Low power real-time data acquisition using compressive sensing

Linda S. Powers, Yiming Zhang, Kemeng Chen, Huiqing Pan, Wo Tak Wu, Peter W. Hall, Jerrie V. Fairbanks, Radik Nasibulin, Janet M. Roveda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

New possibilities exist for the development of novel hardware/software platforms havin g fast data acquisition capability with low power requirements. One application is a high speed Adaptive Design for Information (ADI) system that combines the advantages of feature-based data compression, low power nanometer CMOS technology, and stream computing [1]. We have developed a compressive sensing (CS) algorithm which linearly reduces the data at the analog front end, an approach which uses analog designs and computations instead of smaller feature size transistors for higher speed and lower power. A level-crossing sampling approach replaces Nyquist sampling. With an in-memory design, the new compressive sensing based instrumentation performs digitization only when there is enough variation in the input and when the random selection matrix chooses this input.

Original languageEnglish (US)
Title of host publicationMicro- and Nanotechnology Sensors, Systems, and Applications IX
EditorsAchyut K. Dutta, M. Saif Islam, Thomas George
PublisherSPIE
ISBN (Electronic)9781510608894
DOIs
StatePublished - 2017
EventMicro- and Nanotechnology Sensors, Systems, and Applications IX 2017 - Anaheim, United States
Duration: Apr 9 2017Apr 13 2017

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10194
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

OtherMicro- and Nanotechnology Sensors, Systems, and Applications IX 2017
CountryUnited States
CityAnaheim
Period4/9/174/13/17

Keywords

  • Adaptive Design for Information
  • Compressive sensing
  • low power
  • real-time data acquisition

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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