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Journal of Computer Networks and Communications
Volume 2011, Article ID 235259, 10 pages
http://dx.doi.org/10.1155/2011/235259
Research Article

A Novel Technique for Transmission of M-Ary Signal through Wireless Fading Channel Using Wavelet Denoising

1EEE Department, ADUST, Dhaka, Bangladesh
2WebSatMedia Pte. Ltd., Technopark, Singapore 469003
3School of Engineering and Computer Science, IUB, Dhaka, Bangladesh

Received 30 March 2011; Accepted 24 June 2011

Academic Editor: Youyun Xu

Copyright © 2011 Md. Zahangir Alam et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. M. Shapiro, “Embedded image coding using zerotrees of wavelet coefficients,” IEEE Transactions on Signal Processing, vol. 41, no. 12, pp. 3445–3462, 1993. View at Publisher · View at Google Scholar · View at Scopus
  2. H. Choi and R. Baraniuk, “Multiscale texture segmentation using wavelet-domain hidden Markov models,” in Proceedings of the 32nd Asilomar Conference on Signals, Systems & Computers, vol. 2, pp. 1692–1697, November 1998. View at Scopus
  3. J. Liu and P. Moulin, “Image denoising based on scale-space mixture modeling of wavelet coefficients,” in Proceedings of the International Conference on Image Processing (ICIP '99), pp. 386–390, Kobe, Japan, October 1999. View at Scopus
  4. M. K. Mihçak, I. Kozintsev, K. Ramchandran, and P. Moulin, “Low-complexity image denoising based on statistical modeling of wavelet coefficients,” IEEE Signal Processing Letters, vol. 6, no. 12, pp. 300–303, 1999. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Adaptive Wiener denoising using a Gaussian scale mixture model in the wavelet domain,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '01), pp. 37–40, October 2001. View at Scopus
  6. L. Sendur and I. W. Selesnick, “Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency,” IEEE Transactions on Signal Processing, vol. 50, no. 11, pp. 2744–2756, 2002. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Alfaouri and K. Daqrouq, “ECG signal denoising by wavelet transform thresholding,” American Journal of Applied Sciences, vol. 5, no. 3, pp. 276–281, 2008. View at Google Scholar · View at Scopus
  8. D. Lee Fugal, Conceptual Wavelets in Digital Signal Processing, Space & Signals Technologies LLC, 2006.
  9. F. Abramovich, T. Sapatinas, and B. W. Silverman, “Wavelet thresholding via a Bayesian approach,” Journal of the Royal Statistical Society Series B, vol. 60, no. 4, pp. 725–749, 1998. View at Google Scholar · View at Scopus
  10. A. Chambolle, R. A. DeVore, N. Y. Lee, and B. J. Lucier, “Nonlinear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkage,” IEEE Transactions on Image Processing, vol. 7, no. 3, pp. 319–335, 1998. View at Google Scholar · View at Scopus
  11. M. S. Crouse, R. D. Nowak, and R. G. Baraniuk, “Wavelet-based statistical signal processing using hidden Markov models,” IEEE Transactions on Signal Processing, vol. 46, no. 4, pp. 886–902, 1998. View at Google Scholar · View at Scopus
  12. S. G. Chang, B. Yu, and M. Vetterli, “Adaptive wavelet thresholding for image denoising and compression,” IEEE Transactions on Image Processing, vol. 9, no. 9, pp. 1532–1546, 2000. View at Google Scholar · View at Scopus
  13. M. Ghazel, G. H. Freeman, and E. R. Vrscay, “Fractal-wavelet image denoising revisited,” IEEE Transactions on Image Processing, vol. 15, no. 9, pp. 2669–2675, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Sardy, P. Tseng, and A. G. Bruce, “Robust wavelet denoising,” IEEE Transactions on Signal Processing, vol. 49, no. 6, pp. 1146–1152, 2001. View at Publisher · View at Google Scholar · View at Scopus
  15. Md. Z. Alam, C. Chittaranjon Patra, C. Patra, and M. Abdus Sobhan, “Modeling and performance analysis of a wireless fading channel,” in Proceedings of the 2nd ISECS International Coloquium on Computing, Communication, Control, and Management (CCCM '09), Sanya, China, August 2009.
  16. L. Debnath and D. Bhatta, Integral Transform and Their Application, Chapman & Hall/CRC, 2nd edition, 2007.
  17. A. Boggess and F. J. Narcowich, A First Course in Wavelet with Fourier Analysis, Prentice Hall, Upper Saddle River, NJ, USA, 2001.
  18. G. Oppenheim, Wavelets and Their Applications, ISTE Ltd., London, UK, 1st edition, 2007.
  19. J. G. Proakis, Digital Communication, McGraw Hill, New York, NY, USA, 3rd edition, 1995.
  20. H. Schulze and C. Luders, Theory and Applications of OFDM and CDMA-Wideband Wireless Comunications, John Wiley & Sons, London, UK, 2005.
  21. M. C. Jeruchim, P. Balaban, and K. S. Shanmugan, Simulation of Communication Systems, Kluwer Academic/Plenum, New York, NY, USA, 2nd edition, 2000.
  22. G. K. Stuber, Principles of Mobile Communication, Kluwer Academic Publishers, 2001.
  23. S.-T. Bow, Pattern Recognition and Image Processing, Marcel Dekker, New York, NY, USA, 2nd edition, 2002.
  24. S. M. Alamouti, “A simple transmit diversity technique for wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 16, no. 8, pp. 1451–1458, 1998. View at Google Scholar · View at Scopus