect that occurs in cameras. The Proposed algorithm performs better than the existing spatial domain methods, significantly to cope with the challenge of avoiding intensity leakage at the boundaries of objects when blurring at different depth levels.

Keywords:  minor (sometimes imperceptible) loss of fidelity is acceptable to achieve a substantial reduction in bit rate.

References:
1.       Z. Wang and A. C. Bovik, “Foveated image and video coding,” in Dig-ital Video, Image Quality and Perceptual Coding, H. R. Wu and K. R. Rao, Eds. Boca Raton, FL: CRC, 2006, ch. 14, pp. 431–457.
2.       P. Bradley and F. W. M. Stentiford, “Visual attention for region of interest coding in JPEG 2000,” J. Vis. Commun. Image Representation, vol. 14, no. 3, pp. 232–250, Aug. 2003.
3.       T. Popkin, A. Cavallaro, and D. Hands, “Accurate and efficient method for smoothly space-variant Gaussian blurring,” IEEE Trans. Image Process., vol. 19, no. 5, pp. 1362–1370, May 2010.
4.       R. L. Cook, T. Porter, and L. Carpenter, “Distributed ray tracing,” SIG-GRAPH ’84: Proc. 11th Comput. Graph. Interactive Tech., vol. 18, no. 3, pp. 137–145, Jul. 1984.
5.       M. Kass, A. Lefohn, and J. Owens, “Interactive depth of field using simulated diffusion on a GPU,” Pixar Animation Stu-dios,2006[Online].Available: http://graphics.pixar.com/library/DepthOfField/paper.pdf
6.       Z. Wang, L. Lu, and A. C. Bovik, “Foveation scalable video coding with automatic fixation selection,” IEEE Trans. Image Process., vol. 12, no. 2, pp. 243–254, Feb. 2003.
7.       S. Liu and A. C. Bovik, “Foveation embedded DCT domain video transcoding,” J. Vis. Commun. Image Representation, vol. 16, no. 6, pp. 643–667, Dec. 2005.
8.       B. A. Wandell, Foundations of Vision. Sunderland, MA: Sinauer As-sociates, 1995, pp. 236–236.
9.       S. Lee, G. J. Kim, and S. Choi, “Real-time depth-of-field rendering using anisotropically filtered mipmap interpolation,” IEEE Trans. Vis. Comput. Graph., vol. 15, no. 3, pp. 453–464, 2009.
10.    M. M. Farid, F. Kurugollu, and F. D. Murtagh, “Adaptive wavelet eye-gaze based video compression,” in Proc. SPIE, Mar. 2003, vol. 4877, pp. 255–263.
11.    Ç. Dikici, H. I. Bozma, and M. R. Civanlar, “Fovea based coding for video streaming,” in Proc. Int. Conf. Image Anal. Recognit., Sep. 2004, vol. 3211, pp. 285–294.
12.    B. Huhle, S. Fleck, and A. Schilling, “Integrating 3D time-of-flight camera data and high resolution images for 3DTV applications,” in Proc. 3DTV CON—The True
Vision, May 2007, ISBN 978-1-4244-0722-4.
13.    “Project Natal in Detail,” Microsoft XBox Official Site, 2010 [Online]. Available: http://www.xbox.com/en-GB/ news-fea-tures/news/ Project-Natal-in-detail-050609.htm
14.    Cavallaro, O. Steiger, and T. Ebrahimi, “Semantic video analysis for adaptive content delivery and automatic description,” IEEE Trans. Circuits Syst. Video Technol., vol. 15, no. 10, pp. 1200–1209, Oct. 2005.
15.    P. Viola and M. J. Jones, “Robust real-time face detection,” Int. J. Comput. Vis., vol. 57, no. 2, pp. 137–154, May 2004.

180-182

35.

Authors:

E.Anil Kumar, P.Srinivasulu

Paper Title:

Shadow and Nonshadow Detection Using Tricolor Attenuation Model

Abstract: The shadows are regarded as obstacles in remote sensing image analysis. With high-resolution remote sensing images developed, especially in urban area, shadow detection plays a much more important role in many applications. Shadows, the common phenomena in most outdoor scenes, bring many problems in image processing and computer vision. In this paper ex-tracting shadows from a single outdoor   image   is   presented.   Based on image formation theory relationship between shadow and its nonshadow background is derived based on image formation theory. The parameters of the Tri-color Attenuation Model are fixed by using the spectral power distribution (SPD) of daylight and skylight, which are estimated according to Planck’s blackbody ir-radiance law. The proposed shadow detection algorithm when compared to previous methods can extract shadows significantly than the existing methods.

Keywords:   Remote   sensing,   shadow detection, tricolor attenuation model (TAM).

References:
1.        J.M. Wang, Y.C. Chung, C.L. Chang, S.W. Chen. Shadow detection and removal for traffic images, Networking, Sensing and Control, 2004 IEEE International Conference on Volume1, 21-23 March 2004 Page(s): 649 - 654 Vol.1
2.        Bevilacqua. Effective Shadow Detection in Traffic Monitoring Applications. WSCG 2003.
3.        T. Chen, W. Yin, X.S. Zhou, D. Comaniciu, and T.S. Huang. Illumination Normalization for Face Recognition and Uneven Background Correction Using Total Variation Based Image Models. CVPR (2) 2005: 532-539.
4.        Y. Adini, Y. Moses, and S. Ullman. Face recognition: The problem of compensating for changes in illumination direction.IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7): 721–732, 1997.
5.        W. Zhao and R. Chellappa. Robust face recognition using symmetric shape-from-shading. Technical report, Center for Automation Research, University of
Maryland, 1999.
6.        G.J. Klinker, S.A. Shafer, and T.Kanade. A Physical Approach to Color Image Understanding, Int’l J. Computer Vision, vol.4, pp. 7-38, 1990
7.        M. Fathy and M.Y. Siyal, “An Image Detection Technique Based on Morphological Edge Detection and Background Differencing for Real- Time Traffic Analysis”, Pattern Recognition, Vol. 16, pp. 1321- 1330,1995.
8.        C. Jiang and M. 0. Ward, “Shadow Identification”, Proc. of IEEE Couf. on Computer Vision and Pattern Recognition, pp. 606-612, 1992.
9.        M. Lilger, “A Shadow Handler in a Video-Based Real-Time Traffic Monitoring System”, Proc. Of IEEE Workshop on Applications of Computer Vision, pp.11-18, 1992.
11.     M. Nielsen and C. B. Madsen, “egmentation of soft shadows based ona daylight- and penumbra model,” in Proc. Mirage, 2007, pp. 341–352.
12.     Z. Du, X. Qin, W. Hua, and H. Bao, “Shadow removal in sole outdoor image,” in Proc. PCM, 2006, pp. 999–1007.
13.     C. Lu and M. S. Drew, “A Markov random field framework for finding shadows in a single colour image,” in Proc. 10th Congr. Int. Colour Association, Granada, Spain, May 8–13, 2005.
14.     R. Cucchiara, C. Grana, M. Piccardi, and A. Prati, “Detecting  objects, shadows and ghosts in video streams by exploiting color and motion information,” in Proc. 11th Int. Conf. Image Analysis and Processing,2001, pp. 360–365.

183-186