Editorial

Abstract

Compressed sensing (CS) is a new paradigm for the acquisition/ sampling of signals that violates the intuition behind the theorem of Shannon. In fact, CS theory states that, under surprisingly broad conditions it is possible to reconstruct certain signals or images using far fewer samples or measurements than they are used with traditional methods. To enable this, compressive sensing is based on two concept/principles: 1) sparsity, which is related to the signals of interest, and 2) incoherence, which relates to the methods of measurement/acquisition/ sampling. The aim of this Issue on Circuits, Systems, and Algorithms for Compressive Sensing is to stimulate a further advancement in this challenging scenario by presenting both results in the exploitation of CS techniques in signals and image processing and, for the first time, by offering to the reader a comprehensive collections of contribution dealing with the design of implementation of circuits and systems exploiting compressive sensing techniques and present them in a systematic way. The response to the call for papers overpassed by far our best expectations. In factwe received 91 high quality submission that out of which 27 were finally accepted for this special issue. Of these, 4 deal with advances in applications of CS to the area of communication and channel estimation or equalization, 6 apply these techniques to image and signal processing, while the remaining 17 papers focus on the main theme of the issue, namely to offer the most recent advances in the design and implementation of circuits and systems exploiting CS.