Abstract
Compressive sensing is an efficient way to represent signal with less number of samples. Shannon’s theorem which states that the sampling rate must be at least twice the maximum frequency present in the signal (the so-called Nyquist rate) is a common practice and conventional approach to sampling signals or images. Compressive sensing reveals that signals can be sensed or recovered from lesser data than required by Shannon’s theorem. This paper presents a brief historical background, mathematical foundation, and a theory behind compressive sensing and its emerging applications with a special emphasis on communication, network design, signal processing, and image processing.
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