Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/82294
Title: Data-Driven Sampling Matrix Boolean Optimization for Energy-Efficient Biomedical Signal Acquisition by Compressive Sensing
Authors: Wang, Yuhao
Li, Xin
Xu, Kai
Ren, Fengbo
Yu, Hao
Keywords: Low power sensor
Compressive sensing
Issue Date: 2016
Source: Wang, Y., Li, X., Xu, K., Ren, F., & Yu, H. (2017). Data-Driven Sampling Matrix Boolean Optimization for Energy-Efficient Biomedical Signal Acquisition by Compressive Sensing. IEEE Transactions on Biomedical Circuits and Systems, 11(2), 255-266.
Series/Report no.: IEEE Transactions on Biomedical Circuits and Systems
Abstract: Compressive sensing is widely used in biomedical applications, and the sampling matrix plays a critical role on both quality and power consumption of signal acquisition. It projects a high-dimensional vector of data into a low-dimensional subspace by matrix-vector multiplication. An optimal sampling matrix can ensure accurate data reconstruction and/or high compression ratio. Most existing optimization methods can only produce real-valued embedding matrices that result in large energy consumption during data acquisition. In this paper, we propose an efficient method that finds an optimal Boolean sampling matrix in order to reduce the energy consumption. Compared to random Boolean embedding, our data-driven Boolean sampling matrix can improve the image recovery quality by 9 dB. Moreover, in terms of sampling hardware complexity, it reduces the energy consumption by 4.6× and the silicon area by 1.9× over the data-driven real-valued embedding.
URI: https://hdl.handle.net/10356/82294
http://hdl.handle.net/10220/43517
ISSN: 1932-4545
DOI: 10.1109/TBCAS.2016.2597310
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TBCAS.2016.2597310].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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