Title :
Rate-adaptive compressed-sensing and sparsity variance of biomedical signals
Author :
Vahid Behravan;Neil E. Glover;Rutger Farry;Patrick Y. Chiang;Mohammed Shoaib
Author_Institution :
Oregon State University Corvallis, OR, USA
fDate :
6/1/2015 12:00:00 AM
Abstract :
Biomedical signals exhibit substantial variance in their sparsity, preventing conventional a-priori open-loop setting of the compressed sensing (CS) compression factor. In this work, we propose, analyze, and experimentally verify a rate-adaptive compressed-sensing system where the compression factor is modified automatically, based upon the sparsity of the input signal. Experimental results based on an embedded sensor platform exhibit a 16.2% improvement in power consumption for the proposed rate-adaptive CS versus traditional CS with a fixed compression factor. We also demonstrate the potential to improve this number to 24% through the use of an ultra low power processor in our embedded system.
Keywords :
"Receivers","Electrocardiography","Dictionaries","Adaptive systems","Power demand","Compressed sensing","Databases"
Conference_Titel :
Wearable and Implantable Body Sensor Networks (BSN), 2015 IEEE 12th International Conference on
DOI :
10.1109/BSN.2015.7299419