iosr-JECE   Volume-1 ~ Issue-3

Abstract :Protection of induction motor against possible problems such as overvoltage , overcurrent , overspeed , overtemperature , three-phase unbalanced occurs in course of it's operation is very important because it is used intensively in industry as an actuator. Induction motor can be protected using some components, such as timers, contactors, voltage, and current relays. The voltages, currents,speed, and temperature are the parameters of the motor, andthe problems occurred in the system, are monitored by microcontroller ATMEGA-16 which communicates with GSM and GSM send messages are shown on the LCD as well as on the mobile screen. Experimental resultsare presented in this paper which shows that the parameters of induction motor can be monitored using GSM which costs less, provides higher accuracy as well as safe and motorgets fully protected.
Keywords: ATMEGA-16, GSM,induction motor,monitoring,parameters.

[1] Petervas, ―Parameter estimation, condition monitoring,and diagnosisof electricalmachines.‖ dared on press oxford, 1993. ISBN-10:0198593759, ISBN-13: 978-0198593751.
[2] Atmel Mega16 Datasheet. (www.atmel.com/images/doc2466.pdf-united states)
[3] en.wikipedia.org/wiki/GSM
[4] Search mobilecomputing. techtarget.com.
[5] GSM Module (www.nowsms.com)
[6] ATcommand manual (www.sics.se/~bg/GC75-AT-Commands-R2A.pdf)
[7] Temperature sensors and control ICS –Temp sensor-LM35….(www.ti.com/product/LM35)
[8] Ramazan Bayindir , Member , IEEE, Ibrahim Sefa , Member , IEEE,Ilhami Colak, Member , IEEE and Askin Bektas. (Faults Detection and Protection of Induction Motor Using Sensor)
[9] LCD- Wikipedia, the free encyclopedia. (en.wikipedia.org/wiki/liquid_crystal_display)
[10] ELECTRIC MOTOR-Wikipedia, the free encyclopedia. en.wikipedia.org/wiki/Electric_motor) Similarly , in case of temperature , when temperature is beyond 60 degree then it will stop its working and message will send as‖ overheated‖

ABTRACTS : his application is related to Toll Plaza or check posts on the highways where continuous toll collection takes place by the persons appointed for its collection from the owners of the vehicles. It is intended by the user of this application to make oneself able to verify, "Is the person collecting the amount according to specified class of the vehicle or not?" It is useful for the auditors for verification of collected toll and vehicles passed.
KEYWORDS :Region of interest, Marker image, region growing.

[1] Rafael C.Gonzalez and Richard E. Woods, ―Digital Image Processing, Pearson Education‖, 2004.
[2] Rafael C.Gonzalez and Richard E. Woods and Steven L. Eddins,‖ Digital Image Processing with MATLAB‖, Pearson Education‖, 2006.
[3] Delores M.Etter, David C.Kuncicky and Doug Hull,‖ Introduction to MATLAB‖, Pearson Education‖, 2006.S.
[4] Chapman Stephan,‖MATLAB programming for Engineers‖, second edition, International student edition, Thomson books.
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[6] IEEE journal of,‖Selected Topics In signal processing‖, October 2009, Volume-3,No-5.
[7] IETE journal of research, ―special issue on biomedical image and signal processing‖, Volume 54, No-3
[8] T. Chen and H. R. Wu. Impulse noise removal by multi-state median _filtering. In Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, volume IV, pages 2183.2186, June 2000.
[9] John Canny. A computational approach to edge detection. IEEE Trans. Pattern Analysis and Machine Intelligence, PAMI-8(6):679.698, November 1986.
[10] http://www.eee.bham.ac.uk/spannm/computervision/image segmentation

Abstract: ISpeed and Noise performance of SiGe HBTs in terms of the cut off frequency and the noise figure is investigated by ATLAS software. Noisy transistors disgrace the performance of mobile wireless receivers and put off amplifiers and oscillators from meeting the rigorous requirements forced on them while working at frequencies in the GHz range. Small noise improvements at the device level can have a large blow on overall system presentation. In this paper, a model is projected for speed and noise analysis of SiGe HBTs that is implemented in software ATLAS from SILVACO international and analyzed all the way through a thorough method. The results of experimental and simulated model has been compared and contrasted.
Keywords:SiGe HBT , ATLAS Software , parameter extraction

1. Peter Ashburn, University of Southampton, Southampton, UK-" SiGe Heterojunction Bipolar Transistors", John Wiley & Sons, Ltd ISBN: 0-470-84838-3, 2003.
2. K. Kumar, and A. Chakravorty, "Physics based modeling of RF noise in SiGe HBTs", IEEE Proceedings of International workshop on Electron Devices and Semiconductor Technology IEDST'09', pp. 1-4, 2009.
3. Han-Yu Chen, Kun-Ming Chen, Guo-Wei Huang and Chun-Yen Chang, "Small-Signal Modeling of SiGe HBTs Using Direct Parameter-Extraction Method", IEEE Transactions on Electron Devices, vol. 53, no. 9, 2006.
4. Ankit Kashyap and R.K. Chauhan, "Effect of the Ge profile design on the performance of an n-p-n SiGe HBT-based analog circuit", Microelectronics journal, MEJ: 2554, 2008.
5. Pradeep Kumar and R. K. Chauhan, "Electrical parameter characterization of bandgap engineered Silicon Germainium HBT for HF applications", proceedings of International conference on Emerging trends in signal processing and VLSI design, GNEC Hyderabad, Jun. 11-13, pp. 1157- 1163, 2010.
6. S. Bousnina, P. Mandeville, A. B. Kouki, F. Ghannouchi, Direct parameter-extraction method for HBT small-signal model, IEEE Transactions on Microwave Theory and Techniques, Vol. 20, No. 2, February (2002).
7. L. Degachi, F. Ghannouchi, Systematic and Rigorous Extraction Method of HBT Small-Signal Model Parameters, IEEE Transactions on Microwave Theory and Techniques, Vol. 54, No. 2, February (2006).
8. F. Jun, Small-signal model parameter extraction for microwave SiGe HBTs based on Y- and Z- parameter characterization,Journal of Semiconductors, Vol. 30, No. 8, August (2009).
9. J.M. Zamanillo, A. Tazon, A. mediavilla and C. Navarro, " Simple algorithm extracts SiGe HBT parameters ". Microwaves and RF, October 1999.
10. Atlas user's manual device simulation software, SILVACO international, 2004.

ABSTRACT:Face representation (FR) plays a typically important role in face recognition and methods such as principal component analysis (PCA) and linear discriminant analysis (LDA) have been received wide attention recently. These FR methods will inevitably lead to poor classification performance in case of great facial variations such as expression, lighting, occlusion and so on, due to the fact that the image gray value matrices on which they manipulate are very sensitive to these facial variations .The recognition of faces is very important because of its potential commercial applications, such as in the area of video surveillance, access control systems, retrieval of an identity from a data base for criminal investigations and user authentication. The recognition performance of the face recognition system deteriorates when the system is exposed to the real world scenario. This problem happens because we do not have a complete set of training samples that consists of all types of visual variations. Furthermore, the extendibility of the system to recognize more new people who join the existing groups in the future may cause a problem to the system. In this work, a radial basis function (RBF) neural network with a new incremental learning method based on the regularized orthogonal least square (ROLS) algorithm is proposed for face recognition. It is designed to accommodate new information without retraining the initial network. In addition, it accumulates previous experience and learns updated new knowledge of the existing groups to increase the robustness of the system. The proposed work is to be developed on Matlab platform for its realization.
KEYWORDS: Face recognition, incremental learning, neural network, orthogonal least square, radial basis function (RBF), visual variation.

[1] D. Masip, A. Lapedriza, and J. Vitria, "Boosted online learning for face recognition," IEEE Trans. Syst., Man, Clyburn. B, Cybern, vol. 39, no. 2, pp. 530-538, Apr. 2009.
[2] M. Artac, M. Jogan, and A. Leonardis, "Incremental PCA for online visual learning and recognition, in Proc. ICPR, 2002, vol. 3, pp. 781784.
[3] S.Ozawa, S. L. Toh, S. Abe, S. Pang, and N. Kasabov, "Incremental learning of feature space and classifierfor face recognition", Neural Netw.,vol. 18, no. 5/6, pp. 575-584, Jul./Aug. 2005.
[4] H. Zhao and P. C. Yuen, "Incremental linear discriminant analysis for face recognition," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 38, no. 1, pp. 210–221, Feb. 2008.
[5] S. Chen, E. S. Chng, and K. Alkadhimi," ROLS algorithm for constructing radial basis function networks," Int. J. Control, vol. 64, no. 5, pp. 829–837, Jul. 1996.
[6] Y. W. Wong, K. P. Seng, and L.-M. Ang, "Dual optimal multiband feature for face recognition",Expert Syst. Appl., vol. 37, no. 4, pp. 2957–2962, Apr. 2010.

ABSTRACT: Free Space Optics is the technology which implies the line-of-sight link for the transfer of data between two distant points. The beam of light is used to provide optical connection that can transmit and receive data. The performance of the modulation techniques- Binary Phase Shift Keying (BPSK) and Quadrature Phase Shift Keying (QPSK) are studied in the Nakagami channel and the Rician Channel. The QPSK modulation provides double data rate than the BPSK modulation technique. The certain impairments associated with the FSO system are also studied in this work. The effect of scintillation index and Free Space Path Loss (FSPL) are also considered in this work.
Keywords-BER, FSO, FSPL, Nakagami channel, Rician channel, Scintillation.

[1] S. Bloom, E. Korevaar, J. Schuster, and H. Willebrand, "Understanding the performance of free-space optics", Journal of Optical Networking, Optical Society of America, Vol. 2, Issue 6, 2003, 178-200.
[2] C. Motlagh, V. Ahmadi, Z. Ghassemlooy and K. Abedi, "The Effect of Atmospheric Turbulence on the Performance of the Free Space Optical Communications", 6th IEEE International Symposium on Communication Systems, Networks and Digital Signal Processing, 2008, 540-543.
[3] D. Kedar and S. Arnon, "Urban optical wireless communication networks", IEEE Communications Magazine, 2004, s2-s7.
[4] F. Xu, M.A. Khalighi and S. Bourennane, Ecole Centrale Marseile, Institut Fresnel, "Pulse Position Modulation for FSO Systems: Capacity and Channel Coding", 10th IEEE International Conference on Telecommunication, ConTEL, 2009, 31-38.
[5] B. Barua, "Comparison the Performance of Free-Space Optical Communication with OOK and BPSK Modulation under Atmospheric Turbulence", International Journal of Engineering Science and Technology (IJEST), Vol 3, No. 5, 2011, 4391-4399.
[6] Z. Wang, W.D. Zhong, S. Fu and C. Lin, "Performance Comparison of Different Modulation Formats Over Free-Space Optical (FSO) Turbulence Links with Space Diversity Reception Technique", IEEE Photonics Journal, Volume 1, Number 6, 2009, 277-285.
[7] Z. Ghassemlooy and W.O. Popoola, "Terrestrial Free Space Optical Communication", Mobile and wireless communications: Network layer and Circuit level design, 17 (2010) 355-391.
[8] P.T. Dat, A. Bekkali, K. Kazaura, K. Wakamori and M. Matsumoto, "A Universal Platform for Ubiquitous Wireless Communications Using Radio Over FSO System", Journal of Lightwave Technology, Volume 28, Number 16, 2010, 2258 - 2267.
[9] S.S. Muhammad, E. Leitgeb and O. Koudelkat, "Multilevel Modulation and Channel Codes for Terrestrial FSO links", 2nd IEEE International Symposium on Wireless Communication Systems, 2005, 795-799.
[10] O. Bouchet and H. Sizun, Free-Space Optics Propagation and Communication (Chippenham, Wiltshire, Antony Rowe Ltd, 2006).

ABSTRACT :When measured, the Peak to Average Power Ratio (PAPR) of the discrete Orthogonal Frequency Division Multiplexing (OFDM) signal comes out to be less as compared to its continuous counterpart. This happens due to non-consideration of some of the peaks while measuring PAPR of the discrete time OFDM signal. This paper presents oversampling technique for measuring the accurate value of PAPR of discrete time OFDM signal. Simulations have been performed on Chu sequence for different interpolation factor L.
Key words- Chu sequence, Interpolation, OFDM, Oversampling, PAPR.

[1]. Yong soo cho, Jaekwon Kim, Won young Yang and Chung G. Kang, MIMO- OFDM wireless communication with MATLAB. John Wiley & Sons. (2010).
[2]. Ochiai, H. and Imai, K., The distribution of the peak-to-average power ratio in OFDM signals. IEEE Trans. Commun., 49(2), 282–289 (2001).
[3]. Han, S.H. and Lee, J.H., An overview of peak-to-average power ratio reduction techniques for multicarrier transmission. IEEE Wireless Communication , 12(2), 56–65 (2005).
[4]. Ochiai, H. and Imai, K., Clipping for peak power reduction of OFDM signals. IEEE GTC, vol. 2, pp. 731–735 (2000).
[5]. Chu, D.C., Polyphase codes with periodic correlation properties. IEEE Trans. Info. Theory, 18(4), 531–532 (1972).
[6]. Luke, H.D., Schotten, H.D., and Mahram, H.H., Binary and quadriphase sequences with optimal autocorrelation properties. IEEE Trans. Info. Theory, 49(12), 3271–3282 (2003).