Rate-Adaptive Compressed-Sensing and Sparsity Variance of Biomedical Signals - Behravan, Vahid; Glover, Neil E.; Farry, Rutger; Zarar, Shuayb; Chiang, Patrick Y. (IEEE Int. Conf. Wearable and Implantable Body Sensor Networks (BSN), 2015)
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.