Volume-2 Issue-4

S. No

Volume-2 Issue-4 September 2013, ISSN:  2277-3878 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Kapil Parikh, Ashish Maheshwari, Vinesh Agarwal

Paper Title:

Modeling, Simulation And Perfomance Analysis of AC-DC-AC PWM Converters Based Wind Energy Conversion System

Abstract:   In order to send wind energy to a utility grid, a variable speed wind turbine requires a power electronic converter to convert a variable voltage variable frequency source into a fixed voltage fixed frequency supply. Generic three-phase AC-DC-AC converter, converter control methods for wind power generation, wind turbine, two mass drive train and  PMSG generator are modeled in the thesis using MATLAB / SIMULINK for establishing variable-speed wind energy conversion systems. Variable speed wind power generation system modeling and simulation are essential methods both for understanding the system behavior and for developing advanced system control strategies. The developed wind energy conversion system have been validated through simulation study using MATLAB / SIMULINK, under different input/output conditions like constant wind speed, variable wind speed, and different fault conditions. The simulation results verify the validity of the developed wind energy conversion system and its controls.

Keywords:    AC-DC-AC Converter, , PMSG, Pitch Controller, Two Mass Drive Train, Wind Turbine, ,WECS

References:
1.        Haque, M.E., Negnevitsky, M. and Muttaqi, K.M.,“A novel control strategy for a variable-speed wind turbine with a permanent-magnet synchronous generator,” IEEE Transactions on Industry Applications, , vol. 46, no. 1, pp. 331-339, Jan/Feb. 2010.
2.        Zhang, S., Tseng, K.J., Vilathgamuwa, D.M., Nguyen, T.D. and Wang, X.Y. , “ Design of a robust grid interface system for PMSG-based wind turbine generators,” IEEE Transactions on Industrial Electronics, vol. 58, no. 1, pp. 316-328, Jan. 2011.
3.        Kumar, V., Joshi, R.R. and Bansal, R.C., “Optimal control of matrix converter based WECS for performance enhancement and efficiency optimization,” IEEE Transaction of Energy Conversion, vol. 24, no. 1, pp. 264-273, March. 2009.
4.        Bhende, C.N., Mishra, S. and Malla, S.G., “Permanent magnet synchronous generator-based standalone wind energy supply system,” IEEE Transactions on Sustainable Energy, vol. 2, no. 4, pp. 361-373, Oct.. 2011.
5.        Xia, Y., Ahmed, K.H. and Williams, B.W.  “ A New Maximum Power Point Tracking Technique for Permanent Magnet Synchronous Generator Based Wind Energy Conversion System,” IEEE Transactions on Power Electronics, vol. 26, no. 12, pp. 3609-3620, Dec. 2011.
6.        Koutroulis, E. and Kalaitzakis, K. , “ Design of a maximum power tracking system for wind-energy-conversion applications,” IEEE Transactions on Industrial Electronics, vol. 53, no. 2, pp.486-494,April 2006.
7.        Kenneth E. Okedu,  S. M. Muyeen,RioTaka hashi, and Junji Tamura, “ Wind farms fault ride through using DFIG with new protection scheme,” IEEE trans. On Sustainable Energy, vol. 3, no. 2, pp.242-254, April 2012.
8.        B. Chitti Babu ,and K.B.Mohanty , “ Doubly-fed induction generator for variable speed wind energy conversion systems- modeling & simulation,” International Journal of Computer and Electrical Engineering, vol. 2, no. 1,pp. 1793-8163, Feb. 2010.
9.        Haining Wang, Chem Nayar,  Jianhui Su, and Ming Ding, “ Control and interfacing of a grid-connected small-scale wind turbine generator,” IEEE Trans.On Energy
Convers., vol. 26, no. 2, pp.428-434, June 2011.
10.     Mahmoud M. Amin,  and Osama A. Mohammed, “ Development of high-performance grid-connected wind energy conversion system for optimum utilization of variable speed wind turbines,” IEEE Trans. On Sustainable Energy, vol. 2, no. 3, pp.235-, July 2011.
11.     Johann W. Kolar, Thomas Friedli, Jose Rodriguez, , and Patrick W. Wheeler, “Review of three-phase PWM AC-AC converter topologies,” IEEE Trans. On Industrial Electronics, vol. 58, no. 11, pp.4988-5066, Nov. 2011.
12.     C sasi. and Mohan G., “Performance analysis of grid connected wind energy conversion system with a PMSG during fault conditions, “International journal of engineering and advanced technology ,vol.2, no.4, pp.356-361, April 2013.

1-9

2.

Authors:

Kunal S. Thaker, Kuldeep B. Shukla, Yash K. Sharma, Reenav M. Shukla

Paper Title:

Four Array PSK Using Xilinx Simulator

Abstract:    for any long distance transmission modulation is the basic need for any transmission. At the progress of transmission information frequency will occupy with the carried frequency. Four array PSK work with frequency with four different phase, and then it will transmit in the digital world. With the help of quad phase; overall bandwidth will be decreasing at the end of the four array PSK transmission with less noise. Finally it will give highly accurate output with less bandwidth and long distance transmission of the respective signal. It also consumes low power with Compaq size for the purpose of communication. Xilinx is the software for the rationale of modified any block as well as adjoins some new blocks when required.

Keywords:    Xilinx Simulator, Four Array PSK, Passivefilter, Field Programmable Gate Array

References:
1.     http://www.dsplog.com/2008/07/08/compare-bpsk-qpsk-4pam-16qam-16psk-64qam-32psk/.
2.     http://www.mathworks.in/matlabcentral/newsreader/view_thread/298247.
3.     Wei-Long Sun “Data Sheet Simulation and RF path Modeling of a QPSK Radio Communication System” Kindle Edition, Publication Date March 26-2012.
4.     Syed WahajUI Haq; Shahzad Ahmed; Tehseen Sattar, “Differential QPSK based Communication System”, Publication Date January 23-2013.
5.     Josua Bisuk Mubyarto Purba; Shelia Horan,    “The EffectOf Pulse Shaping QPSK on Bandwidth Efficiency”, Publisher: Biblioscholar, Creator: National Aeronautics And Space Administration.
6.     Xilinx simulator guide.

10-13

3.

Authors:

Akhil Mahajan, Parminder Kaur

Paper Title:

Face Recognition System using EBGM and ANN

Abstract:  There are many challenges associated with face recognition systems which make them a complex and difficult process. These factors include—pose variations, facial expressions, occlusion, age etc. These factors affect the face recognition systems and deteriorate their accuracy. The face recognition problem can be solved by using some statistical techniques such as PCA, ICA and LDA. Some feature based techniques—Elastic Bunch Graph Matching (EBGM), Artificial Neural Network (ANN), etc. have also been used and implemented to solve the face recognition problem. In this paper an insight is provided into various techniques available for face recognition, and a method is proposed that provides an efficient and feasible solution for real-time face recognition system. The proposed method uses EBGM technique, which in turn uses facial features for the identification of the test images that may be captured from a live video. Experimental results show that by involving ANN, better matching results with EBGM were obtained. Moreover, for face recognition in live videos and under low illumination conditions, the proposed system works more efficiently and gives better matching results when compared with the other techniques.

Keywords:  Elastic Bunch Graph Matching (EBGM), fiducial points, Independent Component Analysis (ICA), jets, Linear Discriminant Analysis (LDA and Principal Component Analysis (PCA).

References:
1.        Patil A.M., Kolhe S.R. and Patil P.M., “2D Face Recognition Techniques: A Survey,” International Journal of Machine Intelligence, ISSN: 0975-2927, Vol.2, issue 1, pp 74-83, 2010.
2.        Mohammad Hossein Mahoor, “A Multi-Modal Approach for Face Modeling and Recognition”, open access dissertations, paper 32, 2008.
3.        Yia Dai, Guoqiang Xiao, Kaijin Qiu, “Efficient face recognition with variant pose and illumination in video”, 4th International Conference on Computer Science & Education, pp 18-22, 2009.
4.        Nikunj Kela, Ajita Rattani, Phalguni Gupta, “Illumination Invariant Elastic Bunch Graph Matching for Efficient Face Recognition”, Proceedings of the Conference on Computer Vision and Pattern Recognition Workshop, 2006.
5.        Sheetal Tulsidas Chaudhari and Archana Kale, “Face normalization: enhancing face recognition”, Third International Conference on Emerging Trends in Engg. And Technology, Vol. 3, pp 520-525, 2010.
6.        Prof. V.P. Kshirsagar, M.R. Baviskar, M.E Gaikwad, “Face Recognition Using Eigenfaces”, IEEE, pp 302-306, 2011.
7.        Mayank Agarwal, Nikunj Jain, Manish Kumar and Himanshu Agarwal, “Face Recognition Using Eigen Faces and Artificial Neural Network”, International Journal of Computer Theory and Engineering, Vol. 2, No. 4, pp 624-629, August 2010.
8.        Ahmed Zeeshan Pervaiz, “Real time face recognition system based on EBGM framework”, 12th Int. Conference on Computer Modelling and simulation”, Vol. 12, pp 262-266, 2010.
9.        Kin –Wang Cheung, Jiansheng Chen and Yiu-Sang Moon, “Pose-tolerant Non-Frontal Face Recognition using EBGM”,   IEEE, 2008.
10.     M. A. Jobayer Bin Bakkre, Md. Tajmilur Rahman, Md. Alamin Bhuiyan, “The Enhanced Face Recognition using Binary Patterns of Gabor Features”, IEEE Transaction, pp 01-06, 2009.
11.     Jawad Nagi, “Design and development of an efficient high-speed face recognition system”, open access dissertations, 2007.
12.     Mr. Nilesh Deotale, Ms.S.L.Vaikole, Dr.SD.Sawarkar, “Face Recognition using ANNs”, IEEE Transaction, Vol. 2, pp 446-450, 2010.
13.     Raman Bhati, Sarika Jain, Nilesh Maltare, Durgesh Kumar Mishra, “A Comparative Analysis of Different Neural Networks for Face Recognition Using Principal Component Analysis, Wavelets and Efficient Variable Learning Rate”, Int’l Conf. on Computer & Communication Technology, pp 526-531, 2010.
14.     Ping Zhang, “Complex Wavelet Feature Extraction for  Video-based Face Recognition”, IEEE Transaction, pp 440-443, 2010.
15.     Laurenz Wiskott1, Jean-Marc Fellous, Norbert Kruger and Christoph von der Malsburg; Face Recognition by Elastic Bunch Graph Matching, In Intelligent Biometric Techniques  in Fingerprint and Face Recognition, eds. L.C. Jain et al., Publ. CRC Press, Chapter 11, pp. 355-396, 1999.
16.     Tasweer Ahmad, Ahlam Jameel, Dr. Balal Ahmad, “Pattern Recognition using Statistical and Neural Techniques”, IEEE Transaction, pp 87-91, 2011.
17.     olhandai Yesu, Himadri Jyoti Chakravorty, Prantik Bhuyan, Rifat Hussain, Kaustubh Bhattacharyya, “Hybrid Features Based Face Recognition Method Using Artificial Neural Network”, IEEE Transactions on CISP Proceedings, pp 40-46, 2012.
18.     Teddy Salan, Khan M. Iftekharuddin, “Large Pose Invariant Face  Recognition  Using Feature - based Recurrent  Neural  Network”,   IEEE World Congress  on  Computational Intelligence, pp 10-15,  2012.

14-18

4.

Authors:

A. S. Padampalle, A. D. Suryawanshi, V. M. Navarkhele, D. S. Birajdar

Paper Title:

Structural and Magnetic Properties of Nanocrystalline Copper Ferrites Synthesized by Sol-gel Autocombustion Method

Abstract:     Nanocrystalline copper ferrites are conventionally synthesized by the sol-gel auto combustion method using metal nitrates and citric acid for different temperatures. In this work, X-ray diffractometry (XRD), transmission electron microscopy (TEM), Fourier transform infrared (FT-IR) and vibration sample magnetometry were used to characterize the samples. XRD and selected-area electron diffraction pattern indicates, the synthesized nanocrystalline particles have only the inverse spinel structure without presence of any other phase impurities. Nanocrystalline particles in the range 22-85 nm obtained depending on calcined temperature. FTIR spectra shows, the position of absorption bands are found to be particle size dependent. In magnetic studies, saturation magnetisation increases and coercivity decreases by increasing temperature

Keywords:   Nanocrystalline, CuFe2O4, X-ray, TEM, FTIR, VSM

References:
1.     S. Deka and P. A. Joy, Mater. Chem. Phys.  100(2006) 98.
2.     Z. John Zhang, Z. L. Wang, B. C. Chakoumakos and J. S. Yin, J. Am. Chem. Soc. Vol. 120 No. 8 (1998) 1800.
3.     C. Villette, Ph. Tailhades & A. Rousset,  J. Solid State Chem., 117 (1995) 64.
4.     H. Ohnishi & T. Teranishi, J. Phys. Soc. Jpn., 16 (1961), 35.5.     X.X.Tang,  A. Manthiram,  and J.B. Goodenough, J. Solid State Chem., 79 (1989) 250.
6.     D. Makovec, A. Kodre, K. Arconjzto ,M. Drofenik, J. Nanopart. Res. 11(2009)1145.
7.     Ahniyaz, T. Fujiwara, S. W. Song, M. Yoshimura, solid state ionics 151 (2002) 419.
8.     Kosak, D. Makovec, A. Zindarsic, M. Drofenik, J. Eur. Cream. Soc. 24 (2004)959.
9.     P. Kumar, P. Mishar and S. Kumar Sahu, Int. J. Sci. and Eng. Res. Vol. 2 Issue 8 (2011)1.
10.  Baubet, Ph. Tailhades, C. Bonningue, A. Rousset and  Z. Simsa, Journal of Physics and Chemistry of Solids 61 (2000) 863.
11.  S. Asbrink and A. Waskowska, J. Phys. Condens. Matter. 3 (1991) 8173.
12.  J. Q. Qi, W. P. Chen, M. Lu, Y. Wang, H. Y. Tian, L. T. Li and H. L. W. Chan, nanotechnology 16 (2005) 3098.
13.  J. Z. Jiang, G. F. Goya and H. R. Rechenberg, J. Phys. Condens. Matter 11 (1999) 4065.
14.  Thapa, N. Kulkarni, S. N. Mishra, P. L. Paulose and P. Ayyub, J. Phys. D: Appl. Phys. 43 (2010) 195004.
15.  T. P. Raming, A. J. A. Winnubst, C. M. VanKats and P. Philipse, J. Colloid Interface Sci. 249 (2002) 346.
16.  V. A. M. Brabers, Phys. Status Solid 33 (1969) 563.
17.  S. T. Hafner, Z. Crystallogr. 115 (1961) 331.

19-22

5.

Authors:

Ch. Katyayini, Shaik Meeravali

Paper Title:

Design of Prototypic Army BOT for Landmine Detection and Control Using Hand Gestures

Abstract:  This paper presents three different gesture recognition models which are capable of recognizing seven hand gestures, i.e., up, down, left, right, tick, circle and cross, based on the input signals from MEMS 3-axes accelerometers. The accelerations of a hand in motion in three perpendicular directions are detected by three accelerometers respectively and transmitted to a PC via Bluetooth wireless protocol. An automatic gesture segmentation algorithm is developed to identify individual gestures in a sequence. To compress data and to minimize the influence of variations resulted from gestures made by different users, a basic feature based on sign sequence of gesture acceleration is extracted. This method reduces hundreds of data values of a single gesture to a gesture code of 8 numbers. Finally the gesture is recognized by comparing the gesture code with the stored templates. Results based on 72 experiments, each containing a sequence of hand gestures (totaling 628 gestures), show that the best of the three models discussed in this paper achieves an overall recognition accuracy of 95.6%, with the correct recognition accuracy of each gesture ranging from 91% to 100%. We conclude that a recognition algorithm based on sign sequence and template matching as presented in this paper can be used for non-specific-users hand-gesture recognition without the time consuming user-training process prior to gesture recognition.

Keywords:   Gesture recognition, Interactive controller, MEMS accelerometer, Humidity sensor.

References:
1.        Birk And S. Carpin, “Rescue Robotics – A Crucial Milestone On Th Road To Autonomous Systems,” Advanced Robotics Journal, Vol. 20, J. Peters, Ed. New York: Mcgraw-Hill, 1964, Pp. 15–64.
2.        Davids, “Urban Search And Rescue Robots: From Tragedy To Technology,” Intelligent Systems, Ieee, Vol. 17, No. 2, 2002, Pp. 81-83.
3.        J. Y. Wong, Theory Of Ground Vehicle, 3rd Edition. John Wiley And Sons, Inc.,  2001,  Ch.4.5.
4.        Larry Shapiro, Special Police Vehicles , Isbn 0-7603-07602, Mbi Publications, Pg 44-45.
5.        David Axe, War Bots: Us Military Bots Transforming War In Iraq, Afganisthan And Future, Isbn 978-1-934840-37-5, Nimble Books Inc.
6.        GOURLEY, SCOTT R "JOINT EOD RAPID RESPONSE VEHICLE (JERRV)". ARMY. FINDARTICLES.COM. 03 APR, 2011.
7.        Li Xuewen, Meng Cai, Liang Jianhong, Wang Tianmiao, “Research on Simulation and Training System for EOD Robots”, Industrial Informatics, 2006 IEEE International Conference Singapore,  ISBN: 0-7803-9700-2, Aug. 2006, pp  810 – 814.
8.        Shahin Farhani, “Zigbee wireless and sensor networks”, ISBN 978-0-7506-8393-7, 2008, Newens publications.
9.        Fred Eady, “Hands on Zigbee: implementing 802.15.4 with microcontrollers”, ISBN 978-0-12-370887-8, 2007, pp 63-67.
10.     Andrew N. Sloss, Dominic Symes, Chris Wright, “ARM system developer's guide: designing and optimizing system software”, ISBN 1-55460-874-5, 2004, Morgan Kaufmana publications.
11.     J.R.Gibson, “ARM assembly language- an introduction”, ISBN 978-1-84753-696-9, 2007, pp 26-32.

23-26

6.

Authors:

N. A. Shairi, T. A. Rahman

Paper Title:

Simulation and Analysis of Phase Noise Distortion in RF Transceiver of IEEE 802.11a WLAN Bridge System

Abstract:  In this paper, simulation and analysis of phase noise distortion in radio frequency (RF) transceiver of Wireless Local Area Network (WLAN) bridge system are presented. It is focused on the oscillator in RF transmitter and RF receiver in the transceiver. The effect of phase noise on the constellation error of RF transmitter and the receiver sensitivity of RF receiver is analysed based on IEEE 802.11a standard. By this way, these analyses can be applied in early stage of oscillator design for RF transceiver of IEEE 802.11a WLAN system. RF behavioural models and Agilent Ptolemy simulator are used in Advanced Design System (ADS) software to perform simulation of constellation error and receiver sensitivity. As a result, it is found that the phase noise of oscillator should not be higher than -120 dbc/Hz at 1 MHz offset in order to optimise the receiver sensitivity and also to minimising constellation error in RF transceiver of IEEE 802.11a standard.

Keywords:  Phase noise, transceiver, transmitter, receiver, constellation error, sensitivity, packet error rate, IEEE 802.11a, WLAN.

References:
1.     Supplement to IEEE Standard for Information Technology - Telecommunications and Information Exchange Between Systems - Local and Metropolitan Area Networks - Specific Requirements. Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: High-Speed Physical Layer in the 5
GHz Band," IEEE Std 802.11a-1999 , 1999.
2.     IEEE Std 802.16-2004 (Revision of IEEE Std. 802.16-2001), "IEEE Standard for Local and metropolitan area networks, Part 16: Air Interface for Fixed Broadband Wireless Access Systems", 2004.
3.     Litwin, L., "An introduction to multicarrier modulation," Potentials, IEEE , vol.19, no.2, pp.36-38, Apr/May 2000.
4.     Miss Rashmi Mishra, Dr. Baibaswata Mohapatra, Mr. Nitin Naiyar, "Effect of Cyclic Prefix on OFDM System", International Journal of Recent Technology and Engineering (IJRTE), Vol. 1, Issue 2, page 168-170, June 2012.
5.     Shigematsu, T.; Suematsu, N.; Takeuchi, N.; Iyama, Y.; Mizobuchi, A.; , "A 6-18 GHz 20 W SPDT switch using shunt discrete PIN diodes," Microwave Symposium Digest, 1997., IEEE MTT-S International , vol.2, pp.527-530 vol.2, 8-13 Jun 1997.
6.     Imran, M.I.; Tharek, A.R., "Radial line slot antenna development for outdoor point to point application at 5.8GHz band," RF and Microwave Conference, 2004. RFM 2004. Proceedings , pp.103-105, 5-6 Oct. 2004.
7.     Othman, M.A.; Bakar, S.A.A.; Aziz, M.Z.A.A.; Sinnappa, M.; Ismail, M.M.; Sulaiman, H.A.; Misran, M.H.; Meor Said, M.A.; Ramlee, R.A., "An analysis of UWB double sided circular disc bow-tie antenna," Information Communication and Embedded Systems (ICICES), 2013 International Conference on , pp.584-587, 21-22 Feb. 2013.
8.     Abd.Aziz, M.Z.A.; Shukor, M.Md.; Suaidi, M.K.; Ahmad, B.H.; Othman, M.A.; Hasan, N., "Design a 3.5 slot antenna using coplanar waveguide (CPW) for dual band application," Microwave Techniques (COMITE), 2013l Conference on , pp.31-35, 17-18 April 2013.
9.     B. H. Ahmad, M. M.  Ariffin, H. Nornikman, N. M. S. Roslan, M. Z. A . Abd Aziz, M. A. Atiqa, A. R. Ayuni, Y. W.  Ming, " Parametric Study on the Compact G-Shaped Monopole Antenna for 2.4 GHz and 5.2 GHz Application", International Journal of Engineering and Technology (IJET), Vol. 5, No. 1, page 512-518, 2013.
10.  Yoo, Sang-Sun; Yong-Chang Choi; Hong-Joo Song; Seung-Chan Park; Park, Jeong-Ho; Hyung-Joun Yoo, "A 5.8-GHz High-Frequency Resolution Digitally Controlled Oscillator Using the Difference Between Inversion and Accumulation Mode Capacitance of pMOS Varactors," Microwave Theory and Techniques, IEEE Transactions on , vol.59, no.2, pp.375-382, Feb. 2011.
11.  Zahari, M.K.; Ahmad, B.H.; Shairi, N.A.; Peng Wen Wong, "Reconfigurable dual-mode ring resonator matched bandstop filter," Wireless Technology and Applications (ISWTA), 2012 IEEE Symposium on , pp.71-74, 23-26 Sept. 2012.
12.  Zakaria, Z.; Sam, W.Y.; Aziz, M.Z.A.A.; Isa, A.A.M.; Johar, F.M., "Design of integrated rectangular SIW filter and microstrip patch antenna," Applied Electromagnetics (APACE), 2012 IEEE Asia-Pacific Conference on , pp.137-141, 11-13 Dec. 2012.
13.  Zakaria, Z.; Ahmad, B.H.; , "Design of SIW bandpass filter with 6 dB offset," RF and Microwave Conference (RFM), 2011 IEEE International, pp.87-90, 12-14 Dec. 2011.
14.  M.H. Misran, M.A. Meor Said, M.A. Othman, M.M. Ismail, H.A. Sulaiman, K.G. Cheng, "Design of Low Noise Amplifier using Feedback and Balanced Technique for WLAN Application," Procedia Engineering, Volume 53, 2013, Pages 323-331.
15.  Misran, M.H.; Meor Said, M.A.; Cheng, K.G.; Othman, M.A.; Ismail, M.M.; Sulaiman, H.A.; , "Design of Gaas E-phemt low noise amplifier for WLAN application," Green and Ubiquitous Technology (GUT), 2012 International Conference on , pp.106-109, 7-8 July 2012.
16.  V.J.K.Kishor Sonti, Dr. V.Kannan, "Noise Analysis of Novel Design of MODFET Low Noise Amplifier", International Journal of Recent Technology and Engineering (IJRTE), Vol. 2, Issue 2, page 105-108, May 2013.
17.  Osman, A. F.; Noh, N.M., "Wideband LNA design for SDR radio using balanced amplifier topology," Quality Electronic Design (ASQED), 2012 4th Asia Symposium on , pp.86-90, 10-11 July 2012.
18.  Osman, A. F.; Noh, N.M., "Wideband low noise amplifier design for software defined radio at 136 to 941 MHz," Intelligent and Advanced Systems (ICIAS), 2012 4th International Conference on , pp.232-236, 12-14 June 2012.
19.  Chih-Hao Chen; Pei-Yuan Chiang; Jou, C.F., "A Low Voltage Mixer With Improved Noise Figure," Microwave and Wireless Components Letters, IEEE , vol.19, no.2, pp.92-94, Feb. 2009.
20.  Shairi, N.A.; Rahman, T.A.; Abd Aziz, M.Z.A., "RF receiver system design for Wireless Local Area Network bridge at 5725 to 5825 MHz," Applied Electromagnetics, 2007. APACE 2007. Asia-Pacific Conference on , pp.1-6, 4-6 Dec. 2007
21.  Othman, A.R.; Abd Aziz, A.A.; Pongot, K.; Shairi, N.A.; Mohd Nor, M.N., "Design and sensitivity analysis of direct conversion RF receiver for IEEE 802.11a WLAN system at 5.8 GHz frequency," Research and Development (SCOReD), 2012 IEEE Student Conference on , pp.262-265, 5-6 Dec. 2012.
22.  Shairi, N.A.; Rahman, T.A., "Adjacent channel rejection analysis due to channel select filter in RF superheterodyne receiver of WLAN IEEE 802.11a," Research and Development (SCOReD), 2012 IEEE Student Conference on , pp.140-143, 5-6 Dec. 2012.
23.  Shairi, N.A.; Rahman, T.A.; Abd Aziz, M.Z.A., "RF transmitter system design for Wireless Local Area Network bridge at 5725 to 5825 MHz," Computer and Communication Engineering, 2008. ICCCE 2008. International Conference on , pp.109-112, 13-15 May 2008.
24.  Shairi, N.A.; Ibrahim, I.M.; Rahman, T.A., "Third order intermodulation distortion effect on the constellation error in RF transmitter of IEEE 802.11a WLAN system," Industrial Electronics and Applications (ISIEA), 2011 IEEE Symposium on , pp.223-226, 25-28 Sept. 2011.
25.  Heung-Gyoon Ryu; Ying-Shan Lee, "Phase noise analysis of the OFDM communication system by the standard frequency deviation," Consumer Electronics, IEEE Transactions on , vol.49, no.1, pp.41-47, Feb. 2003.
26.  Heung-Gyoon Ryu; Ying Shan Li; Jin-Soo Park, "Nonlinear analysis of the phase noise in the OFDM communication system," Consumer Electronics, IEEE Transactions on , vol.50, no.1, pp.54-63, Feb 2004.
27.  Mahmoud, M.; Riad, M., "Combined Effect Analysis of Wiener Phase Noise and Doppler Spread in OFDM Communication System," Radio Science Conference, 2006. NRSC 2006. Proceedings of the Twenty Third National , pp.1-8, 14-16 March 2006.
28.  Seong-Min Kim; Byung-Soo Kang; Yoon-Soo Ko; Jae-Ho Jeong; Kwang-Cheon Lee, "Influence of phase noise according to pilot symbol structures in OFDM," Consumer Electronics, 2006. ISCE '06. 2006 IEEE Tenth International Symposium on , pp.1-4, 2006.
29.  Qiyue Zou; Tarighat, A.; Sayed, A.H., "Compensation of Phase Noise in OFDM Wireless Systems," Signal Processing, IEEE Transactions on , vol.55, no.11, pp.5407-5424, Nov. 2007.
30.  Wanli Peng; Qunyi Gao; Xiujun Zhang; Shidong Zhou; Xibin Xu, "Analysis of Transmitter Phase Noise in OFDM System and its Mitigation," Vehicular Technology Conference, 2009. VTC Spring 2009. IEEE 69th , pp.1-5, 26-29 April 2009.
31.  Pandey, K.K.; Bhatt, U.R.; Upadhyay, R., "Investigation effect of phase noise of OFDM system and realization LO (local oscillator) with and without phase locked loop (PLL)," Wireless and Optical Communications Networks (WOCN), 2012 Ninth International Conference on , pp.1-5, 20-22 Sept. 2012.
32.  Mini-Circuit Inc. Characterizing Phase Noise. RF Design Magazine, p.p. 58 – 59, January 2003.
33.  Rizzoli, V.; Mastri, F.; Masotti, Diego, "General noise analysis of nonlinear microwave circuits by the piecewise harmonic-balance technique," Microwave Theory and Techniques, IEEE Transactions on , vol.42, no.5, pp.807-819, May 1994.
34.  Georgiadis, A., "A Model for Stability, Noise, and Angle Modulation Analysis of Injection-Locked Oscillators," Microwave Theory and Techniques, IEEE Transactions on , vol.56, no.4, pp.826-834, April 2008.
35.  Xiaolue Lai; Roychowdhury, J., "Capturing oscillator injection locking via nonlinear phase-domain macromodels," Microwave Theory and Techniques, IEEE Transactions on , vol.52, no.9, pp.2251-2261, Sept. 2004.
36.  Demir, A.; Mehrotra, A.; Roychowdhury, J., "Phase noise in oscillators: a unifying theory and numerical methods for characterization," Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on , vol.47, no.5, pp.655-674, May 2000.
37.  Farzaneh, F.; Mehrshahi, E., "A Novel Noise Analysis Method in Microwave Oscillators Based on Harmonic Balance," Microwave Conference, 1998. 28th European , vol.2, pp.255-260, Oct. 1998.
38.  Hieu Nguyen; Trung Kien Nguyen; Feng Zheng; Kupferschmidt, C.; Kaiser, T.; , "Signal design to mitigate the impact of RF impairment in OFDM communication systems," Personal, Indoor and Mobile Radio Communications, 2009 IEEE 20th International Symposium on , pp.3159-3163, 13-16 Sept. 2009.
39.  Hsin-Hung Kuo; Yang-Han Li; Yi-Hsin Pang, "A 0.13 μm CMOS transmitter with 72-dB RF gain control for mobile WiMAX/WiBro applications," Radio Frequency Integrated Circuits Symposium, 2008. RFIC 2008. IEEE , pp.105-108, June 17 2008-April 17 2008.
40.  Ildu Kim; Junghwan Moon; Jangheon Kim; Jungjoon Kim; Cheol Soo Seo,; Kae-Oh Sun,; Cheol Woo Ahn,; Kim, Bumman, "Envelope injection consideration of high power hybrid EER transmitter for IEEE 802.16e mobile WiMAX application," Microwave Symposium Digest, 2008 IEEE MTT-S International , pp.411-414, 15-20 June
2008.
41.  van Zeijl Paul, T.M.; Collados, M., "A Digital Envelope Modulator for a WLAN OFDM Polar Transmitter in 90 nm CMOS," Solid-State Circuits, IEEE Journal of , vol.42, no.10, pp.2204-2211, Oct. 2007.
42.  Webster, A.P.; Paviol, J.; Liu, J.; Arslan, H.; Dunleavy, L.P., "Measurement-based modeling of a 5 GHz WLAN transmitter," Radio and Wireless Conference, 2004 IEEE , pp.403-406, 19-22 Sept. 2004.
43.  Christian Olgaard. "Using Advanced Signal Analysis to Identify Sources Of WLAN Transmitter Degradations". RF Design Magazine. , pp.28-37, October 2004.
44.  David M. Pozar. Microwave and RF Wireless System. Third Avenue, N.Y.: John Wiley & Sons, Inc. 2001.
45.  Behzad Razavi. RF Microelectronics. Upper Saddle River, N.J.: Prentice Hall PTR. 1998.
46.  Cotter W. Sayre. Complete Wireless Design. New York: McGraw-Hill. 2001.

27-32

7.

Authors:

Sooraj Narayan A, Mohd. Z. A. Ansari

Paper Title:

Simulation Investigations on Flywheel Energy Ride Through Systems

Abstract:   Nowadays static UPS systems are preferred for low-power applications, although rotary UPS systems offer some interesting advantages. The rotary UPS now having only high end applications and is not available for, hospitals-critical loads like operation theatre and ICU’s, utility, small collocations, military applications- data centers , telephone equipments - mobile towers, storage devices and various data centers. In this paper, simulation investigations for a diesel rotary UPS system have been carried out using MATLAB / SIMULINK for possible implementation of this system to feed the critical loads as described above uninterruptedly. The name Flywheel energy ride through came from the operation of Flywheel in the rotary UPS. Flywheel is the kernel of the system described above.

Keywords: Diesel rotary UPS, Flywheel energy storage, Kinetic Energy, rotary UPS.

References:
1.        W Mervin Burger, Penbro Kelnick  “The rotary UPS: an alternative source of electrical power  “ , energize- December 2006 .
2.        R. Arghandeh Jouneghani, , M. Pipattanasomporn, ,and S. Rahman, “Flywheel Energy Storage Systems for Ride-through Applications in a Facility Microgrid” Ieee Transactions On Smart Grid, VOL. 10, NO.10, 2012
3.        Haichang Liu*, Jihai Jiang  , “ Flywheel energy storage—An upswing technology for energy sustainability “ , School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, Heilongjiang Province, China , science – Elsvier direct .page no 599-600
4.        T. Loix, E. Haesen, K. De Brabandere, J. Driesen and R. Belmans  K.U.Leuven ESAT / ELECTA “Drive Model for a Low-Power Rotary Uninterruptible Power Supply”, 3rd IEEE B Enelux Young Researchers Symposium In Electrical Power Engineering 27-28 APRIL 2006, GHENT, B ELGIUM
5.        Jennifer K. H. Ratner, James B. “composite Flywheel rotor technology – a      review” IEEE Transactions on Smart Grid- 2007
6.        Allen Windhorn, Member, IEEE. “ A Hybrid Static/Rotary UPS System “IEEE Transactions on Industry Applications, VOL. 28, NO. 3, MAYIJUNE 1992
7.        Florian Herrault, Chang-Hyeon Ji, and Mark G. Allen,IEEE “Ultraminiaturized High-Speed Permanent-Magnet Generators for Milliwatt-Level Power Generation” journal of micro electromechanical systems, vol. 17, no. 6, december 2008
8.        B J Beck “The Design And Performance Of A Rotary Ups System” Holec Ltd, Leatherhead, Surrey, 2001
9.        Alexander kusko and Stephen Fairfax “Survey of Rotary Uninterruptable Power Supplies.” Electrical Division Failure Analysis Associates, Inc.Three Cambridge Center Cambridge, MA 02142
10.     Hansjoacihn Dolezal “Dynamic-Rotary Systems with  Flywheel and Diesel Engine” Ad . STR'JVER KG (GMBH & C0' Niendorfer  Weg 11 2000 Hamburg 61
11.     Alexander Kusko and  Stephen Fairfax “Survey of Rotary Uninterruptible Power Supplies” , Electrical Division Failure Analysis Associates, Inc.Three Cambridge
Center Cambridge, MA 02142
12.     Simulation of Flywheel energy storage system – Claus R Danielson, Nicolas W. Frank. Conference paper – 2-06-2011
13.     Flywheel energy storage system for ride through applications in a facility macro grid. R. Arghandeh Jouneghani, M. Pipattanasomporn,  S. Rahman, Fellow, IEEE 2012.

33-36

8.

Authors:

Kiran S. Patil, N.G.Gore, P.J.Salunke

Paper Title:

Optimum Design of Reinforced Concrete Flat Slab with Drop Panel

Abstract:   In this present study optimum design of reinforced concrete flat slab with drop panel according to the Indian code (IS 456-2000) is presented. The objective function is the total cost of the structure including the cost of slab and columns. The cost of each structural element covers that of material and labour for reinforcement, concrete and formwork. The structure is modelled and analysed using the direct design method. The optimization process is done for different grade of concrete and steel. The comparative results for different grade of concrete and steel is presented in tabulated form. Optimization for reinforced concrete flat slab buildings is illustrated and the results of the optimum and conventional design procedures are compared. The model is analysed and design by using MATLAB software. Optimization is formulated is in nonlinear programming problem (NLPP) by using sequential unconstrained minimization technique (SUMT).

Keywords:  Flat slab, Reinforced concrete, Structural optimization, Punching shear stress, slab size, drop panel size..

References:
1.        R.C.Gupta and Dr. M.R.Sethia “computer aided design of flat slab-column-footing structure”, The Bridge and Structural Engineering. Vol.23 (1) pp.39-54. 1993.
2.        Ronaldo B. Gomes and Paul E. Regan,”Punching Resistance of RC Flat Slabs with Shear Reinforcement”, Journal of Structural Engineering, Vol.125 (6), pp- 684-692. 1999.
3.        M.G. Sahab, A.F. Ashour, And V.V. Toropov “Cost optimisation of reinforced concrete flat slab buildings”, Engineering Structures.Vol.27 pp.313–322. 2005.
4.        K.M.A. Hossain, And O. Olufemi “Design optimization of simply supported concrete slabs by finite element modeling”, Struct. Multidisc Optimization.Vol.30, pp.76–88. 2005.
5.        H.S. Kim, And D.G.Lee “Efficient analysis of flat slab structures subjected to lateral loads” Engineering Structures Vol.27 pp.251–263. 2005.
6.        N. Subramanian “Evaluation and enhancing the punching shear resistance of flat slabs using HSC”, The Indian Concrete Journal, Vol. 79(5), pp.31-37. 2005.
7.        S.S.F. Mehanny, B.M. Sobhy and M.M. Bakhoum “Strength versus drift limitation effects on code compliant seismic-resistant flat slab buildings”, The Indian Concrete Journal .vol.36 (6) pp.-1-2. 2008.
8.        Miguel Fernández Ruiz, Aurelio Muttoni, and Jakob Kunz, “Strengthening of Flat Slabs Against Punching Shear Using Post-Installed Shear Reinforcement”, ACI Structural Journal, Vol.107 (4), pp. 434-442. 2010.
9.        M.A. Eder, R.L. Vollum, A.Y. Elghazouli, T. Abdel-Fattah “Modelling and experimental assessment of punching shear in flat slabs with spearheads”, Engineering Structures.Vol.32, pp.3911–3924. 2010.
10.     Vikunj k.Tilva Prof. B.A.Vyas and Assit.Prof. Parth Thaker “Cost comparison between flat slabs with drop and without drop in four storey lateral load resisting building”, National Conference on Recent Trends in Engineering & Tech. pp1-5. 2011.
11.     Adeola A Adedeji “Application: simplifying design of RC flat slab using taboo search”, Trends in applied science research (Academic Journals Inc.)Vol.6 (4) pp 375
385.2011.
12.     I.S: 456-2000.
13.     “Engineering Optimization” by S.S. Rao

37-39

9.

Authors:

M  Basavaraju, S Ranganatha

Paper Title:

Effect of Soft Material Hardness and Hard Material Surface Morphology on Friction and Transfer Layer Formation; Dry Condition

Abstract:  The morphological features of the surface in both micro and macro levels are important factors governing the tribological behavior of the contacting surfaces. Surface hardness is also an important factor which governs the friction and wear behaviors of the contacting surfaces. Surface morphology of a tool is an important factor as it primarily controls the tribological behavior at the interface which in turn controls the surface finish of products. In the present investigation a pin-on-plate sliding tester was used to identify the effect of surface morphology and hardness on co-efficient of friction and transfer layer which characterizes the tribological behavior. The morphology of mild steel (EN8)  plate surfaces were modified by employing three different surface modification methods like grinding (silicon carbide wheel polishing),  shot blasting and  electric discharge machining methods. Surface roughness parameters which characterize the morphology of the steel plates were measured using a three dimensional optical profilometer. Role of hardness is studied by employing lead, copper and Aluminum (Al6082) pins which were slid against steel plates. Experiments were conducted for plate inclination angles of 1, 1.5,2 and 2.5 degrees.  Normal load was varied from 1 to 150N during the tests. Experiments were conducted under dry condition in ambient environment. Scanning electron microscope was used to study the formation of transfer layer on plate and pin surfaces. It was observed that the co-efficient of friction and transfer layer formation were found to depend on the surface morphology of the harder surface. The quantum of transfer layer formation on the surfaces is found to increase with increase in surface roughness. 

Keywords:   Friction, co-efficient of friction, surface morphology and transfer layer formation.

References:
1.        J. F. Archard, “Elastic deformation and the laws of friction”, Proceedings of Royal. Society of London, Ser. A (243), 1957, 190-205.
2.        T. Nellemann, N. Bay and T. Wanheim, Real area of contact and friction stress – The role of trapped lubricant, Wear 43(1), 1977, 45-53.
3.        J. R. Whitehead, Surface deformation and friction of metals at light loads, Proc. Royal. Soc., London, A (201), 1950, 109-124.
4.        F. P. Bowden and D. Tabor, The friction and lubrication of solids, volume-I; Clarendon Press, Oxford, UK, 1950.
5.        K. Endo and H. Goto, Effects of environment on fretting fatigue, Wear, 48(2), 1978, 347-367.
6.        Y. Tsuya, Microstructure of wear, friction and   solid lubrication, Tech. Rep. of   Mech. Engg.  Lab., no. 81,Tokyo, Japan, 1975.
7.        K. Hiratsuka, A. Enomoto, T. Sasada, Friction and wear of Al2O3, ZrO2 and SiO2 rubbed against pure metals, Wear, 153(2), 1992, 361-373. 
8.        H E Staph ,P.M. Ku, H J Carper, Effect of Surface Roughness and Surface Texture   on Scuffing Mechanism and Machine Theory 8(1973) 197-208.
9.        M M Koura, The effect of surface texture on friction mechanisms, Wear 63(1980) 1-12.
10.     P L Menezes, Effect of surface roughness parameter and grinding angle on co-efficient of    friction when sliding of Al-Mg alloy over EN8 steel, Transactions of ASME:Journal of Tribology 128(2006) 697-704.

40-46

10.

Authors:

M. Arulselvan, G. Ganesan

Paper Title:

A Study on Compression Test on Ti-6Al-4V in Various Strain Rates and Various Temperature

Abstract:   The study investigates the plastic deformation of titanium alloy under moderate strain rates and warm temperature condition. The compression tests are carried out at constant strain rates of 0.01, 0.1, and 1 s-1 to the reductions of 30, 50 and 70% in height at temperatures of 25,100,200,300,400 and 500°C. The flow stress data are analyzed in terms of strain rate and temperature sensitivities. The flow stress decreases with the increase of temperature, but its variation with strain rate is low. Micro structural characteristics of  Ti-6Al-4V solid material after compressive plastic deformation were studied in the temperature  range 25-500 °C and at strain rates of 0.01, 0.1 and 1.0 s−1. The fracture of the material occurs at a reduction in height of 70%  and shows shear banding when compressed uniaxially at strain rates above 0.01s -1 up to a temperature of 500oC

Keywords:    Titanium, Compression, Microstructure, Plastic deformation, Temperature .

References:
1.     S.H. Wang a, M.D. Wei a, L.W. Tsay b, “Tensile properties of LBW welds in Ti–6Al–4V alloy at evaluated temperatures below 4500C”, Materials Letters 57, 2003, pp 1815– 1823
2.     Madsen, H. Ghonem, Materials Science and Engineering. A177, 1994, pp63–73.
3.     R. Boyer, E.W. Collings, G.E. Welsch (Eds.), Materials Properties Handbook: Titanium Alloys, ASM International, Materials Park, OH, 1994, pp.488
4.     S.R. Seagle *, K.O. Yu, S. Giangiordano “Considerations in processing titanium”, Materials Science and Engineering A263,1999, pp237–2425.     M.W. Mahoney, in: R. Boyer, E.W. Collings, G.E. Welsch (Eds.), Materials Properties Handbook: Titanium Alloys, ASM International, Materials Park, OH, 1994, pp. 1101.
6.     Hee Y. Kim and Soon H. Hong,” High Temperature Deformation Behavior And Microstructural Evolution Of Ti-47al-2cr-4nb Intermetallic Alloys”, ScriptaMaterialia, Vol. 38, No. 10,1998, pp. 1517–1523
7.     C.M. Sabinash, S.M.L. Sastryb, K.L. Jerinab,High “Temperature Deformation Of Titanium Aluminium Alloys”, Materials Science and Engineering A192/193,1995,pp 837-847
8.     S.R. Seagle *, K.O. Yu, S. Giangiordano,”Considerations in processing titanium”, Materials Science and Engineering A263,1999,pp 237–242
9.     R. Mahapatra a,c,*, Y.T. Chou a, D.P. Pope b, “The anomalous flow behavior in single-phase Ti44Al56 single crystals The effect of Materials Science and Engineering A239–240deformation history”, ,1997,pp 97–101
10.  V. Seetharamen, L. Boothe, C.M. Lombard, “Microstructural Property Relationships in Titanium Aluminides and Alloys,Metals and Materials Society”, Warrendale, 1991, pp. 605-622.
11.  Xicheng Zhao,a Wenjie Fu,b Xirong Yanga and Terence G. Langdonc,d,” Microstructure and properties of pure titanium processed by equal-channel angular pressing at room temperature”, Scripta Materialia 59, 2008,pp542–545

47-51

11.

Authors:

D.Vijaya Bhavani, Shaik Meeravali

Paper Title:

Development of Vision-Based Sensor of Smart Robot for Industrial Applications

Abstract:   Robotic manipulators are widely used to replace human operators in tasks that are repetitive in nature. However, there are many tasks that are non-repetitive, unpredictable, or hazardous to thehuman operators. Clear in gupanu clear power plant leak or exploring the extreme depths of ocean are just some examples. The most developed robot in practical use to day is the roboticarmandit is seen in applications throughout the world. Robotic are used to carryout working outer space where man can not survive and also used to do work in them edicalfieldsuch asconducting experiments without exposingthe researcher.In early days, robotic manipulators have been implemented in different control techniques like mechanical control andtheremotecontrolor tele-opertation. But with the adventofhigh per formance, anewway of control using mobile has been implemented whichi sintroduced in this project. All the abovesy stemsare controlled by the Microcontroller. Inour project weareusing thepopular8bitmicrocontrollerAT89S52. It’s a 40 pinmicrocontroller. The Microcontroller AT89S52is used to control the decimators. Two DC motorcar eased do riveter obo tinfrontd erect noise. Front the robot isalsodeveloped to given alert when any fire accidents occurs and to give alert. Here we are using fire sensor and IR-pairs for altering fire sensors and for obstacle detection also.

Keywords: Mobilerobots, Navigation, Robotvisionsystems, Intelligentrobots, Learningsystems, Cooperative systems .

References:
1.     Water Heating Base on Multithreading¡, Measurement and Control
2.     Technique, Vol. 28,No.8, pp. 79-81, 2009(inChinese).
3.     M.Gianluigi,G.Italian,¡GSMand GPRS performanceofIPSEC
4.     Datacommunicaitons, J. Ascensoet al. (eds.),e-Business
5.     Telecommunication Networks, pp125¨133,2006
6.     Managementsystem¡,LectureNotesin ComputerScience,Vol.
7.     M.Weske,¡Object-orienteddesign ofaflexibleworkflow
8.     1475,  Advances    in  Databases  and InformationSystems,pp.119, 1998.
9.     Scarpazza, P.Raghavan, D. Novo, F. Catthoor, and Diederik
10.  Verkest, ¡ Software simultaneous multi- threading, a technique to exploit task-level parallelism to improve instruction- and data-level.

52-55

12.

Authors:

M. Antony Sundarsingh, S.P.Victor

Paper Title:

Fiscal Implementation of Encoding and Decoding Schema for Graph Mining Technique using Realtime Data Base Management System

Abstract:    Graph mining in DatabaseBaseManagementSystem has become an important topic of research recently because of numerous applications to a wide variety of identification problems in current educational system. Nowadays Graphs play a vital role everywhere, occupying the social networks and mobile networks to biological net-works and the World Wide Web. Mining big graphs leads too many interesting applications including marketing, news groups, community mining, and many more. In this paper we describe a technique for the implementation of encoding schema problem for confidentiality management to a Graph Mining pattern. Our findings include designs to survey different aspects of graph mining and encoding-decoding environment, and provide a compendium for other researchers in the field. The results are revealed for selecting the optimized encoding and decoding schema for the cricket player identification based implemenation towards selection strategies. In the future we will extend our research to propose a Graph-Analysis Implementer for any real-time complex entities.

Keywords:   Graph mining, Graph pattern, Graph template, Graph learning..

References:
1.     J. Leskovec, K. J. Lang, A. Dasgupta, and M. W. Ma-honey. Statistical properties of   community structure in large social and information networks. In WWW, pages      695-704, 2008.
2.     Liu, F. Guo, and C. Faloutsos. Bbm: Bayesian browsing model from petabyte-scale data. In KDD, pages 537-546, 2009.
3.     Y. Low, J. Gonzalez, A. Kyrola, D. Bick son, C. Guestrin, and J. M. Heller stein. Graph lab:  A new framework for parallel machine learning. In UAI, pages 340-349,
2010.
4.     R. Gemulla, E. Nijkamp, P. Haas, and Y. Sisma-nis. Large-scale matrix factorization with distributed stochastic gradient descent. In Proceedings of the 17th ACM SIGKDD international  conference on Knowledge discovery and data mining, pages 69-77. ACM, 2011.
5.     Ghoting, R. Krishnamurthy, E. P. D. Pednault,B. Reinwald, V. Sindhwani, S. Tatikonda, Y. Tian,and S. Vaithyanathan. System: Declarative machine learning on map reduce. In ICDE, pages 231-242, 2011
6.     U. Kang, H. Tong, J. Sun, C.-Y. Lin and C. Faloutsos.Gbase: an ancient analysis platform for large graphs.VLDB J., 21(5):637-650, 2012.
7.     Dr.S.P.Victor,Antony Sundar singh:” Design and Development of Abstractness in Graph Mining Technique using  Structural Datum “-IJSCE-Vol-3,Issue-3-Jun
2013.
8.     http://www.elsevierdirect.com/companions/9780123814791 /chapters_from_the_second_edition/chapter_9.pdf

56-58

13.

Authors:

M  Basavaraju, S Ranganatha

Paper Title:

Effect of Soft Material Hardness and Hard Material Surface Morphology on Friction and Transfer Layer Formation; Lubricated Condition

Abstract:      Hot and cold forming of metals is carried out in industry for manufacturing engineering components. Such manufacturing processes employ dies, whose surface condition is one of the factors which characterize the surface finish of engineering components. The surface finish of engineering components is largely influenced by the tribological phenomenon at die and components interface.  Lubrication, morphology and hardness of die surface are found to control surface finish of the products.  In the present investigation a pin-on-plate sliding tester was used to identify the effect of surface morphology, lubrication and hardness on co-efficient of friction and transfer layer which characterizes the tribological behaviour. The morphology of mild steel (EN8)  plate surfaces were modified by employing three different surface modification methods like grinding (silicon carbide wheel polishing),  shot blasting and  electric discharge machining methods. Surface roughness parameters which characterize the morphology of the steel plates were measured using a three dimensional optical profilometer. Role of hardness is studied by employing lead, copper and Aluminum (Al6082) pins which were slid against steel plates. Experiments were conducted for plate inclination angles of 1, 1.5,2 and 2.5 degrees.  Normal load was varied from 1 to 150N during the tests. Experiments were conducted under lubricated condition in ambient environment. Scanning electron microscope was used to study the formation of transfer layer on plate and pin surfaces. It was observed that the co-efficient of friction and transfer layer formation were found to depend on the surface morphology of the harder surface under lubricated condition. The quantum of transfer layer formation on the surfaces is found to increase with increase in surface roughness.  .

Keywords:    friction, lubrication, hardness, surface morphology and transfer layer formation

References:
1.        Y. Tsuya, Microstructure of wear, friction and   solid      lubrication, Tech. Rep. of   Mech. Engg.  Lab., 3(81), Tokyo, Japan, 1975.
2.        R. Holm, P. Charsley   A study of indentation in an Al Cu  Mg alloy, Materials Science and Engineering: A, Volume 229( 1–2), (1997), 10-22
3.        K.L.Johnson, Proceedings of institute of Mechanical Engineers, volume 196(1982)363-378.
4.        S.Ranganatha, Ph.D. thesis, 2008, Transfer layer formation and      friction in extrusion of aluminium; an experimental study using a high temperature vacuum based pin-on-disc machine. Department of Mechanical engineering, IISc, Bangalore, India
5.        R.D.Mindlin, Journal of Applied Mechanics, volume 71(1949)259
6.        J. F. Archard, Elastic deformation and the laws of friction, Proceedings of Royal. Society of London, Ser. A, 243 (1957), 190-205.
7.        J.A.Greenwood and J.B.P.Williamson, Proceedings of Royal. Society of London, Ser. A, 295 (1996) 300-319.
8.        F P Bowden, D Tabor, The friction and lubrication of solids, Clarendon press, Oxford, UK, 1954.
9.        H E Staph,P.M Ku, H J Carper, Effect of Surface Roughness and Surface Texture   on Scuffing Mechanism and Mechine Theory 8(1973) 197-208.
10.     S.Abtahi,, Ph.D Thesis 1995 ,Department of  Mechanical Design and materials Technalogy,The Norwegian institute of Technalogy,University of Trondenhim, Trondenhim, Norway,1995.
11.     W.W.Thedja, K.B.Muller and D.Ruppin, Proceedings of the 5th international Extrusion Technalogy seminar, Chicago (1992)467-474.
12.     J. F. Archard and W. Hirst, The wear of metals under unlubricated conditions, Proceedings of Royal. Society of London A, 236 (1956) 397-410.
13.     Azushima, M. Sakuramoto, Effects of Plastic Strain on Surface Roughness and Coefficient of Friction in Tension-Bending Test, CIRP Annals - Manufacturing Technology, Volume 55(1), (2006), 303-306.
14.     M M Koura, The effect of surface texture on friction mechanisms, Wear 63(1980) 1-12.
15.     J. R. Whitehead, Surface deformation and friction of metals at light loads, Proc. Royal. Soc., London, A, 201, 1950, 109-124.
16.     M. Kerridge and J. K. Lancaster, The Stages in a Process of Severe Metallic Wear Proc. R. Soc. Lond. A , 236(1956 ) 250-264.
17.     T. Nellemann, N. Bay and T. Wanheim, Real area of contact and friction stress – The role of trapped lubricant, Wear 43(1), 1977, 45-53.
18.     Theng-Sheng Yang, Prediction of surface topography in lubricated sheet metal   forming, International Journal of Machine Tools and Manufacture, Volume 48 ( 7–8)( 2008), 768-777.
19.     A. Rigney and J. P. Hirth, Plastic deformation and sliding friction of metals, wear, 53(2), 1979, 345-370.
20.     N. P. Suh and H. C. Sin, The genesis of Friction, Wear, 69(1), 1981, 91-114

59-66

14.

Authors:

G.K.Viju, Md. Jassim Mohammed Jassim

Paper Title:

Ensuring Better Service Assurance using Scalable Monitoring and Resource Management

Abstract:    In this project a scalable monitoring system was constructed for monitoring the network in both online and offline. The goal of this system is continuous monitoring of network status and its resources like host, router etc. in a scalable manner to ensure proper network operation. The monitoring system is scalable in terms of network size, speed and number of customers subscribed to value-added services. It provides measurements for network provisioning, dynamic resource allocation, route management and in-service verification of services..

Keywords:  IP, Monitoring, Traffic Engineering, Differentiated Services, Active/Passive Measurements, Scalability

References:
1.        D.Goderis et al., “Service Level Specification Semantics and Parameters,” Internet draft, draft-tequila-sls-02.txt.
2.        S. Blake, D. Black, et al., “An Architecture for Differentiated Services”, Informational RFC-2475.
3.        Feldman et al., “Netscape: Traffic Engineering for IP Networks”, IEEE Network Magazine, Vol. 14, No. 2, pp. 11-19, March/April 2000.
4.        “A Scalable Real-time Monitoring System for Supporting Traffic Engineering” Abolghasem Asgari, Panos Trimintzios, Mark Irons, George Pavlou, Richard Egan, Steven V. den Berghe.
5.        www.snmplink.org

67-69

15.

Authors:

N. Venkateswara Rao, M.V.S. Murali Krishna, P.V.K.Murthy

Paper Title:

Effect of Injector Opening Pressure and Injection Timing on Exhaust Emissions and Combustion Characteristics of High Grade Low Heat Rejection Diesel Engine with Tobacco Seed Oil Based Biodiesel

Abstract:  Experiments were conducted to determine exhaust emissions and combustion characteristics of a conventional diesel engine (CE) and high grade low heat rejection diesel engine (LHR) (with air gap insulated piston with superni (an alloy of nickel) crown, air gap insulated liner with superni insert and ceramic coated cylinder head)with different operating conditions [normal temperature and pre-heated temperature] of tobacco seed oil based biodiesel with varied injection timing and injector opening pressure. Exhaust emissions [smoke and oxides of nitrogen] and combustion characteristics [peak pressure, time of occurrence of peak pressure and maximum rate of pressure] were determined at peak load operation of the engine fuelled with  tobacco seed oil based biodiesel with different versions of the engine. Comparative studies on exhaust emissions and combustion characteristics were made between different versions of the engine with biodiesel operation with varied engine parameters. Smoke levels decreased and NOx levels increased with LHR engine with biodiesel operation on LHR engine. Advanced injection timing and increase of injector opening pressure reduced exhaust emissions from LHR engine with biodiesel operation.

Keywords:  Alternate Fuels, Vegetable Oils, Biodiesel, LHR engine, Exhaust emissions, Combustion characteristics

References:
1.        Matthias Lamping, Thomas Körfer, Thorsten Schnorbus, Stefan Pischinger, Yunji Chen : “Tomorrows Diesel Fuel Diversity – Challenges and Solutions,”,  SAE 2008-01—1731, 2008.
2.        Agarwal,A.K, “Bio-fuels (alcohols and biodiesel) applications as fuels for internal combustion engines,” International Journal Energy Combustion Science, vol.33, 2006, Pp.233-271
3.        Babu, A.K. and Devarajane,G. “Vegetable oils and their derivatives as fuels for CI engines: an overview,” SAE Paper No.2003-01-0767, 2003.
4.        Surendra, R, K., Suhash, and D.V. “Jatropha and karanj bio-fuel: as alternate fuel for diesel engine,”  ARPN Journal of Engineering and Applied Sci, vol.3 (1), 2008.
5.        Devan, P.K. and Mahalakshmi, N.V. “Performance, emission and combustion characteristics of poon oil and its blends in a DI diesel engine,” Fuel, vol.88, 2009,       pp.861-870.
6.        Misra, R.D., Murthy, M.S., “Straight vegetable oils usage in a compression ignition engine—A review,” Renewable and Sustainable Energy Reviews, vol.14, 2010, pp.3005–      3013.
7.        Bari, S., Lim, T.H.,Yu, C.W, “Effect of preheating of crude palm oil on injection  system, performance and emission of a diesel engine,”  Renewable Energy, vol.27 (3),  2002, pp. 339-351. 
8.        Nwafor, O.M.L., “The effect of elevated fuel inlet temperature on the performance of diesel engine running on a neat vegetable oil at constant speed conditions,” Renewable energy, vol.28,  2003, pp.171-180.  
9.        Senthil Kumar, M., Kerihuel, A., Bellettre, J. and Tazerout, M. (2005). Experimental investigations on the use of preheated animal fat as fuel in a compression ignition engine. Renewable Energy, 30, 2314-2323.   
10.     Agarwal, D., Agarwal, A.K., “Performance and emissions characteristics of jatropha oil (preheated and blends) in a direct injection compression ignition engine,”  Int. J. Applied Thermal Engineering, vol. 27, 2007, pp.2314-23.
11.     Canakei, M., “Performance and emission characteristics of biodiesel from soyabeen  Oil,” Proc. I Mech E, Part-D, Journal of Automobile Engineering, vol.219, 2005, pp.       915-922. 
12.     Jiwak Suryawanshi, “Performance and emission characteristics of CI engine fueled by  coconut oil methyl ester,”   SAE Paper No. 2006-32-0077, 2006.
13.     Marek Tatur, Harsha Nanjundaswamy, Dean Tomazic, Matthew Thornton, “Effects of Biodiesel Operation on Light-Duty Tier 2 Engine and Emission Control Systems,” SAE 2008-01-0080, 2008.
14.     Murugesan, A., Umarani, C., Subramanian,R., Nedunchezhian, N., “Bio-diesel as an alternate fuel for diesel engines,”  Renewable and Sustainable Energy Reviews, vol. 3(3), 2009, pp.653-662. 
15.     Venkatramn. And Devaradjane, G., “Experimental investigation of performance and emission characteristics of diesel-pungam oil, methyl esters diesel blends fueled DI engine at optimum engine operating parameters,” International Journal of Green energy and env, vol.1, 2010, pp.7-12.  
16.     Heywood, J.B, “Fundamentals of Internal Combustion Engines,” Tata McGraw Hills, New York, 1988.
17.     Celikten, I. “An experimental investigation of the effect of the injection pressure          on the engine performance and exhaust emission in indirect injection diesel engines,”  Applied          Thermal Engineering, vol.23, 2003, pp.2051–2060.
18.     Cingur, Y., & Altiparmak, D. “Effect of cetane number and injection pressure on a DI diesel engine performance and emissions,” Energy Conversion and Management, vol.44,  2003, pp.389–397.
19.     Hountalas, D.T., Kouremenos, D.A., Binder, K.B., Schwarz, V., & Mavropoulos, G.C. “Effect of injection pressure on the performance and exhaust emissions of a heavy duty DI diesel engine,”  Warrendale, PA , SAE Technical Paper No. 2003-01-              0340, 2003.
20.     Venkanna, B.K., & Venkataramana, R.C. “Influence of fuel injection rate on the performance, emission and combustion characteristics of DI diesel engine running on calophyllum inophyllum linn oil (honne oil)/diesel fuel blend,”  SAE Technical Paper                  No. 2010-01-1961,, 2010..
21.     Chandrakasan Solaimuthu and Palani Swamy Govindaraju, “Effect of injection timing on performance, combustion and emission characteristics       of diesel engine using mahua oil methyl ester,”  Journal of Scientific and Industrial Research, vol.71, 2012, pp. 69-74.
22.     Ratna Reddy, T., Murali Krishna, M.V.S., Kesava Reddy, Ch and Murthy, P.V.K. “Performance evaluation of mohr oil based biodiesel in low           grade low heat  rejection diesel  engine,”  International Journal of Innovative Research in Science,  Engineering and Technology, vol.1 (1), 2012, pp. 80-94.
23.     Murali Krishna, M.V.S., Durga Prasada Rao, N., Anjeneya Prasad, B. and Murthy, P.V.K. “Improving of emissions and performance of rice             brawn oil in medium grade low       heat rejection diesel engine,” International Journal of Renewable Energy Research, (Turkey),  vol.3 (1), 2013, pp.98-108.
24.     Janardhan, N., Murali Krishna, M.V.S., Ushasri, P. and Murthy, P.V.K. “Comparative performance, emissions and combustion characteristics of      jatropha oil in crude form and biodiesel form in a medium grade low heat rejection diesel engine,” International Journal of Soft Computing and Engineering, International Journal of Innovative Technology and Exploring Engineering, vol.2 (5), 2013,  5-15.
25.     Parlak, A., Yasar, H., ldogan O. “The effect of thermal barrier coating on a  turbocharged Diesel engine performance and exergy potential of the exhaust gas,”  Energy Conversion and Management, vol.46 (3), 2005, pp.489–499.
26.     Ekrem, B., Tahsin, E., Muhammet, C. “Effects of thermal barrier coating on gas emissions and performance of a LHR engine with different injection   timings and valve adjustments,”  Journal of Energy Conversion and Management,   vol.47, 2006, pp.1298-             1310.
27.     Ciniviz, M., Hasimoglu, C., Sahin, F., Salman, M. S. “Impact of thermal barrier coating application on the performance and emissions of a turbocharged diesel engine,” Proceedings of The Institution of Mechanical Engineers Part D-Journal Of Automobile Eng, vol.222 (D12),                2008, pp. 2447–2455
28.     Murali Krishna, M.V.S., Chowdary, R.P., Reddy, T.K.K.  and Murthy,P.V.K “Performance evaluation of waste fried vegetable oil in la low grade low heat rejection diesel engine,”  International Journal of Research in Mechanical Engineering and    Technology, vol.2 (2), 2012, pp.        35-43.
29.     Kesava Reddy, Ch., Murali Krishna, M.V.S., Murthy, P.V.K. and Ratna    Reddy,T. “Performance evaluation of a low grade low heat rejection   diesel engine with crude jatropha oil,”  International Scholarly Research Network (ISRN) Renewable Energy (USA),  Article ID 489605, 2012, pp.1-10.
30.     Ratna Reddy, T., Murali Krishna, M.V.S., Kesava Reddy, Ch. and Murthy, P.V.K. “Comparative performance of ceramic coated diesel engine with Mohr oil in crude and biodiesel form,”  International Journal of Engineering and Advanced Technology (CSIR), vol.2 (3), 2012, pp.588-     596.
31.     Parker, D.A. and Dennison, G.M. “The development of an air gap insulated Piston”, SAE Paper No. 870652, 1987.
32.     Rama Mohan, K., Vara Prasad, C.M. and Murali Krishna, M.V.S. “Performance of a low heat rejection diesel engine with air gap insulated piston,” ASME Journal
of Engineering for Gas Turbines and Power, vol.121 (3),  1999, pp.530-540.
33.     Ratna Reddy, T., Murali Krishna, M.V.S., Kesava Reddy, Ch  and Murthy, P.V.K.“Performance evaluation of a medium grade low heat rejectiondiesel engine with Mohr oil,” International Journal of Recent Advances in Mechanical Engineering (IJMECH), vol. 1(1), May 2012, pp. 1-17.
34.     Chennakesava Reddy, Murali Krishna, M.V.S., Murthy, P.V.K., and Ratna Reddy,T.“Potential of low heat rejection diesel engine withcrude pongamia oil,” International Journal of Modern Engineering Research (IJMER), vol.1 (1), 2011, pp.210-224.
35.     Janardhan, N., Murali Krishna, M.V.S., Ushasri, P. and Murthy, P.V.K. “Potential of a medium low heat rejection diesel engine with  crude jatropha oil,” International Journal of Automotive Engineering and Technologies, vol.1 (2), 2012, pp. 1-16.
36.     Kesava Reddy, Ch., Murali Krishna, M.V.S., Murthy, P.V.K. and Ratna Reddy, T. “A comparative study of the performance evaluation of a low heat rejection engine with three different levels of insulation with crude pongamia oil operation,” Canadian Journal on Mechanical Sciences & Engineering, vol.3 (3), 2012, pp. 59-71.
37.     Murali Krishna, M.V.S., Chowdary, R.P., Reddy, T.K.K and Murthy, P.V.K.”  A comparative study of the performance of a low heat rejection diesel engine with three different levels of insulation with waste fried vegetable oil operation,” International Journal of Science & Technology, vol.2 (6), June 2012,pp 358-371. 
38.     Ratna Reddy, T., Murali Krishna, M.V.S., Kesava Reddy, Ch. and Murthy, P.V.K. “Comparative performance of different versions of the low heat rejection diesel Engine with mohr oil based bio-diesel,” International Journal of Research & Reviews in Applied Sciences (IJRRAS), vol.1 (1), 2012, pp. 73-87. 
39.     Velikovic, V.B., Lakicevic, S.H., Stamenkovic, O.S., Todorovic, Z.B. and  Lazic., M.L. “Biodiesel production from tobacco seed oil (nicatiana tabacom.L) seed oil wit high content of free fatty acids,” Fuel, vol.85, 2006, pp. 2671-2675.
40.     Tapasvi D, Wiesenborn D, Gustafson C “Process Model for Biodiesel Production  from various Feedstock’s,” Trans. ASAE, vol.48(6),  2005, pp.2215- 2221.
41.     Jindal S, Bhagwati PN, Narendra SR, “Comparative Evaluation of Combustion, Performance and Emissions of Jatropha Methyl Ester and Karanja Methyl Ester in a Direct Injection Diesel Engine,” Energy Fuels, vol.24, 2010, pp. 1565-1572.
42.     Rao, P.V. “Effect of properties of Karanja methyl ester on combustion and NOx emissions of a diesel engine,”  Journal of Petroleum Technology and Alternative Fuels       Vol. 2(5), 2011, pp. 63-75.
43.     Murali Krishna, M.V.S. “Performance evaluation of low heat rejection diesel engine with alternate fuels.,” PhD Thesis, J.N.T. University, Hyderabad, 2004.
44.     Gattamaneni, L.N., Saravanan, S., Santhanam, S. and Kuderu, R. “Combustion and emission characteristics of diesel engine fuelled with rice bran oil methyl ester and its diesel blends,” Thermal Science, vol.12, 2008, pp139–150.
45.     Aziz, A.A., Said, M.F., & Awang, M.A.Performance of palm oil-based biodiesel fuels in a single cylinder direct injection engine. Palm Oil Developments, vol.42, 2012, pp.15–27.
46.     Venkanna, B.K and Venkataramana Reddy, C. “Effect of injector opening pressure on erformance, emission and combustion characteristics of DI diesel engine fueled with diesel and honne oil methyl ester,” International Journal of Environmental   Progress and Sustainable Energy, vol.32 (1), 2013, pp148-155.
47.     Gumus, M.A. “Comprehensive experimental nvestigation of combustion and  heat release characteristics of a biodiesel (hazelnut kernel oil methyl ester) fueled  direct injection compression ignition engine,” Fuel, vol.89, 2010, pp.2802– 2814.
48.     Yamane K, Ueta A, Shimamoto Y, “Influence of Physical and Chemical Properties of Biodiesel Fuel on Injection, Combustion and Exhaust Emission Characteristics in a DI-CI Engine,” Tran. Of the Jap. Soc. Mech. Eng., vol.32 (2), 2001, pp. 25-30.

70-79

16.

Authors:

Shivani Dhiman, A.J Singh

Paper Title:

Tesseract Vs Gocr A Comparative Study

Abstract:   Optical Character Recognition (OCR) is a technique used to convert scanned images into machine readable text formats. Different types of Optical Character Recognition (OCR) Tools are used in market from earlier times have their own strengths and weaknesses. They provided different results on the basis of different metrics or parameters. But in this paper we are going to compare two open source tools i.e. Tesseract and GOCR. This paper firstly provides the introduction of open source tools Tesseract and GOCR, architecture of Tesseract and description about their working. In this paper, Tools are compared on the basis of Precision as well as Accuracy by considering different parameters that are Image Type, Resolution, Brightness and Font Type.

Keywords: Optical Character Recognition (OCR), Open Source, Tesseract and GOCR

References:
1.        O.PSharma, M.K Ghose, K.B Shah and B.K Thakur, “Recent Trends and Tools for Feature Extraction in OCR Technology.” International Journal of Soft Computing and Engineering (IJSCE), 2013, ISSN: 2231-2307, Volume-2, Issue-6.
2.        R.Smith,“An Overview of the Tesseract OCR Engine.” In proceedings of Document analysis and Recognition, ICDAR 2007, IEEE Ninth International Conference3.
3.        The Tesseract open source OCR engine, http://code.google.com/p/tesseract-ocr.
4.        GOCR Reference, http://www.redhat.com/archives/blinux-list/2004-March/msg00201.html
5.        Stromme and R. Carlson,“Minimally Supervised Methods to Correct Optical Character Recognition.” Swarthmore College, Swarthmore, PA 19081.
6.        T. Kanungo, G.A Marton and O. Bulbul,“Omni Page vs. Sakhr: Paired Model Evaluation of Two Arabic OCR Products.”Centre for Automation Research University of Maryland College Park, MD 20742.
7.        R.D Lins and N.F Alves, “A New Technique for Accessing the Performance of OCRs.” IADIS International Conference on Applied Computing, 2005.
8.        C.A.B Mello and R.D Lins,“A Comparative Study on OCR Tools.” Vision Interface '99, Trois-Rivières, Canada, 19-21 May.
9.        R. Mithe, S. Indalkar and N. Divekar,“Optical Character Recognition.” International Journal of Recent Technology and Engineering (IJRTE) 2013, ISSN: 2277-3878, Volume-2, Issue-1.
10.     Patel, A. Patel and D. Patel,“Optical Character Recognition by Open Source OCR Tool Tesseract: A Case Study.” International Journal of Computer Applications (0975 – 8887) Volume 55– No.10, October 2012.
11.     GOCR open source OCR engine, http:// jocr.sourceforge.net/‎.

80-83

17.

Authors:

Merugu Suresh, Kamal Jain

Paper Title:

Sub Pixel Analysis on Hypothetical Image by Using Colorimetry

Abstract:  Color, more generally it is a signature associated to each object which makes it recognizable and is highly dependent on the nature of the light source, which can be either natural (sun) or artificial (light bulbs). As the perception of color involves a complex processing by the brain, and could not be restricted to something like “the spectrum of the light captured by the human eye”. The increased use of color has brought with its new challenges. In order to meaningfully record and process color images, it is essential to understand the mechanisms of color vision and the capabilities and limitations of color imaging devices. It is also necessary to develop algorithms that minimize the impact of device limitations and preserve color information as images are exchanged between devices. The colours in real world have sharp boundaries we know exactly where a colour starts and where it ends. But when we take image of such an area the image is expressed in pixels, each pixel representing one value often should be a grey value in each band. These pixels don’t express the boundaries exactly as sharp as they are in reality; we observe a transition from one colour to some colour other than the second colour.

Keywords: Colorimetry, CIE Chromaticity Diagram, Tristimulus values, Pixel Analysis, Statistical Measures, Spectral colors, Color Matching Function.

References:
1.        K, Csuti P, Schanda J (2007) “Colour Appearance of Metameric Lights and Possible Colorimetric Description”, Poster on the CIE Expert Symposium on Appearance, Paris, France, Oct 2006, CIE x032.
2.        CIE Techn. Report (2006): Fundamental Chromaticity Diagram with Physiological Axes - Part 1 Publ. CIE 170.
3.        CIE TC 1-36 draft technical report (2006) Development of chromaticity diagrams based upon the principles of the CIE XYZ system .
4.        CIE (1926), Commission Internationale de l’ ´ Eclairage Proceedings 1924.Cambridge: Cambridge University Press.
5.        CIE Draft Standard DS 014-1.2/E (2004): Colorimetry - Part 1: CIE Standard Colorimetric Observers.
6.        CIE (1932), Commission Internationale de l’ Eclairage Proceedings 1931.Cambridge: Cambridge University Press.
7.        Csuti P, Schanda J (2009) A better description of metameric experience of LED clusters.  Light & Lighting Conf. Budapest.
8.        De Vries, H. (1948), Physica14, 319–348.
9.        Eisner, A., MacLeod, D. I. A. (1981), J. Opt. Soc. Am.71, 705–718.
10.     From Schanda, J (2007) CIE colorimery, in Colorimetry, Understanding the CIE system, ed. J. Schanda, pp. 25-78. Wiley 2007.
11.     Grassmann HG (1853) Zur Theorie der Farbenmischung/Theory of compound colours.      Original published in Poggendorf’ Ann. Phys.,89 69  original translation in English in       Phil. Mag. 4(7) 254-264 1854.
12.     Guild, J (1931) The colorimetric properties of the spectrum, Phil. Trans. Roy. Soc. Lond., Ser. A, 149-187.
13.     Guild, J Philos. Trans. R. Soc. (1931), London, Ser.A230, 149–187.
14.     Hunt RWG (1998) Measuring colour, third edition. Fountain Press, England.
15.     Judd, D. B. (1951), Proceedings of the TwelfthSession of the CIE, Stockholm.Paris: Bureau Central CIE,p.1–60.
16.     Newton, I. (1704), Opticks: Or, A Treatiseof the Reflexions, Refractions, Inflexons and Colours of Light. London: Sam. Smith and Benj Walford.
17.     Sándor N, Schanda J (2006) Visual colour rendering based on colour difference evaluations. Lighting Res. & Technol. 38/3 225-239.
18.     Smith, V. C., Pokorny, J., Zaidi, Q. (1983),in J. D. Mollon, L. T. Sharpe (Eds.), Colour Vision: Physiology and Psychophysics.Lon-don: Academic Press.
19.     Speranskaya, N. I. (1959),Opt. Spectrosc.7, 424–428.20.     Sperling, H. G. (1958), Visual Problems of Colour, Vol. 1. London: Her Majesty’s  Stationery Office, pp. 249–277.
21.     Stiles, W. S., Burch, J. M. (1959),Opt. Acta 6, 1–26.
22.     Stiles, W. S. (1955),Opt. Acta2, 168–181.
23.     Stockman, A., Sharpe, L. T. (1999), in K. Gegen furtner, L. T. Sharpe (Eds.), Color Vision: From Genes to Perception. Cambridge: Cambridge University Press, pp. 53–87.
24.     Vos, J. J. (1978),Color Res. Appl.3, 125–128.
25.     Wagner, G., Boynton, R. M. (1972), J. Opt. Soc. Am.62, 1508–1515.
26.     Wright, W. D. (1928-29),Trans. Opt. Soc.30,141–164.
27.     Wyszecki, G., Stiles, W. S. (1982), Color Science: Concepts and Methods, Quantitative Data and Formulae,(2nded.), NewYork: Wiley.
28.     Stockman, A., Sharpe, L. T. (2000),Vis. Res.40, 1711–    1737.

84-91

18.

Authors:

Naseer M. Basheer, Mustafa Mushtak Mohammed

Paper Title:

Image Denoising Using FPGA Based 2D-DWT Architecture

Abstract:   In this work, a simple design is implemented for removing noise from gray scale images, that depends on Two Dimensional Discrete Wavelet Transform (2D-DWT) and a threshold stage. The proposed design is used to remove two types of noise (the Salt and pepper noise, and the Gaussian noise) from the corrupted images. The proposed architecture is based on lifting scheme approach using the (5/3) wavelet filter. This architecture consists of a control unit, a processor unit, two on-chip internal memories to speed up system operations, and an on-board off-chip external memory (Intel strata parallel NOR flash PROM). The proposed architecture is designed and synthesized with the VHDL language and then implemented on the FPGA Spartan 3E starter kit (XC3S500E) to check validation of the results and performance of the design.

Keywords:   Two Dimensional Discrete Wavelet Transform (2D- DWT), image denoising,  lifting scheme, (5/3) wavelet  filter, and FPGA applications.

References:
1.        w. chang, y. lee, w. peng, and c. lee; "a line-based, memory efficient and programmable architecture for 2d dwt using lifting scheme",  ieee, 0-7803-6685-9/01/2001.
2.        die, m. zeghid, t. saidani, m. atri, b. bouallegue, m. machhout and r. tourki; "multi-level discrete wavelet transform architecture design", proceedings of the world congress on engineering , london, u.k, vol 1,wce 2009.
3.        w. sweldens; "the lifting scheme: a construction of second generation wavelets", tech. rep. 1995. industrial mathematics initiative, department of mathematics, university of south carolina.
4.        l. donoho and i. m. johnstone; "ideal spatial adaptation by  wavelet shrinkage", biometrika, vol. 81, NO. 3 (AUG., 1994), PP. 425-455.
5.        B. Toufik and N. Mokhtar; (2012). "The Wavelet Transform for Image   Processing Applications", Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology, Dr. Dumitru Baleanu (Ed.), ISBN: 978-953-51-0494-0, InTech.
6.        Y. M. Hawwar, A. M. Reza, and R. D. Turney; "Filtering (Denoising) In The Wavelet Transform  Domain", Department Of Electrical       Engineering and Computer Science University  Of Wisconsin-Milwaukee, Core Solutions Group, XILINX, INC., 2002.
7.        J. Joshi, and N. Nabar; "Reconfigurable Implementation of Wavelet based Image Denoising", IEEE international Midwest symposium on Vol.1, pp. 475-478, 2006.
8.        M. I. Mahmoud, M. I. Dessouky, S. Deyab, and F. H. Elfouly; "Signal Denoising by Wavelet Packet Transform on FPGA Technology", UBICC journal. Vol. 3, pp. 54-58, January 2008.
9.        M. Maamoun, M. Neggazi, A. Meraghni, and D. Berkani; "VLSI Design of 2-D Discrete Wavelet Transform for Area-Efficient and High-Speed Image Computing", World Academy of Science, Engineering and Technology, 44, 2008.
10.     K. Andra, C. Chakrabarti, and T. Acharya, "A VLSI Architecture for Lifting-Based Forward and Inverse Wavelet Transform", IEEE Transactions On Signal
rocessing, Vol. 50, No. 4, April 2002.
11.     G. Strang, and T. Nguyen; (1996),"Wavelets and Filter   Banks", Wellesley, Massachusette, Wellesley-cambridge Press.
12.     R. J. E. Merry, "Wavelet Theory and Applications", A literature study, Eindhoven University of Technology, Department of Mechanical Engineering, June 7, 2005.
13.     B. Ergen (2012). "Signal and Image Denoising Using Wavelet Transform",  Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology, Dr. Dumitru Baleanu (Ed.), ISBN: 978-953-51-0494-0, InTech.

92-97

19.

Authors:

K. Chenna Kesava Reddy, P.Venkatrao

Paper Title:

Real Time Field Monitoring and Controlling System

Abstract:  Modern agriculture management relies strongly on many different sensing methodologies to provide accurate information on temperature, status of the land, ambient pressure in the field etc. Almost every sensing technique may find an application in agriculture. A real time field monitoring and controlling system is implemented using ARM controller in the present study. Experiments were carried out at lab scale to sense temperature, nature of the land and pressure. All these parameters will be uploaded to the server and field information can be monitored.

Keywords:   Agricultural Field monitoring, ARM controller, Server, Sensor.

References:
1.        K. Nirmal Kumar, P.Ranjith, and R.Prabakaran, “Real Time Paddy Crop Field Monitoring Using Zig bee Network”, International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT), pp 1136 – 1140, 2011.
2.        Correia, V. Realinho, R. Braga, J. Turégano, A. Miranda, J. Gañan,  Development of a Monitoring System for Efficient Management of Agriculture Resources, Proceeding of the VIII International Congress on Project Engineering, 2004.
3.        Zhang xihai, Zhang changli Fang junlong. Smart Sensor Nodes forWireless Soil Temperature Monitoring Systems in Precision Agriculture 2009. 237-241.
4.        Jeonghwan Hwang, Changsun Shin, and Hyun Yoe “Study on anAgricultural Environment Monitoring Server System using Wireless Sensor Networks”, 2010.
5.        Bogena H R, Huisman J A, OberdÊrster C, et al. Evaluation of a low cost soil water content sensor for wireless network applications [J].Journal of Hydrology,
2007, 32-42.
6.        Khalid EL-Darymli, Faisal Khan, Mohamed H. Ahmed“Reliability    Modeling of Wireless Sensor Network for Oil and Gas Pipelines Monitoring”, Sensor & Transducers  Journal, Vol.106, Issue 7, July 2009, pp. 6-26, ISSN 1726.
7.        Hirafuji M, Fukatsu T. 2002. Architecture of Field Monitoring Servers.Proceedings of the Third Asian Conference for Information Technology in Agriculture. P 405-409.
8.        Laurenson M, Kiura T, Ninomiya S. 2002. Providing agriculturalModels with mediated access to heterogeneous weather databases.Appl. Engin. Agric. 18:617-625.

98-100

20.

Authors:

OTOBO Firstman Noah, BAAH Barida, TAYLOR Onate Egerton

Paper Title:

Evaluation of Student Performance Using Data Mining Over a Given Data Space

Abstract:   The volume of data generated every year in our institutions is enormous, due to this large volume of data there is the need to provide a efficient system support to aid in good decision making process; this is what necessitated this research paper which is all about the evaluation of student performance using data mining technique over a given data space. In this paper we are going to look at the various data mining techniques, data mining algorithms and the k-means clustering technique. In this paper, the performance evaluation of students, were presented using data mining technique and cluster checking. The system examined students who gained admission into the University of Port-Harcourt through the University Matriculation Examination (UME) and through Basic studies programme with the aim of finding out variations in their performance when they graduate from the university. The evaluation was done using data mining technique to find out the ratio that falls into grouping of the grading in the various classes using the cumulative grade point average (CGPA) and the students who failed out. The system was able to cluster, analyze and report the relative performance of each of the groups of the students used in the research work. Finally, the system was implemented using Apache, MySQL, PHP, internet explorer, NetBeans IDE 6.8 and XAMPP web server.

Keywords:    Data Mining, Data Space, Clustering, Database

References:
1.     Karoline Schönbrunn, Andreas Hilbert (2006), Data Mining in Higher Education.pg 489-496
2.     Michael V. Mannino (2004), Database Design, Application Development, and Administration
3.     Berzal, Fernando, Cubero, Juan-Carlos, Marin Nicolas, Serrano, Jose-Maria (2001). TBAR: An Efficient Method for Association Rule Mining in Relational Databases. Knowledge Engineering  37: 47-64.
4.     Comaford, Christine. (1997). Unearthing Data Mining Methods, Myths. PC Week 14, no. 1 (January 6): 65.
5.     Goebel, Michael and Gruenwald, Le. (1999), A Survey of Data Mining and Knowledge Discovery Tools. SIGKDD Explorations, ACM SIDKDD 1, no. 1 (June): 20-33.
6.     Ramakrishna R. and Johannes G. (2003) Database Management System. McGraw-Hill
7.     Ansari E., G.H. Dastghaibifard, M. Keshtkaran, H.Kaabi  (2008).Distributed  Frequent Itemset Mining using Trie Data Structure.

101-104

21.

Authors:

Viralkumar Solanki, Sanjay R Joshi, Jiten Chavda, Kashyap Mokariya

Paper Title:

Simulation of Induction Furnace and Comparison with Actual Induction Furnace

Abstract:    In this paper, at first, a matlab simulation of induction furnace model optimized resonant capacitor is designed for a practical induction furnace with parallel resonant inverter. Then rectifier and inverter snubber circuit are designed and voltage, current, THD and power were measured. This measured value is compared with actual working industry furnace data and conclusion is made that when furnace is not operate at full load that time its power factor is very low and THD is high.

Keywords:     Induction furnace, Harmonics, THD, Simulation.

References:
1.        Barry Davis and Brooks Simpson,Induction Heating Hand Book.1979. McGraw Hill Book company (UK).
2.        VRudnev, D. Loveless, R. Cook, M.Black,hand Book of Induction Hearing, 2003, Marchel Dekker. Inc, New York. Basel.
3.        JahonKassakian, J.G. Schlecbt and M. F. Vaghese, Principles of Power Electronics, 1991 ,Addison Wesley.
4.        N. Mohan, T. M. Undeland and W. P. Robbins, Power Electronics Converters Applications and Design.2003, Hamilton Printicompany (USA)..
5.        J. Lee, S. Lirn, K. Nam and D. choi, “An Optimal Selection of Induction Heater Capacitance Considering Dissipation Loss caused by ESR,” Power Electronics Conference and Exposition, APEC 04, Vol. 3, pp.1858-1863,2004.
6.        M. Espi, A. E. Navarro, J. Maicas, J Ejea and S. Casans, “Control Circuit Design of the L-LC Resonant Inverter for Induction Heating.” IEEE Power Electronics Specialists Conference PESC 00,Vol.3,pp.1430-1435,2000.
7.        “Harmonic distortion in a steel plant with induction furnaces” I.Zamora1, I. Albizu2, A. J. Mazon, K. J. Sagastabeitia, E. Fernandez.Department of Electrical Engineering University of the Basque Country Alda.
8.        Hasan EROĞLU, Musa AYDIN,” Simulation of a large electric distribution system having intensive harmonics in the industrial zone of Konya” Department of Electrical & Electronic Engineering, Gümüşhane University, Gümüşhane-TURKEY.
9.        Enrique Acha, Manuel Madrigal,” Power systems Harmonics Computer Modeling and Analysis”, ISBN 0-471-52175-2.
10.     Domijan, Jr and E. Embriz-Santander, “Harmonic Mitigation Techniques for the Improvement of Power Quality of Adjustable Speed Drives (ASDs), “ in IEEE 1990
11.     ArashKiyoumarsi, Rahmat-o-Allah Houshmand, Rasoul Ali-Zargar and mohammad Reza, Department of Electrical engineering. University of Isfahan,Iran ,Islamic Azad University of Abhar, Ghazwin,Iran.

105-109

22.

Authors:

Jan Haase

Paper Title:

Power Simulation and Power Profiling of Wireless Sensor Networks

Abstract:  Wireless sensor networks (WSN) are battery-powered, thus they should be optimized for low energy consumption already at design time. Current tools offer different levels of power simulation, showing the designer where the energy was spent, where hot spots might be optimizable, and so on. However, a semantic gap still remains open: The question why a specific part of the software was executed and therefore energy was consumed. Especially for WSN this question not only encompasses single devices, but whole networks, which can suffer e.g. from network congestion, attenuation, routing errors, lost packets, etc. This paper presents an overview over existing power simulation tools as well as the new approach of power profiling, which collects much data about many aspects that lead to energy consumption in a network of wireless sensor nodes. The outcome is a statistic about the energy consumption for each single generated transaction, from the moment it was generated until the resulting message is finally received. Using this, the designer is able to optimize the whole system, e.g. by running several simulations with different communication protocols, network topologies, duty cycles, or wake-up timings.

Keywords: wireless sensor networks, WSN, power estimation, power profiling, energy efficiency.

References:
1.        J. Conti, “The internet of things,” Communications Engineer, vol. 4, no. 6, pp. 20 –25, 2006.
2.        Shye, B. Scholbrock, G. Memik, and P. A. Dinda, “Characterizing and modeling user activity on smartphones,” Northwestern University, Tech. Rep., Mar. 2010.
3.        H.-Y. Zhou, D.-Y. Luo, Y. Gao, and D.-C. Zuo, “Modeling of node energy consumption for wireless sensor networks,” Wireless Sensor Network, vol. 3, pp. 18–23, Jan. 2011.
4.        A.Willig, “Recent and emerging topics in wireless industrial communications: A selection,” Industrial Informatics, IEEE Transactions on, vol. 4, no. 2, pp. 102 –124, May 2008.
5.        Q. Wu, M. Pedram, and X. Wu, “Clock-gating and its application to low power design of sequential circuits,” Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on, vol. 47, no. 3, pp. 415 –420, Mar. 2000.
6.        Egea-Lopez, J. Vales-Alonso, A. Martinez-Sala, P. Pavon-Mario, and J. Garcia-Haro, “Simulation scalability issues in wireless sensor networks,” Communications Magazine, IEEE, vol. 44, no. 7, pp. 64 – 73, July 2006.
7.        Bruckner, J. Haase, P. Palensky, G. Zucker, „Latest trends in integrating building automation and smart grids“, IECON 2012-38th Annual Conference on IEEE Industrial Electronics Society, pp. 6285-6290, 2012
8.        M. Rathmair, J. Haase, “Simulator for Smart Load Management in Home Appliances”, Proceedings of SIMUL 2012, The Fourth International Conference on Advances in System Simulation, pp. 1-6, 2012
9.        J. Wenninger, J. Moreno, J. Haase, “Model Based Design of Smart Appliances”, book chapter in “Embedded Systems for Smart Appliances and Energy Management”, pp. 41-51, Springer 2013
10.     L.W. Nagel and D. Pederson, “Spice (simulation program with integrated circuit emphasis),” EECS Department, University of California, Berkeley, Tech. Rep. UCB/ERL M382, Apr 1973. [Online]. Available: http://www.eecs.berkeley.edu/Pubs/TechRpts/1973/22871.html
11.     “Synopsys HSPICE,” homepage: http://www.synopsys.com/tools/Verification/AMSVerification/ CircuitSimulation/HSPICE/Pages/default.aspx.12.     “Mentor ModelSim,” homepage: http://model.com.
13.     “Cadence Spectre,” homepage: http://www.cadence.com/products/rf/spectre circuit/pages/- default.aspx.
14.     R. Dömer, A. Gerstlauer, and D. Gaijski, “SpecC language reference manual (version 2.0),” http://www.ics.uci.edu/ specc/reference/SpecC LRM 20.pdf.
15.     “SystemC,” Open SystemC Initiative. [Online]. Available: http://www.systemc.org
16.     “SystemC AMS,” homepage: http://www.accellera.org/downloads/standards/systemc/ams.
17.     “MATLAB Simulink,” homepage: http://www.mathworks.de/products/simulink.
18.     UCLA Compilers Group, “Avrora: The avr simulation and analysis framework,” 2011. [Online]. Available: http://compilers.cs.ucla.edu/avrora/
19.     B. L. Titzer, D. K. Lee, and J. Palsberg, “Avrora: scalable sensor network simulation with precise timing,” in Proceedings of the 4th international symposium on Information processing in sensor networks, ser. IPSN ’05. Piscataway, NJ, USA: IEEE Press, 2005.
20.     R. de Paz Alberola and D. Pesch, “AvroraZ: extending Avrora with an IEEE 802.15.4 compliant radio chip model,” in Proceedings of the 3nd ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks, ser. PM2HW2N ’08. New York, NY, USA: ACM, 2008, pp. 43–50.
21.     Center for Adaptive Wireless Systems, “Avroraz: enabling ieee 802.15.4 compliant emulations,” 2011. [Online]. Available: http://citavroraz.sourceforge.net/docextensions.html
22.     Österlind, A. Dunkels, J. Eriksson, N. Finne, and T. Voigt, “Cross-Level Sensor Network Simulation with COOJA,” in Local Computer Networks, Proceedings 2006 31st IEEE Conference on, Nov. 2006, pp. 641 –648.
23.     J. Eriksson, F. Österlind, N. Finne, A. Dunkels, and T. Voigt, “Accurate power profiling for sensor network simulators,” 2008.
24.     J. Eriksson, F. Österlind, N. Finne, A. Dunkels, N. Tsiftes, and T. Voigt, “Accurate networkscale power profiling for sensor network simulators,” in Wireless Sensor Networks, ser. Lecture Notes in Computer Science, U. Roedig and C. Sreenan, Eds. Springer Berlin / Heidelberg, 2009, vol. 5432, pp. 312–326, 10.1007/978-3-642-00224-3 20.
25.     Österlind, J. Eriksson, and A. Dunkels, “Cooja TimeLine: a power visualizer for sensor network simulation,” in Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, ser. SenSys ’10. New York, NY, USA: ACM, 2010, pp. 385–386.
26.     C. A. Boano, K. Römer, F. Österlind, and T. Voigt, “Demo Abstract: Realistic Simulation of Radio Interference in COOJA,” in European Conference on Wireless Sensor Networks (EWSN 2011), Feb. 2011.
27.     P. Levis, N. Lee, M. Welsh, and D. Culler, “TOSSIM: accurate and scalable simulation of entire TinyOS applications,” in Proceedings of the 1st international conference on Embedded networked sensor systems, ser. SenSys ’03. New York, NY, USA: ACM, 2003, pp. 126–137.
28.     V. Shnayder, M. Hempstead, B.-r. Chen, G. W. Allen, and M. Welsh, “Simulating the power consumption of large-scale sensor network applications,” in Proceedings of the 2nd international conference on Embedded networked sensor systems, ser. SenSys ’04. New York, NY, USA: ACM, 2004, pp. 188–200.
29.     E. Perla, A. O. Cath´ain, R. S. Carbajo, M. Huggard, and C. Mc Goldrick, “PowerTOSSIM z: realistic energy modelling for wireless sensor network environments,” in Proceedings of the 3nd ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks, ser. PM2HW2N ’08. New York, NY, USA: ACM, 2008, pp. 35–42.
30.     D. B. Johnson, “Validation of wireless and mobile network models and simulation,” in In Proceedings of the DARPA/NIST Network Simulation Validation Workshop, May 1999.
31.     X. Zeng, R. Bagrodia, and M. Gerla, “GloMoSim: a library for parallel simulation of largescale wireless networks,” in Proceedings of the twelfth workshop on Parallel and distributed simulation, ser. PADS ’98. Washington, DC, USA: IEEE Computer Society, 1998, pp. 154–161.
32.     S. N. Technologies, “Qualnet.” [Online]. Available: http://www.scalablenetworks. com/products/qualnet/
33.     J. Haase, J. Molina, and D. Dietrich, “Power-aware system design of wireless sensor networks: Power estimation and power profiling strategies,” Industrial
Informatics, IEEE Transactions on, vol. 7, no. 4, pp. 601 –613, Nov. 2011.
34.     “The network simulator - ns-2.” [Online]. Available: http://www.isi.edu/nsnam/ns/
35.     K. Fall and K. Varadhan, “The ns manual,” 2010. [Online]. Available: http://www.isi.edu/nsnam/ns/doc/ns doc.pdf
36.     S. Park, A. Savvides, and M. B. Srivastava, “SensorSim: a simulation framework for sensor networks,” in Proceedings of the 3rd ACM international workshop on
Modeling, analysis and simulation of wireless and mobile systems, ser. MSWIM ’00. New York, NY, USA: ACM, 2000, pp. 104–111.
37.     Simon, P. Volgyesi, M. Maroti, and A. Ledeczi, “Simulation-based optimization of communication protocols for large-scale wireless sensor networks,” in Aerospace Conference, 2003. Proceedings. 2003 IEEE, vol. 3, Mar. 2003, pp. 1339–1346.
38.     “J-Sim Official.” [Online]. Available: http://sites.google.com/site/jsimofficial/
39.     Sobeih, J. Hou, L.-C. Kung, N. Li, H. Zhang,W.-P. Chen, H.-Y. Tyan, and H. Lim, “J-Sim: a simulation and emulation environment for wireless sensor networks,” Wireless Communications, IEEE, vol. 13, no. 4, pp. 104 –119, Aug. 2006.
40.     P. Baldwin, S. Kohli, E. A. Lee, X. Liu, Y. Zhao, C. C. T. Ee, C. Brooks, N. V. Krishnan, S. Neuendorffer, C. Zhong, and R. Zhou, “VisualSense: Visual modeling for wireless and sensor network systems,” UCB ERL Memorandum UCB/ERL M04/8, Tech. Rep., 2004.
41.     “Ptolemy II,” UC Berkeley EECS Dept. [Online]. Available: http://ptolemy.berkeley.edu/ptolemyII/
42.     E. A. Lee, “Finite State Machines and Modal Models in Ptolemy II,” EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2009-151, Nov 2009. [Online]. Available: http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-151.html
43.     G. Chen, J. Branch, M. Pflug, L. Zhu, and B. Szymanski, “SENSE: A Wireless Sensor Network Simulator,” in Advances in Pervasive Computing and Networking, B. K. Szymanski and B. Yener, Eds. Springer US, 2005, pp. 249–267, 10.1007/0-387-23466-7 13.
44.     C. of Pervasive Computing and Networking, “SENSE: Sensor Network Simulator and Emulator.” [Online]. Available: http://www.ita.cs.rpi.edu/sense/
45.     G. Chen and B. Szymanski, “COST: a component-oriented discrete event simulator,” Winter Simulation Conference, vol. 1, pp. 776–782, 2002.
46.     S. Mahlknecht, J. Glaser, and T. Herndl, “PAWiS: Towards a Power Aware System Architecture for a SoC/SiP Wireless Sensor and Actor Node Implementation,”
in Proceedings of 6th IFAC International Conference on Fieldbus Systems and their Applications, 2005, pp. 129–134.
47.     “OMNeT++ Network Simulator Framework,” OMNeT++ Community. [Online]. Available: http://www.omnetpp.org/
48.     “SNOPS - Sensor Network Simulation by Power Simulation,” project funded by FFG (FITIT program), Austria.
49.     Moreno Molina, J. Haase, and C. Grimm, “Energy consumption estimation and profiling in wireless sensor networks,” in ARCS ’10 - 23th International Conference
on Architecture of Computing Systens 2010 Workshop Proceedings, Feb. 2010, pp. 259–264.
50.     W. Du, F. Mieyeville, and D. Navarro, “IDEA1: A SystemC-based system-level simulator for wireless sensor networks,” in Wireless Communications, Networking and Information Security (WCNIS), 2010 IEEE International Conference on, June 2010, pp. 618 –622.
51.     Haase, J. Molina, and C. Grimm, “High level energy consumption estimation and profiling for optimizing wireless sensor networks,” in Industrial Informatics (INDIN), 2010 8th IEEE International Conference on, July 2010, pp. 537 –542.
52.     Markus Damm, Jan Haase, Christoph Grimm, Fernando Herrera, and Eugenio Villar. 2008. Bridging MoCs in SystemC specifications of heterogeneous systems. EURASIP J. Embedded Syst. 2008, Article 7 (January 2008), 16 pages. DOI=10.1155/2008/738136 http://dx.doi.org/10.1155/2008/738136
53.     Haase, J., Molina, J. M., & Dietrich, D. (2011). Power-aware system design of wireless sensor networks: Power estimation and power profiling strategies. Industrial Informatics, IEEE Transactions on, 7(4), 601-613.
54.     “Transaction-Level Modeling,” Open SystemC Initiative - Transaction-Level Modeling Working Group. [Online]. Available: http://www.systemc.org/downloads/standards/tlm20/
55.     Aynsley, “TLM-2.0 Language Reference Manual,” Open SystemC Initiative, Tech. Rep., 2009.
56.     J. Haase, M. Damm, J. Glaser, J. Moreno, and C. Grimm, “Systemc-based power simulation of wireless sensor networks,” in Specification Design Languages, 2009. FDL 2009. Forum on, sept. 2009, pp. 1 –4.
57.     Damm, J. Moreno, J. Haase, and C. Grimm, “Using transaction level modeling techniques for wireless sensor network simulation,” in Design, Automation Test in Europe Conference Exhibition (DATE), 2010, march 2010, pp. 1047 –1052.
58.     J. Haase, M. Lang, C. Grimm, “Mixed-level simulation of wireless sensor networks” in Proceedings of Forum on Specification and Design Languages (FDL) 2010.
59.     J. Moreno, J. Wenninger, J. Haase, and C. Grimm, “Energy profiling technique for networklevel energy optimization,” in AFRICON, 2011, 2011, pp. 1–6.
60.     J. Glaser, J. Haase, M. Damm, and C. Grimm, “Investigating power-reduction for a reconfigurable sensor interface,” in Austrochip. Institut für Elektronik - TU Graz, 2009, pp. 27–32.

110-116

23.

Authors:

Md Irfanullah, Vishwanath. B. Patil

Paper Title:

Seismic Evaluation of RC Framed Buildings with Influence of Masonry Infill Panel

Abstract:   RC framed buildings are generally designed without considering the structural action of masonry infill walls present. These walls are widely used as partitions and considered as non-structural elements. But they affect both the structural and non-structural performance of the RC buildings during earthquakes. RC framed building with open first storey is known as soft storey, which performs poorly during earthquakes. A similar soft storey effect can also appear below plinth, when the ground material has removed during excavation and refilled later. To observe the effect of masonry infill panel, it is modeled as an equivalent diagonal strut. In order to study these six RC framed buildings with brick masonry infill were designed for the same seismic hazard. In the present paper an investigation has been made to study the behavior of RC frames with various arrangement of infill when subjected to earthquake loading. The results of bare frame, frame with infill, soft ground floor, soft basement and infill in swastika pattern in ground floor are compared and conclusions are made. It is observed that, providing infill below plinth and in swastika pattern in the ground floor improves earthquake resistant behavior of the structure when compared to soft basement.  

Keywords:   Masonry infill panel, bare frame, soft basement, diagonal strut.

References:
1.        Sachin. R. Patel and Sumant. B. Patel ,”Effect of Brick Infill Panel in Design of High Rise Building”, National Conference on Recent Trends in Engineering & Technology,(2011).
2.        C.V.Murthy and Sudhir. K. Jain, “Benefecial Influence of Masonry Infill Walls on Seismic Performance of RC Frame Buildings”, 12WCEE2000, paper 1790, pg.2-5.
3.        Amit. V. Khandve, “Seismic response of RC frames buildings with soft first storeys”, International Journal of Engineering Research and Applications (IJERA), ISSN 2248-9622, Vol.2, Issue 3,May-June 2012,pp 2100-2108.
4.        Das, D. & Murty, C. V. R. 2004. “Brick masonry infills in seismic design of RC framed buildings” Part 1 –Cost implications. The Indian Concrete Journal. July 2004, vol78 No7: 39-43.
5.        Pankaj Agarwal and Manish Shrikhande “ Earthquake Resistant Design of Structures”, New Delhi.
6.        IS 1893 (Part I), (2002) “Criteria for Earthquake Resistant Design of Structures.” Bureau of Indian Standards, New Delhi.

117-120

24.

Authors:

Sonia Sharma, Vinay Chopra

Paper Title:

Association Rule Mining: A Multi-objective Genetic Algorithm Approach Using Pittsburgh Technique

Abstract:    Association rules [4] usually found out the relationship between different data entities in given data set and moreover it is very much important task of data mining. Basically, association rule mining is a multi-objective problem, instead of a single objective problem. A multi-objective genetic algorithm approach using Pittsburgh technique is introduced in this paper for discovering the interesting association rules with multiple criteria i.e. support, confidence and simplicity and complexity With Genetic Algorithm. In this paper we have discussed the results on various datasets and show effectiveness of the new proposed algorithm.

Keywords: Data Mining, Genetic Algorithm, Optimization, Association Rule, Measure, Apriori, Genetic Operators, Interestingness, Frequent Item-set

References:
1.     Indra k and kanmanis “ Performance analysis of genetic algorithm for mining association rules” International Journal Of Computer Science, issues Vol. 9, issue 2, March 2012, pp: 318-376.
2.     Rajul Anand, Abhishek Vaid, Pramod Kumar Singh “Association Rule Mining Using Multi-Objective evolutionary algorithm: Strengths and challenges” IEEE Conference, 2009, pp:385-389.
3.     Rupali Haldulakar and Prof. Jitender Aggarwal “optimization and association rule mining through genetic algorithm” International Journal Of Computer Sciences And Engineering Vol. 3, No. 3,  March 2011,  pp: 1252-1259.
4.     Jian Hu and Xing Yang Li “Association rule mining using multi-objective co evolutionary algorithm” Ieee International Conference On Computational Intelligence And Security Workshop, 2007, pp: 405-408.
5.     Basheer Mohamad “Discovering interesting association rules a multiobjective genetic algorithm” International Journal Of Applied Information System, Vol. 5 No. 3, February 2013, pp: 47-52.
6.     Sanat Jain, Swati Kabra “Mining and optimization of association rules using effective algorithm” International Journal Of Emerging Technology And Advanced Engineering, Vol. 2 issue 4 April 2012.
7.     J.Malar Vizhi and Dr. T.Bhuvaneswari “ Data quality measurement on categorical data using genetic algorithm” International Journal And Determining And Knowledge Management Process, Vol. 2, 01 Jan 2012, pp: 33-42,.

121-124

25.

Authors:

Prince Verma, Dinesh Kumar

Paper Title:

IP- Apriori: Improved Pruning in Apriori for Association Rule Mining

Abstract:  Association rule mining which is of great importance and use is one of a vital technique for data mining. Main among the association rule mining techniques have been Apriori and many more approaches have been introduced with minute changes to Apriori but their basic concept remains the same i.e use of support and confidence threshold(s). According to best of our knowledge we came to know that no work has been done in the field of improving the pruning step of Apriori. This paper introduces a new algorithm IP-APRIORI i.e. ‘Improved Pruning in Apriori’. This algorithm improves the pruning procedure of Apriori algorithm by using average support (supavg) instead of minimum support (supmin), to generate probabilistic item-set instead of large item-set.

Keywords:  Data Mining, KDD Process, Association Rule Mining, Pruning

References:
1.        R. Agrawal, T. Imielinski, and A. N. Swami, “Mining association rules between sets of items in large databases” ACM SIGMOD International Conference on Management of Data, Washington, D.C., 1993, pp 207-216.
2.        U. Fayyad, G. Piatetsky-Shapiro and P. Smyth, “From data mining to knowledge discovery: an overview” Advances in Knowledge Discovery and Data Mining, MIT Press, Cambridge, 1996, MA.
3.        U. Fayyad, S. G. Djorgovski and N. Weir, “Automating the analysis and cataloging of sky surveys” Advances in Knowledge Discovery and Data Mining, MIT Press, Cambridge, MA, 1996, pp. 471-94.
4.        Technology Forecast, Price Waterhouse World Technology Center, Menlo Park, CA, 1997.
5.        R. Agrawal, T. Imielinski, and A. N. Swami, “Mining association rules between sets of items in large databases” ACM SIGMOD International Conference on Management of Data, Washington, D.C., 1993, pp 207-216.
6.        U. Fayyad, G. Piatetsky-Shapiro and P. Smyth, “From data mining to knowledge discovery: an overview” Advances in Knowledge Discovery and Data Mining, MIT Press, Cambridge, 1996, MA.
7.        U. Fayyad, S. G. Djorgovski and N. Weir, “Automating the analysis and cataloging of sky surveys” Advances in Knowledge Discovery and Data Mining, MIT Press, Cambridge, MA, 1996, pp. 471-94.
8.        Technology Forecast, Price Waterhouse World Technology Center, Menlo Park, CA, 1997.
9.        Sotiris Kotsiantis, Dimitris Kanellopoulos, “Association Rules Mining: A Recent Overview” GESTS International Transactions on Computer Science and Engineering, vol.32 (1), 2006, pp. 71-82.
10.     Rakesh Agarwal, Ramakrishna Srikant, “Fast Algorithm for mining association rules” VLDB Conference Santiago, Chile, 1994, pp 487-499.
11.     Suhani Nagpal, “Improved Apriori Algorithm using logarithmic decoding and pruning” International Journal of Engineering Research and Applications, vol. 2, issue 3, 2012, pp. 2569-2572.
12.     Sang Jun Lee, Keng Siau, “A review of data mining techniques. Industrial Management and Data Systems”, University of Nebraska-Lincoln Press, USA, 2001, pp 41-46.
13.     Huan Wu, Zhigang Lu, Lin Pan, Rong Seng XU and Wenbao jiang, “An improved Apriori based algorithm for association rule mining” IEEE Sixth international conference on fuzzy systems and knowledge discovery, 2009, pp 51-55.
14.     Farah Hanna AL-Zawaidah, Yosef Hasan Jbara and Marwan AL-Abed Abu-Zanana, “An improved Algorithm for mining Association Rule in large database” World of Computer and Information technology, vol. 1, no. 7, 2011, pp 311-316.
15.     S. A. Abaya, “Association Rule Mining based on Apriori Algorithm in Minimizing Candidate generation” International Journal of Scientific & Engineering Research, vol-3, issue 7, 2012.
16.     Zhuang Chen, Shibang Cai, Quilin Song, Chonglai Zhu, “An Improved Apriori Algorithm based on pruning Optimization and transaction reduction” IEEE transactions on evolutionary computation, 2011, pp 1908-1911.
17.     M. S. Chen, J. Han, and P. Yu, “Data mining: an overview from a database perspective” IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 6, 1996, pp. 866-883.
18.     R. Patel Nimisha, Sheetal Mehta, “A Survey on Mining Algorithms” International Journal of Soft Computing and Engineering, vol. 2, issue 6, 2013, pp 460-463.

125-131

26.

Authors:

B. Rajneesh Kumar, S. Ranganatha

Paper Title:

Dynamic Characteristics of a Squeeze Film Damper Lubricated With Electro - Rheological Fluid in Terms of Reynolds Number

Abstract:   Smart fluid technology is an emerging field of research that leads to the introduction of Electro-rheological (ER) fluids. ER fluids are such smart materials whose rheological properties (viscosity, yield stress, shear modulus etc.) can be readily controlled upon external electric field. The use of ER fluids introduces a new philosophy on the fact that the stiffness and damping can be changed by applying high electric field and thus minimizing the vibration of the structure during normal operation. In the rotor vibration control of high speed engines squeeze film dampers are currently used. The dynamic characteristics (stiffness and damping) of a squeeze film damper lubricated with electro-rheological fluids are important in many practical engineering applications are studied for high accuracy and efficiency. The Reynolds equation of hydro-dynamic lubrication is normally used to determine the dynamic characteristics in the analysis of rotor dynamic system with squeeze film damper, which neglect the inertia effects. At high speeds both inertia and visco-elasticity introduce phase shifting effects into the fluid motion. As a result, prediction derived from Reynolds equation can be significantly in error.  Here an improved expression is developed for the dynamic characteristic in terms of Reynolds’s number for a particular electro-rheological fluid. Bingham model has been used to describe the behavior of the electro-rheological fluids. The result leads to improvements and explain why it is significant to include fluid inertia forces which have large effects on dynamic characteristics.

Keywords:   Dynamic characteristics, Squeeze film damper, Electro-rheological fluid, Reynolds number.

References:
1.        Luis san andres, Oscar de Santiago, “Forced response of a squeeze film damper and identification of force co-efficients from large orbital motions”, transactions of the ASME, 126, pp. 292.
2.        E.J.Gunter, “Design Of Nonlinear Squeeze Film Dampers For Aircraft Engines”, Journal of Lubrication Technology, 99, Pp.57-64, 1977.
3.        FuleiChu , Roy Holmes,  “The damping capacity of the squeeze film damper in suppressing vibration of a rotating assembly”, tribology international, 33, pp. 81-97, 2000.
4.        Jianjum Li, DewenJin,Xiaoning Zhang, Jichuan Zhang, And William.A,Gruver , “An Electrorheological Fluid Film Damper For Robots ”, IEEE international conference on robotics and automation, 33, pp. 2631-2636, 1995.
5.        Jonathan Krimm, Nicholas Szczerba , “Electro Rheological Fluid”, Conference Session B2, Pp. 1-8, April 14,2007.
6.        C.Carmignani., P.Forte, “Active Squeeze Film Dampers In Rotor Dynamics”, AIMETA International Tribology Conference 1, Aquilia, September,20-22, 2000
7.        S.Danaila and L Moraru “On the validity of classical hydrodynamic lubrication theory applied to squeeze film dampers”, 25th IAHR Symposium on Hydraulic Machinery and Systems, pg.1-8, 2010.
8.        Lucien Johnston, Dorothea Adams, Fluidicon GmbH, Darmstadt,”Adaptive Components in Transport Technologies – new”,Adaptronic Congress 2007, 23 - 24 May, Göttingen
9.        Zamm.Z, angew, Butz.T , Von Stryk, “Modeling And Simulation Of Electro And Magneto Rheological Fluid Dampers”, math.mech 78,pp.1-22,78,1998.
10.     E.Switonski, A.Mezyk, W.Klein, “Application of Smart Materials In Vibration Control Systems”, Journal of AMME, 24, pp .291-296.
11.     JanuszGoldasz, BogdanSapinski,” Nondimensional characterization of flow mode magnetorheological/electrorheological fluid dampers “, journal of intelligent
material system and structures September 2012 vol 23 no 14 1545-1562.
12.     J.SharanaBasavaraja, S.C. Jain, Satish.C.Sharma, “A Study of Smart Fluid Lubricated Non-Recessed Hybrid Journal Bearing”, International Conference On SmartMaterials Structures And Systems, July 24-26,2008,Bangalore, India
13.     J.A.Tichy “Measurement of squeeze film bearing forces and pressures, including the effects of fluid inertia [ALSE Trans]” page. 520- 526, vol[28] year [1984].

132-136

27.

Authors:

Ira Gaba, Paramjit Kaur

Paper Title:

Biometric Identification on The Basis of BPNN Classifier with Other Novel Techniques Used For Gait Analysis

Abstract:  Gait is the manner of the limb movement or the manner of moving a foot of an individual and recognition of an individual  is the task to identify a people. Gait recognition is the biometric process  by which an individual can be identify by manner they walk. The advantage of gait over other biometric traits such as face ,iris ,and fingerprint etc is that it is non-invasive  and less unobtrusive biometric, which offers to identify people at the distance, without any interaction from the subject or at low resolution. In this paper, firstly the input video is converted into frame then, binary silhouette of a walking person is detected from each frame. Secondly, feature from each frame is extracted using image processing operation. Here distance between head and feet, distance between both hands,  length of one hand, length of leg etc using hanavan’s model are taking as key feature. And then CBIR method is also used.  At last BPNN+MDA and BPNN+LDA techniques are used for training and testing purpose. Here all experiments are done on gait database and input video. Therefore, by using the combination of BPNN with LDA and MDA, in this paper, it obtains the better accuracy results.

Keywords:    BPNN, CBIR, Feature Extraction, Gait Recognition, LDA, MDA, PCA, Silhouette Extraction.

References:
1.        Hayder Ali, Jamal Dargham, Chekima Ali, Ervin Gobin Moung|, “Gait Recognition using principle Component Analysis” International Conference on Machine Vision 2011.
2.        Qinghan, “Technology review- Biometrics Technology, Application, Challenge and Computational Intelligence Solution” ,IEEE Computational Intelligence Magazine, Vol 2,pp5-25,2007.
3.        K.L.Kroeker, “Graphics And Security: exploring visual biometrics” IEEE Computer Graphics And Applications, Volume 22, Issue 4, pp. 16-22, July-August 2002.
4.        N.V.Boulgouris, D.Hatzinakos and K.N.Plataniotis, “ Gait Recognition: a challenging signal processing technology for biometrics identification”, IEEE Signal Processing Magazine, Volume 22, Issue 6, pp. 78-90, November 2005.
5.        C.Y.Yam, M.S Nixon and J.N. Carter, “Extended model based automatic gait recognition of walking and running”, 3rd. proc. AVBPA 2001, pp 278-283 june 2001.
6.        Bobick and A. Johnson, “gait recognition using static, activity-specific parameters,” Proc, IEEE Conf. computer Vision and Pattern Recognition, 2001.
7.        Xiaxi Huang, Nikolaos V. Boulgouris, 2009 , “ model based human gait recognition using fusion of features,” in processing of IEEE international conference on Acoustics, speech and signal, ICASSP 2009, pp. 1469-1472.
8.        Edward Guillen, Daniel Padilla, Adriana Hernandez, Kenneth Barner, “Gait Recognition System : Bundle Rectangle Approach ”, World Academy of Science, Engineering and Technology 34, 2009.
9.        Lili Liu, Yilong Yin, Wei Qin, Ying Li, “Gait Recognition based on Outermost Contour ,” International Journal of Computational Intelligence Systems,  Vol. 4, September 2011, pp. 1090–1099
10.     A.Hayder, J.Dargham, A.Chekima, G.M.Ervin,, “Person Identification Using Gait”, International Journal of Computer and Electrical Engineering, Vol. 3, No. 4, August 2011.
11.     Krishna Kumar Pandey, Nishchol Mishra and Hemant Kumar Sharma, “Enhanced of color matching Algorithm for Image Retrieval  ”, International Journal of Computer Sciences Issues, Vol. 8, Issue 3, No. 2, May 2011.
12.     Wang, Jin, She, Mary, Nahavandi, Saeid and Kouzani, Abbas 2010, “ A Review of Vision-Based Gait Recognition Methods of Human Identification”, in DICTA 2010: Proceedings of the Digital Image Computing: Techniques and Application, IEEE, Piscataway, N.J., pp. 320-327,2010.
13.     Qiong Cheng, Bo Fu, Hui Chen,  “ Gait Recognition Based on PCA and LDA,” proc: 2nd Symposium International Computer Science and Computational Technology, ISBN 978-952-5726-07-7, pp. 26-28, December 2009.
14.     Daniel L. Swets and Juyang Weng, “Using Discriminant Eigen features for image retrieval ”, IEEE Transactions on Patten Analysis and Machine Intelligence, 18(8):831-836, 1996.
15.     R.O. Duda, P.E. Hart, D.G. Strok, “ Pattern Classification”, Second Edition, Wiley,2000.

137-142

28.

Authors:

Abdullah A. Mohamed, Dia M. Ali

Paper Title:

Creating Real-Time operation System Based on xPC Target Kernel

Abstract:   Real-Time system is the base for many applications today. Whatever the designed system is a high accuracy and flexibly, it still not efficient unless be a real-time with the overall system. In this paper a real-time Operation System (OS) is created based on xPC Target Kernel.  xPC gives the ability to convert a designed model to a real-time OS. Two models are built, Target-Host transmission using UDP and Spectrum Analyzer, and implemented separately on the created OS. The first model to test the ability of the created OS when dealing with network systems, the second to test the ability of dealing with High Speed Systems and Analyzing units. There result shows that the created OS is very efficient and give short average Task Execution Time (TET) equal to (0.636 micro sec) for the first model and (0.3117 micro sec) for the second model and it shows that it can easily communicate with other systems.

Keywords:  Personal Computer (PC), Task Execution Time (TET), Snapshot from Simulink-Matlab R2010 (SSMR10)..

References:
1.     Anton Cervin, “Integrated Control and Real-Time Scheduling”, PhD. Thesis, Department of Automatic Control, Lund Institute of Technology, Lund, Sweden, pp. 139-161, April 2003.
2.     Stefan Molyneux, “Real-Time Relationships-The Logic of Love", Freedomain Library, Volume 4 Version 1.0 Extended Edition, pp. 205-230, January 2008, www.freedomainradio.com.
3.     Emeka Eyisi, Jia Bai, Derek Riley, Jiannian Weng, Wei Yan, Yuan Xue, Xenofon Koutsoukos,  Janos Sztipanovits, “An integrated modeling and simulation tool for networked control systems”, Simulation Modelling Practice and Theory 27 (2012),  Elsevier.
4.     Abdullah A. Mohamed, “Designing of Intrusion Detection System Based on Image Block Matching”, International Journal of Computer and Communication Engineering, Vol. 2, No. 5, September 2013.
5.     The MathWorks Inc., “xPC Target User’s Guide for Use with Real-Time Workshop”, The MathWorks Inc., pp. 85-92, 2000.
6.     The MathWorks Inc., “xPC Target Getting Started Guide”, The MathWorks Inc., pp. 10-31, 2010.
7.     The MathWorks Inc., “xPC Target”, The MathWorks Inc., 2012.
8.     The MathWorks Inc., “xPC_Target_Supported_Ethernet_Chipsets”, The MathWorks Inc., 2010.

143-146

29.

Authors:

Nanda S. Korde, Suresh T. Gaikwad, Seema S. Korde, Anjali S. Rajbhoj

Paper Title:

Ultrasound Synthesis, Characterization and Thermal Study of Some Transition Metal Complexes of Β – Diketone Ligand

Abstract:  Complexes of Fe(III), Co(II), Ni(III), Cu(II) and Cr(III) with cyclic β-diketone ligand were synthesized by ultrasound irradiation and characterized by elemental analysis, conductometry, magnetic susceptibility, UV-Vis, IR, 1H-NMR, 13C-NMR, X-ray diffraction analysis of powdered samples thermal analysis, and screened for antimicrobial activity. The IR spectral data suggested that the ligand behaves as a bidentate ligand towards the central metal ion with   an O-O, O-O donor atoms sequence. From the microanalytical data, the stoichiometry of the complexes 1:2 (metal :  ligand) was found. The physico-chemical data suggested octahedral geometry for all these complexes. The thermal behavior   (TGA/DTA) of the complexes were studied and kinetic parameters were determined by Horowitz- Metzer method. The powder X-ray diffraction data suggested a monoclinic crystal system for the Fe(III) and Co(II) complexes. The ligand and their metal complexes were screened for antibacterial activity against Staphylococcus aureus, B. subtilis and E. coli and fungicidal activity against A. niger and F. oxysporum.

Keywords:  Cyclic β-diketone; Baker-Venkatraman transformation; transition metal complexes; ultrasoun irradiation;  XRD; Thermal analysis; antimicrobial activity

References:
1.          A.S.Rajbhoj,N.S. Korde, S.T. Gaikwad, S.S. Korde,  2012 Scholars Research Library Der Pharma Chemica 4(5) (2012) 1868
2.          A.Siedle  in comprehensive Coordination chem. Wilkinson, pergamon press,Oxford, vol.2 (1987) 365
3.          L.Tang, S.Zhang , J.Yang , W.Gao, J. Cui, T.Zhuang,  Molecules 9 (2004)842
4.          R.Kumar ,  Y.Joshi,  Arkivoc 9 (2007)142
5.          S.Heller, S.Natarajan   Org. Lett 8 (2006) 2675
6.          D.Simoni, F.Invidiata, R.Rondanin, S. Grimaudo, G.Cannizzo, E.Barbusca, F.,Porretto,N.Alessandro, M.Tolomeo, J. Med. Chem 42(1999)  4961
7.          O.Kuzueva, Y.Burgart, V.Saloutin, O.Chupakhin, Chem Heterocyclic Compds 37 (2001) 1130
8.          V.Alekseev, K.Zelenin, S.Yakimovich, Russ J Org Chem 31(1995) 868.
9.          T.Dziemboska, Z. Rozwadowski, Curr. Org. Chem  5 (2001) 289
10.       L.Tchertanov, J. Mouscadet, J. Med. Chem 50 (2007) 1133
11.       K.Singletary, C. Macdonald, M. Lovinelli, C. Fisher, M. Wallig,  Carcinogenesis 19(1998)  1039
12.       C.Lin, G.Wei, M.Huang, C.Ho, J. Food and drug analysis 13  (2005)284
13.       C.Lin, Y.Tsai, M.Huang, Y.Lu, C.Ho, S.Tseng, Carcinogenesis 27(2006)  131
14.       O.Sharma, R.Singla,  B.Shrivastava, V. Bhat,  G.Shenoy, K.Sreenivasan, Indo Global J. of Pharmaceutical Sciences 2(1) (2012)  70-75
15.       R.Natrajan, K.Antonysamy, C.Thangaraja, Transition Met. Chem 28 (2003) 29
16.       K. Nakamoto, Infrared Spectra of Inorganic and Cooredination Compounds, 3rd ed., Wiley Interscience, New York 159 (1970) 167 214
17.       A.Verma, N.Bhoslae, S.P.Sangel J. Indian Chem. Soc 72(1995)685
18.       ABP Lever, Inorg. Electronic Spectroscopy Elsevier, Amsterdam, 275 (1968)
19.       E.Sinn, C.M. Harris, Coord. Chem. Rev 4 (1969) 391
20.       K.C.Patel, D.E.Goldberg, J. Inorg Nucl. Chem 34 (1972) 637
21.       Meena Nagar, J. Chil. Chem. Soc 4 (2011) 56
22.       Rajendra Jain, J. Serb. Chem. Soc 77 (2012) 1-27
23.       N.H.Gaur, R.Sharma, R.S.Shindhu, J. Indian Chem.Soc 78(1) (2001), 26-27
24.       A.S. Munde, T.K. Chondhekar, J.Serb.Chem.Soc.,75(3), 349-359(2010)
25.       AI A.Vogel, Text Book of Quatitative Inorg. Analysis, 3rd Edn, Longmans : London; (1975), p.540
26.       J.Sestak, V.Satava, W.Wendland, Therm. Chem. Acta 7 (1973)333
27.       H.Horowitz, G. Metzer, Anal. Chem 35(1963)1464
28.       Impura, Y. Inoue, I. Yasumori, Bull. Chem. Soc. Jpn. 56 (1983) 2203
29.       J. R. Carvajal, T. Roisnel, Winplotr, A Graphic Tool for Powder Diffraction, Laboratoire Leon Brillouin (ceal/cnrs) 91191 Gif sur Yvette Cedex, France, 2004
30.       P. Shoemaker, C. W. Garland, Experiments in Physical Chemistry, 5th ed., McGraw-Hill International Edition, New York, 1989
31.       M. B. Deshmukh, S. Dhongade-Desai, S. S. Chavan, Indian J. Chem. 44 (2005) 1659
32.       Z.Chohan, M.Arif, A. Akhtar,  C.Supuran, J. Bioinorganic Chemistry and Applications, 2006,  Article ID 83131, 13

147-151

30.

Authors:

Poonam A. Arbat, P.D. Gawande

Paper Title:

Improved Adaptive Block Truncation Coding For Image Compression in LCD Overdrive

Abstract:   Block truncation coding (BTC) technique has attracted much attention during the last few years as a simple source coding method to achieve good quality image reproduction by preserving the first two statistical moments of an image: the mean and the variance. It significantly reduces the amount of computation required by the traditional coding methods, such as transform. In this paper, we have proposed a method called the Improved Adaptive Block Truncation Coding (IABTC) based on Adaptive Block Truncation Coding (ABTC).And we are implemented it on LCD overdrive for image compression. The feature of inter-pixel redundancy is exploited to reduce the bit-rate further by retaining the quality of the reconstructed images. The proposed method outperforms the existing BTC based methods both in terms of bit-rate and PSNR values.

Keywords:   Compression, BPP, Bit plane, PSNR

References:
1.        S.Vimala, P. Uma, B. Abidha “Improved Adaptive block truncation coding for image compression” international journal of computer application (0975-8887) vol 19-No.7,April 2011.
2.        Dr.K.Somasundara, Ms.S.Vimala “Multi-Level Coding Efficiency with Improved Quality for Image Compression based on AMBTC” International Journal of Information Sciences and Techniques (IJIST) Vol.2, No.2, March 2012.
3.        Jun Wang and Jong-Wha Chong, Member, IEEE “High Performance Overdrive Using Improved Motion Adaptive Codec in LCD” IEEE Transactions on Consumer Electronics, Vol. 55, No. 1, FEBRUARY 2009.s
4.        Edward j. Delp, Robert Mitchell “ Image compression using block truncation coding” IEEE transactions on communications, vol. com-27, no. 9, September 1979.
5.        Jun Wang, Jong-Wha Chong “Adaptive Multi-level Block Truncation Coding for Frame Memory Reduction in LCD Overdrive” IEEE Transactions on Consumer Electronics, Vol. 56, No. 2, May 2010.
6.        Yun-Ho Ko, Jin-Hyung Kim, Si-Woong Lee, and Hyun-Soo Kang “Dual Block Truncation Coding for Overdriving of Full HD LCD Driver” IEEE Transactions on Consumer Electronics, Vol. 58, No. 1, February 2012.
7.        Doaa Mohammed, Fatma Abou-Chadi “Image compression using block truncation coding” Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Telecommunications (JSAT), February Edition, 2011.
8.        ] Meftah M. Almrabet , Amer R. Zerek, Allaoua Chaoui , Ali A. Akash “Image compression using block truncation coding” IJ-STA, Volume 3, No 2, December 2009.
9.        Maxim D. lema, Robert Mitchell “Absolute Moment Block Truncation Coding and Its Application to Color Images” IEEE transactions on communications, vol. com-32, no. 10, october 1984.
10.     Jong-Woo Han, Min-Cheol Hwang, Seong-Gyun Kim, Tae-Ho You, Sung-Jea Ko” Vector Quantizer based Block Truncation Coding for Color Image Compression in LCD Overdrive” IEEE Transactions on Consumer Electronics, Vol. 54, No. 4, NOVEMBER 2008.

152-154

31.

Authors:

Hanumantharayagouda, Amaresh.S. Patil

Paper Title:

Flexural Fatigue Studies for SFRC under Compound Loading For Different Stress Ranges

Abstract:  This paper presents a study on fatigue behavior of non-fibrous concrete and SFRC subjected to repeated loading. It is aimed to study the behavior of Non fibrous concrete and SFRC under constant amplitude and compound amplitude loading. A total number of 140 prism specimens of size 75x100x500 mm were tested under flexural fatigue loading using havier sine wave loading in order to obtain the fatigue lives of Non fibrous concrete and SFRC at different stress levels. About 10 prism specimens are subjected to static flexural test to determine the static flexural strength of Non fibrous concrete and SFRC prior to fatigue testing. The specimens incorporated 1% volume fraction of crimped steel fibers of size 0.55mmɸ X30 mm. About 30 prism specimens were tested under constant amplitude fatigue loading. Fatigue life data obtained has been analyzed in attempt to determine the relationship among stress level and number of cycles of failure. About 110 prism specimen was tested under compound fatigue loading in order to check the validity of miner’s hypothesis.

Keywords:    Fatigue; compound loading; steel fiber reinforced concrete (SFRC); flexural strength

References:
1.     Tarun R. Naik, Shiw.S.Singh, Congli Ye, “Fatigue Behaviour of Plain Concrete made with or without Flyash” Electric Power Research Institute, 3412 Hillview Avenue Palo Alto, California 94304
2.     Batson, G., Ball, C., Bailey, L., Lenders, E. and Hooks, j., Flexural Fatigue Strength of Steel Fiber Reinforced Concrete Beams. ACI Journal. Procedings, V ol. 69, N0. 11 November 1972, pp 673 – 677.  
3.     Cachim, P.B., Figueiras, J. A., Pereira, P. a. a., Fatigue Behaviour of Fibre Reinforced Concrete in Compression, Cement & Concrete Composites, Elsevier Science, 2002, 24, no. 2, pp. 211 – 217
4.     Daniel, L. and Loukili, A., Behavior of High Strength Reinforced Concrete Beams under Cyclic loading, ACI Structural Journal, May – June 2002, pp. 248 – 256.
5.     Jun, Z. and Stang, H., Fatigue Performance in Flexure of Fibre Reinforced Concrete, ACI Materials Journal, January – February 1998, pp 58 – 67.
6.     Ballinger, C.A. Cumulative Damage characteristics of Plain Concrete. Highway Research Record, NO. 370, 1972, pp. 48 -60.
7.     A.J.M. Siemes. Miner’s Rule with Respect to Plain Concrete Variable Amplitude Tests. ACI Special Publications, No. SP – 75, 1987, pp. 71 – 109
8.     Aravindkumar. B. Harwalkar and Dr. S.S. Awanti “Fatigue Behivour of High Volume Fly Ash Concrete under Constant Amplitude and Compound Loading” IJCIET Vol 3 Dec (2012), pp. 404 – 414
9.     Girish M.G, A. Chandrashekar and A.U. Ravishankar“Flexural Fatigue Analysis of Steel Fibre Reinforced Concrete” International Journal of Earth Sciences and Engineering.Vol 05, Oct (2012) pp 1352 – 1357
10.  S.P. Singh & B.R. Ambedkar, Y. Mohammadi, S.K Kaushik “Flexural Fatigue Strength Predication of Steel Fiber Reinforced Concrete Beams” Electronic Journal of
Structural Engineering 2008.
11.  IRC 58 2011, Guidelines for the design of plain jointed rigid pavements for highways.
12.  IRC SP: 46 – 1997, Guidelines for Steel Fiber Reinforced Concrete for Pavements, Indian Road Congress” New Delhi.

155-159

32.

Authors:

Raju U. Bhabhor, Jitendrakumar Amrutlal Jadav, Rinal K. Ahir

Paper Title:

Isolated DC-DC UPS Based in a Fly back Converter Analysis and Design

Abstract: This paper presents the analysis, design and simulation from a simple DC-UPS converter with integrated back-up and automatic transition. This converter makes automatic transition between the main AC and the battery when a failure occurs, and it delivers uninterrupted DC power to the load through two independent power sources of commercial input power and battery power. The converter has the following characteristics: automatic transition between the main and the battery, no additional control to detect failure in the main, single structure, galvanic isolation, multi output voltages capability and only one switch control for two operation modes normal and back-up. The analysis, design, simulation and experimental results for this converter are presented.

Keywords: Automatic transition, battery charger, DC-DC converter.

References:
1.        J. Perkinson, “UPS System : A Review,” in Proc. Third IEEE application Power Electronic Con. Exposition 1988,PP. 151-154.
2.        P. Hunter, “Battery backed switcher provides UPS capability,” Power conversion and Intelligent Motion. Oct. 1989, pp. 56-59.
3.        Ma, K.-W.; Lee, Y.-S., “A novel uninterruptible DC-DC converter for UPS applications”, Industry Applications Society Annual Meeting, 1991.B. Smith, “An approach to graphs of linear forms (Unpublished work style),” unpublished.
4.        Kwok-Wai Ma; Yim-Shu Lee, “An integrated flyback converter for DC uninterruptible power supply”, Applied Power Electronics Conference and Exposition (APEC), 1994

160-163

33.

Authors:

Mehmet Altinkaya, Metin Zontul

Paper Title:

Urban Bus Arrival Time Prediction: A Review ofComputational Models

Abstract:  Traffic  flow  in  major  urban  roads  is  affected  by  several  factors.   It is often interrupted by stochastic conditions, such as traffic lights, road conditions, number of vehicles  on  the  road,  time  of  travel,  weather  conditions,  driving  style  of  vehicles. The  provision  of  timely  and  accurate  travel  time  information  of  transit  vehicles is  valuable  for  both  operators  and  passengers,  especially  when  dispatching  is  based on estimation of potential passengers waiting along the route rather than the predefined time schedule. Operators manage their dispatches in real time, and passengers can form travel preferences dynamically.    Arrival time estimation for time    scheduled  public  transport  busses  have  been  studied  by  many  researchers  using  various paradigms.  However, dynamic prediction on some type of transit vehicles,  which do not  follow  any  dispatch  time  schedule,  or  stop  station  constrains  introduces  extra complexities. In  this  paper,  a  survey  on  the  recent  studies  using  historical  data,  statistical methods,  Kalman  Filters  and  Artificial  Neural  Networks  (ANN)  have  been  applied to GPS data collected from transit vehicles, are collected with an emphasis on their model  and  architecture.

Keywords: Bus Travel Time Prediction, Intelligent Transportation Systems (ITS), Advanced Traveller Transportation Systems (AITS), Kalman Filtering, Machine Learning, Artificial Neural Networks (ANN).

References:
1.        Y. Bin, Y. Zhongzhen,  and Y. Baozhen , “Bus arrival time prediction using support vector machines”, Journal of Intelligent Transportation Systems, 2006, 10 (4),151-158.
2.        M. Chen and S. Chien, "Dynamic freeway travel-time prediction with probe vehicle data, link based versus path based,", Transportation Research  Record, 2001, pp. 157-161.
3.        M. Chen  M, X.B.  Liu,  and J.X.Xia,” A  dynamic  bus  arrival  time prediction model based on APC data”,. Computer Aided Civil and Infrastructure Engineering , 2004,19 :364 -376.
4.        S.I.J  Chien, and C.M. Kuchipudi,   "Dynamic Travel Time Prediction with Real-Time and Historic Data.", Journal of Transportation Engineering, 2003,Volume 129,
umber 6, pp. 608-616.
5.        S.I.J . Chien, Y. Ding, and C. Wei, "Dynamic Bus Arrival Time  Prediction with  Artificial  Neural  Networks."  Journal  of  Transportation  Engineering, 2002, Volume 128, Number 5, pp. 429-438.
6.        L.Y.  Chu,  J.S. Oh,  and W.  Recker,"Adaptive  kalman  filter  based freeway  travel time  estimation" , the  84th  TRB  Annual Meeting,  Washington, DC, 2005.
7.        E.-H.  Chung  and  A.  Shalaby, "Expected  time of  arrival  model  for school  bus  transit  using  real-time  global  positioning  system based  automatic  vehicle location  data",  Journal  of  Intelligent  Transportation  Systems, 2007, 11(4):157—167.
8.        W. Chun-Hsin, W. Chia-Chen,S. Da-Chun, C. Ming-Hua,  and H. Jan-Ming, Travel  time  prediction  with  support  vector  regression,  in  'Proceedings of the2003 IEEE Conference on Intelligent Transportation Systems', IEEE, Shanghai,China, 2003.
9.        M.  P.  D'Angelo,  H.  M.  Al-Deek,  and  M.  C.Wang,  "Travel  Time Prediction  for  FreewayCorridors78,"  in  78th  Annual  Meeting  of  the Transportation Research Board, Washington D.C., 1999.
10.     S.  A.  DeLurgio, Forecasting Principles and Applications, New York:  McGraw-Hill., 1998,12 .
11.     X.  Fei, C.-C.  Lu,  and  K. Liu,  "A  Bayesian  dynamic linear model approach for real-time short-term freeway travel time prediction," Transportation Research Part C: Emerging Technologies, vol. 19, no. 6,pp. 1306-1318, 2011.
12.     M.T. Hagan,  H.B. Demuth,   and  M. Beale,  Neural Network Design,  PWS, Boston, 1996.
13.     R.H. Jeong , "The  Prediction  of  Bus  Arrival  time  Using  Automatic Vehicle Location Systems Data", A Ph.D. Dissertation at Texas A&M University , 2004.
14.     R. Jeong,  and L. Rilett, "The Prediction of Bus Arrival Time using AVL data.", 83rd Annual General Meeting, Transportation Research Board, National Research Council, Washington D.C., USA, 2004.
15.     Y. Jin and B. Sendhof, "Pareto-Based Multi Objective  Machine  Learning:  An Overview  and  Case  Studies," EEE Transactions on Systems, Man and Cybernetics—Part C: Applications and  Reviews, vol. 38, no. 3, pp. 397-415, May 2008.
16.     R.E. Kalman , “A  new  approach  to  linear  filtering  and  prediction problems”, Transactions  of  the  ASME-Journal  of  Basic  Engineering,  82  (Series  D), 1960,pp.35-45.
17.     B. Kidwell,  "Predicting  Transit Vehicle  Arrival  Times,"  Geo Graphics Laboratory , Bridgewater State College, Bridgewater, Mass., 2001.
18.     C.M. Kuchipudi and S. Chien,"Development of a hybrid model for dynamic travel-time prediction", Transportation Research Record , vol. 1855, pp. 22-31, 2003.
19.     W.-H. Lee,S.-S. Tseng,and S.-H. Tsai, "A knowledge based real time travel time prediction system for urban network", Expert Systems with Applications36, vol. 36, no. 3, pp. 4239-4247, 2009.
20.     Lesniak, T. Danken, and M. Wojdyla , "Application of Kalman Filter to Noise Reduction in Multichannel Data", Schedae Informaticae, Vol. 17/18, Poland, 2009.
21.     W.-H.  Lin  and  J.  Zeng,  "Experimental  Study on  Real-Time Bus  Arrival  Time  Prediction with  GPS  Data,"  In  Transportation  Research Record: Journal of the Transportation Research Board , No. 1666, pp. 101-109,1999.
22.     R. P. S.  Padmanaban,  L.Vanajakshi, and  S. C. Subramanian, "Estimation of Bus Travel Time Incorporating Dwell Time for APTS Applications", in IEEE Intelligent Vehicles Symposium, vol. IV, 2009, pp. 955-959, 2009.
23.     T. Park and S. Lee, "A Bayesian Approach for Estimating  Link Travel  Time  on  Urban  Arterial  Road Network",  Springer  ICCSA , LNCS , vol.3043, pp. 1017-1025, 2004.
24.     Park and L. R. Rilett, "Forecasting Freeway Link Travel Times  with  a Multilayer  Feedforward  Neural  Network", Computer-Aided  Civil  and Infrastructure Engineering, pp. 357-367, 1999.
25.     J.Patnaik, S.Chein,and A.Bladihas "Estimation of Bus Arrival  Times Using APC  Data",  Journal  of  Public  Transportation,  Vol.  7, No.  1, pp. 1-20, 2004.
26.     Y. Ramakrishna ,  P. Ramakrishna,  and R. Sivanandan ,"Bus Travel Time Prediction Using GPS Data", Proceedings Map India, 2006.
27.     Recknagel , "Applications of machine learning to ecological modeling", Ecological Modeling, vol. 146, no. 1, pp.303-310, December 2001.
28.     A. Shalaby,   and  A.  Farhan, "Bus  Travel  Time Prediction  for Dynamic  Operations  Control  and  Passenger  Information  Systems", CD-ROM.  82nd Annual  Meeting  of  the  Transportation  Research  Board,  National Research Council, Washington D.C. ,2004.
29.     B. L. Smith and M.J. Demetsky, "Short-term traffic flow prediction:   Neural  network approach",                  Transportation  Research Record,  vol.  1453,  pp. 98-104, 1995.
30.     Y. J. Stephanedes, E. Kwon, and P.Michalopoulos, "Online  diversion  prediction for  dynamic  control  and  vehicle  guidance",  Transportation Research Record, vol. 1287, pp. 11-19, 1990.
31.     D. Sun, H. Luo, L. Fu, W.Liu, X. Liao, and M. Zhao," Predicting Bus Arrival Time on the Basis of Global Positioning System Data", Transportation Research Record, Washington, DC; National Academy, 2007.
32.     H. Sun,  H. Liu,  and B. Ran,  "Short term traffic forecasting using the local linear regression model", in 82nd Annual Meeting, Washington, 2003.
33.     T. Thomas, W.A.M.  Weijermars and E.C. Van Berkum, “Predictions of Urban Volumes in Single Time Series, IEEE Transactions on intelligent transportation systems, vol. 11, no1, pp. 71-80, 2010.
34.     W.-H.  Tin and  R. L  Bertini,"Modeling schedule recovery processes in transit operations for bus arrival time prediction," in The IEEE 5th International Conference on Intelligent Transportation Systems, 2002, pp. 857-862. 
35.     H. Tu, "Monitoring Travel Time Reliability on Freeways". Transportation and Planning. Delft,. Delft University of Technology. PhD thesis, 2008.
36.     J.W.C. van Lint, S.P. Hoogendoorn, and H.J.  van Zuylen, "Accurate freeway travel time prediction with state-space neural networks under missing data", Transportation, vol. 13, no. 5, pp. 347-369 ,2005.
37.     L. Vanajakshi and L. R. Rilett,"Support Vector Machine Technique for the Short Term Prediction of Travel Time," in Proceedings of the IEEE Intelligent Vehicles Symposium, Istanbul, Turkey, 2007, pp. 13-15.
38.     L. Vanajakshi, S.C. Subramanian, and R. Sivanandan, “Travel time prediction under heterogeneous traffic conditions using global positioning system data from
uses”, IET Intell. Transp. Syst. 3, p1 -9.
39.     Z. Wall, and D. J. Dailey , "An Algorithm for Predicting the Arrival Time of Mass Transit Vehicles Using Automatic Vehicle Location Data",  CD-ROM.78th  Annual  Meeting  of  the  Transportation  Research  Board,  National Research Council, Washington D.C , 1999.
40.     C.-C.  Wei,  D.-C.  Su,  M.-H. Chang,  and  J.-M. Ho,  "Travel  Time Prediction  With  Support  Vector  Regression", in  IEEE  Transactions  on Intelligent  Transportation Systems,  vol.  5,  2004,  pp. 1438-1442.
41.     L. Weigang, W. Koendjbiharie,, R.C. Juca, Y. Yamashita, and A.  Maciver, "Algorithms for estimating bus arrival times using GPS data", Intelligent Transportation  Systems,  2002,  Proceedings,  The  IEEE  5th  International  Conference on Volume, Issue ,Page(s):  868 – 873.
42.     B. Williams, and L. Hoel, “Modeling and forecasting vehicle traffic flow as a seasonal arima process:  Theoretical basis and empirical results”, Journal of Transportation Engineering, 129(6):664-672, 2003.
43.     M.  Xue  and  C.  Zhu, "A  Study  and Application  on Machine Learning of Artificial Intellligenc," ,in InternationalJoint Conference on Artificial Intelligence, Changchun, China , 2009, pp. 272-274.
44.     J.-S.  Yang,” Travel time prediction using the GPS test vehicle and Kalman filtering techniques”, In American Control Conference, Portland, Oregon, USA, 2005.
45.     B.YU,  B.YU,  J. LU,  and  Z. YANG,  "An  Adaptive Bus  Arrival  Time Prediction  Model,"  in  Proceedings  of  the Eastern  Asia  Society  for Transportation
Studies, vol. 7, 2009, 15.
46.     B. Yu, W. H.K. Lam, and M. L. Tam, "Bus arrival time prediction at bus stop with multiple routes", Transportation Research Part C : Emerging Technologies, vol. 19, no. 6, pp. 1157-1170, December 2011.
47.     J. Zhang, L. Yan, Y. Han, and J. Zhang, "Study on the Prediction Model of Bus Arrival Time ", in International Conference on Management and Service Science, MASS, Shanghai, China , 2009, pp. 1- 3.
48.     T. Zhu, F. Ma, T. Ma, and C. Li, "The Prediction of Bus Arrival Time Using Global Positioning System Data And Dynamic Traffic Information",  in  Wireless and Mobile Networking Conference (WMNC), 2011 4th Joint IFIP,  Beijing, China , 2011, pp. 1-5.
49.     H. Liu,  H. van Zuylen, H. van Lint, and M. Salomons, "Predicting  urban  arterial  travel time  with  state-space  neural  networks  and Kalman  filters", Transportation Research Record , vol. 1968, pp. 99-108, 2006.

164-169