iosr-JECE   Volume-2 ~ Issue-3

Abstract: The rise of the Internet and multimedia techniques in the mid-1998s has prompted increasing interest in hiding data in digital media. Early research concentrated on watermarking to protect copyrighted multimedia products (such as images, audio, video, and text).Data embedding has also been found to be useful in covert communication, or Steganography. The goal was and still is to convey messages under cover, concealing the very existence of information exchange. There have been a number of Steganography embedding techniques proposed over the past few years.
Key words: Steganography, Discrete cosine transforms (DCT), JPEG, Quantization, Embedding, Extraction

[1] .J. R. Smith and B. O. Comisky, "Modulation and information hiding in images," in Information Hiding, First International Workshop, Lectu e Notesin Computer Science, R. Anderson, Ed. Berlin, Germany:Springer-Verlag, 1996, vol. 1174, pp. 207–226.
[2] I.J. Cox, J. Killian, T. Leighton and T .Shamoon, "Secure spread spectrum watermarking for images ,audio and video" in Proc. IEEE Int .Conf. Image Processing, Lausanne,Switzerland,Sept.2000,vol.111,pp.243-246.
[3] F.Johnsonand S. Jajodia, "exploring Steganography: seeing the unseen" IEEE computer magzinne,pp26-34 FEB1998
[4] P. Davern and M. Scott," Fractal based image steganography" ,in Information Hiding ,First International Workshop ,Lecture Notes in computer science M.D.Swanson,B.Zhu8 and
[5] A.H. Tewfik," Robust data hiding for images "in Proc. IEEE digital Signal procesising Workshop, Loen,Norway,Sept 1996 pp 37-40.
[6] I.J. Cox, S.roy and S .L Hingorani," Dyanamic histogram warping of images pairs for constant image brightness," in IEEE Int .Conf. Image Processing.
[7] S. Dumitrescu, X.WU and Z.Wang ," Detection of LSB Steganography via sample pair analysis ",IEEE transction on Signal processing,vol.51,no.7,july 2003,pp.1995-2007
[8] F. A.P. Petitcola s, R.J. Anderson and M.G.Kuhn,"Information Hiding –A Survey", Proceeding of the IEEE ,vol.87,no.7,pp.1062-1078,july 1999
[9] G. Caronni, "Assuring ownership rights for digital images," in Proc .Reliable IT Systems, VIS'95.
[10] J. Brassil, S. Low, N. Maxemchuk, and L.O'Gorman, "Electronic marking and identification techniques to discourage document copying," in Proc. Infocom'94, pp. 1278–1287.

Abstract: The multiband behavior of the Fractal Sierpinski Gasket Antenna is described in this paper. An analysis is performed to examine the parameters of an antenna with a frequency range in between 1 GHz to 6 GHz. The behaviors of an antenna are investigated such as return loss and bandwidth. Simulations have been done by using different iterations. This multiband Fractal antenna is also used for different wireless applications. Sierpinski gasket antenna using a coplanar waveguide (CPW) feed which is proposed for multiband applications.
Keywords: Fractal antenna, CPW feeding, multiband, Sierpinski gasket,

[1] D L. Jaggard, "On Fractal electrodynamics," in Recent Advances in Electromagnetic Theory, H. N. Kritikos and D. L. Jaggard, Eds. New York: Springer-Verlag, 1990, pp. 183–224.
[2] M. Waqas, Z. Ahmad and M.Ihsan, "Multiband Sierpinski Fractal Antenna", in 13th IEEE International Multitopic Conference, 2009, pp. 1-6
[3] G.F. Tsachtsiris, C.F. Soras, M.P. Karaboikis, and V.T. Makios, "Analysis of a Modified Sierpinski Gasket Monopole Antenna Printed on Dual Band Wireless Devices", IEEE Transactions on Antennas and Propagation, vol. 52, no. 10, pp. 2571-2579, October 2004.
[4] L. Lizzi and G. Oliveri, " Hybrid design of a Fractal-Shaped GSM/UMTS Antenna," J. Of Electromagnetics Waves and Applications, vol. 24, pp. 707-719, 2010.
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[6] C. Baliarda, J. Romeu, R. Pous, A. Cardama, "On the behavior of the Sierpinski multiband Fractal antenna", IEEE Transactions on Antennas and Propagation, vol. 46, no. 4, pp. 517-524, April 1998.
[7] S. T. Fang, "Analysis and design of triangular microstrip antennas", Ph.D. Dissertation, Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan, 1999.
[8] Thomas M. Tirpak, Sam M. Daniel, John D. Lalonde, and Wayne J. Davis, "A Note On A Fractal Architecture For Modeling And Controlling Flexible Manufacturing Systems", IEEE Transactions On Systems, Man, And Cybernetics, Vol. 22, No. 3, May-June 1992.
[9] R Garg, P. Bhartia, I. Bahl, and A. Ittipiboon, Microstrip Antenna Design Handbook, Artech House, Norwood, MA, 2001.
[10] H. R Hassani, D. Mirshekar-Syahkal, "Analysis of triangular patch antennas including radome effects", Proceedings H Microwaves, Antennas and Propagation, vol. 139, no. 3, pp. 251 – 256, June 1992.

Abstract: This project proposes a robust single-image super-resolution method for enlarging low quality web image/video degraded by down sampling and compression. To improve the resolution and perceptual quality of such web image/video, we bring forward a practical solution which combines adaptive regularization and learning-based super-resolution. The contribution of this work is twofold. First, we propose to analyze the image energy change characteristics during the iterative regularization process, i.e., the energy change ratio between primitive (e.g., edges, ridges and corners) and nonprimitive fields. Based on the revealed convergence property of the energy change ratio, appropriate regularization strength can then be determined to well balance compression artifacts removal and primitive components preservation. Second, we verify that this adaptive regularization can steadily and greatly improve the pair matching accuracy in learning based super resolution. The suggested approach has to be developed using matlab tool.
Index Terms: Adaptive regularization, learning-based super-resolution (SR), artifacts, down sampling.

[1] R. G. Keys, ―Cubic convolution interpolation for digital image processing,‖ IEEE Trans. Acoust., Speech, Signal Process., vol. 29, no.12, pp. 1153–1160, Dec. 1981.
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[4] Z. Xiong, X. Sun, and F.Wu, ―Fast directional image interpolator with difference projection,‖ in Proc. IEEE Int. Conf. Multimedia & Expo, 2009, pp. 81–84.
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Abstract: The objective of the research is to design a voice controlled car and use it as a platform for obtaining feedback from a critically controlled environment. The system comprises of a user interface module and a Robocar. The user interface includes an electret microphone through which the voice commands will be accepted by the voice recognitionchip(HM2007).Microcontrollers(PIC16F877A) in the transmitting and receiving side controls the motion of Robocar. The control commands and data are transmitted and received using ZigBee (IEEE 802.15.4).Sensors and transducers are present in the Robocar for collecting application specific datas.
Keywords:Voice controlled,Zigbee,User interface,Real time.

[1] Xiaoling Lv, Minglu Zhang and Hui Li, "Robot Control based on Voice Command", The Proceeding of the IEEE International Conference on Automation and Logistics, China 2008.
[2] Chomtip Pornpanomchai, Thammarat Saengsopee and Teravit Wongseree, "Robot Arm Control by Using Thai Voice Commands", The 1st Northeastern Computer Science and Engineering Conference, Thailand, 2005.
[3] Parichart Leechor, Chomtip Pornpanomchai, "Operation of a Radio-Controlled Car by Voice Commands", 2nd International Conference on Mechanical and Electronics Engineering, Thailand, 2010.
[4] Zigbee/IEEE 802.15.4 Networking Examples, Zigbee Wireless Network and Transceivers, 2008.
[5] Paolo Baronti, Prasant Pillai.Wireless Sensor Networks:A survey on the state of the art and the 802.15.4 and ZigBee Standards,Computer Communications, Volume 30,Issue 7,26 May 2007
[6] N.Yamasaki and Y.Anzai,"Active Interface for Human Robot Interaction,"Proccedings of the 1995 International Conference on Robotics and Automation,Nagoyo,Japan,May 1995.
[7] HM2007 Manual,Images Company
[8] Holmes J and Holmes W 2001 Speech Recognition and Synthesis 1st Edition pp2,ISBN-10.
[9] Nedevshi S,Patra R A and Brewer E A 2005 Hardware Speech Recognition for User Interfaces in Low Cost ,Low Power Devices
[10] Li Wenfeng,Hanfei,"Short Distance Wireless Voice Communication Based on ZigBee"University Of Science and Technology,China,2007

Abstract: The navigation problem is to direct the movement of vehicle so as to arrive at a given destination.The single model unscented filter gives better accuracy for non-linear navigation problems using unscented transform.But it fails to address noise variations in vehicle dynamics.Hence the multiple model filtering[3] is required to deal with both non-linearity and noise variations in vehicle movement.The interacting multiple model unscented filter(IMMUF) gives better navigation results compared to individual model filters like EKF ,UKF and IMM-EKF.In this paper, the Interacting multiple model unscented smoothing approach (IMMUS) which combines the estimates of two separately running forward and back ward IMMUF is introduced to improve navigation results.The simulation result proves that IMMUS approach provides better estimation accuracy compared to IMMUF and single model UKF.
Keywords- GPS; INS; Interacting Multiple Model (IMM);Extended Kalman Filter(EKF); Unscented Kalman Filter (UKF);Unscented Two Filter Smoother(UTFS); Interacting Multiple Model Unscented Filter(IMMUF);Interacting Multiple Model Unscented Smoother(IMMUS)

[1] Woei-Leong Chan and Fei-Bin Hsiao "Implementation of the Rauch –Tung-Striebel Smoother for sensor Compatibility correction of a Fixed-Wing Unmanned Air vehicle", Sensor Publications,11,3738-3764,2011.
[2] Helmick,R.E.,Blair,W.D., and Hoffman "Fixed-interval smoothing for Markovian switching systems",IEEE Trans.Information Theory,Vol.41,No.6,pp.1845-1855.Nov.1995.
[3] Jilkov,V.P.,Li,X.R., "Performanence enhancement of the IMM estimation by smoothing",in Proc.Int.Conf.Information Fusion,vol.1,pp.713-720,2002.
[4] Chen,B., and Tugnait,J.K., "Interacting multiple model fixed –lag smoothing algorithm for markovian switching systems",IEEE Trans.Aerospace and Electronic systems,Vol.36,No.1,pp.432-500,jan2002.
[5] Modelling and parameter Estimation of Dynamic Systems by J.R.Raol,G.Girija and J.Singh.
[6] A solution manual and Notes for:Applied Optimal Estimation by Arthur Gelp
[7] Ali Almagbile, Jinling Wang, "Evaluating the performances of Adaptive Kalman Filter Methods in GPS /INS Integration", journal of global positioning systems, 9, 33-40, 2010
[8] Chien-hao tseng and Dae-Jung jwo "GPS navigation processing using the IMM based Extended kalman filter for integrated navigation sensor fusion", Sensor Publications, 6, 4-17, 2009.
[9] Fong-Chi Chung "Fuzzy adaptive Unscented Kalman Filter for Ultra-Tight GPS/INS Integration", International Symposium on computational Intelligence and Design, 2010.
[10] YanLIng Hao and Zhen Guo "Adaptive Extended Kalman Filtering for INS/GPS integrated navigation systems", International joint conference on Computational Sciences and Optimization, 2009.

Abstract: Signature has been a distinguishing biometric feature through ages. They are extensively used as a means of personal verification; therefore an automatic verification system is needed. Even today thousands of financial and business transactions are being authorized via signatures. Signature verification finds its application in a large number of fields starting from online banking, passport verification systems to even authenticating candidates in public examinations from their signatures. This paper represents a brief survey on various off-line signature recognition & verification schemes. Categories and Subject Descriptors 1.4 Image Processing and Computer Vision General Terms: Systems, Forgeries, Methods, Skilled, Performance.
Keywords: Off-line Signature verification, on-line signature verification, biometrics, authentication systems

[1] P. Deng, H. Yuan Mark Liao & H. Tyan, "Wavelet ` Based Off-line Signature Recognition System", Proceedings 5th Conference on Optical Character Recognition and Document Analysis, Beijing, China., 1996.
[2] Mehdi Dehghan, Karim Faez & Mahmood Fathi "Offline signature verification system using shape descriptors & multiple neural networks" , IEEE , 1997.
[3] T. Kaewkongka, K. Chamnongthai, B. Thipakom, "Off-Line Signature Recognition using parameterized Hough Transform ", ISSPA Brisbane, Australia, 1999.
[4] H. Baltzakis, N. Papamarkos, "A new signature verification technique based on a two-stage neural network classifier", Engineering Applications of Artificial Inteligence ,2001.
[5] J Edson, R. Justino, F. Bortolozzi and R. Sabourin, "An off-line signature verification using HMM for Random, Simple and Skilled Forgeries", 2001.
[6] J Edson, R. Justino, F. Bortolozzi and R. Sabourin, "The Interpersonal and Intrapersonal Variability Influences on Off-line Signature Verification Using HMM", Brazilian Symp, Computer Graphics and Image Processing, 2002.
[7] Martinez, L.E., Travieso, C.M, Alonso, J.B., and Ferrer, M. Parameterization of a forgery Handwritten Signature Verification using SVM. IEEE, International Carnahan Conference on Security Technology, 2004.
[8] Emre Özgündüz, Tülin Şentürk and M. Elif Karslıgil "Offline Signature verification and Recognition by Support Vector Machine", EUSIPCO, 2005.
[9] R. Abbas and V. Ciesielski, "A Prototype System for Offline Signature Verification Using Multilayered Feed forward Neural Networks", 1995.
[10] L. S. Oliveira, E. Justino, and R. Sabourin, "Off-line signature verification using writer-independent approach", IJCNN, 2007.

Abstract :This paper proposes a speaker specific source information at different levels.speaker recognition system exploits the source information (LP residual) present at different levels namely subsegmental, segmental & suprasegmental. The subsegmental analysis considers LP residual in blocks of 5 msec with shift of 2.5 msec to extract speaker information. The segmental analysis extracts speaker information by processing in blocks of 20 msec with shift of 2.5 msec. The suprasegmental speaker information is extracted by viewing in blocks of 250 msec with shift of 6.25 msec. The speaker recognizer studies performed using TIMIT (Texas Instruments and Massachusetts Institute of Technology) databases demonstrate that the segmental analysis provides best performance followed by subsegmental analysis. The suprasegmental analysis gives the least performance. However, the evidences from all the three levels of processing seem to be different and combine well to provide improved performance, demonstrating different speaker information captured at each level of processing. Finally, the combined evidence from all the three levels of processing together with vocal tract information further improves the speaker recognition performance.
Keywords: LP residual,Segmental,Source information ,Subsegmental,Suprasegmental.

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[6] A. E. Rosenberg, "Automatic speaker verification: A review," Proc. IEEE, vol. 64, pp. 475-487, Apr. 1976.
[7] Rama Chellapa, Charles L. Wilson and Saad Sirohey, "Human and machine recognition of faces: A survey," Proc. IEEE vol. 83, no. 5, pp. 705-740, May 1995.
[8] Pati, D., & Prasanna, S. R. M. (2010). Speaker information from subband energies of linear prediction residual. In Proc. NCC (pp. 1–4).
[9] Atal, B. S. (1972). Automatic speaker recognition based on pitch contours. The Journal of the Acoustical Society of America, 52(6), 1687–1697.

Abstract : A multiple-input multiple output (MIMO) communication system is used with orthogonal frequency division multiplexing(OFDM) modulation technique to achieve high and reliable data rate transmission over broadband wireless channels. The need of MIMO OFDM is important because of only MIMO improves the system capacity without additional bandwidth.[1] The performance of MIMO-OFDM system is evaluated on the basis of Bit Error Rate(BER) and Mean Square Error(MSE). The correct channel estimation requires channel response of subcarriers between the pilot tones. Usually the received signal is distorted by channel characteristics. To recover the transmitted bits channel effects must be estimated. By orthogonality principle, each component of received subcarrier is to be expressed as product of transmitted signal and channel frequency response of subcarriers. So, the transmitted signal is recovered by estimating the channel response just at each subcarrier. Generally, data signal as well as training signal, or both , can be used for channel estimation. In this Paper, we will discuss channel estimation techniques in brief. The estimation of channel estimation technique will be carried out through MATLAB simulation.[3]
Keywords: Channel estimation, MIMO OFDM, Pilot carriers, PSAM, LS, MMSE, DFT based and DD
Estimation Techniques.

Examples follow:
Journal Papers:
[1] MM Rana "Channel estimation techniques and LTE Terminal implementation challenges" in Proc. International Conference on
Computer and Information Technology pp. 545-549 December 2010
[2] M. Simko, D. Wu, C. Mehlführer, J. Eilert, D. Liu "Implementation Aspects of Channel Estimation for 3GPP LTE Terminals" in
Proc. Proc. European Wireless 2011.
Books:
[3] MIMO OFDM Wireless communications with MATLAB by Yong Soo Cho\ Jaekwon Kim Won Young Yang/Chung-Gu Kang.
Theses:
[4] Michall David Larsen ,Studies o the Performance and Impact of channel estimation in MIMO and OFDM System, Brigham Young
University,2010 .