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Volume-5 Issue-2: Published on May 05, 2015
13
Volume-5 Issue-2: Published on May 05, 2015
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S. No

Volume-5 Issue-2, May 2015, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Mustafa Abdulkadhim

Paper Title:

Service Performance Evaluation for WiMAX Networks Based on Node Trajectory

Abstract:    WIMAX is a wireless communications standard designed to provide 30 to 40 megabit-per-second data rates, [1] with the 2011 update providing up to 1 Gbit/s [1] for fixed stations. The name "WiMAX" was created by the WiMAX Forum, which was formed in June 2001 to promote conformity and interoperability of the standard. The forum describes WiMAX as "a standards-based technology enabling the delivery of last mile wireless broadband access as an alternative to cable and DSL". [2]. This paper aim to spot the light on how node trajectory within the WiMAX cell may effects the network performance, also how QoS parameters and the choice we make in the network configuration might changes how the network react and how it may have a direct effect on its performance.

Keywords:
component; WIMAX, QoS, Trajectory.


References:

1.                 K. Fazel and S. Kaiser, Multi-Carrier and Spread Spectrum Systems: From OFDM and MC-CDMA to LTE and WiMAX, 2nd Edition, John Wiley & Sons, 2008, ISBN 978-0-470-99821-2
2.                 M. Ergen, Mobile Broadband - Including WiMAX and LTE, Springer, NY, 2009 ISBN 978-0-387-68189-4 .

3.                 V.Mehta, Dr. N.Gupta "Performance Analysis of QoS Parameters for Wimax Networks" International Journal of Engineering and Innovative Technology (IJEIT) Volume 1, Issue 5, May 2012

4.                 Sedoyeka, E  “Evaluation of WiMAX QoS in a developing country's" International Conference on environment Computer Systems and Applications (AICCSA), 2010 IEEE/ACS

5.                 Othman, H.R " Performance analysis of VoIP over mobile WiMAX (IEEE 802.16e) best-effort class " IEEE 5th Control and System Graduate Research Colloquium (ICSGRC), 2014

6.                 Boone, P " Using time-of-day and location-based mobility profiles to improve scanning during handovers" IEEE International Symposium on a World of Wireless Mobile and Multimedia Networks (WoWMoM), 2010  


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2.

Authors:

Muluneh L. Woldesemayat, K. D. Badgujar, Won Sangchul

Paper Title:

A Simplified Design of Space Vector Modulation for Speed and Torque Control of Induction Motor

Abstract: This paper proposes a simplified Space Vector Modulation technique which is used to control an inverter that supplies voltage to an induction motor. A simplified dynamic model of an induction motor model was also designed and voltage is supplied to it using SVM technique. A step by step design procedure with the help of matlab and Simulink made the complexity of the system simpler than existing models. This paper briefly explains design of space vector modulation technique and induction motor modeling. With the help of appropriate interfacing the design method will be used in industrial applications where the space vector modulation technique is used to achieve smooth control of speed and torque. Finally, on-line starting of the designed Induction Motor model was simulated. Moreover comparison of existing and the simplified SVM-based direct torque control method was simulated and results were shown.

Keywords:
 Decoupling, Dynamic Model, Reference frame, Squirrel-cage.


References:

1.          Santhi Kumar and K.Nagalinka, “Flux vector control with space vector modulation for PWM inverter fed induction motor drive,” International Journal of application or innovation in Engineering and Management (IJAIEM), vol.2, 2013.
2.           http://www.ijaiem.org/volume2issue8/IJAIEM-2013-08-25-054.pdf

3.           Fouad Giri, “Ac Electric Motors Control, Advanced design techniques and applications,” A John Willey and Sons, Ltd., 2013, pp. 17-31.

4.           R.Krishnan, “Electric Motor Drives, Modeling Analysis and control,” Prentice Hall, pp. 188-191, 2001.

5.           Andres Diaz, Roger Saltarez, Christian Rodrigues, Roberto F.Nunez, Eduardo I. Ortiz-Rivera and Jesus Gonzalez-Liorente, “Induction Motor Equivalent Circuit for Dynamic Simulation,” Electrical Machines and Drives IEEE International Conference, IEMDC, 2009, pp. 858-863.

6.           http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5075304

7.           Aleck W. Leedy, “Simulink/Matlab Dynamic Induction Motor Model for Use as A Teaching and Research Tool,” International Journal of Soft Computing and Engineering (IJSCE), vol. 3, Sep. 2013, pp. 102–107.

8.           http://www.ijsce.org/attachments/File/v3i4/D1786093413.pdf

9.           Mohamad H. Moradi and Pouria G. Khorasani, “A New Simulation of Induction Motor,” Australasian Universities Power Engineering Conference, (AUPEC), 2008, pp. 1-6.

10.        http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4812992

11.        Burak Ozpineci and Leon M. Tolbert, “Simulink Implementation of Induction Machines-A Modular Approach,” IEEE, 2003, pp. 728-734.

12.        http://web.eecs.utk.edu/~tolbert/publications/iemdc_2003_im_model.pdf

13.        Adel Aktaibi and Daw Ghanim, “Dynamic Simulation of a Three-Phase Induction Motor Using Matlab Simulink (unpublished work style),” unpublished. file:///C:/Users/Muluneh/Downloads/Dynamic%20simulation%20of%20a%20three%20phase%20induction%20motor.pdf

14.        Anjana Manuel and Jebin Francis, “ Simulation of direct torque controlled induction motor drive by using space vector pulse width modulation for torque ripple reduction,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE), 2013,  pp. 4471-4478.

15.        http://www.ijareeie.com/upload/2013/september/34_Simulation.pdf

16.        Implementing Space Vector Modulation with ADMCF32X, ANF32X-17, Analog Devices Inc., pp.1-12, 2000.

17.        ftp://ftp.analog.com/pub/www/marketSolutions/motorControl/applicationCode/admcf32x/pdf/svpwm.pdf

18.        Kwang Hee Nam, “Ac Motor Control and Electric Vehicle Application,” CRC Press, 2010.

19.        Rohit Chandan, “Three phase space vector pulse width modulation using generalized multiphase space vector approach (unpublished style),” unpublished.

20.        http://kr.mathworks.com/matlabcentral/fileexchange/44343-three-phase-svpwm/content//ph3_svmp.m

21.        Jose Andres Santisteban, and Richard M. Stephan, “Vector control Methods for Induction Machines: An overview,” IEEE Transactions on Education, Vol. 44, No. 2, 2001, pp. 170-175.

22.        http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=925828

23.        E. Mageswari, Yuvaleela. , M.Rajeshwari and P. Amuthithini, “Implementation of Low-Cost Direct Torque Control Algorithm for Induction Motor without AC Phase Current Sensors,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, (IJAREEIE), Vol.3, October 2014, pp. 12587-12593.

24.        http://www.ijareeie.com/upload/2014/october/22T_Implementation.pdf


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3.

Authors:

Alanoud Al Mazroa, Mohammed Arozullah

Paper Title:

Detection and Remediation of Attack by Fake Base Stations in LTE Networks

Abstract:  Rogue base station attack can compromise the privacy of user equipment (UE) in LTE networks.  To address this issue, we propose a rogue base station identification protocol to protect UE privacy.  Our protocol utilizes the mobile property of the UE and is designed based on the observation that rogue base station can only cover a small area. We use the measurements of UE in different locations to estimate the power and location of the base stations. The UE also tracks the signatures of each legitimate base station.  If the base station is already verified by the detection protocol, then the UE connects to the base station according to LTE standard. For any new appearing base stations, it sends the power of the base station and the GPS location itself to a cloud server to verify the legitimacy of the base station. The cloud server maintains a database of real base stations. Our proposed protocol does not need to change existing LTE standard and no base station modification is required.  Our protocol is implemented on NS3 LTE module and evaluated with various practical settings. The results indicate our protocol can ensure that the UE can successfully detect rogue base stations and avoid sending privacy data to rogue base station.

Keywords:
   user equipment (UE) in LTE networks, identification protocol to protect UE privacy, GPS, legitimacy, NS3 LTE module.


References:

1.             M. Arapinis,  L. Mancini,  E. Ritter, M. Ryan, N. Golde, K. Redon,  and R. Borgaonkar. New privacy issues in mobile telephony:  fix and verification.  In Proceedings of the 2012 ACM conference on Computer and communications security.  ACM, 2012.
2.             C.-M. Chen,  Y.-H. Chen,  Y.-H. Lin, and H.-M.

3.             Sun. Eliminating rouge femto cells based on distance bounding protocol and geographic information. Expert Systems with Applications, 2014.

4.             J. A. Del Peral-Rosado, J. A. Lopez-Salcedo, G. Seco-Granados, F. Zanier, and M. Crisci. Analysis of positioning  capabilities of 3gpp lte. In Proceedings of the ION GNSS, pages 1–10, 2012.

5.             N. Golde, K. Redon,  and R. Borgaonkar.

6.             Weaponizing femto cells:  The effect of rogue devices on mobile telecommunications. In NDSS, 2012.

7.             C.-K. Han, H.-K. Choi, and I.-H. Kim. Building femtocell more secure with improved  proxy signature. In Global Telecommunications Conference, 2009.
GLOBECOM  2009. IEEE. IEEE,  2009.

8.             N. K. M. Mishra  Sandip  D. False base station attack in gsm network environment. In International Journal of Advanced Research  in Computer Engineering and Technology (IJARCET), 2014.

9.             D. Peral-Rosado, J. A. Lopez-Salcedo,

10.          G. Seco-Granados, F. Zanier, M. Crisci, et al. Achievable localization accuracy of the positioning reference signal of 3gpp lte. In Localization and GNSS (ICL-GNSS), 2012 International Conference on, pages 1–6. IEEE,  2012.

11.          Y. Song, K. Zhou, and X. Chen.  Fake bts attacks of gsm system on software radio platform.  Journal of Networks, 2012.

12.          D. Strobel. Imsi catcher.  2007.


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4.

Authors:

Ekta Gupta, Shrisha Vanga, Sonal Kachare, Jay Borade

Paper Title:

Fashion Accessories using Virtual Mirror

Abstract:       In this paper, we are comparing various technologies for e-business trying to create a Virtual Mirror made up by a large digital screen like a computer monitor, a sensory device like a webcam. The system will work in the following way: When a person enters the field of view of the camera, the camera will start capturing the image of the person, and the algorithm of the system will start tracking the image of the person in order to recognize various body feature of that person. Each new person entering the camera’s field of view will trigger the computer to track the movements of the person. Thereafter, whenever the person selects an item from the shopping list, the item will be directly placed on that particular body part. This way, the person can try out different items and buy those items that suit him/her.

Keywords:
    Virtual Mirror (VR), Try It Online (TIO), Camera (CAM), Webcam Social Shopper (WSS), Virtual Mirror Application (VMA), Graphic User Interface (GUI), Fitting Box(FB).


References:

1.             M. Usman, K. Sporsheim, E. Bergstøl, I. Milanovic, R. Hercz, “The Virtual Mirror” , Human-Computer-Interface course, University of Oslo, pp. 6-7, 2007.
2.             Tortoise and Blonde, ” The Tortoise and Blonde Experience”, http://tortoiseandblonde.com/about-us.

3.             SiliconIndia, “LensKart.com Launches A Virtual Mirror For Eye Gear”, http://www.siliconindia.com/shownews/LensKartcom_Launches_A_Virtua l_Mirror_For_Eye_Gear-nid-100662-cid-100.html, 2011.

4.             Zugara, ”Our Story”, http://zugara.com/about-zugara/zugaras-story. 2008.

5.             Radar, "Virtual Shopping Made Easy with Zugara's Fashionista App", http://www.ladylux.com/articles/virtual-shopping-made-easy-with-Zugaras -fashionista-app. 2009.

6.             D.Adams, "Augmented Reality Virtual Mirror: Try Before You Buy Online",http://www.bitrebels.com/technology/augmented-reality-virtual-mi rror-try-before
you-buy-online.

7.             EZface,"EZface   Virtual Mirror",

8.             http://www.info4u.com.br/index.php/videos.

9.             EZface,"EZface            Virtual     Mirror      Application", http://staging.ezface.com/about-us.html

10.          FrameFish virtual try-on software, "Better Virtual Try-On Solutions For Eyewear", http://virtualmirror.framefish.com/features.

11.          Virtual Try On Solutions for Glasses, “TURNKEY SOLUTIONS FOR THE EYEWEAR AND EYECARE INDUSTRIES" http://www.trylive.com/solutions/trylive-eyewear-virtual-mirror/overview.

12.          C.O'Brien, "Ray-Ban Uses Augmented Reality For Their Virtual Mirror",http://thefutureofads.com/ray-ban-uses-augmented-reality-for-their -virtual-mirror.

13.          Innovation Offerings, “Magic Mirror Technology", https://www.etpl.sg/innovation-offerings/technologies-for-license/tech-offe rs/1911.


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5.

Authors:

Mustafa Tunay, Rahib H. Abiyev

Paper Title:

Hybrid Local Search Based Genetic Algorithm and its Practical Application

Abstract:        This paper presents an intense hybrid search method that uses Genetic Algorithms (GAs) and local search procedure for global optimization.  The Genetic Algorithms (GAs) comprise a selection process, a crossover process and a mutation processes and local search procedure that uses Powell’s method for updating the parameters of the objective functions. The performance of the designed algorithm is tested on specific benchmarking functions namely; Rastrigin function, Rosenbrock function, Schwefel’s function 2.22, Schwefel’s function 2.21 and Sphere's function. The computational results have demonstrated that the performance of Genetic Algorithms with Powell’s Method is much improved specific benchmarking functions. The use of a hybrid search method approach allows it to speed up the learning of the system with faster convergence rates. The Genetic Algorithm with Local Search Procedure (GALSP) is applied for soling exam timetabling problem. The GALSP seems to be a promising approach and is comparable to specialized algorithm for solving a set of global optimization problems. The algorithms of these processes have been designed and presented in the paper.

Keywords:
     Genetic algorithms, local search procedure, evolutionary theory, search methods.


References:

1.          Abiyev Rahib H. (2009). Fuzzy Wavelet Neural Network for Prediction of Electricity Consumption. AIEDAM: Artificial Intelligence for Engineering Design, Analysis and Manufacturing , 23(2) 109-118.
2.          Abiyev Rahib H., Tunay M. (2015). Optimization of High Dimensional Functions through Hypercube Evaluation, Computational Intelligence and Neuroscience, Volume 2015, 2015.

3.          Burke E. K, Elliman DG, Weare RF. ‘‘A Hybrid Genetic Algorithm for    Highly Constrained Timetabling Problems,’’ 6th International Conference on Genetic Algorithms (July 1995,Pittsburgh, USA), 15-19.    

4.          Burke E. K, Newall JP.‘‘A multistafe Evolutionary Algorithm for Timetable Problem’’ Trans. On Evolutionary Computation 1999, 3(1).

5.          Eiben A. E, Smith JE. Introduction to evolutionary computing. Springer 2003, New York.

6.          Goldberg D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading MA

7.          Hansen N, Auger A, Ros R, Finck S, Posik P. Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009, in Proceedings of the 12th International Conference on Genetic and Evolutionary Computation Conference (GECCO’10), Companion Material, Portland, ed. by M. Pelikan, J. Branke (ACM, New York, 2010), pp. 1689–1696.

8.          Hansen N, Kern S. Evaluating the CMA evolution strategy on multimodal test functions, in Proceedings of the 9th International Conference on Parallel Problem Solving from Nature (PPSN VIII), Birmingham. Volume 3242 of Lecture Notes in Computer Science, ed. by X. Yao et al. (Springer, 2004), pp. 282–291.

9.          Holland J. H. Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor.

10.       Kalayci C. B., Gungor A. A Genetic Algorithm Based Examination Timetabling Model Focusing on Student Success for the Case of the College of Engineering at Pamukkale University, Turkey. Gazi University Journal of Science, 25(1) (2012) 137-153.      
11.       Karaboga D., Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing , 687–697.
12.       Kennedy J. (1997). The particle swarm: social adaptation of knowledge, Proc. IEEE International Conference on Evolutionary Computation (Indianapolis, Indiana), IEEE Service Center 1997, Piscataway, NJ, 303–308.

13.       Kramer O., Ciaurri D. E, Koziel S. (2011). Derivative-free optimization, in Computational Optimization and Applications in Engineering and Industry, Studies in Computational Intelligence (Springer, New York, 2011). pp. 61–83.

14.       Lee K. Y., El-Sharkawi MA. Modern heuristic optimization techniques: theory and applications to power systems. Wiley 2008, New York.

15.       Michalewicz Z. (1996). Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag 1996, 3rd edition, New York.

16.       Moscato P. Memetic algorithms: a short introduction. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 219–234. McGraw-Hill 1999, New York.

17.       Mukhopadhyay D., Maulik U., Bandyopadhyay S., and Coello Coello, CA. A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I. IEEE Trans. On Evolutionary Computation 2014, 18(1).

18.       Naji-Azimi Z. Comparison of metaheuristic algorithms for examination timetabling problem. Applied Mathematics and Computation 2004, 16(1-2)  337-354.

19.       Ostermeier A., Gawelczyk A, Hansen N.  A derandomized approach to self-adaptation of evolution strategies. Evol. Comput. 1994, 2(4), 369–380. 

20.       Pohlheim H. (December 2006). Genetic and Evolutionary Algorithm. Retrieved from website: http://www.Geatbx.com.

21.       Rothlauf F. Representations for genetic and evolutionary algorithms. Springer 2006, Berlin.

22.       Sudholt D. On the analysis of the (1+1) memetic algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006), pp. 493–500. ACM Press 2006, New York

23.       Vaisakh K, Srinivas LR, Meah K. Genetic evolving ant direction particle swarm optimization algorithm for optimal power flow with non-smooth cost functions and statistical analysis. Applied Softcomputing, 13, 4579-4593.

24.       Zhu G., Kwong S. Gbest-Guided Artificial Bee Colony Algorithm for Numerical Function Optimization, 2010.


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6.

Authors:

M. Manojkumar, R. Venkateshwaran, S. Yukesh, S. Rajiv Gandhi

Paper Title:

Risk Management on Construction Project

Abstract: There is an increase in the number of construction project experiencing extensive risks leading to exceeding the initial time and cost budget. Managing risks in construction project has been recognized as a very important management process in order to achieve the project objectives in terms of time, cost, quality, safety and environmental sustainability. To implement construction project, a proper planning and scheduling is of vital important in order for the project to be executed and run smoothly. The software tool Microsoft Project Planner is used for planning, scheduling and controlling. The activities required to complete the building structure excluding the interiors was identified and was fed as an input to Microsoft along with their durations. We have made a study about the risk management and identified the risk involved in the building and found out the causes for the risk occurred throughout the construction work. Finally we have provided the suggestions to avoid that risk. Apart from that we have made a study of journals about risk management on various buildings (hospital, commercial, etc.) and listed out the risk occurring during construction work and provided solution to avoid those risks.

Keywords:
 Microsoft Project, (hospital, commercial, etc.), software, management


References:

1.              Cost And Schedule-Control Integration Issues And Needs:by William J.Rasdorf and Osama Y.Abudayyeh
2.              A Risk Management System For Preconstruction Phases Of Large Scale Development Projects In Developing Countries by Mohammad Baydoun, Project Manager, Millennium Development International & DBA Candidate, Grenoble Ecole de Management.

3.              Construction Delays Causing Risk On Time And Cost- A Critical Review by Chidambaram Ramanathan, SP Narayanan and Arazi B Idrus.

4.              Risk Management in Construction Project Management by Martin Schieg.

5.              Methodology of Risk And Uncertainity Management In Construction’s Technological And Economical Problems by Darius Migilinskas, LeonasUstinovicius

6.              Risk Management In Building Projects by AdanEnshassi, Jaser Abu Mosa

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7.

Authors:

K. Joshil Raj, S Siva Sathya, Kalyan Nandi

Paper Title:

A Modified Group Search Optimizer for Feature Selection and Parameter Determination of SVM

Abstract:  Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Group Search Optimizer (GSO) is a new population based optimization algorithm inspired by animal searching behavior for developing optimum searching strategies to find out solutions for continuous optimization problems. This paper presents an experimental analysis of modifications to classical GSO & studies its effects on a GSO-SVM hybrid combination for feature selection and kernel parameters optimization. In the proposed algorithm, three   modifications are introduced over classical GSO to improve its global search mechanism. The quality and effectiveness of the proposed methodology has been evaluated on standard machine learning datasets.

Keywords:
   Evolutionary algorithm; Group Search Optimizer; GSO; Support Vector Machine; Machine learning; Feature Selection; Kernel parameters.


References:

1.             Pang-Ning Tan, Michael Steinbach and Vipin Kumar, Introduction to Data Mining. Pearson Education Inc., 2006..
2.             Han, J., and Kamber, M., Data Mining Concepts and Techniques, Elsevier Inc., Second Edition, 2006.

3.             Alexandra M. Carvalho, Teemu Roos, Arlindo L. Oliveira, "Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood," Journal of Machine Learning Research 12 , pp. 2181-2210,2011.

4.             H. Witten, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Series in Data Management Systems, 2005.

5.             Keller, J. M. , Gray, M. R. , Givens JR. , J. A. , A Fuzzy K-Nearest Neighbor Algorithm, IEEE Transactions on Systems, Man, and Cybernetics, v. SMC-15, n. 4, pp. 258-260, 1985.

6.             Kenneth A. De Jong, Evolutionary Computation A Unified Approach. The MIT Press, Cambridge, England, 2006

7.             Marco Dorigo and Thomas Stützle, Ant Colony Optimization, The MIT Press Cambridge, England, 2004.

8.             J. Kennedy and R. Eberhart, "Particle swarm optimization," Proc. IEEE Int. Conf. Neural Networks, pp. 1942–1948,1995.

9.             Keerthi, S. S., & Lin, C.-J., Asymptotic behaviors of support vector machines with gaussian kernel. Neural Computation, 15, pp.1667–1689, 2003.

10.          K. Joshil Raj, S Siva Sathya, Kalyan Nandi, GSO-SVM for Feature Selection and  kernel Parameter Optimization, International Journal of Advanced Information Science and Technology(IJAIST), ISSN: 2319 – 2682, pp. 33-39, 2014.

11.          L. Zhang, L.B. Jack, A.K. Nandi, Fault detection using genetic programming, Elsevier, Mechanical Systems and Signal Processing. 19 , pp. 271–289,2005.

12.          P.-F. Pai, W.-C. Hong, Software reliability forecasting by support vector machines with simulated annealing algorithms, Elsevier Inc, The Journal of Systems and Software 79 pp.747–755, 2006.

13.          Felipe Alonso-Atienza, José Luis Rojo-Álvarez, Alfredo Rosado-Muñoz, Juan J. Vinagre, Arcadi García-Alberola & Gustavo Camps-Valls, Feature selection using support vector machines and bootstrap methods for ventricular fibrillation detection, Elsevier Ltd, Expert Systems with Applications 39,pp. 1956–1967, 2012.

14.          Yukun Bao, ZhongyiHu & TaoXiong, A PSO and pattern search based memetic algorithm for SVMs parameters optimization, Elsevier B.V, Neurocomputing 117,  pp.  98–106, 2013.

15.          Vapnik, V.N, “The Nature of Statistical Learning Theory”, Springer,  Berlin Heidelberg New York ,1995.

16.          S. He, Q. H. Wu, and J. R. Saunders, “Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior,” IEEE Transactions On Evolutionary Computation, vol. 13,no. 5, pp. 973-990, 2009.

17.          Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer,,Peter Reutemann, Ian H. Witten; The WEKA Data Mining Software: An Update; SIGKDD Explorations, Volume 11, Issue 1,2009.

18.          Lichman, M., UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science,2013.


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8.

Authors:

G.Sai Manoj, T.Sreevatsav, V.Vidya Priyanka, S.V.K.S. Prasad, P.Rajesh

Paper Title:

An Area Efficient Low Power High Speed Pulse Triggered Flip Flop Using Pass Transistor

Abstract:   The performance of flip-flop is an important element to determine the efficiency of the whole synchronous circuit. This paper presents an efficient explicit pulsed static single edge triggered flip flop with an improved performance and overcomes the drawbacks of the implicit type pulsed flip flops. The proposed flip flop is having a structure of explicit pulse-triggered with a modified true single phase clock latch based on signal feed through scheme. The proposed flip-flop is compared with existing explicit pulsed single edge triggered flip-flops in terms of power, speed and area. Simulation results based on PTM 90nm CMOS technology reveal that the proposed design features the best power, area and delay performance in several FF designs under comparison.

Keywords:
    Explicit, Edge-Triggered, Feed through, Latch, Synchronous


References:

1.          H. Kawaguchi and T. Sakurai, “A reduced clock-swing flip-flop (RCSFF) for 63% power reduction,” IEEE J. Solid-State Circuits, vol. 33, no. 5, pp. 807–811, May 1998.
2.          K. Chen, “A 77% energy saving 22-transistor single phase clocking D-flip-flop with adoptive-coupling configuration in 40 nm CMOS,” in Proc. IEEE Int. Solid-State Circuits Conf., Nov. 2011, pp. 338–339.

3.          V. Stojanovic and V. Oklobdzija, “Comparative analysis of masterslave latches and flip-flops for high-performance and low-power systems,” IEEE J. Solid-State Circuits, vol. 34, no. 4, pp. 536–548, Apr. 1999.

4.          J. Tschanz, S. Narendra, Z. Chen, S. Borkar, M. Sachdev, and V. De, “Comparative delay and energy of single edge-triggered and dual edge triggered pulsed flip-flops for high-performance microprocessors,” in Proc. ISPLED, 2001, pp. 207–212.

5.          F. Klass, C. Amir, A. Das, K. Aingaran, C. Truong, R. Wang, A. Mehta, R. Heald, and G. Yee, “A new family of semi-dynamic and dynamic flip-flops with embedded logic for high-performance processors,” IEEE J. Solid-State Circuits, vol. 34, no. 5, pp. 712–716, May 1999.

6.          M. Alioto, E. Consoli, and G. Palumbo, “General strategies to design nanometer flip-flops in the energy-delay space,” IEEE Trans. Circuits Syst., vol. 57, no. 7, pp. 1583–1596, Jul. 2010.

7.          B. Kong, S. Kim, and Y. Jun, “Conditional-capture flip-flop for statistical power reduction,” IEEE J. Solid-State Circuits, vol. 36, no. 8, pp. 1263–1271, Aug. 2001.

8.          N. Nedovic, M. Aleksic, and V. G. Oklobdzija, “Conditional precharge techniques for power-efficient dual-edge clocking,” in Proc. Int. Symp. Low-Power Electron. Design, Aug. 2002, pp. 56–59.

9.          P. Zhao, T. Darwish, and M. Bayoumi, “High-performance and low power conditional discharge flip-flop,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 12, no. 5, pp. 477–484, May 2004.

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9.

Authors:

Hussein Jaddu, Amjad Majdalawi

Paper Title:

Recursive Approximation Method for Solving Constrained Nonlinear Optimal Control Problems Using Legendre Polynomials

Abstract:    A computational method is presented to solve  a nonlinear quadratic optimal control problems subject to terminal state constraints, path inequality constraints on both the state and the control variables. The method is based on using  a recursive approximation technique  to replace the original constrained nonlinear dynamic system by a sequence of constrained linear time-varying systems. Then each of constrained linear time-varying quadratic optimal control problems is approximated by a quadratic programming problem by parameterizing each of the state variable by a finite length  Legendre polynomials with unknown parameters. To show the effectiveness of the proposed method, simulation results of two constrained nonlinear optimal control problems are presented.

Keywords:
  Nonlinear constrained quadratic optimal control problem;  Iterative Technique; Legendre polynomials; State parameterization.


References:

1.           J. Vlassenbroeck, A Chebyshev polynomial method for optimal control with state constraints, Automatica 24 (1988) 499–506.
2.           P. A. Frick and D. J. Stech, "Epsilon-Ritz method for solving optimal control problems: Useful parallel solution method",  Journal of Optimization Theory and Applications,  vol. 79,  no. 1, (1993), pp. 31-58.

3.           C. J. Goh  and K. L. Teo, "Control parameterization: A unified approach to optimal control problems with general constraints",  Automatica,  vol. 24,  no. 1, (1988),  pp 3-18.

4.           Troch I., F. Breitenecker and M. Graeff, Computing optimal controls for system with state and control constraints, IFAC Control Applications of Nonlinear Programming and Optimization, France, pp. 39-44, 1989.

5.           H. Jaddu, "Numerical methods for solving optimal control problems  using Chebyshev polynomials", PHD Thesis (1998).

6.           H. Jaddu, "Direct solution of nonlinear optimal control problems using quasilinearization and Chebyshev polynomials", Journal of the Franklin Institute,vol. 339,  no. 4, (2002),  pp 479-498.

7.           H. Jaddu and M. Vlach, "Successive approximation method for non-linear optimal control problems with applications to a container crane problem", Optimal Control Applications and Methods, vol. 23,  no. 5,  (2002), pp 275-288.

8.           Cimen T., Banks S. P.," Global optimal feedback control for general nonlinear systems with nonquadratic performance criteria", Systems & Control Letters, Vol  53, PP.  327 – 346, 2004

9.           Cimen T., Banks S. P., " Nonlinear optimal tracking control with application to super-tankers for autopilot design", Automatica, Vol. 40, PP. 1845 – 1863, 2004.

10.        Topputo  F., Bernelli-Zazzera  F., " Approximate Solutions to Nonlinear Optimal Control Problems in Astrodynamics", ISRN Aerospace  Engineering, Vol. 2013, 2013

11.        S. P. Banks and K. Dinesh, "Approximate optimal control and stability of nonlinear finite and infinite-dimensional systems", Annals of Operations Research, vol.  98,  (2000),  pp. 19-44.

12.        H. Jaddu and A. Majdalawi, " Legendre Polynomials Iterative Technique for Solving A Class of Nonlinear Optimal Control Problems," International Journal of Control and Automation, vol. 7, pp. 17-28, 2014

13.        W. Bell, "Special Functions For Scientist and Engineers",  D. Van Nostrand  Company, London, (1968).

14.        Neuman C. P. and A. Sen, A suboptimal control algorithm for constrained problems using cubic splines, Automatica, Vol. 9, pp. 601-613, 1973.

15.        Fegley K., S. Blum, J. Bergholm, A. Calise, J. Marowitz, G. Porcelli and L. Sinha, Stochastic and deterministic design and control via linear and quadratic
programming, IEEE Trans. Automatic Control, Vol. AC-16-6, pp. 759-766, 1971.

16.        J. T. Betts, "Practical Methods for Optimal Control using Nonlinear Programming", SIAM, Philadelphia, (2001).

17.        G. Bashein and M. Enns, "Computation of optimal control by   a method combining quasi-linearization and quadratic programming", International Journal  of  Control, vol. 16,  no. 1,  (1972),  pp. 177-187.

18.        Sakawa Y. and Y. Shindo, Optimal control of container cranes, Automatica, Vol.18-3, pp. 257-266, 1982.

19.        Teo K., C. Goh and K. Wong, A Unified Computational Approach to Optimal Control Problems, Longman Scientific & Technical, England, 1991.

20.        Jaddu, H., M. Vlach, Successive approximation method for non-linear optimal control problems with applications to a container crane problem, Optimal Control Applications and Methods, Vol. 23, pp. 275-288, 2002.

21.        Jaddu H. and A. Majdalawi, " An Iterative method for Solving the Container Crane Constrained Optimal Control Problem Using Chebyshev Polynomials", International Journal of Emerging Technology and Advanced Engineering, Vol. 5, pp. 344-351, 2015 


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10.

Authors:

Aivars Helde

Paper Title:

Advertise with Social Discourse, as a Brand Positioning Technique: Review of  Reseach with Special Reference to the Latvian Media

Abstract:     This study examines the nature of the social discourse of advertising used as a brend positioning discourse. The focus is on consumer advertising, which is directed towards the promotion of some product or service to the general public. The study, however, is not meant to exhaust all the aspects of this particular discourse, or present an answer to all the problems it poses. This paper aimed at analyzing some different comercial advertisements (product/non-product ads) to investigate the intentions and techniques of consumer product companies to reach more consumers and sell more products. Norman Fairclough‟s 3-D model and Kress and van Leeuwen‟s grammar of visual design were used to analyze the data for professionals, but we are pointed on using stereotypes.Tradicionally,stereotypes are defined as patterns or shemes via which peopleorganise their behaviors and activities.Psychologists have been extremely interested in the persuasion techniques used by advertisers. The implicite question that most of such studies have entertained is whether advertising has becom aforce molding cultural mores and individual behaviors,or whether it constitutes no more than a”mirror”of deeper cultural tendencies within urbanaized contemporary society.The one thing which evryone agrees is that advertising has become one of the most recognizable and appealing forms of aocial communication to which evryone in sociecty is exposed. However, it could be understood from the results that the producers, generally tend to use their power and ideology to change people’s behaviour and thought. Some time more efficiently is used” old” stereotypes and do not try to going to change people’s behaviuor but do conversaly use their power to preserve previous behaviour try to reinforces this behaviour,shown this like some tradicional  value what confirmed customers identity. When  we consider gender stereotypes we look at notinos about the supposedly traditional behaviours of men and women and the characteristics and standardsof this behaviours,as grounded in our culture and society.   This idea allows to producers make customer feel belonging to this society and psychologically be involved into story what is shown by advertisers. Culture covershuman values,action patterns,ideas,and material and artificial surraunding which enable interaction among people.The content of culturedetermines the particular qualities of certain groups of people,and it also determines their consumer characteristics.That is why it is essential to understand the way in witch culture reaches individuals.In today’s information area,the media are the primary means for the transmission and reproduction of cultural information.They shape the image of culture in people’s consciousness. In addition this study provides analyses of some ads, using different ways of interpretations. All materials are taken  from Latvian media.

Keywords:
   Social discourse analysis, stereotypes  brend , customer behaviour, print advertisement, Image, Fairclough-3D, Krees and van Leeuwen’s grammer, Gestalt psychology,culture.


References:

1.             Al Ries.,Jack Trout - „Positioning: the battle for Your mind”- Mc Graw-Hill ,2001.
2.             D.Meerman Scott -„The new rules of marketing &Pr”-Wiley,2010.

3.             Ferrell.O.C.;M.D.Hartline – „Marketing Strategy Text and Cases”South-Western Cengage Learning ,2014

4.             Hohlova P.-Finansovaja reklama.Kak eta delajut v Britanii”:M,:Media Mir, 2007

5.             E.A.Pesockii -”Reklamai motivācija potrebitelei”,Moskva,2009. 

6.             A.Lebedev-Ljubimov –„Psihologija reklami”. Piter 2003.

7.             Lails E. Borns, Nensija F.Ruso-„Psiholoģija” Raka,2001.

8.             Harald Business Review September 2013-„Seven steps to strategy making”

9.             International Journal Of Linguistics, ISSN 1948-5425,2012,Vol4,No.4

10.          Cook,.G the discurse of advertising.London and NY: Routledge,1992.

11.          Wodak R. –„ Gender and discourse”Sage Publications , 1997.

12.          Teun A. Van Dijk-„Discourse and manipulation”, Discurse and society Sage,2006. 

13.          Kelly, Aidan, Katrina Lawlor, and Stephanie O'Donohoe. "Chapter 8- Encoding Advertisements: The Creative Perspective." The Advertising and Consumer Culture Reader. By Joseph Turow and Matthew P. McAllister. New York: Routledge, 2009. 

14.          Hall, Stuart. Encoding /Decoding, in Culture, Media, Language.London, Hutchinson 1980.

15.          Kress G.,and  Van Leeuwen T.Reding images-Victoria, Deakin Univesity Press ,1990.

16.          http://www.academia.edu/2645766/Critical_Discourse_Analysis_and_Faircloughs_Model-_Mirzaee_and_Hamidi_2012_

17.          http://www.uv.es/gimenez/Recursos/criticaldiscourse.pdf  


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11.

Authors:

Anil Kumar, Vijay Mittal

Paper Title:

Recursive Approximation Method for Solving Constrained Nonlinear Optimal Control Problems Using Legendre Polynomials

Abstract:     A new approach for the implementation of quality philosophy Zero Quality Defects with usage of the Poka-Yoke method in the Assembly Line has been presented. The possibility of usage of mistake proofing device is connected with monitoring and improvement of operations in the process. The Poka-Yoke method of preventing errors by putting limits on how operation can be performed in order to force the correct completion of the operation has been presented. The possibility of implementing of the Poka-Yoke method as a factor of improving operation in the process in the assembly line has been shown. The aim of method Poka-Yoke in those practical examples is to eliminate or minimizes human error in manufacturing process and management as a result of mental and physical human imperfections.

Keywords:
   Lever Combination Switch; Mistake proofing; Poka Yoke ; Mindarika Company Limited


References:

1.             M. Dudek-Burlikowska, D. Szewieczek, Vol-36, Issue 1, (2009), “The Poka-Yoke method as an improving quality tool of operations in the process”, Journals of achievements in Materials and manufacturing. pp 95-102
2.             Arash Shahin and Maryam Ghasemaghaei, Vol. 2, No.-2; November 2010, “Service Poka Yoke”, International Journal of Marketing Studies. pp 190-201.

3.             Zero Quality Control:  Source Inspection and the Poka-Yoke System, Shigeo Shingo, Cambridge Productivity Press, 1986.

4.             Ramin Sadri, Pouya Taheri, Pejman Azarsa and Hedayat Ghavam, vol.5 (2011), “Improving Productivity through Mistake-proofing of Construction Processes”, International Conference on Intelligent Building and Management, pp 280-284.

5.             www.mindagroup.com

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12.

Authors:

Valliappan Raman, Sundresan Perumal

Paper Title:

Matlab Implementation Results: Detection and Counting of Young Larvae and Juvenile by Image Enhancement and Region Growing Segmentation Approach

Abstract:  This paper describes techniques to perform efficient and accurate recognition in larvae images captured from the hatcheries for counting the live and dead larvae’s. In order to accurately model small, irregularly shaped larvae and juvenile, the larvae images are enhanced by three enhancement methods, and segmentation of larvae and juvenile is performed by orientation associated with each edge pixel of region growing segmentation method. The two vital tasks in image analysis are recognition and extraction of larvae and juvenile from an image. When these tasks are manually performed, it calls for human experts, making them more time consuming, more expensive and highly constrained. These negative factors led to the development of various computer systems performing an automatic recognition and extraction of visual information to bring consistency, efficiency and accuracy in image analysis. This main objective of this paper is to study on the various existing automated approaches for recognition and extraction of objects from an image in various scientific and engineering applications. In this study, a categorization is made based on the four principle factors (Input, Segment the larvae, Recognition, Counting) with which each approach is drive .The achieved result of recognition and classification of larvae is around 85%. All the results achieved through matlab implementation are discussed in this paper are proved to work efficiently in real environment.

Keywords:
    Enhancement, Segmentation and Counting


References:

1.             Official Portal: Department of Fisheries Malaysia,” Department of Fisheries Malaysia, http://www.dof.gov.my/59, 2011.
2.             D.J. White, C. Svellingen, N.J.C. Strachan, Automated measurement of species and length of fish by computer vision, Fisheries Research 80 (2006) 203-210.

3.             R.N.Williams, T.J.L ambert, A.F. Kelsall, T. Pauly, Detecting marine animals in underwater video: Let’s start with salmon, in: Proceedings of the Twelfth Americas Conference on Information Systems, Acapulco, Mexico,2006.

4.             Boles, W.W,Geva, S,Busch, A, "An image processing approach for estimating the number of live prawn larvae in water" in Signal Processing and Its Applications, 1999, Brisbane, Australia.

5.             D.J. Lee, R. Schoenberger, D. Shiozawa, X.Q. Xu, P.C.Zhan, Contour matching for a fish recognition and migration monitoring system, SPIE Optics East, Two and Three-Dimensional Vision Systems for Inspection, Control, and Metrology II 56 (2004).

6.             J. Broersen, “Towards a detection and recognition system for freshwater fish,” MSc report, 2009.

7.             H. de Pontuala, R. Roberta, b, P. Minerb, " Study of bivalve larval growth using image processing" in Journal of Aquaculture Engineering, Elsevier, Vol 17, Issue 2, 1998.

8.             Johnson I, Harman M, Forrow D, Norris M, "An assessment of the feasibility of using image analysis in the oyster embryo-larval development test" PubMed , Environ Toxicol, 16(1):68-77, 2001.

9.             Borenstein, E., Ullman, S.: Class-specific, top-down segmentation. In: ECCV (2), 2002.

10.          Felzenszwalb, P.F., Huttenlocher, D.P,  "Pictorial structures for object recognition", International Journal of Computer Vision 61(1), 2005.

11.          Belongie, S., Malik, J., Puzicha, J." Shape matching and object recognition using shape contexts", IEEE Trans. Pattern Anal. Mach. Intell. 24(4) 2002

12.          J. Canny. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell., 8(6):679–698, 19.

13.          D. de Ridder, P.W. Verbeek R.P.W. Duin, and L.J. van Vliet. The applicability of neural networks to non-linear image processing. Pattern Analysis and Applications, 2:111–128, 1999.

14.          L.J. Latecki, R. Lakamper, and U. Eckhardt. Shape descriptors for non-rigid shapes with a single closed contour. IEEE Conf. on Computer Vision and Pattern Recognition, 5:424 429, 2000.

15.          L.A. Torres-Mendez, J.C. Ruiz-Suarez, L.E. Sucar, and G. Gomez. Translation,rotation, and scale-invariant object recognition. IEEE Transactions on Systems, Man and Cybernetics, 30:125–130, February 2000.

16.          Valliappan Raman, Brian Loh and Patrick Then,” First Prototype of Aquatic ToolKit: Towards Low-cost Intelligent Larval Fish Counting in Hatcheries” Proceedings of 9th International Conference on Pervasive Intelligence and Computing (PICom2011), Sydney, Australia, Pp: 193-195, Dec.12-14, 2011.


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13.

Authors:

Navdeep Sharma, Sameer Sharma, S.P. Guleria, N.K. Batra

Paper Title:

Mechanical Properties of Urea Formaldehyde Resin Composites Reinforced with Bamboo, Coconut and Glass Fibers

Abstract:   Composite materials, plastics and ceramics have been the dominant emerging materials from last thirty years. Polymeric materials reinforced with natural and synthetic fibres such as coconut, bamboo, jute glass, carbon and aramid provide advantages of high stiffness, good thermal, acoustic insulating properties, excellent formability and strength to weight ratio as compared to conventional construction materials, i.e. wood, concrete, iron and steel. The increase interest in using natural fibres as reinforcement in plastics is to substitute the conventional synthetic fibres in some structural applications and it has become one of the main concerns to study the potential of using natural fibres as reinforcement for polymers. In this research paper, seven different fiber reinforcement polymer composite were fabricated by wet hand-lay-up method using short coconut, short bamboo and short glass fibers binded with amino resin like urea formaldehyde. The urea formaldehyde was selected due to its low cost, less weight, easier to field fabricate, long durability and high temperature withstand ability. The different mechanical properties like density, tensile strength, hardness, flexural strength and percentage elongation of specimens were calculated and were compared with the pure urea formaldehyde.

Keywords:
     Composite, Polymeric materials, coconut, bamboo, glass fibers, urea formaldehyde.


References:

1.              H. Hishi , M. Yoshioka , A. Yamanoi , N. Shiraishi , Mokuzai Gakkaishi, “Studies on composites from wood and polypropylenes (I)”,  Vol. 34(2), 133–9, 1998.
2.              F. Febrianto , M. Yoshioka , Y. Nagai ,  M. Mihara , N. Shiraishi , “Composites of wood and trans-1,4-isoprene rubber I: mechanical, physical, and flow behaviour”, Journal of  Wood Science, Vol.45(1) , pp.38–45, 1999.

3.              F. Febrianto, M. Yoshioka, Y. Nagai, M. Mihara, N. Shiraishi, “Composites for wood and trans-1,4-isoprene rubber II: processing conditions for production of the composites”, Wood Science and Technology, Vol.35, pp. 297–310, 2001.

4.              M.M. Sain,  B.V. Kokata, D. Maldas, “Effect of reactive additives on the  performance cellulose fiber-filled polypropylene composities”, Journal of  Adhesive  Science and  Technology, Vol.7(1) ,  pp.49–61, 1993.

5.              S. Takase, N. Shiraishi, “Studies on composites from wood and polypropylenes (II)”, Journal of Applied Polymer Science, Vol.37, pp. 59-645, 1989.

6.              S. Mishra, J.B. Naik, “Absorption of water at ambient temperature and steam in wood-polymer composites prepared from agrowaste and polystyrene”, Journal
of Applied Polymer Science, Vol.68,  pp.6-681, 1998.

7.              J. Simonsen , R. Jacobsen , R. Rowell , “Wood-fiber reinforcement of styrene-maleic anhydride copolymers”, Journal of  Applied  Polymer  Science, Vol.68, pp.1567–73,1998.

8.              B. Xu ,  J. Simonsen ,  W.E. Rochefort , “Creep resistance of wood-filled polystyrene/high-density polyethylene blends” Journal of  Applied  Polymer  Science,  Vol.79, pp. 418–25, 2001.

9.              F. Zhang, T. Endo, W. Qiu W, L.Yang ,  T. Hirotsu , “Preparation and mechanical properties of composite of fibrous cellulose and maleated polyethylene”, Journal of  Applied  Polymer  Science, Vol.84, pp.1971–80, 2002.

10.           P.W. Balasuriya, L.Ye, Y.W Mai, J. Wu, “Mechanical properties of wood flake-polyethylene composites, II. Interface modification”, Journal of   Applied Polymer Science, Vol.83, pp.  2505–21, 2002

11.           A.Valadez-Gonzalez, J.M. Cervantes-Uc, R. Olayo, and P.J. Herrera-Franco, “Effect of fiber surface treatment on the fiber–matrix bond strength of natural fiber reinforced composites”, Composite B, Vol. 30, pp. 309-330, 1999.

12.           R M Rowell, R A Young, J K Rowell, “Paper and composites from agro-based resources”. CRC Lewis Publishers, Boca Raton FL, 1997.

13.           John M. J and Anandjiwala R. D, “Recent Developments in Chemical Modification and Characterization of Natural Fiber-Reinforced Composites”, Polymer Composites, 29(2), pp.187-207, 2008.

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14.

Authors:

Yousif Ismail Mohammed

Paper Title:

Virtual Solar Cell Tester System Based on Modified Interval Type-2 Fuzzy Logic Controller

Abstract:    The most fundamental of solar cell characterisation techniques is the measurement of cell efficiency. Standardised testing allows the comparison of devices manufactured at different companies and laboratories with different technologies to be compared. This paper presents a new design of solar cell testers for monocrystalline, polycrystalline, cadmium telluride (CdTe), and copper indium diselenide (CIS) cells. Each cell is tested for efficiency and categorized accordingly into four groups (A to D). A Virtual Reality (VR) model was built to simulate the system, keeping in mind real world constraints. Two photoelectric sensors were used to make detections for both the testing process and the robot movement. A handling robot with vacuum end-effectors was designed based a Modified Interval Type-2 Fuzzy Logic Controller (MIT2FLC) and command line programming for construction, editing, and simulation of the MIT2FLC for control of movement for solar cell and then distributed the cells according to the categories of test for efficiency. The MIT2FLC guides the trajectory of the robot according to the results of the efficiency testing. It was seen that the system worked very well, with the testing process and the robot movement interacting smoothly. The robot trajectory was seen to be highly accurate, and the pick and place operations were done with great precision.

Keywords:
 Handling robot, Solar cell tester, Virtual reality, a Modified Interval Type-2 Fuzzy Logic Controller (MIT2FLC).


References:

1.           M. A. Green, K. Emery, Y. Hishikawa. Warta W. Solar cell efficiency tables (version 37), progress in photovoltaic: research and applications, 2011, 19, pp:84–92
2.           S. Mohammad, C. Suria, Z. Zurita, J, J. Faridah, Kasmiran. Portable Wireless CATV Tester Unit with Solar Panel, IEEE International Conference on Space Science and Communication,2009, PP: 15-18

3.           P. Hacke, K. Terwilliger, R. Smith, S. Glick, J. Pankow, M. Kempe, S Kurtz. System voltage potential-induced degradation mechanisms in PV modules and methods for test. The 37th IEEE Photovoltaic Specialists Conference (PVSC 37), 2011, pp: 1-9

4.           M. Meiqin1, S. Jianhui, L. Chang, P. Kai1, Z. Guorong, Ming D. Research and development of fast field tester for characteristics of solar array. IEEE electrical and computer engineering, 2009, pp: 1055-1060

5.           C. Osterwald, Terrestrial photovoltaic module accelerated test-to-failure protocol. Technical report, National Renewable Energy Laboratory 1617 Cole Boulevard, Golden, Colorado 80401-3393 303-275-3000 • www.nrel.gov, March 2008, pp:1-16

6.           Y. Al Mashhadany, “Design, Analysis, Simulation, and Virtual-Reality-Verified Intelligent Controller for Industrial-Application SCARA Robot”,  International Journal of Advanced Computing, June, 2013

7.           R. Lathrop, K. Pfluke, Novel approaches to benchmarking solar cell tabbing solder ability. Proceedings of the 26th European Union Photovoltaic Solar Energy Conference, 2011 , pp: 1-6

8.           Student Guide, Robotics with the Boe-Bot,  Ver. 2.2, ISBN 1-928982-03-4, 2011 , pp:1-360

9.           C. Park, D. Park, H. Min. Controller design and motion simulation of solar cell substrate handling robot in vacuum environment, 11th International conference on control, automation and systems, 2011, pp: 1017-1019

10.        Y. Al Mashhadany, “ Hybrid ANFIS Controller for 6-DOF Manipulator with 3D Model”, International Journal of Computers & Technology, Vol. 4, No. 2, ISSN 2277-3061, www.cirworld.com,  April, 2013.

11.        C. Park, D. Park, H. Min. Motion simulation model for beam type solar cell substrate transport robot, The 8th IEEE International Conference on Ubiquitous Robots and Ambient Intelligence, 2011, pp:796-799

12.        X. Zhangl, H. Panland, G. Wul. Photovoltaic generation and Its applications in DC-motor, IEEE International conference on communications, circuits and
systems, 2010, pp: 609-611

13.        L. Leottau, M. Melgarejo, “An Embedded Type-2 Fuzzy Controller for a Mobile Robot Application”, chapter 18, Recent Advances in Mobile Robotics, 2011, pp.365-384

14.        X. Ling, Y. Zhang, “Operations on Triangle Type-2 Fuzzy Sets” Elsevier, Journal of Advanced in Control Engineering and Information Science,12, 2011,  pp. 3346-3350

15.        T. C. Yang,  J.G. Juang, “Application of Adaptive Type-2 Fuzzy CMAC to Automatic Landing System”,  2010 IEEE International Symposium on Computational Intelligence and Design, ISCID.2010, pp. 221-224

16.        M. Manceur, N. Essounbouli, A. Hamzaoui, “Robust Smooth Sliding Type-2 Interval Fuzzy Control For Uncertain System”, 978-1-4244-8126-2/10/$26.00 ©2010
IEEE

17.        Y. Al Mashhadany, “Advance 6-DOF Manipulator Controller Design Using DMRAC Based ANFIS” , Wulfenia Journal, Austria, ISNN: 1561-882X, Vol 20, No. 3;Mar 2013.

18.        P.A.S. Birkin, J. M. Garibaldi, “A Comparison of Type-1 and Type-2 Fuzzy Controllers in a Micro-Robot Context”, IEEE international Fuzzy conference, Korea, August 20-24, 2009, pp. 1857-1862

19.        Z. Lv, H. Jin, P. Yuan, “The Theory of Triangle Type-2 Fuzzy Sets”’ IEEE Ninth International Conference on Computer and Information Technology,  2009, pp. 57-62

20.        X. Du, H. Ying, “Derivation and Analysis of the Analytical Structures of the Interval Type-2 Fuzzy-PI and PD Controllers”, IEEE transactions on fuzzy system, Vol, 18, No. 4, Aug. 2010, pp. 802-814

21.        Y. I. Al Mashhadany, S. Adel, A. Abdu sattar, A. Khuder, “Novel Controller for PUMA 560 Based on PIC Microcontroller”, has been accepted for publication in Wulfenia Journal, Vol. 21, Iss. 4, 2014

22.        M. Biglarbegian, W. W. Melek, J. M. Mendel, “Design of Novel Interval Type-2 Fuzzy Controllers for Modular and Reconfigurable Robots: Theory and Experiments”, IEEE transactions on industrial electronics, Vol. 58, No. 4, April 2011, pp. 1371-1384

23.        J. T. Rickard, J. Aisbett, G. Gibbon, “Type-2 Fuzzy Conceptual Spaces”, 978-1-4244-8126-2/10/$26.00 ©2010 IEEE

24.        Y. Al Mashhadany,"High-Performance of Power System Based upon ANFIS (Adaptive Neuro-Fuzzy Inference System) Controller", Journal of Energy and Power Engineering 8 , 729-734, 2014.

25.        M. Manceur, N. Essounbouli,  A. Hamzaoui, “Higher Order Sliding Fuzzy Type-2 Interval Control for SISO Uncertain Nonlinear Systems”, IEEE International Conference on Fuzzy Systems, June 27-30, 2011, Taipei, Taiwan, pp. 1388-1396

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15.

Authors:

Deniz Kılınç, Fatma Bozyiğit, Alp Kut, Muhammet Kaya

Paper Title:

Overview of Source Code Plagiarism in Programming Courses

Abstract:     Plagiarism of programming source codes is an undesirable situation in the many fields of software development world. Especially in educational field, it is obviously realized that plagiarism in programming courses increases consistently. The aim of this study is attempting to answer questions such as “which codes are similar?”, “what similarity ratios are?” in order to prevent plagiarism among university students who attend programming courses.  While developing the proposed methodology, N-gram similarity calculation method and Vector Space Model (VSM) were considered. Information Retrieval (IR) System and Cosine Normalization (CN) methods were utilized to calculate similarity ratios. Experimental study was performed on the dataset yielded by changing source code examples in different forms. The results obtained provide convincing evidence that the study is fit the purpose.

Keywords:
 Plagiarism source code, n-gram, vector space model, cosine normalization.


References:

1.                 Parker and J. Hamblen, “Computer algorithms for plagiarism detection”, IEEE Trans. Education, vol. 32, May 1989, pp. 94–99.
2.                 Intellectual and Artistic Works Law.  Available: http://mevzuat.meb.gov.tr/html/7981_5846.html

3.                 G. Whale, “Plague: Plagiarism Detection Using Program Structure”, Dept. Comput. Sci., Univ. New South Wales, Kensington, Australia, Tech. Rep. 8805,1988.

4.                 G. Malpohl. JPlag: Detecting Software Plagiarism. Available: http://www.ipd.uka.de:2222/index.html

5.                 “YAP3: Improved detection of similarities in computer program and othertexts,” In Proc. 27th SCGCSE Tech. Symp., Philadelphia, PA, 1996, pp.130–134

6.                 R. Dale, H. Mois, H. Somers, “Handbook of NLP”, Marcel Dekker, 2000.

7.                 G. Salton, “The SMART Retrieval System – Experiments in Automatic Document Processing”, NJ, Englewood Cliffs: Prentice-Hall, 1971.

8.                 G. Salton, A. Wong, C.S. Yang. “A vector space model for information retrieval”, Journal of the American Society for Information Science, 1975 , 18(11):613-620.

9.                 C.D. Manning, P. Raghavan., H. Schütze, “An Introduction to Information Retrieval”, Cambridge University Press, 2009.

10.              M. Joy and M. Luck, “Plagiarism in programming assignments,” IEEE Trans. Educ., vol. 42, no. 1, Feb. 1999, pp. 129–133.

11.              E. L. Jones, “Metrics based plagiarism monitoring “, In: 6th  Annual CCSC Northeastern Conference, Middlebury, Vermont, April 20-21, 2001.

12.              F. Culwin, T. Lancaster, "Plagiarism issues for higher education", VINE, Vol. 31 Iss 2, 2001, pp. 36 – 41.

13.              G. Frantzeskou, E. Stamatatos, S. Gritzalis, and C. E. Chaski. “Identifying authorship by byte-level n-grams: The source code author profile (SCAP)”, Journal of Digital Evidence, 2007.

14.              Bozyigit, D. Kılınç, A. Kut, M. Kaya, “Bulanık Mantık Algoritmaları Kullanarak Kaynak Kod Benzerliği Bulma”,In: AB15, 2015, submitted for publication.

15.              Vector Space Model. Available: http://nlp.stanford.edu/IR-book/html/htmledition/the-vector-space-model-for-scoring-1.html


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16.

Authors:

Alem Čolaković, Tarik Čaršimamović

Paper Title:

The Corresponding Options of TCP Variants for Fairness Problem in AD HOC Networks

Abstract:      The ad hoc network is a continuously self-configuring and decentralized network where nodes communicate with each other without the fixed network infrastructure or centralized administration. TCP (Transmission Control Protocol) is a connection-oriented transport protocol that provides a reliable exchange of data streams. Implementation of TCP in wireless networks has many challenges such as the issues of the efficiency and TCP fairness problem. The fairness means that network nodes (users or applications) are receiving a fair share of overall resources. In this paper, we study the problem of maintaining the fairness for TCP connections in ad hoc networks. Our research has been made to present the TCP fairness problem in MANET (ad hoc mobile networks) while considering the sending and receiving of traffic. Achieving fairness in these networks is a challenge due to specific characteristics of an ad hoc environment and it is necessary to adapt TCP for ad hoc networks. The primary goal of this paper is to present fairness in ad hoc networks using combinations of different TCP variants and routing protocols. We evaluated the results of our research by using the proper simulation method. .

Keywords:
   Ad hoc, MANET, VANET, TCP, fairness.


References:

1.              Saylee Gharge, Ajinkya Valanjoo: “Review of different TCP variants in Ad hoc networks”, VESIT, International Technological Conference-2014 (I-TechCON), Jan. 03 – 04, 2014.
2.              Hatim Mohamad Tahir, Abas Md Said, Alex W.M. Tan: „Improving TCP fairness Flows in Wireless Ad Hoc Networks“, Information Systems International Conference (ISICO), 2-4. December 2013.

3.              Ahmad Al Hanbali, Eitan Altman, Philippe Nain: “A Survey of TCP over Ad hoc Networks”, Communications Surveys & Tutorials, IEEE  (Volume:7 ,  Issue: 3 ), 2006.

4.              Linfang Dong, Shang Liu: "Research on TCP Fairness Improvement over Wireless Ad Hoc Networks", Distributed Computing and Applications to Business Engineering and Science (DCABES), 2010 Ninth International Symposium on 10-12 August 2010.

5.              Ms.M.Santhiya ,Mr.M.Sunil karthick, Ms.M.keerthika: “Performance of various TCP in Vehicular Ad Hoc network based on timer management”, International Journal of Advanced Research in  Electrical, Electronics and Instrumentation Engineering, Vol. 2, Issue 12,  December, 2013.

6.              Vijendra Rai: “Simulation of Ad-hoc Networks Using DSDV, AODV And DSR Protocols And Their Performance Comparison” Proceedings of the 4th National Conference; INDIACom-2010 Computing For Nation Development, February 25 – 26, 2010.

7.              Yi Lu, Yuhui Zhong, Bharat Bhargava: „Packet Loss in Mobile Ad Hoc Networks“, Center for Education and Research in Information Assurance and Security, Department of Computer Sciences, Purdue University, Wes Lafayette, USA.

8.              Dimitry Kuptsov, Boris Nechaev, Andrey Lukyanenko: “A novel Demand-Aware Fairness Metric for IEEE 802.11 Wireless Networks“, Combra, Portugal, SAC'13 March 18-22, 2013.

9.              Jain, R.; Chiu, D.M.; Hawe, W.: "A Quantitative Measure of Fairness and Discrimination for Resource Allocation in Shared Computer Systems", DEC Research Report TR-30, 1984.

10.           Saar Pilosof, Ramachandran Ramjee, Danny Raz, Yuval Shavitt, Prasun Sinha: „Understanding TCP fairness over Wireless LAN“, IEEE INFOCOM 2003.

11.           Tan G., Guttag J.: „Time-based fairness Improves Performance in Multi-rate WLANs”. In Proc. of USENIX'04, Boston, MA, 2004.

12.           G. Boggia, P. Camarda, L. A. Grieco, T. Mastrocristino, and G. Tesoriere: „A Cross-layer Approach to Enhance TCP Fairness in Wireless Ad-hoc Networks“, Wireless Communication Systems, 2005. 2nd International Symposium, Siena, September 2005.

13.           Chi Ho Ng, Jack Chow, Ljiljana Trajkovic: „Performance Ecaluation of TCP over WLAN 802.11 with Snoop Performance Enhancing Proxy“, In Proceedings of OPNETWORK, School of Engineering Science Simon Fraser University, Vancouver Canada V5A 1S, 2002.

14.           Awedeh R.: „Compatibility of TCP Reno and TCP Vegas in wireless ad hoc networks“, IET Communications, vol. 1, issue 6, 2007.

15.           V. Jacobson, R. Braden: “TCP Extensions for Long-Delay Paths” Network Working Group, Request for Comments: 1072, 1988.

16.           M. Mathis, J. Mahdavi, S. Floyd, A. Romanow: “TCP Selective Acknowledgment Options” Network Working Group, Request for Comments: 2018, 1996.

17.           K. Ramakrishnan, S. Floyd, D. Black: "The Addition of Explicit Congestion Notification (ECN) to IP", Network Working Group, Request for Comments: 3168,  September 2001.

18.           Forouzan Pirmohammadi, Mahmood Fathy, Hossein Ghaffarian: „TCP and UDP Fairness in Vehicular Ad hoc Networks“, International Journal of Emerging Technology and Advanced Engineering, Volume 2, Issue 6, June 2012.

19.           Sabina Baraković, Jasmina Baraković: “Comparative Performance Evaluation of Mobile Ad HocRouting Protocols” MIPRO, Opatija, Croatia, 2010.

20.           Kaixin Xu, Mario Gerla, Lantao Qi, Yantai Shu: „Enhancing TCP Fairness in Ad Hoc Wireless Networks Using Neighborhood RED“, MobiCom '03, USA, 2003.  

21.           Yahia M: „Enhanced congestion control for TCP fairness in ad hoc wireless networks“, Industrial Electronics and Applications (ICIEA), 7th IEEE Conference on 18-20 July 2012.

22.           Linfang Dong, Yantai Shu, Sanadidi M., Gerla M.: "A Method for Improving the TCP Fairness in Wireless Ad Hoc Networks", Wireless Communications, Networking and Mobile Computing, 2008. WiCOM '08. 4th International Conference on 12-14 Oct. 2008.

23.           Zhifei Li, Sukumar Nandi, Anil K. Gupta: "Achieving MAC fairness in wireless ad-hoc networks using adaptive transmission contro", Computers and Communications, 2004. Proceedings. ISCC 2004. Ninth International Symposium on July 2004.

24.           Jian Liu, Singh Suresh: "ATCP: TCP for mobile ad hoc networks", Selected Areas in Communications, IEEE Journal on  (Volume:19 ,  Issue: 7), July 2001.


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17.

Authors:

Harsh Khatter, Anjali Jain, Poonam Pandey

Paper Title:

Classification and Categorization of Blood Infection Using Fuzzy Inference System

Abstract:       From last few decades the human body infections and diseases are growing in exponential manner. As per the medical report, in every three months a new infection or viral comes in existence with some new explode to effect the human race. To test whether the infection is in body or not, Blood tests are the common methods. Most of the diseases are beyond the doctor’s study or some recently spread virus infected the blood or human body. In such cases doctors use to give the treatment of other disease having same symptoms or same blood test cases. In this paper we are trying to make such a system which will spread awareness among doctors about the infections. The proposed system will work on the basis of fuzzy logic and neural network with the help of inference engine and its rules. The simulation will be done using Matlab. The proposed approach of using fuzzy logic and inferences with neural networks training in blood samples on real test cases of blood report is a novel idea.

Keywords:
    About four key words or phrases in alphabetical order, separated by commas.


References:

1.           W.H. Chang, J.H. Wang, W.S. Ling “An integrated microfluidic system for detecting human immunodeficiency virus in blood samples”, IEEE, Taiwan, January 20-24, 2013.
2.           A.P. Reddington, J.T. Tureb, A. Tuysuzoglu, G.G. Daaboul, C.A. Lopez, W.C. Karl “An Interferometric Reflectance Imaging Sensor for Point of Care Viral Diagnostic”, IEEE transaction, vol.60, no.12, December 2013.

3.           W.Shitong, K.F.L. Chung, F.Duan “Applying the improved fuzzy cellular neural network IFCNN to white blood cell detection”, Elsevier, Neurocomputing 70 (2007), pp 1348–1359, 2006.

4.           W. Shitong, F. Duan, X. Min, H. Dewen “Advanced fuzzy cellular neural network: Application to CT liver images”, Elsevier, Artificial Intelligence in Medicine (2007) 39, pp 65—77, 2006.

5.           D. Elsheakh, H. Elsadeki, E. Abdullah, S. Atteya. W. N. ELmazny “Novel Rapid Detection of Different Viruses in Blood Using Microimmuno-Sensor”, 7th European Conference on Antennas and Propagation, Gothenburg, 8-12 April, 2013, pp. 1128-1131.

6.           D.S. Campo, Z. Dimitrova, G.L. Xia, P. Skums, L.G. Raeva, Y. Khudyakov “New Computational Methods for Assessing the Genetic Relatedness of Close Viral Variants”, IEEE 4th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), Miami, 2-4 June 2014.

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18.

Authors:

Apeksha Rani H. M, Prathibha Kiran

Paper Title:

A Novel Method for Analysis of EEG Signals Using Brain Wave Data Analyzer

Abstract:   The present day research allow us to develop a new class of bioengineering control devices and robots to provide daily life assistance to handicapped and elderly people. This proposed method describes how the brain activity is measured using mind wave EEG signal data transmission device. The brain electric signal are measured by EEG (Electroencephalograph) which shows a demand for better accuracy and stability and facilitates the graphical illustration of spatial features of electric brain activity. It provides a very promising technology for physically disabled people who are unable to access their hands and in this paper we will discuss briefly how the data acquisition can be done by using biosensor.

Keywords:
 EEG, Mind wave, Biosensor.


References:

1.              J.Katona, I.Farkas, T.Ujbanyi, P.Dukan, and A.Kovari,”Evaluation of the Neurosky MindFlex EEg Headset Brain Waves Data”  (Ed.), 2014.
2.              Chin-Teng Lin, Bor-Shyh Lin, Fu-Chang Lin, and Che-Jui Chang, “Brain Computer Interface-Based Smart Living Environmental Auto-Adjustment Contol System in UPnP Home Networking, IEEE System Journal, Vol..8, No.2, 2014

3.              Giovanni, Topo Suprihadi, Kanisius Karyono,” Drowtion: Drowsiness detection software using Mind wave”, IEEE August Co., 2014

4.              Gy. Buzsaki, Rhythms of the Brain, Oxford University Press, 2006

5.              J. Malmivuo, R. Plonsey, Bioelectromagnetism, Principles and Applications of Bioelectric and Biomagnetic Fields, Oxford University Press, New York, 1995

6.              Neurosky Inc, MindSet Communications Protocol, Neurosky Inc., 2010

7.              Lebedev M.A., Nicolelis M.A., Brain-machine interfaces: past, present and future, Trends Neurosci, 29, 536-546, 2006

8.              NeuroSky Inc, The brain Wave Signal (EEG), NeuroSky Inc, 2009

9.              J. Katona, A. Kovari, T. Ujbanyi, Visualization of brainwaves, Dunakavics, DF Press, in press.

10.           J. Katona et al. • Evaluation of The Neurosky MindFlex EEG Headset Brain Waves Data


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19.

Authors:

Havyas V B, Choodarathnakara A L, Thribhuvan R, Chethan K S

Paper Title:

Decision Tree Approach for Classification of Satellite Imagery

Abstract:    Various practical systems capable of extracting descriptive decision making knowledge from data have been developed and evaluated. Techniques that represent knowledge about classification tasks in the form of decision trees are focused on. A sample of techniques is sketched, ranging from basic methods of constructing decision trees to ways of using them non-categorically. Some characteristics that suggest whether a particular classification task is likely to he amenable or otherwise to tree-based methods are discussed. Many urban land cover types show spectral similarity in remote sensing data. Further, the finer the spatial resolution of the data, the larger is the number of detectable subclasses within classes. This high within-class spectral variance of some classes results in multimodal distribution of spectra and may decrease their spectral separability. Hence, the existing traditional hard classification techniques which are parametric type do not perform well on high resolution data in the complex environment of the urban area as they expect datasets to be distributed normally. The aim of this paper is to investigate a non-parametric classifier as an alternative approach to classify an image data of a semi urban area

Keywords:
  Remote Sensing, Image Classification, Parametric Classifier, Non-parametric and Decision Tree Classifier

References:

1.              Navalgund, R. R., “Remote sensing: Basics and applications”. Resonance, 2001, 6, 51–60.
2.              Patel, N. K., Medhavy, T. T., Patnaik, C. and Hussain, A., “Multi-temporal ERS-1 SAR data for identification of rice crop”. J. Indian Soc. Remote Sensing, 1995, 23, 33–39.

3.              Panigrahy, S., Chakraborty, M., Sharma, S. A., Kundu, N., Ghose, S. C. and Pal, M., “Early estimation of rice acre using temporal ERS-1 synthetic aperture radar data – A case study for Howrah and Hooghly districts of West Bengal”, India. Int. J. Remote Sensing, 1997, 18, 1827–1833

4.              Srivastava, H. S., Patel, P. and Navalgund, R. R., “Incorporating soil texture in soil moisture estimation from extended low-1 beammode Radarsat-SAR data”. Int. J. Remote Sensing, 2006, 27, 2587– 2598.

5.              Jayaraman, V., Srivastava, S. K., Kumaran Raju, K. and Rao, U.R., “Total solution approach using IRS-1C and IRS-P3: A perspective of multi-resolution data fusion and improved vegetation indices”. IEEE Trans. Geosci. Remote Sensing, 2000, 38, 587–604

6.              Navalgund, R. R., Parihar, J. S., Ajai and Nageshwar Rao, P. P., “Crop inventory using remotely sensed data”. Curr. Sci., 1991, 61, 162–171.

7.              Vasudevan, B. G., Gohil, B. S. and Agarwal, V. K., “Backpropagation neural network based retrieval of atmospheric water vapour and cloud liquid water from IRS-P4 MSMR”. IEEE Trans Geosci. Remote Sensing, 2004, 42(5), 985–990.

8.              C. Apte and S. Weiss, “Data mining with decision trees and decision rules,” Future Generation Computer Systems,” pp. 197-210, Nov. 1997

9.              Selim Aksoy, K. Koperski, Carsten Tusk, and Giovanni Marchisio, “Interactive training of advanced classifiers for mining remote sensing image archives,” KDD’04, Seattle, Washington, USA, August 22-25,2004.

10.           Jiawei Han and Micheline Kamber, “Data Mining Concepts and Techniques”, 2nd ed., Elsevier, 2006.

11.           S. Rasoul Safavian and David Landgrebe, “A survey of decision tree classifier methodology,” IEEE Trans. Syst. Man Cybnet., vol. 21, no. 3, pp. 660-674, May/ June 1991

12.           Lior Rokach and Oded Maimon, “Top-down induction of decision trees classifiers-A survey,” IEEE Trans. Syst. Man Cybnet., part C, vol. 35, no. 4, pp. 476-487, Nov. 2005.

13.           R. G. Congalton and A. Roy Mead, “A review of three discrete multivariate analysis techniques used in assessing the accuracy of remotely sensed data from error matrix,” IEEE Trans. Geosci. Remote Sensing, vol. GE 24, no. 1, pp. 169-174, Jan. 1986.

14.           R. G. Congalton, “Considerations and techniques for assessing the accuracy of remotely sensed data,” Geosci. Remote Sens. Symp., vol. 3, Jul. 10-14, 1989, pp. 1847-1850.


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20.

Authors:

Etleva Beliu, Ermira Pajaj

Paper Title:

What Affects the Memory

Abstract:     Physical exercise affects our body on multiple fronts. It increases heart rate, which means more blood pumped to the brain. It also helps the body release some hormones, that participate in aiding and providing a nourishing environment for the growth of brain cells. Physical exercise training can modify hippocampal and medial temporal lobe volumes. Both of these regions are involved in memorization. The aim of this study is to analyze the effect of physical exercise, of smoking, and using alcohol on the memorizing ability. By using the questionnaires and face-to-face interviews, data is collected from around 300 people of both genders. They have an age range of 15 – 20 years old and are from different schools in Albania ; namely in Tirana and the outskirts of Tirana, in Durres, in Shkodra, Gjirokastra and Vlora.  They are asked to read 40 words in 5 minutes and then are tested to see how many of them they can memorize. The questionaire includes demographic information such as age, gender and birth city and questions to measure their lifestyle. In the lifestyle area it is asked about the  smoking habbit, alcohol consumption, eating and exercise frequency. The data is analized using SPSS. A normalization of number of memorized words is done. Then, this modified variabel is analized using stepwise multiple regression. The most important independent variables of this model are exercise frequency and alcohol consumption. Exercise frequency is organized in three groups: those who train 0-2 days a week, Level Group 1 (LG1); those who train 3-4 days a week (LG2); and the ones that train more than 4 days a week (LG3).  The method of MANOVA shows that there  is  a statistically obvious increase between two consecutive levelgroups. But there is a very significant difference between the number of memorized words of LG1 and LG2. This research provides evidence that physical exercise and  alcohol consumation affect   memory.

Keywords:
   memory, BDNF molecule, hippocampus, exercise frequency, alcohol consumption, stepwise multiple regression, and MANOVA.


References:

1.             http://www.brainfacts.org/across-the-lifespan/diet-and-exercise/articles/2013/physical-exercise-beefs-up-the-brain/
2.             http://www.ncbi.nlm.nih.gov/pubmed/21722657

3.             Griffin ÉW, et al. Aerobic exercise improves hippocampus function and increases BDNF in the serum of young adult males. Physiol Behav. 2011 104(5):934-41

4.             Devin K. Binder and Helen E. Scharfman. Brain-derived Neurotrophic Factor Growth Factors. 2004 September; 22(3): 123–131.

5.             Ka Lok Chan et al., Relationship of serum brain-derived neurotrophic factor (BDNF)  and health-related lifestyle in healthy human subjects  (The results of this study were approved by the Research Ethnic Committee of Hong Kong Hospital Authority and the Hong Kong Polytechnic University Human Subjects Ethnics Sub-Comittee)

6.             http://www.pnas.org/content/108/7/3017.short Kirk I. Erickson et al., Exercise training increases size of hippocampus and improves memory, PNAS February 15, 2011 vol. 108 no. 7  3017–3022

7.             Kirk I. Erickson et al Brain-Derived Neurotrophic Factor Is Associated with Age-Related Decline in Hippocampal Volume The Journal of Neuroscience, April 14, 2010 • 30(15):5368 –5375

8.             Applied Longitudinal Analysis, G. Fitzmaurice, N. Laird and  Ware, pp.490-513

9.             Practical  Multivariate Analysis, A.A. Afifi , S.May, V. Clarc, 2011, pp.231-264

10.          An introduction to applied multivariate analysis, Tenko Raikov and George Marcoulides , 2008, pp.120-314

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21.

Authors:

Aditya Kumar Singh, Apurva Anand, Anindya Sundar Das

Paper Title:

First Passage Monte-Carlo Simulation for Charge Distribution and Capacitance

Abstract:      A novel scheme has been studied and demonstrated for Monte Carlo simulations of diffusion-reaction processes. The new algorithm skips the traditional small diffusion hops and propagates the diffusing particles over long distances through a sequence of super-hops, one particle at a time. By partitioning the simulation space into non- overlapping protecting domains each containing only one or two particles, the algorithm factorizes the N-body problem of collisions among multiple Brownian particles into a set of much simpler single-body and two-body problems. Efficient propagation of particles inside their protective do- mains is enabled through the use of time-dependent Green's functions (propagators) obtained as solutions for the first-passage statistics of random walks. The resulting Monte Carlo algorithm is event-driven and asynchronous; each Brownian particle propagates inside its own protective domain and on its own time clock. The algorithm reproduces the statistics of the underlying Monte-Carlo model exactly. The new algorithm is efficient at low particle densities, where other existing algorithms slow down severely. Thus we have analyzed the application of this algorithm in the charge distribution and the capacitance detection.

Keywords:    Monte Carlo Simulation, Charge distribution, capacitance, Markov chain


References:

1.              James A. Given, Chi-Hwang, Michel Mascagni, “First and last passage Monte Carlo algorithm for charge distribution on a conducting surface”, Phys. Rev.  66,0567042002.
2.              Sydeny Redner, “A Guide to First Passage Process” Cambridge university press, 2001.

3.              M.Strobel, K.-H. Heinig, and W. Moller. “Three-dimensional domain growth on the size scale of the capillary length:  Effective growth exponent and comparative atomistic and mean-field simulations”. Phys.  Rev. B, 64(24):245422, 2001.

4.              J. S. Reese, S. Raimondeau, and D. G. Vlachos. Monte Carlo Algorithms for Complex Surface Reaction Mechanisms:  Efficiency and Accuracy.  J. Comp. Phys. 173(1):302–321, 2001.

5.              M. Biehl.   “Lattice gas models and Kinetic Monte Carlo simulations of epitaxial growth”.   In Voigt, editor, Int.  Series of Numerical Mathematics 149, pages 3–18. Birkhaeuser, 2005.

6.              S. K. Theiss, M.-J.  Caturla, M. D. Johnson, J. Zhu, T.  J.  Lenosky, B. Sadigh, and  T.  Diazde la Rubia.   Atomic scale models of ion implantation and  dopant  diffusion in silico.  Thin Solid Films, 365:219–230, 2000.

7.              C. Domain, C. S. Becquart, and L. Malerba.  “Simulation of radiation damage in Fe alloys: an object kinetic Monte Carlo approach. Journal of Nuclear Materials”, 335(1):121–145, 2004.

8.              D. P. Tolle and N. Le Novere. “Particle-based stochastic simulation in systems biology”. Current Bioinformatics, 1(3):315–320, 2006.

9.              J. S. van Zon and P. R. ten Wolde.  “Green’s-function reaction dynamics:  A particle-based approach for simulating biochemical networks in time and space”. J. Chem. Phys., 123(23):234910, 2005.

10.           S. J. Plimpton and A. Slepoy. “Microbial cell modeling via reacting diffusive particles”.  J. Phys.: Conf. Ser., 16:305–309, 2005.

11.           M. H. Kalos, D. Levesque, and L. Verlet.  “Helium at zero temperature with hard-sphere and other forces”. Phys.  Rev. A, 9(5):2178–2195, 1974.

12.           J. Dalla Torre, J.-L. Bocquet, N. V. Doan, E. Adam, and  A. Barbu.   “JERK an event-based Kinetic Monte Carlo model to predict microstructure evolution of materials under irradiation”. Philosophical Magazine, 85:549–558, 2005.

13.           T. Oppelstrup, V. V. Bulatov, G. H. Gilmer, M. H. Kalos, and B. Sadigh. “First-Passage Monte Carlo Algorithm:  Diffusion without All the Hops”.  Phys.  Rev. Lett., 97(23):230602, 2006.Donev.  

14.           T. Oppelstrup, V. V. Bulatov, G. H. Gilmer, M. H. Kalos, and B. Sadigh. “Asynchronous event-driven particle algorithms.  SIMULATION”.  Transactions of The Society for Modeling and Simulation International, 85(4):229–242, 2008.

15.           Daniel Ben Abraham.   “Computer simulation methods for diffusion-controlled reactions”.    J. Chem. Phys., 88(2):941–948, 1988.

16.           F. Leyvraz and S. Redner. “Spatial organization in the two-species annihilation reaction A +B → 0”. Phys.  Rev. Lett., 66(16):2168–2171, 1991.

17.           Y. Shafrir and D. ben Avraham. “Large scale simulations of two-species annihilation, A+B → 0, with drift”.  Phys.  Lett.  A, 278(4):184–190, 2001.

18.           M. Smith and T. Matsoukas. “Constant-number Monte Carlo simulation of population balances”. Chemical Engineering Science, 53(9):1777–1786, 1998.

19.           D. Zhong, R. Dawkins, and D. Ben Abraham.   “Large-scale simulations of diffusion-limited n-species annihilation”. Phys.  Rev. E, 67(4):040101, 2003.

20.           S. S. Andrews and D. Bray.  “Stochastic simulation of chemical reactions with spatial resolution and single molecule detail”.  Physical biology, 1(3):137–151, 2004.

21.           D. Toussaint and F. Wilczek.  “Particle–antiparticle annihilation in diffusive motion”.  J. Chem. Phys., 78(5):2642–2647, 1983.

22.           H. Kim and K. J. Shin.  Exact Solution of the Reversible   Diffusion-Influenced Reaction for an Isolated Pair in Three Dimensions.  Phys.  Rev. Lett., 82(7):1578–1581, 1999


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22.

Authors:

Merina Devi Hemam, N.V. Uma Reddy

Paper Title:

An Energy-Efficient, Delay-Aware, Lifetime-Balancing and Data Collection Protocol for Heterogeneous Wireless Sensor Networks

Abstract:   The technique that is used in this paper is to make it more simpler for wireless sensor networks problem .To make the energy more efficient a protocol is used that is called EDAL. It is rebuilt from the existing system called OVR which uses NP-hard algorithm. To make more prominent a centralized heuristic is design to make the computational overhead more smaller and to detect the dead nodes. As it has some limitation distributed heuristic is design which is the best for large scale networks.

Keywords:
     Power consumption, delay, energy efficient, heuristic algorithm, wireless sensor networks.


References:

1.           N. Xu, S. Rangwala, K. K. Chintalapudi, D. Ganesan, A. Broad, R. Govindan, and D. Estrin, “A wireless sensor network for structural monitoring,” in Proc. 2nd ACM SenSys, New York, NY, USA, 2004, pp. 13–24. 1472–1483, 2009.
2.           An Energy-Aware Routing Protocol in Wireless Sensor Networks Ming Liu 1, Jiannong Cao 2, Guihai Chen 3 and Xiaomin Wang Sensors 2009.

3.           B. Eksioglu, A. V. Vural, and A. Reisman, “The vehicle routing problem: A taxonomic review,” Comput. Ind. Eng., vol. 57, no. 4, pp. 1472–1483, 2009.

4.           O. Bräysy and M. Gendreau, “Vehicle routing problem with time windows, Part I: Route construction and local search algorithms,” Transport. Sci., vol. 39, no. 1, pp. 104–118, Feb. 2005.

5.           Z. Ozyurt, D. Aksen, and N. Aras, “Open vehicle routing problem with time deadlines: Solution methods and an application,” in Operations Research Proceedings 2005, ser. Operations Research Proceedings, H.-D. Haasis, H. Kopfer, and J. Schnberger, Eds. Berlin, Germany: Springer, 2006, vol. 2005, pp. 73–78.

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23.

Authors:

Sweta, Sushmitha Reddy I, Maddipatla Mounika, Priyanka Agrawal, Pallavi G. B

Paper Title:

A Survey to Justify the Need for Carpooling

Abstract:    In India people mostly prefer road transportation to move around places. The increasing number of vehicle on road lead to several issues as congestion, environmental degradation and energy problems. Research and development have been progressively done in this field to reduce the environmental degradation and for the better utilization of fossil fuels. Different approaches and techniques to solve these issues emerged which address fields of emission reduction, increase efficiency of vehicle, energy alternative, decrease the road density with care of safety and comfort, etc. In this paper survey on these emerging drifts and elaborate on one of the ways to reduce the vehicular density and emission, we have identified carpooling as one such solution to provide user, flexibility in time, enjoyable, efficient and safe ride.

Keywords:
   Drivers, Efficiency, GHG emission, HOV [high occupancy vehicle], Passenger, Road density, Safety.


References:

1.              Geetam Tiwari. "URBAN TRANSPORTION IN INDIAN CITIES".  London School of Economics.
2.              “ D. s.Jonathan Handbook”,  in www.delhi.gov.in, 2013.           

3.              Norman1; Heather L. MacLean; and Christopher A. Kennedy3. “Comparing high Or Low Residential Density: Life-Cycle Analysis of Energy Use and Greenhouse Gas Emission” in JOURNAL OF URBAN PLANNING AND DEVELOPMENT, MARCH 2006.

4.              Hirschman, K. Zallinger, M. Fellendorf, M. Hansberger, S.  “A New Method Traffic Calculate Emission with Simulated Traffic Conditions” in Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on 19-22 September 2010 Funchal.

5.              Vivek Tyagi, Member, IEEE, Shivkumar Kalyanarman, Fellow, IEEE and Raghuram        Krishnapuram, Fellow, IEEE, IBM India Research Laboratory, India “Vehicular Traffic Density State Estimation Based on Cumulative Road Acoustics”

6.              Dimitrakopoulos, G. ; Department of Digital system, University of Piraeus, Greece; Dem’estichas P.  in “Intelligent Transportation Systems”, 2011.

7.              Heinz Jansen, Cecile; “A welfare cost assessment of various policy measures to reduce pollutant emissions from passenger road vehicles” , Published in Transport Research, volume 4, November 1999.

8.              Moshe Ben-Akiva Massachusetts Institute of Technology, Andre de Palma Queen's University, Kingston, Ontario, Canada, Pavlos Kanaroglou McMaster University, Hamilton, Ontario, Canada, “Dynamic Model of Peak Period Traffic Congestion with Elastic Arrival Rates”.

9.              Jason Hill, Erik Nelson, David Tilman, Stephen Polasky , and  Douglas Tiffany  “Environmental, economic, and energetic costs and benefits of biodiesel and ethanol biofuels”,. Published in 2006.

10.           Olof Johansson and Lee Schipper “Measuring the Long-Run Fuel Demand of Cars: Separate Estimations of Vehicle Stock, Mean Fuel Intensity, and Mean
Annual Driving Distance.”  in Journal of Transport Economics and Policy.

11.           Sisinnio Concas,  Philip L.Winters, “ Impact of Carpooling on Trip-Chaining Behavior and Emission Reductions”,  published in Transportation Research Record: Journal of the Transportation Research Board, December 26, 2007.

12.           Moshie Ben-Akiva and Terry  J. Atherton “Methodology for Short –Range Travel Demand Prediction. An Analysis for carpooling Incentive”

13.           Shrishti Garg “Carpools: Driving into greener pastures Carpooling not only helps save fuel but also contributes to the environment”, in The Business Standard, June 3, 2014.

14.           Kum Kum Dewan and Israr Ahmad “Carpooling: A Step To Reduce Congestion (A Case Study of Delhi)” , in The times of India, 2014.

15.           Burmeister, B. ; Daimler-Benz Res. Syst. Technol., Berlin, Germany ; Haddadi, A. ; Matylis, G. “Application of multi-agent systems in traffic and  transportation”  in  Software Engineering. IEE Proceedings, 06 August 2002.

16.           Lalos, P.  Dept. of Electron., Computer., Telecomm., Univ. of Athens, Athens, Greece ; Korres, A. ; Datsikas, C.K. ;  “ A Framework for Dynamic Car and Taxi Pools with the Use of Positioning Systems”  in Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns, 2009. COMPUTATION WORLD '09.

17.           Kavita Sheoran (Guide) Assistant Professor / Reader, Vaibhav Jatana, Rachit Gulati, Nikhil Ahuja and Ankit Kapoor, Student, CSE Department Maharaja Surajmal Institute of Technology “Intelligent Transportation System Architecture for a Carpool System”. Published in International Journal of Computer Applications.

18.           Tejas Talele, Gauresh Pandit and Parimal Deshmukh  “Dynamic ridesharing using social media”, in International Journal on AdHoc Networking Systems.

19.           Swati. Tare Neha B. Khalate Ajita and A.Mahapadi Department of C.S.E J.S.P.M’s BSIOTR(W) University of Pune , “Review Paper On CarPooling Using Android Operating System-A Step Towards Green Environment”, in International Journal of Advanced Research in Computer Science and Software Engineering,  April 2013.

20.           Carpooling and carpool clubs: Clarifying concepts and assessing value enhancement possibilities through a Stated Preference web survey in Lisbon, Portugal.

21.           J. D. Hunt, J. D. P. McMillan, “Stated-Preference Examination of Attitudes Toward Carpooling to Work in Calgary”,  in Transportation Research Record: Journal
of the Transportation Research Board  journal, January 30, 2007.

22.           Prathibha Joy “What's stopping people in Bengaluru from carpooling?”, in The Times of India, 11 November 2014. 


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24.

Authors:

Shubha Agarwal, Govind Narain Bajpai

Paper Title:

The Brand Extension Strategy: An Analysis

Abstract:     Branding has emerged as a top management priority in the last decade due to the growing realization that brands are one of the most valuable intangible assets that firms have. This paper identifies some of the influential work in the branding area, highlighting what has been learned from an academic perspective on important topics such as brand positioning, brand integration, brand equity measurement, brand growth, and brand management. It is also discussed how branding and society affect each other. Based on the knowledge of how branding theories have been developed as dependent variables of each other and the society, we are able to form a better understanding of the past, the present, and the future of branding.

Keywords:
    top management priority, most valuable intangible assets, branding area, brand positioning, brand integration, brand equity measurement, brand growth.

References:

1.                Aaker, David A., Joachimsthaler, Erich (2000): Brand Leadership. The Free Press, New York
2.                Burk, James (1996): Satisfied customers. Scientific American. Vol. 274 Issue 3, p116

3.                Ailawadi, K. L., D. R. Lehmann, S. A. Neslin. 2001. Market response to a major policy change in the marketing mix: Learning from Procter & Gamble’s value pricing strategy. J. Marketing. 65 (January) 44-61.: Research and Best Practices, Jossey Bass, 52-82.

4.                Ambler, T., C.B. Bhattacharya, J. Edell, K. L. Keller, K. N. Lemon, V. Mittal. 2002. Relating brand and customer perspectives on marketing management. J. Service Res. 5(1) 13-25.

5.                Kapferer, Jean-Noël (2008): The New Strategic Brand Management – Creating and sustaining brand equity long term. London; Philadelphia, Kogan Page

6.                Kim, Hong-bumm, Kim, Woo Gon, and An, Jeong A. (2003): The Effect of Consumer based Brand Equity on Firms’ Financial Performance. Journal of Consumer Marketing. 20(4), pg. 335-351

7.                D., J.-P. Dubé, S. Gupta. 2005. Own-brand and cross-brand retail pass-through.Marketing Sci. 24(1) 123-137.

8.                Muniz, Albert M., Jr. & O'Guinn, Thomas C. (2001): Brand Community. The Journal of Consumer Research. 27(4), pg. 412-432 


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25.

Authors:

H. Mohssine, M. Kourchi, H. Bouhouch F. Debbagh

Paper Title:

Perturb and Observe (P&O) and Incremental Conductance (INC) MPPT Algorithms for PV Panels

Abstract:      In this work we present a study on the comparison between two MPPT algorithms: Perturb and Observe (P&O) and Incremental Conductance (INC). We base our approach on the difference between computed results using an adapter bloc Buck DC-DC converter. The MPPT algorithms are combined with it to complete the PV simulation system. We show that the MPPT control with both P & O and INC keeps the system power operating point at its maximum. For this purpose the conventional P&O, the converter input reference voltage is perturbed in fixed steps until the maximum power is reached. However, depending on the step size, the system operating point will oscillate around the MPP resulting in a loss of energy.

Keywords:
 Photovoltaic (PV), Maximum Power Point Tracking (MPPT), Perturb and Observe (P&O), Incremental Conductance (INC). 


References:

1.             F. Ansari, A. K. Jha‘  Maximum power point tracking using perturbation and observation as well as incremental  conductance algorithm’ international journal of research in engineering & applied sciences, issn: 2294-3905, PP 19-30, 2011.
2.             S. Jain and V. Agarwal, “A New Algorithm for Rapid Tracking of Approximate Maximum Power Point in Photovoltaic  Systems”’ IEEE Power Electronic Letter, Vol.2, pp. 16-19,   Mar.2004. 

3.             J.A.Jiang et. Al.,”Maximum Power Tracking for Photovoltaic Power Systems,” Tamkang Journal of Science and   Engineering, Vol. 8, No. 2, pp. 147-153, 2005.

4.             D. Rekioua and E. Matagne, Optimization of Photovoltaic Power Systems, Modelization, Simulation and Control, Springer, 2012.

5.             Y.Kuo, et. Al., “Maximum power point tracking controller for photovoltaic energy conversion system”, IEEE Trans. Ind.  Electron., Vol.48, pp. 594-601, 2001. 

6.             Rafia Akhter and Aminul Hoque, “Analysis of a PWM Boost  Inverter for Solar Home Application”, Proceedings of World  Academy Of Science, Engineering And Technology Volume 17  December 2006 ISSN 1307-6884.

7.             Huan-Liang Tsai, Ci-Siang Tu and Yi-Jie Su, “Development of  Generalized Photovoltaic Modeling Using  MATLAB/Simulink “, Proceedings of the World Congress on  Engineering and  Computer Science 2008 WCECS 2008, October 22-24, 2008,  San Francisco, USA.

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