Data-Driven Sampling Matrix Boolean Optimization for Energy-Efficient Biomedical Signal Acquisition by Compressive Sensing

Yuhao Wang, Xin Li, Kai Xu, Fengbo Ren, Hao Yu

Research output: Contribution to journalArticle

13 Scopus citations

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.

Original languageEnglish (US)
Article number7742902
Pages (from-to)255-266
Number of pages12
JournalIEEE Transactions on Biomedical Circuits and Systems
Volume11
Issue number2
DOIs
StatePublished - Apr 1 2017

Keywords

  • Compressive sensing
  • low power sensor
  • quantization
  • resistive random-access memory (RRAM)
  • Sampling matrix optimization

ASJC Scopus subject areas

  • Biomedical Engineering
  • Electrical and Electronic Engineering

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