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A Deep Learning Framework of Quantized Compressed Sensing for Wireless Neural Recording
2016
IEEE Access
In low-power wireless neural recording tasks, signals must be compressed before transmission to extend battery life. Recently, compressed sensing (CS) theory has successfully demonstrated its potential in neural recording applications. In this paper, a deep learning framework of quantized CS, termed BW-NQ-DNN, is proposed, which consists of a binary measurement matrix, a non-uniform quantizer, and a non-iterative recovery solver. By training the BW-NQ-DNN, the three parts are jointly optimized.
doi:10.1109/access.2016.2604397
fatcat:lsdgockgtnhaze5xs4hbhq57je