Design of Energy-efficient Sensing Systems with Direct Computations on Compressively-sensed Data
Mohammed Shoaib · 2013
The aim of this thesis is to explore the energy limits that can be achieved by signal-processing systems when they explicitly utilize signal representations that encode infor-mation efficiently. Compressive sensing is one method that enables us to efficiently repre-sent data. The challenge, however, is that in compressive sensing, signals get substantially altered due to the random projections involved, posing a challenge for signal analysis. Moreover, due to the high energy costs, reconstructing signals before analysis is also of-ten infeasible. In this thesis, we develop methodologies that enable us to directly perform analysis on embedded signals that are compressively sensed. Thus, our approach helps potentially reduce the energy and/or resources required for computation, communication, and storage in sensor networks. We specifically focus on transforming linear signal-processing computations so that they can be applied directly to compressively-sensed signals. We show that this can be achieved by solving a system of linear equations, where we solve for a projection of the processed signals as opposed to the processed signals themselves. This opens up two ap-