Adapted Compressed Sensing for Effective Hardware Implementations

The aim of this book is to give a concrete answer to the following question: can Compressed Sensing effectively yield optimized means for signal acquisition, encoding, and encryption, either in analog or digital circuits and systems, when implementation constraints are considered in its realization? The reason why this question is important is that Compressed-Sensing (CS) has been intensely discussed in the engineering community for more than a decade as a hot research topic, gathering a great deal of effort from a large community that unites scientists in applied mathematics and information theory, as well as engineers of analog/digital circuits and optical systems. Yet, several investigations have been dominated by a few misconceptions that somehow hindered the application of this promising technique to real-world systems. The first concept is that optimization and adaptivity are fundamentally pointless since CS is born as a universal technique that cannot be significantly improved. The second is that even if one wants to optimize CS, the degrees of freedom to do it are not there, since it is a technique that spreads information so uniformly that no criteria are able to tell important parts to emphasize from less important parts to neglect.