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dc.contributor.authorPowers, Linda S.
dc.contributor.authorZhang, Yiming
dc.contributor.authorChen, Kemeng
dc.contributor.authorPan, Huiqing
dc.contributor.authorWu, Wo-Tak
dc.contributor.authorHall, Peter W.
dc.contributor.authorFairbanks, Jerrie V.
dc.contributor.authorNasibulin, Radik
dc.contributor.authorRoveda, Janet M.
dc.date.accessioned2017-11-06T23:51:12Z
dc.date.available2017-11-06T23:51:12Z
dc.date.issued2017-05-18
dc.identifier.citationLinda S. Powers, Yiming Zhang, Kemeng Chen, Huiqing Pan, Wo-Tak Wu, Peter W. Hall, Jerrie V. Fairbanks, Radik Nasibulin, Janet M. Roveda, "Low power real-time data acquisition using compressive sensing", Proc. SPIE 10194, Micro- and Nanotechnology Sensors, Systems, and Applications IX, 101940C (18 May 2017); doi: 10.1117/12.2263220; http://dx.doi.org/10.1117/12.2263220en
dc.identifier.issn0277-786X
dc.identifier.doi10.1117/12.2263220
dc.identifier.urihttp://hdl.handle.net/10150/626011
dc.description.abstractNew possibilit ies 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.
dc.language.isoenen
dc.publisherSPIE-INT SOC OPTICAL ENGINEERINGen
dc.relation.urlhttp://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2263220en
dc.rights© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).en
dc.subjectCompressive sensingen
dc.subjectAdaptive Design for Informationen
dc.subjectlow poweren
dc.subjectreal-time data acquisitionen
dc.titleLow power real-time data acquisition using compressive sensingen
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Dept Elect & Comp Engnen
dc.contributor.departmentUniv Arizona, Biomed Engn Grad Interdisciplinary Programen
dc.identifier.journalMICRO- AND NANOTECHNOLOGY SENSORS, SYSTEMS, AND APPLICATIONS IXen
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
dc.contributor.institutionThe Univ. of Arizona (United States)
dc.contributor.institutionThe Univ. of Arizona (United States)
dc.contributor.institutionThe Univ. of Arizona (United States)
dc.contributor.institutionThe Univ. of Arizona (United States)
dc.contributor.institutionThe Univ. of Arizona (United States)
dc.contributor.institutionThe Univ. of Arizona (United States)
dc.contributor.institutionThe Univ. of Arizona (United States)
dc.contributor.institutionThe Univ. of Arizona (United States)
dc.contributor.institutionThe Univ. of Arizona (United States)
refterms.dateFOA2018-09-11T23:58:09Z
html.description.abstractNew possibilit ies 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.


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