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Issue-2 Published on December 30, 2016

Volume-6 Issue-2 Published on December 30, 2016
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Volume-6 Issue-2, December 2016, ISSN:  2249-8958 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.



Katsikides S., Markoulis S., Papaminas M.

Paper Title:

Corporate Social Responsibility and Stock Market Performance: An Event Study Approach

Abstract: This paper examines the relationship between Corporate Social Responsibility and stock market performance. To examine this relationship the “event-study” methodology is utilised to examine five events, two from the oil industry (BP and Exxon oil spills) and three from the banking industry (HSBC – money laundering; Barclays and Royal Bank of Scotland – Libor scandal). Results suggest that, apart from the HSBC money laundering event, all other events appear to have a significant effect on stock market performance as the shares of the firms involved tend to exhibit significant negative average abnormal returns during the period which followed the event. We also find some differences regarding the time-frame of the effect, since for some events it took more time to get into “full swing” and lasted longer.

  Event-study; Corporate Social Responsibility; Stock market performance.


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Ramazan ŞENER

Paper Title:

Design and Thermal Analysis of Free Piston Linear Generator using In Range Extended Electric Vehicles

Abstract: Today, battery electric vehicles (BEV) have zero emission (tank to wheel) and very high efficiency. However, the most important obstacle of BEV is insufficient range. This disadvantage can be eliminated in term of range extender systems. Range extender system like generator can charge battery when required. Free Piston Linear Generator (FPLG), Wankel engine, Piston Internal Combustion Engine, Gas Turbine Engine and Fuel Cell Engine can be used as range extender unit. In this study, opposed-piston free-piston linear generator which can be used in low weight electric vehicles, which has spark ignition, 153 cm3 volume, and gasoline direct fuel injection was designed via SOLIDWORKS® software. Thermal analysis of the engine was performed by means of ANSYS® software using temperature in the literature. Finally, the engine design is determined to suit thermal operating conditions. It is find out that this system can be used as a range extender unit.

Finite Element Method, Thermal Analysis, Free Piston Linear Generator, Computer Aided Design.


1.       Ferrari, C., Offinger, S., Schier, M., Philipps, F., et al., “Studie zu Range Extender Konzepten für den Einsatz in einem batterieelektrischen Fahrzeug – REXEL, DLR,  Hacker  Media, Stuttgart, Germany, 2012.
2.       Virsik, R., Heron, A., “Free piston linear  generator in  comparison  to  other range-extender  Technologies”  EVS  27  Electric  Vehicle  Symposium  & Exhibition, Spain, 2013.

3.       Varnhagen,  S.J., “Experimental  Investigation  of  the  Wankel  Engine  for Extending the Range of Electric Vehicles” Master thesis, University of California, Davis, 2011.

4.       (2016) Freikolben website. [online]. Available:

5.       Narayan, K.L., Rao, K.M., Sarcar, M.M.M., “Computer Aided Design and Manufacturing” New Delhi: Prentice Hall of India, ISBN 812033342X, 2008.

6.       (2016) Beetron website. [online]. Available:

7.       Ferziger, J.H., Peric, M., “Computational methods for fluid dynamics” Springer, 3rd edition, 2002.

8.       Huebner, H.K., Thornton, E.A., Byrom T.G., “The finite element method for engineers” 3rd edition, John Wiley & Sons, 1999.

9.       Durat, M., Kapsiz, M., Nart, E., Ficici, F, Parlak, A., “The effects of coating materials in spark ignition engine design” Materials and Design, s. 540-545, 2012.

10.    Cerit, M., Soyhan, H.S., “Thermal analysis of a combustion chamber surrounded by deposits in an HCCI engine” Applied Thermal Engineering, s. 81-88, 2013.

11.    Çakır, U., “Seramik Kaplı Bir Dizel Motor Yanma Odasının Termal Analizi” M.Sc. Thesis, Sakarya University, 2007.

12.    Cerit, M., “Thermo mechanical analysis of a partially ceramic coated piston used in an SI engine” Surface & Coatings Technology, s. 3499-3505, 2007.

13.    Varol, B., “Turbo Dizel Bir Motorda Bir Pistonun Termal Ve Mekanik Yükler Altında Sonlu Elemanlar Yöntemiyle Gerilim Analizi” M.Sc. Thesis, Hacettepe University, 2012.

14.    Ceylan, S., “Seramik Kaplı Dizel Pistonlarda Termal Gerilmelerin Sonlu Elemanlar Metoduyla Belirlenmesi” M.Sc. Thesis, Sakarya University, 2009.

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18.    Okur,  M., “Dört  Zamanlı,  Tek  Silindirli,  Buji  İle  Ateşlemeli  Bir  Benzin Motorunun  Sonlu  Elemanlar  Yöntemi  Kullanılarak  Tasarımı  Ve  İmali” Ph.D. thesis, Gazi University, 2007.

19.    Cornforth, J.W., “Finite element analysis of engines” Materials and Design, 1985.






N. Maksimov, A. Panas

Paper Title:

Modified Ultra-Wideband Microwave Chaotic Colpitts Oscillator with a Simplified Structure: Implementation, Experiments

Abstract:  Modified Colpitts oscillator with SiGe bipolar transistor as an active element was introduced, implemented and experimentally studied. It enables generation of ultra-wideband chaotic oscillations in the microwave range. Compared to its classical analogue, the oscillator has an extremely simple structure comprising only one single external reactive element (an inductor). The transistor p-n junction capacitance performs the function of oscillator external capacitors. Stable generation of chaotic oscillations in the range of 1 to 8.5 GHz (at 10 dB level) with highest ever efficiency values (7%) for a given class of oscillators has been obtained.

 Chaotic Colpitts oscillator, ultra wideband chaotic oscillations, microwave band, power spectra, power efficiency, implementation, bipolar SiGe transistor


1.       Dmitriev, A.S., Panas, A., Starkov, S.O.: Experiments on speech and music signals transmission using chaos. Int. J. Bifurc. Chaos 5, 1249-1254 (1995)
2.       Dmitriev, A., Panas, A., Starkov, S., Kuzmin, L.: Experiments on RF band communication using chaos. Int. J. Bifurc. Chaos 7, 2511-2527 (1997)

3.       Dmitriev, A.S., Kyarginsky, B.Ye., Panas, A.I., Starkov, S.O.: Experiments on ultra wideband direct chaotic information transmission in microwave band. Int. J. Bifurc. Chaos 13, 1495-1507 (2003)

4.       Panas, A.I., Kyarginsky, B.E., Maximov, N.A.: Single-transistor microwave chaotic oscillator. Proc. NOLTA-2000 2, Dresden, Germany, 445-448 (2000)

5.       Kyarginsky, B.E., Maximov, N.A., Panas, A.I., Starkov, S.O.: Wideband microwave chaotic oscillators. Proc. 1st IEEE Conf. Circuits and Systems for Communications (Circuits and Systems in Broadband Communication Technologies), St. Petrsburg, Russia, 296-299 (2002)

6.       Panas A.I., Kyarginsky B.E., Efremova E.V.: Ultra-wideband microwave chaotic oscillator. Proc. 12th Mediterranean microwave symposium MICROCOLL-2007, Budapest, Hungary, 14-16 May, 145-148 (2007)

7.       Efremova E.V., Nikishov A.Yu., Panas A.I.: UWB Microwave Chaotic Oscillator: from Distributed Structure to CMOS IC Realization. Proc. of 5th European Conf. Circuits and Systems for Communications ECCSC’10. Belgrade, Serbia, November 23-25, 67-70 (2010)

8.       Panas: Ultra wideband microwave chaotic oscillator. Eurasian physical technical journal. 9, 50-56 (2012)

9.       Chong, S.K. Young: UWB Direct Chaotic Communications Technology for Low-Rate WPAN Applications. IEEE Trans. on Vehicular Technology. 57, 1527-1536 (2008)

10.    Efremova E.: Generator of 3-10 GHz ultrawideband microwave chaos. Proc. of 21th Int. Conf. Nonlinear Dynamics of Electronics Systems (NDES). Bari, Italy, (2013)

11.    Kennedy M.P.: Chaos in the Colpitts oscillator. IEEE Trans. on Circuits and Systems I: Theory ans Applications. 41, 771-774 (1994)

12.    G.M. Maggio, O. De Feo, and M.P. Kennedy: Nonlinear analysis of the Colpitts oscillator and application to design. IEEE Trans. on Circuits and Systems. 46(9), 1118-1130 (1999)

13.    Tamasevicius A., Mykolaitis G., Bumelene S., Baziliauskas A., Krivickas R., Lindberg E.: Chaotic Colpitts oscillator for the ultrahigh frequency range. Nonlinear Dynamics. 44, 159-165 (2006)

14.    N.A. Maksimov, and A.I. Panas: Three-point circuits for generating band-limited chaotic oscillators. Proc. Int. Symp. Signals, Circuits, Systems (SCS’2001). Iasi, Romania, 10-11 July, 65-68 (2001)

15.    Maximov N.A., Panas A.I.: Microwave chaotic oscillators with controlled bandwidth. Proc. ICCSC’2004. Moscow, Russia, June 30-July 2, (2004)

16.    Z.G. Shi, L.X. Ran.: Microwave chaotic Colpitts oscillator: design, implementation and applications. Int. J. of Electromagn. Waves and Appl. 20, 1335-1349 (2006)

17.    W. Chen, Yu Guo, Huai Gao, G.P. Li: A novel ultra-wideband microwave chaotic Colpitts oscillator. Proc. Wireless and Microwave Technology Conference (WAMICON). Orlando, Florida, April 16, 1-4 (2013)

18.    Panas A., Maximov N.: Modified microwave chaotic Colpitts oscillator. Proc. of 23th Int. Conf. on Nonlinear Dynamics in Electronic Systems. Como, Italy, (2015)

19.    Jing Xia Li, Yun Cai Wang, Fu Chang Ma.: Experimental demonstration of 1.5 GHz chaos generation using an improved Colpitts oscillator. Nonlinear Dyn. 72, 575-580 (2013)






John Kiplagat Biwott, Wanyona Githae, Charles Kabubo

Paper Title:

Challenges Facing Construction of Affordable Decent Low Cost Housing in Turkana County

Abstract: Housing problems in developed countries are characterized by overcrowding, dilapidated structures and shared bathrooms. On the other hand, developing countries like Kenya, the problem is largely complicated by lack of serviced land, lack of access to housing finances, rigid legal framework and over dependence on non-local construction materials, techniques and technologies. Although there have been significant interventions in the effort to reverse this trend in Kenya, some counties especially in marginalized areas like Turkana County have continued to lag behind in provision of decent and affordable houses for its residents. In an effort to establish where the problem is, this study, seek to determine and describe challenges faced by different stakeholders and residents of Turkana County in their endeavor to put up decent low cost housing. 

Affordable housing, decent housing and alternative building technologies and Turkana County


1.       Ayedun, C. A. & Oluwatobi, A. O. (2011). Issues and Challenges Militating against the Sustainability of Affordable Housing Provision in Nigeria. Business Management Dynamics Journal 1(4), pp 1-8.
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6.       Kenya National Bureau of Statistics (KNBS). 2010. The 2009 Kenya Population and Housing Census. Nairobi: Government Printer.

7.       Kenya National Bureau of Statistics (March 2012). Analytical Report on Housing Conditions, Amenities and Household Assets (Vol. 9). Nairobi: Government Printer.

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10.    Macoloo, G. (1994). The Changing Nature of Financing Low Income Urban Housing Development in Kenya. Housing Studies (Volume 9, Issue 2), pp 189-281.

11.    Ministry of Housing (2004). Sessional Paper No. 3 of 2004: National Housing Policy for Kenya. Nairobi: Government Printers.

12.    Nabutola, W. (2004). Affordable Housing in Kenya: A Case Study of Policy on Informal Settlements, Land Administration and Housing Issues in Informal Environments. 3rd FIG Regional Conference, Jakarta, Indonesia.

13.    Otiso, K.M. (2003). State, Voluntary and Private Sector Partnerships for Slum Upgrading and Basic Service Delivery in Nairobi City. Kenya Cities, 20(4), pp 221-229.

14.    Republic of Kenya (2010). The Constitution of Kenya. Nairobi: Government Printer.

15.    Schussheim, M. J. (2004). Housing Low-Income Families: Problems, Programs Prospects.  Journal of Housing and Community Development Washington; 56(5).

16.    Shitemi, K. (2014, May 9).  Exploring Equalization Fund under Devolution. Retrieved from

17.    Wilcox, S. & Fitzpatrick, S. (2010). The Impact of Devolution Housing and Homelessness. London: Joseph Rowntree Foundation.






Seyed Amin Ahmadi Olounabadi, Avula.Damodaram, V Kamakshi Prasad, Mahdi Hosseini

Paper Title:

Impact of Multi-Path Security in Wireless Ad Hoc Networks in Indoor Environments by using AOMDV Methods

Abstract:  Ad hoc Network is a decentralized type of wireless network and also is a local area network (LAN) that is built spontaneously as devices connect. , Instead of relying on a base station to coordinate the flow of messages to each node in the network, the individual network nodes forward packets to and from each other. Basically, an ad hoc network is a temporary network connection created for a specific purpose (such as transferring data from one computer to another). Multipath routing is the routing technique of using multiple alternative paths through a network, which can yield a variety of benefits such as fault tolerance, increased bandwidth, or improved security. Ad-hoc On-demand Multipath Distance Vector Routing (AOMDV) protocol is an extension to the AODV protocol for computing multiple loop-free and link disjoint paths and also increases the reliability through transmitting the messages in multiple paths with minimal redundancy, which used in present work. Simulations were conducted using the NS2 network simulator. In order to simulate most of the proposed Byzantine attacks in NS2, a protocol independent Byzantine attack simulation module was developed. This module provides the capability to simulate the black hole, Byzantine wormhole, and Byzantine overlay network wormhole attacks without modifying the routing protocol. We are considering our communication path is changeable even path or node is node failed. So data is sending through different paths, it provide high security than single path.

 wireless network, Ad hoc, AOMDV, Byzantine attacks


1.    Reza Curtmola Cristina Nita-Rotaru, “BSMR: Byzantine- Resilient Secure Multicast Routing in Multi-hop Wireless Networks”, IEEE Transactions on Mobile Computing, vol. 8, Issue. 4, pp. 445 - 459, February 2009.
2.    A.Tsirigos and Z.J.Hass (2004), “Analysis of multi path routing, Part 1: The effects on the packet delivery ratio” IEEE Transactions on Wireless Communication., vol.3, no.2, pp: 500- 511.

3.    Banner, R. Orda, A, “Multipath Routing Algorithms for Congestion Minimization”. This paper appears in: Networking, IEEE/ACM Transactions on Publication Date: April 2007 Volume: 15, Issue: 2, on page(s): 413-424.

4.    Jun Peng, Biplab Sikdar and Liang Cheng (2009) “Multicasting with Localized Control in Wireless Ad Hoc Networks” IEEE Transaction on Mobile Computing.

5.    Papadimitratos, P. Haas, Z.J, “Secure data communication in mobile ad-hoc networks” , This paper appears in: Selected Areas in Communications, IEEE Journal on Publication Date: Feb. 2006,Volume: 24, Issue: 2,On page(s): 343- 356.

6.    Banner , R. Orda, A. “Multipath Routing Algorithms for Congestion Minimization”Conference version in Proc. IFIP Networking 2005.

7.    Papadimitratos, P. Haas, Z.J, Sirer, E, G.”Path Set Selection in Mobile Ad Hoc   Networks”. June 09 - 11, 2002. Pages 1 - 11.






Immandi Solomon Raju, I Prudhvi Kumar Raju, D Krishna Chaitanya

Paper Title:

Performance Analysis of a Grid Current Compensator using Fuzzy Logic Controller

Abstract: This paper introduces an advanced current control strategy for distributed generation into the utility grid despite the distorted grid voltage and RC loads. The proposed current controller is designed in synchronous reference frame and composed of a fuzzy logic controller. The fuzzy logic controller greatly simplifies the control strategy. It does not require the local load current measurement and harmonic analysis of the grid voltage. Therefore, the proposed control method can be easily adopted into the traditional DG control system without installation of external hardware. The operation principle of the proposed control method is analyzed in detail, and its effectiveness is validated through simulated results.

Distributed Generation (DG), RC load, Fuzzy Logic Controller (FLC), PI Controller, PI-RC Controller


1.       Quoc-Nam Trinh and Hong-HeeLee,”An enhanced grid current compensator for Grid-connected Distribuited Generation uinderNoninear loads and Grid voltage distortions”, IEEE Trans. Ind. Electron., vol. 61, no. 12, pp. 6528– 6536, December,2014.
2.       Q. Zeng and L. Chang, “An advanced SVPWM-based predictive current controller for three-phase C. A. Busada, S. Gomez Jorge, A. E. Leon, and J. A.

3.       Solsona, “Current controller based on reduced order generalized integrators for distributed generation systems,”IEEE Trans. Ind. Electron., vol. 59, no. 7, pp. 2898– 2909, Jul. 2012.

4.       M. Liserre, R. Teodorescu, and F. Blaabjerg, “Multiple harmonics control for three-phase grid converter systems with the use of PI-RES current controller in a rotating
frame,” IEEE Trans. Power Electron., vol. 21, no. 3, pp. 836–841, May 2006.

5.       M. Castilla, J. Miret, A. Camacho, J. Matas, and L. G. de Vicuna, “Reduction of current harmonic distortion in three-phase grid-connected photo- voltaic inverters via resonant current control,” IEEE Trans. Ind. Electron., vol. 60, no. 4, pp. 1464–1472, Apr. 2013.

6.       R.-J. Wai, C.-Y. Lin, Y.-C. Huang, and Y.-R. Chang, “Design of high- performance stand-alone and grid-connected inverter for distributed generation applications,”IEEE Trans. Ind. Electron., vol. 60, no. 4, pp. 1542–1555, Apr. 2013.

7.       J. Balaguer, Q. Lei, S. Yang, U. Supatti, and F. Z. Peng, “Control for grid-connected and intentional islanding operations of distributed power generation,” IEEE Trans.Ind. Electron., vol. 58, no. 1, pp. 147–157, Jan. 2011.

8.       R. C. Dugan and T. E. McDermott, “Distributed generation,” IEEE Ind. Appl. Mag., vol. 8, no. 2, pp. 19–25, Mar./Apr. 2002.

9.       F. Blaabjerg, R. Teodorescu, M. Liserre, and A. V. Timbus, “Overview of control and grid synchronization for distributed power generation systems,” IEEE Trans. Ind.Electron., vol. 53, no. 5, pp. 1398–1409, Oct. 2006.

10.    Z. Yao and L. Xiao, “Control of single-phase grid-connected inverters with nonlinear loads,” IEEE Trans.Ind. Electron., vol. 60, no. 4, pp. 1384– 1389, Apr. 2013.

11.    Z. Liu, J. Liu, and Y. Zhao, “A unified control strategy for three-phase inverter in distributed generation,”IEEE Trans. Power Electron., vol. 29, no. 3, pp. 1176– 1191, Mar. 2014.

12.    IEEE Application Guide for IEEE Std 1547, IEEE Standard for Interconnecting Distributed Resources with Electric Power Systems, IEEE Std. 1547.2-2008, 2008.

13.    Q.-N. Trinh and H.-H. Lee, “Improvement of current performance for grid connected converter under distorted grid condition,” in Proc. IET Conf. RPG, Sep. 6–8, 2011, pp. 1–6





M. Bhanu Divya Bharathi, P. Krishna Chaitanya, K. Sandhya Rani

Paper Title:

Power Quality Improvement of DFIG using FLC Based Variable Wind Turbines by IPC Method

Abstract:  Because of the wind speed variation, breeze shear along with tower shadow effects, grid connected wind generators are the options for power fluctuations which could produce sparkle during constant operation. This paper presents a type of an MW-level varying speed windmill with a new doubly feasted induction generator to analyze the Flicker emission along with mitigation difficulties. Fuzzy logic controller (FLC) was designed to obtain maximum power extraction at low wind speeds to limit power extraction at 1.5MW nominal power set point. The Fuzzy logic based IPC (Individual Pitch Control) scheme is proposed along with the individual message controller is made using generator active power along the wind turbine. A 1.5MW horizontal axis breeze turbine model was designed for tuning as well as simulation performance is studied and the results show the damping of this generator active power by IPC is an efficient means for flicker minimization of varying speed wind generators during constant operation.

 Flicker mitigation, IPC, variable speed wind turbine, DFIG, FLC.


1.       T. Sun, “Power Quality of grid-connected wind turbines with DFIG and their interaction with the grid,” Ph.D. dissertation, Aalborg Univ., Aalborg, Denmark, 2004.
2.       L. Rossetto, P. Tenti, and A. Zuccato, “Electromagnetic compatibility issues in industrial equipment,” IEEE Ind. Appl. Mag., vol. 5, no. 6, pp. 34–46, Nov./Dec. 1999.

3.       A° .Larsson, “Flicker emission of wind turbines during continuous operation,” IEEE Trans. Energy Convers., vol. 17, no. 1, pp. 114–118, Mar. 2002.

4.       H. Sharma, S. Islam, T. Pryor, and C. V. Nayar, “Power quality issues in a wind turbine driven induction generator and diesel hybrid autonomous grid,” J. Elect. Electron.Eng., vol. 21, no. 1, pp. 19–25, 2001.

5.       M. P. Papadopoulos, S. A. Papathanassiou, S. T. Tentzerakis, and N. G. Boulaxis, “Investigation of the flicker emission by grid connected wind turbines,” in Proc. 8th Int. Conf. Harmonics Quality Power, Athens, Greece, 1998, vol. 2, pp. 1152–1157.

6.       T. Sun, Z. Chen, and F. Blaabjerg, “Flicker study on variable speed wind turbines with doubly fed induction generators,” IEEE Trans. Energy Convers., vol. 20, no. 4, pp. 896–905, Dec. 2005.

7.       K. Yun-SeongandW. Dong-Jun, “Mitigation of the flicker level of a DFIG using power factor angle control,” IEEE Trans. Power Del., vol. 24, no. 4, pp. 2457–2458, Oct. 2009.

8.       W. Hu, Z. Chen, Y. Wang, and Z. Wang, “Flicker mitigation by active power control of variable-speed wind turbines with full-scale back-toback power converters,”
IEEE Trans. Energy Convers., vol. 24, no. 3, pp. 640–649, Sep. 2009.

9.       Bossanyi, “Individual blade pitch control for load reduction,” Wind Energy, vol. 6, pp. 119–128, 2002.

10.    Bossanyi, “Further load reductions with Individual pitch control,” Wind Energy, vol. 8, pp. 481–485, 2005.

11.    Y. Zhang, Z. Chen, M. Cheng, and J. Zhang, “Mitigation of fatigue loads using Individual pitch control of wind turbines based on FAST,” in Proc.46th Int. Conf. Universities’ Power Eng., Soest, Germany, 2011.

12.    J. Jonkman and M. L. J. Buhl, “FAST User’s Guide,” National Renewable Energy Laboratory (NREL), Golden, CO, USA, Tech. Rep. NREL/EL-500-38230, (2005). [Online]. Available: http://wind.

13.    S. M. Muyeen,M. Hasan, R. Takahashi, T.Murata, J. Tamura, Y. Tomaki, A. Sakahara, and E. Sasano, “Comparative study on transient stability analysis of wind
turbine generator system using different drive train models,” IET Renewable Power Generation, vol. 1, no. 2, pp. 131–141, 2007.

14.    D. Wright and L. J. Fingersh, “Advanced control design for wind turbines—Part I: Control design, implementation, and initial tests,” National Renewable Energy Laboratory, NREL Rep. TP-500–42437, National Renewable Energy Laboratory, Mar. 2008.

15.    Electromagnetic Compatibility (EMC)—Part 4: Testing and Measurement Techniques—Section 15: Flickermeter—Functional and Design Specifications,IEC Std. 61 000–4–15, Nov. 1997.

16.    A° .Larsson, “Flicker emission of wind turbines during continuous operation,” IEEE Trans. Energy Convers., vol. 17, no. 1, pp. 114–118, Mar. 2002





Begard Salih Hassen

Paper Title:

The Powerful Activity of DSDV Algorithm in WSN System

Abstract:   Lately, technological developments in the strategy of processors, memory and radio communications have pushed an attention in the field of sensor network. Networks of those devices are denoted as Wireless Sensor Networks (WSNs). WSNs make possible information accumulation and investigation on an unmatched scale. Indeed, they have concerned care and get wide range of application in diverse areas. The Choice of the protocols and routing are the greatest common schemes that are to be dedicated when manipulative every type of wireless networks likes WSNs. In this paper, performance investigation of “Destination Sequenced Distance Vector DSDV” protocol is done. All the cases for working the protocol are discussed and the time for transmission the information is calculated within multi cases. The results show that this protocol is more strong and robust against the worst cases of losing the nodes or link failure within the network with minimum time for transfer the information through the WSN.

  WSN, DSDV, transmission time, sending and receiving node.


1.       Shio Kumar Singh, M P Singh and D K Singh, “Routing Protocols in Wireless Sensor Networks A Survey”, International Journal of Computer Science & Engineering Survey (IJCSES), Vol. 1, No. 2, DOI : 10.5121/ijcses.2010.1206, November 2010.
2.       Fengju An, “Density Adaptive Sleep Scheduling in Wireless Sensor Networks”, Master of Science Thesis, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Netherlands, 2013.

3.       Bilal Mustafa and Umar Waqas Raja, “Issues of Routing in VANET”, Master thesis, School of Computing, Blekinge Institute of Technology, Sweden, 2010.

4.       Luis Gironés Quesada, “A Routing Protocol for MANETs”, Master of Science in Communication Technology, Norwegian University of Science and Technology, Department of Telematics, 2007.

5.       Heng Luo, “A Best Effort QoS Support Routing in Mobile ad hoc Networks”, Ph.D. thesis, The University of Edinburgh, 2011.

6.       Ilker Demirkol, Cem Ersoy and Fatih Alagöz, “MAC Protocols for Wireless Sensor Networks: A Survey”, IEEE Communications Magazine, 0163-6804/06, April 2006.

7.       Jennifer Yick, Biswanath Mukherjee and Dipak Ghosal, “Wireless sensor network survey”, Computer Networks, 52 (2008) 2292–2330, journal homepage: , 2008.
8.       Ravi Kumar Bansal, “Performance Analysis of Cluster Based Routing Protocol in Manets”, Master Thesis of Engineering, Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, 2006.
9.       Yujie Zhu, “Energy-Efficient Communication Strategies for Wireless Sensor Networks”, Ph.D. thesis, School of Electrical and Computer Engineering, Georgia Institute of Technology, 2007.

10.    Rajeshwar Singh, Dharmendra K Singh and Lalan Kumar, “Performance Evaluation of DSR and DSDV Routing Protocols for Wireless Ad Hoc Networks”, Int. Journal of Advanced Networking and Applications, Volume: 02, Issue: 04, Pages: 732-737, 2011.

11.    Kumar Prateek, Nimish Arvind and Satish Kumar Alaria, “MANET-Evaluation of DSDV, AODV and DSR Routing Protocol”, International Journal of Innovations in Engineering and Technology (IJIET), Vol. 2 Issue 1, ISSN: 2319 – 1058, 2013.

12.    Yatendra Mohan Sharma and Saurabh Mukherjee, “Comparative Performance Exploration of AODV, DSDV & DSR Routing Protocol in Cluster Based VANET Environment” International Journal of Advances in Engineering & Technology, IJAET ISSN: 2231-1963, Vol. 4, Issue 2, pp. 120-127, 2012.

13.    Aman Kumar and  Barinderpal Singh, “Performance Analysis of DSDV, I-DSDV Routing Protocol in Monile Ad Hoc Networks in IPv6 under Black Hole Attack”, International Journal of Future Generation Communication and Networking (IJFGCN), Vol. 8, No. 4 , pp. 155-160,, ISSN: 2233-7857, 2015.

14.    M. Pushpadevi and M.Sakthi, “Improved Minimum Delay Routing Using TBETX Routing Over DSDV Routing Protocol in Wireless Ad Hoc Networks”, International Journal of Innovative Research in Computer and Communication Engineering, , Vol. 2, Issue 9, ISSN(Online): 2320-9801, ISSN (Print): 2320-9798, 2014.

15.    B.N. Jagdale1, Pragati Patil, P. Lahane and D. Javale, “Analysis and Comparison of Distance Vector, DSDV and AODV Protocol of MANET”, International Journal of Distributed and Parallel Systems (IJDPS) Vol.3, No.2, DOI: 10.5121/ijdps.2012.3210 121, 2012.

16.    Biswaraj Sen and Sanku Sinha, “A Simulation Based Performance Analysis of AODV and DSDV Routing Protocols in MANETs”, International Journal of Modern Engineering Research (IJMER), Vol.2, Issue.4, pp-2404-2408, ISSN: 2249-6645, 2012.






Vandana Vinayak, Sonika Jindal

Paper Title:

A Review on various Image Compression Methods in Content Based Image Retrieval

Abstract: This paper provides an overview about the various compression techniques available in the research area of Image retrieval, especially Content-Based Image Retrieval (CBIR), an evocative and authentic research area for the last decades. CBIR is used for the retrieval of the images based on the content of the images generally known as features. These features may be low level features i.e. color, shape, texture and spatial relationship or the high level features that use the concept of human brain. Now a days, the development and demand of multimedia product grows increasingly fast, contributing to insufficient storage of memory device. Therefore, the theory of data compression becomes more and more significant for reducing the data redundancy to save more hardware space. Compression is the process of reducing the amount of data required to represent the quality of information. Compression is also useful as it helps to reduce the consumption of expensive resources such as hard disk space.

Especially Content-Based Image Retrieval (CBIR), Therefore, increasingly fast, provides.


1.       J. O. A. Tamer Mehyar, “An enhancement on content based image retrieval using color and texture features,” vol. 3, no. 4. Journal of Emerging Trends in Computing and Information Sciences, April 2012.
2.       S. J. Nitika Sharma, “A review on global features based cbir system.” International Conference on information and mathematical sciences.

3.       G. V. Tcheslavski, “Basic image compression methods,” 2008.

4.       M. Sharma, “Compression using huffman coding,” vol. 10, no. 5. IJCSNS International Journal of Computer Science and Network Security, May 2010.

5.       K. S. Julie Zelenski, “Huffman encoding and data compression.” Springer 2012, CS106B, May 23 2012.

6.       D. D. S. Mridul Kumar Mathur, Seema Loonker, “Lossless huffman coding technique for image compression and reconstruction using binary trees,” vol. Vol 3 (1). International Journal of Computer Technical Applications, pp. 76–79.

7.       J. Glen G. Langdon, “An introduction to arithmetic coding,” vol. 28, no. 2. IBM J. RES. DEVELOP., March 1984.

8.       P. P. Venkataram, Lossless Compression Algorithms, 2016, ch. 6.

9.       R. E. W. Rafael C. Gonzalez, Digital Image Processing, 3rd ed. Pearson Education, 2014.

10.    O. N. Pasi Franti and T. Kaukoranta, “Compression of digital images by block truncation coding:a survey,” no. 37(4). The Computer Journal, 1994, pp. 308–332.

11.    H. P. Jing-Ming Guo and J.-H. Chen, “Content-based image retrieval using error diffusion block truncation coding features,” vol. 25, no. 03. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,, 2015.






Paul Thomas, R.S. Moni

Paper Title:

ANN based Multilevel Classification Technique with Optimum Measurement Period for Accurate Diagnosis using Biomedical Signals

Abstract: Biomedical signals are representations of the mechanical and electrical activities within the human body. These signals contain a lot of information on the state of health of a person and their analysis have a significant role in the diagnosis of various health disorders and medical abnormalities, such as activation levels and the biomechanics of the muscles and other human organs. Of the many Biomedical signals, focus of this work is on Electro-cardiogram (ECG) and Electro-myogram (EMG). ECG provides information on the rhythm and functioning of the heart. EMG is the recording of human muscular activity. ECG signals used in this work are taken from the standard MIT-BIH, and CU data bases of PhysioNet database and EMG signals are taken from the EMGLab and PhysioNet database. Automated analysis of Biomedical signals can largely assist the physicians in their diagnostic process. The extracted spectral and temporal features represent the diverse characteristics of a Biomedical signal. In this work, more emphasis is given to spectral features since a lot of critical information on the health of a person are hidden in the spectral content of the signal. A subset from a larger set of available features is experimentally selected for optimum performance. The feature vector has a size of 11 for ECG signal analysis and a size of 9 for EMG signal analysis. Accuracy of detecting a health disorder depends on the quality of the features extracted from a Biomedical signal. A few techniques are proposed to achieve improved quality for the features. Also a method is developed to arrive at the optimum length of the Biomedical signal to be used for analysis. Accordingly, the length of the ECG signal used in this work is 10 s and the length of the EMG signal is 11 s. It is observed that the variance of the features is minimum when the signal for analysis is taken from the mid portion of the whole Biomedical signal. To make the value of a feature close to its true value, each feature value is taken as the average of the values of the feature extracted from 20 consecutive signal segments. A technique is also proposed to reduce the effect of wild points in the computation of spectral parameters. It is observed that classification accuracy also depends on the sampling rate of the Biomedical signal. The sampling rate of ECG signal in this work is 128 Hz and that of EMG signal is 750 Hz. Classifying a Biomedical signal is the process of attaching the signal to a disease state or healthy state. The work proposes a Multi level classification approach for Biomedical signals. Each classifier is a cascade of two ANN classifiers, the first ANN has a linear transfer function and the second ANN has a sigmoid transfer function. First level classification is to the broad categories of the disorders. In the second level, these disorders are drilled down to more specific categories. This concept can be extended further to achieve finer classification of Biomedical signals. In this work the classification is demonstrated to two levels for ECG signals and one level for EMG signals. The performance of the proposed method is evaluated using the standard parameters of specificity, sensitivity and classification accuracy (CA). The performance is found to be better than the reported figures in the case of both ECG and EMG signals.

 ECG, EMG, FFT, DWT, Pattern recognition ANN, Feature extraction, Multilevel classification, Wavelet, PhysioNet database, CA, Atrial arrhythmias, Ventricular arrhythmias, NSR, MI, MUAP, Myopathy, ALS.


1.  Martis, Roshan Joy, U. Rajendra Acharya, Lim Choo Min, “ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform,” Biomedical Signal Processing and        Control, vol 8, issue 5, pp. 437-448, 2013.
2.  Naik, Ganesh, S. Selvan, and Hung Nguyen. "Single-Channel EMG Classification With EnsembleEmpirical-Mode-Decomposition-Based ICA for Diagnosing Neuromuscular  Disorders.",pp.1-11,2015.
3.  Rahime Ceylan, Yuksel ozbay, "Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural network," Expert Systems with  Applications,vol 33, issue 2, pp. 286-295, 2007.
4.  Swati Banerjee, Madhuchanda Mitra. "A classification approach for Myocardial infarction using voltage features extracted from four standard ECG leads" IEEE International  conference on recent trends in information systems, 2011, pp.325-330.
5.  Sambhu D, Umesh A. C, “Automatic Classification of ECG Signals with Feature Extraction using Wavelet Transform and Support Vector Machine,” IJAREEIE, vol.2, special issue 1,  pp. 235-241, 2013.
6.  Felipe Alonso Atienza, Eduardo Morado, Lornea Fernandez Martinez, Arcadi Garcia Alberola, Jose Luis Rojo Alvarez, "Detection of life-threatening arrhythmias using feature  selection and support vector machines." Biomedical Engineering, IEEE Transactions vol. 61, no.3, pp.832-840, 2014.
7.  Roger Dzwonczyk, Charles G. Brown, H. A. Werman, “The median frequency of the ECG during ventricular fibrillation: its use in an algorithm for estimating the duration of  cardiac arrest,” IEEE Transactions on Biomedical Engineering, vol. 37, no. 6, pp. 640-646, 1990.
8.  Subasi, Abdulhamit. "Classification of EMG signals using combined features and soft computing techniques." Applied soft computing, Vol.12, Issue 08, pp. 2188-2198, 2012.
9.  Phinyomark, Angkoon, Pornchai Phukpattaranont, and Chusak Limsakul. "Feature reduction and selection for EMG signal classification." Expert Systems with Applications Vol.39, Issue 08, pp. 7420-7431, 2012.





M. Brindha, K. Saranya, S. Rajesh

Paper Title:

Certain Investigation on Image Classification and Segmentation using Different Techniques

Abstract: A brain cancer is a tissue that structured by an addition of anomalous cells and important to detect and classify brain tumors from MRI (Magnetic Resonance Imaging) for treatment. Brain tumor segmentation and classification is considered to be more important tasks in medical imaging. MRI is used for the study of the human brain. A fully automated method plays an important role in the prediction of brain cancer. In this review paper, different classification and segmentation techniques are discussed.

 Image Segmentation, Classification and Mining Techniques.


1.       Zeng, Hong, and Aiguo Song. "Optimizing Single-Trial EEG Classification by Stationary Matrix Logistic Regression in Brain-Computer Interface." (2015).
2.       Al-Shaikhli, Saif Dawood Salman, Michael Ying Yang, and Bodo Rosenhahn. "Brain tumor classification using sparse coding and dictionary learning." Image Processing (ICIP), 2014 IEEE International Conference on. IEEE, 2014.

3.       Pereira, Sérgio, et al. "Brain Tumor Segmentation using Convolutional Neural Networks in MRI Images." (2016).

4.       Anitha, V., and S. Murugavalli. "Brain tumour classification using two-tier classifier with adaptive segmentation technique." IET Computer Vision 10.1 (2016): 9-17.

5.       Nandpuru, Hari Babu, S. S. Salankar, and V. R. Bora. "MRI brain cancer classification using support vector machine." Electrical, Electronics and Computer Science (SCEECS), 2014 IEEE Students' Conference on. IEEE, 2014.

6.       Jui, Shang-Ling, et al. "Brain MR image tumor segmentation with 3-Dimensional intracranial structure deformation features." (2015).

7.       Yang, Xiaofeng, and Baowei Fei. "A MR brain classification method based on multiscale and multiblock fuzzy C-means." Bioinformatics and Biomedical Engineering,(iCBBE) 2011 5th International Conference on. IEEE, 2011.

8.       Ibrahim, Walaa Hussein, Ahmed AbdelRhman Ahmed Osman, and Yusra Ibrahim Mohamed. "MRI brain image classification using neural networks." Computing, Electrical and Electronics Engineering (ICCEEE), 2013 International Conference on. IEEE, 2013.

9.       Joshi, Dipali M., N. K. Rana, and V. M. Misra. "Classification of brain cancer using artificial neural network." Electronic Computer Technology (ICECT), 2010 International Conference on. IEEE, 2010.

10.    Othman, Mohd Fauzi Bin, Noramalina Bt Abdullah, and Nurul Fazrena Bt Kamal. "MRI brain classification using support vector machine." Modeling, Simulation and Applied Optimization (ICMSAO), 2011 4th International Conference on. IEEE, 2011.

11.    Boberek, Marzena, and Khalid Saeed. "Segmentation of MRI brain images for automatic detection and precise localization of tumor." Image Processing and Communications Challenges 3. Springer Berlin Heidelberg, 2011. 333-341.

12.    Ji, Zexuan, et al. "Fuzzy local Gaussian mixture model for brain MR image segmentation." Information Technology in Biomedicine, IEEE Transactions on 16.3 (2012): 339-347.

13.    Dawngliana, Malsawm, et al. "Automatic brain tumor segmentation in MRI: Hybridized multilevel thresholding and level set." Advanced Computing and Communication (ISACC), 2015 International Symposium on. IEEE, 2015.





Revathi Nath H A, Jeena R S

Paper Title:

An Efficient Algorithm for Reversible Data Hiding in Encrypted Images by RRBE

Abstract: Recently reversible data hiding in encrypted images is gaining importance as this technique of watermarking can reconstruct the original image after extracting the desired data hidden in the image. In all the previous works, room was reserved for data in the image and the image would be encrypted using a standard stream cipher. This work proposes a technique for reversible data hiding in encrypted images where the data to be hidden is encrypted using Advanced Encryption Standard (AES) that can improve the PSNR. Also the encrypted image holding data would be permuted and transmitted, that can increase the level of security. Experimental results show that this method can achieve a PSNR of more than 60db thereby increasing the embedding rate.

Advanced Encryption Standard, Block merging, Image permutation, Reserving Room before encryption


1.       W. Bender, D.Gruhl, N.Morimoto and A.Lu., Techniques For Data Hiding, IBM Systems Journal ,Vol.35,Pp 313-336,1996
2.       C.W.Honsinger, P.W.Jones, M.Rabbani and J.C.Stoffel, Lossless Recovery Of An Original Image Containing Embedded Data, U S Patent, Ed, 2001

3.       T.Kalker and F.M.Willems. “Capacity bounds and code construction for reversible datahiding,” in proc.14th Int. Conf. Digital Signal Processing (DSP2002), 2002, pp. 71-76.

4.       W. Zhang, B. Chen, and N. Yu, “Capacity-approaching codes for reversible data hiding,” in Proc 13th Information Hiding (IH’2011).LNCS 6958, 2011, pp. 255-269, Springer - Verlag.

5.       W. Zhang, B. Chen, and N. Yu, “ Improving various reversible data hiding shemes via optimal codes for binary covers,” IEEE Trans. Image Process., vol. 21, no. 6, pp. 2991-3003, Jun. 2012

6.       J. Fridric h and M. Goljan, “Lossless data embedding for all image for-mats,” in Proc. SPIE proc. Photonics West, Electronic Imaging, Security and Watermarking of
Multimedia Contents, San Jose, CA, USA, Jan. 2002, vol. 4675, pp. 572-583

7.       J. Tian, “Reversible data embedding using a difference expansion,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 8, pp. 890-896, Aug. 2003

8.       Z Ni, Y. Shi, N. Ansari, and S. Wei, “Reversible data hiding,” IEEE Trans. Circuits Syst. Video Technol., vol. 16, no. 3, pp. 354-362, Mar. 2006

9.       D.M. Thodi and J.J. Rodriguez, “Expansion embedding techniques for reversible watermarking,” IEEE Trans. Image Process., vol. 16, no. 3, pp. 721-730, Mar. 2007.

10.    X.L. Li, B. Yang, and T. Y. Zeng, “Efficient reversible watermarking based on adaptive prediction-error expansion and pixel selection,” IEEE Trans. Image Process., vol. 20, no. 12, pp. 3524-3533, Dec. 2011.

11.    P. Tsai, Y. C. Hu, and H. L. Yeh, “Reversible image hiding scheme using predictive coding and histogram shifting,” Signal Process., vol. 89, pp. 1129-1143, 2009.

12.    L. Luo et al., “Reversible image watermarking using interpolation technique,” IEEE Trans. Inf. Forensics Security, vol. 5, no. 1, pp. 187-193, Mar. 2010.

13.    Sachnev, H. J. Kim, J. Nam, S. Suresh, and Y.-Q. Shi, “Reversible Waterm- arking algorithm using sorting and prediction,” IEEE Trans. Circuits Syst. Video Technol., vol. 19, no. 7, pp. 989-999, Jul. 2009.

14.    J. Menezes, P. C. van Oorschot, and S. A. Vanstone, Handbook of Applied Cryptography. Boca Raton, FL, USA: CRC, 1996

15.    K. Hwang and D. Li, “Trusted cloud computing with secure resources and data coloring,” IEEE Internet Comput., vol. 14, no. 5, pp. 14-22, Sep./Oct. 2010.

16.    M. Johnson, P. Ishwar, V. M. Prabhakaran, D. Schonberg, and K. Ramachandran, “On compressing encrypted data,” IEEE Trans. Signal Process., vol. 52, no. 10, pp.2992-3006, Oct. 2004.

17.    W. Liu, W. Zeng, L. Dong, and Q. Yao, “Efficient compression of encrypted grayscale images,” IEEE Trans. Image Process. vol. 19, no. 4, pp. 1097-1102, Apr. 2010.

18.    X. Zhang, “Reversible data hiding in encrypted images,” IEEE Signal Process. Lett., vol. 18, no. 4, pp. 255-258, Apr. 2011.

19.    W. Hong, T. Chen, and H. Wu, “An improved reversible data hiding in encrypted images using side match,” IEEE Signal Process. Lett., vol. 19, no. 4, pp. 199-202, Apr. 2012.

20.    X. Zhang, “Separable reversible data hiding in encrypted image,” IEEE Trans. Inf. Forensics Security, vol. 7, no. 2, pp. 826-832, Apr. 2012.

21.    Kese Ma, Weiming Zhang, Xianfeng Zhao, “Reversible data hiding in Encrypted images by reserving room before encryption” IEEE Transactions on information forensics and security, Vol.8, No.3, March 2013

22.    Wien Honga, Tung-Shou Chen, Reversible Data Embedding For High Quality Images Using Interpolation And Reference Pixel Distribution Mechanism., Elsevier Journal For Visual Image R.22(2011) 131-140.

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Bhagwat P. Dwivedi, Shiv Kumar, Babita Pathik

Paper Title:

Intrusion Detection over Networking KDD Dataset using Enhance Mining Algorithm

Abstract: The intrusion detection systems (IDSs) generate large number of alarms most of which are false positives. Fortunately, there are reasons for triggering alarms where most of these reasons are not attacks. In this research, a rule based technique which is the enhancement of genetic algorithm has been developed. For this, The networking data and intrusion over the data is find to extract to recognize various entities into it. Data mining and its algorithm to process, data extraction, and data analysis is an important phase to monitor the features in it. Intrusion detection process follows the clustering and classification technique to monitor the data flow in it. In this paper our investigation is about to observe available algorithm for the intrusion detection. Algorithm such as Genetic, SVM etc have been processed over KDD cup 10% of dataset which contain 41 attributes and large number of data availability. Here our experiment also conclude that the proposed feature extraction algorithm outperform as best than the existing algorithm with computation parameter such as precision, recall and its accuracy.

 Intrusion Detection, Clustering Technique, Data Mining, KDD.


1.       Zhan Jiuhua Intrusion Detection System Based on Data Mining Knowledge Discovery and Data Mining, 2008. WKDD 2008.
2.       Bane Raman Raghunath Network Intrusion Detection System (NIDS)Emerging Trends in Engineering and Technology, 2008. ICETET '08.

3.       Changxin Song Design of Intrusion Detection System Based on Data Mining Algorithm 2009 International Conference on Signal Processing Systems.

4.       Wang Pu Intrusion detection system with the data mining technologies Communication Software and Networks (ICCSN), 2011 IEEE 3rd International Conference.

5.       Gaikwad, D.P. Sonali Jagtap, Kunal Thakare, Vaishali Budhawant Anomaly Based Intrusion Detection System Using Artificial Neural Network and fuzzy clustering International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181, 1 (9.) (2012 November).

6.       Goyal, C. Kumar GA-NIDS: A Genetic Algorithm based Network Intrusion Detection System, Electrical Engineering and Computer Science, North West University Technical Report (2008).

7.       G. Gu, P. Porras, V. Yegneswaran, M. Fong, W. Lee BotHunter: detecting malware infection through IDS-driven ialog correlation Proc. of 16th USENIX Security Symp. (SS’07) (2007 Aug), pp. 12:1–12:16.

8.       G. Gu, J. Zhang, W. Lee BotSniffer: detecting botnet command and control channels in network traffic Proc. of 15th Ann. Network and Distributed Sytem Security Symp. (NDSS’08) (2008 Feb).

9.       Ketan Sanjay Desale, Roshani Ade,” Genetic algorithm based feature selection approach for effective intrusion detection system”, IEEE 2015.

10.    Kajal rai, “Decision Tree Based Algorithm for Intrusion Detection”, Volume: 07 Issue: 04 Pages: 2828-2834 (2016) ISSN: 0975-0290.




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