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Title

Beyond The Sampling Theorem- Compressive Sensing and its Applications

Author

AISHA SULTHANA, R.BALAMURALI

Citation

Vol. 14  No. 1  pp. 77-82

Abstract

The compressive sensing (or compressive sampling, CS) theorem states that a sparse signal can be perfectly reconstructed even though it is sampled at a rate lower than the Nyquist rate. It has gained an increasing interest due to its promising results in various applications. There are two popular reconstruction methods for CS: basis pursuit (BP) and matching pursuit (MP).Introductory papers on CS often concentrated either on mathematical fundamentals or reconstruction algorithms for CS. Newcomers in this field are required to study a number of papers to fully understand the idea of CS. This paper aims to provide both the basic idea of CS and how to implement BP and MP, so that newcomers no longer need to survey multiple papers to understand CS and can readily apply CS for their works.

Keywords

Nyquist rate, basis pursuit, matching pursuit

URL

http://paper.ijcsns.org/07_book/201401/20140114.pdf