Adaptive Compressive Sensing and Sparsity-Aware Techniques: Design, Algorithms and Applications


This project has studied and developed adaptive compressive sensing and sparsity-aware techniques and their applications for wireless communications, system identification, sensor array signal processing and distributed signal processing in wireless sensor networks. We have developed and investigated novel algorithms for compressive sensing that can exploit the sparsity in signals, resulting in improved performance and/or reduced complexity in a number of applications. We have also studied the fundamental limits of the proposed algorithms and considered the derivation of analytical formulas to help in the design of practical algorithms.  The proposed algorithms have been applied to problems in electronic, sensing and wireless communications systems.



York: Yuriy Zakharov, Rodrigo de Lamare, Amelia Gully, Ran Meng, Benjamin Henson, Jianghui Li


USP: Vítor Nascimento, Fernando Gonçalves de Almeida Neto



York-FAPESP Project

FAPESP Project number 2011/06994-2




               Publications and Matlab codes