@inproceedings{behravan2015rate-adaptive,
author = {Behravan, Vahid and Glover, Neil E. and Farry, Rutger and Zarar, Shuayb and Chiang, Patrick Y.},
title = {Rate-Adaptive Compressed-Sensing and Sparsity Variance of Biomedical Signals},
booktitle = {IEEE Int. Conf. Wearable and Implantable Body Sensor Networks (BSN)},
year = {2015},
month = {June},
abstract = {Biomedical signals exhibit substantial variance in sparsity. This variance can be exploited to save power in compressed-sensing systems. In this paper, we propose and implement an adaptive compressed-sensing system wherein the compression factor is modified automatically depending on the sparsity of the input signal. Experimental results based on our embedded sensor platform show a 16.2% improvement in power consumption when compared with a traditional compressed-sensing system 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.},
publisher = {IEEE - Institute of Electrical and Electronics Engineers},
url = {http://approjects.co.za/?big=en-us/research/publication/rate-adaptive-compressed-sensing-and-sparsity-variance-of-biomedical-signals/},
}