Publication

Abstract : Several papers have appeared on the subject of compressed sensing (CS) in the last decade. Many insights to this subject were given in the literature. Smaller number of random projections also preserves distances in a signal space with high probabilities. The most important fact about compressed sensing is that it gives a new algorithmic approach to perception, revealing that global information is embedded in the local information. This paper is an attempt in exposing the basic resources from signal processing, random matrix and information theory, function spaces and sparsity from transform domain point of view. The process of compressed sensing is not covered here, however, necessary foundations to understand this method are provided.