On signal reconstruction algorithms and speedup opportunities

Abstract

Compressive Sensing (CS) reduces sampling data at the cost of increased signal reconstruction time. This problem has been addressed by a different set of algorithmic approaches under various realistic assumptions. Graphical Processing Units (GPU), Multicore architectures, distributed cloud computing are among technologies used to speed up data intensive and compute intensive algorithms. In this study, state of the art sample reconstruction algorithms (SRA) have been presented and surveyed. Opportunities to speed up their performances using parallel & distributed computing platforms have been also investigated and summarized. Finally, the study concludes with a detailed list of project ideas for further developments of these algorithms.