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