A Systems Perspective on Compressed Sensing and its Use in Reconstructing Sparse Networks

Compressed sensing (or compressive sensing), a relatively new area in signal processing, improves the efficiency of algorithms for compression, coding, and recovery of natural signals, including audio, still images, and video. Specifically, compressed sensing is concerned with the recovery of a sparse signal from a small number of projections onto random vectors. This paper discusses how systems thinking contributed to uncovering key concepts of compressed sensing and presents the development of an extension of compressed sensing to the sparse network reconstruction problem. The sparse network reconstruction problem is a reverse engineering problem of interest in a wide variety of fields, ranging from remote sensing to social networks.