IMAGE COMPRESSION AND ANAYLIS IN WIRELESS ADHOC NETWORKS

Hassan Azwar

Abstract


In today’s technological world as our use of and reliance on computers continues to grow, so too does our need for efficient ways of storing large amounts of data and due to the bandwidth and storage limitations, images must be compressed before transmission and storage. In this paper, we will be implementing lossy techniques for image compression. The techniques used will be Discrete Fourier Transform (DFT), Cosine Transform (CT), Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA). These four techniques are very much used in the field of Digital Signal Processing for processing many Signals and have many other applications. The software used in our project is MATLAB 7.0. We use this software to implement all the techniques with our algorithms and compare the result.

Full Text:

PDF

References


R. V. Kulkarni and G. K. Venayagamoorthy,“Computational

Intelligence in Wireless Sensor Networks: A Survey,” IEEE Communications Surveys & Tutorials, Vol. 13, No. 1, pp. 68-96, 2011.

M.Dankova, M.Stepnicka, Fuzzy transform as an additive normal form, Fuzzy Sets Syst. 157 (2006) 1024-1035

N. Kimura and S. Latifi, “A survey on data compression in wireless sensor networks," In Proc. the Information Technology: Coding and Computing, Vol. 2, pp:8 – 13, 2005

. A.N. Akansu, W.A. Serdijn, and I.W. Selesnick, Wavelet Transforms in Signal Processing: A Review of Emerging Applications, Physical Communication, Elsevier, vol. 3, issue 1, pp. 1–18, March 2010.

. Frigo, M.; Johnson, S. G. (February 2005). "The Design and Implementation of FFTW3" (PDF). Proceedings of the IEEE. 93 (2): 216–231. doi:10.1109/JPROC.2004.840301

. Warmuth, M. K.; Kuzmin, D. (2008). "Randomized online PCA algorithms with regret bounds that are logarithmic in the dimension". Journal of Machine Learning Research. 9: 2287–2320.

. C. Candan; M. A. Kutay; H. M.Ozaktas (2000). "The discrete fractional Fourier transform". IEEE Trans. on Signal Processing. 48 (5):1329-1337. Bibcode:2000ITSP...48.1329C. doi:10.1109/78.839980

. K. Dolfus and T. Braun, “An Evaluation of Compression Schemes for Wireless Networks”, Technischer Bericht IAM, vol 24, pp. 010-003, May 2010

. Ahmed E.A.A. Abdulla, Hiroki Nishiyama, and Nei Kato. “Extending the Lifetime of Wireless Sensor Networks: A Hybrid Routing Algorithm”, Computer Communications Journal, vol. 35, no. 9, pp. 1056-1063, May 2012

. Elmannai, Wafa, Khaled Elleithy, Ajay Shrestha, Mohamed Alshibli, and Reem Alataas. "A new algorithm based on discrete fourier transform to improve the lifetime of underwater Wireless Sensor Networks communications." InAmerican Society for Engineering Education (ASEE Zone 1), 2014 Zone 1 Conference of the, pp. 1-5. IEEE, 2014.

. C.M. Bishop, et al., Pattern Recognition and Machine Learning, 1, Springer, New York, 2006.

. H.C. Woo, Variable step size LMS algorithm using squared error and autocorrelation of error, Proc. Eng. 41 (2012) 47–52.

. S. Zhao, Z. Man, S. Khoo, H.R. Wu, Variable step-size LMS algorithm with a quotient form, Signal Process. 89 (1) (2009) 67–76

. M. A. Razzaque, C. Bleakley, S. Dobson, Compression in Wireless Sensor Networks: A Survey and Comparative Evaluation, ACM Trans. Sensor Networks 10(1) (2013) 5:1–43.

. G.J. Pottie, W.J. Kaiser, Wireless integrated network sensors, Communications of ACM, 43 (2000) 51-58.

. M. Gaeta, V. Loia, S. Tomasiello, Multisignal 1-D compression by Ftransform for wireless sensor networks applications, Appl. Soft Comput. 30 (2015) 329–340

. F. Di Martino, P. Hurtik, I. Perfilieva, S. Sessa, A color image reduction based on fuzzy transforms, Inform. Sci. 266 (2014) 101-111.


Refbacks

  • There are currently no refbacks.