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.
[5] C. Puente, J. Romeu, R. Pous, X. Garcia and F. Benitez, "Fractal multiband antenna based on the Sierpinski gasket", Electronic Letters, vol. 32, no. 1, pp. 1-2, 1996.
[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.
[2] J. Allebach and P. W. Wong, ―Edge-directed interpolation,‖ in Proc. IEEE Int. Conf. Image Processing, 1996, vol. 3, pp. 707–710.
[3] L. Xin and M. T. Orchard, ―New edge-directed interpolation,‖ IEEE Trans. Image Processing, vol. 10, no. 10, pp. 1521–1527, Oct. 2001.
[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.
[5] M. Irani and S. Peleg, ―Motion analysis for image enhancement: Resolution, occlusion and transparency,‖ J. Vis. Commun. Image Represent., vol. 4, pp. 324–335, Dec. 1993.
[6] B. S. Morse and D. Schwartzwald, ―Image magnification using level-set reconstruction,‖ in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2001, pp. 333–340.
[7] C. B. Atkins, C. A. Bouman, and J. P. Allebach, ―Optimal image scaling using pixel classification,‖ in Proc. IEEE Int. Conf. Image Processing, 2001, pp. 864–867.
[8] S. Baker and T. Kanade, ―Limits on super-resolution and how to break them,‖ IEEE Trans. Pattern Anal. Mach. Intell., vol. 2, no. 9, pp. 1167–1183, Sep. 2002.
[9] C. Liu, H. Y. Shum, and C. S. Zhang, ―A two-step approach to hallucinating faces: Global parametric model and local non-parametric model,‖ in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2001, pp. 192–198.
[10] W. T. Freeman and E. C. Pasztor, ―Learning low-level vision,‖ in Proc. IEEE Int. Conf. Computer Vision, 1999, pp. 1182–1189.