Author
Powers, Linda S.Zhang, Yiming
Chen, Kemeng
Pan, Huiqing
Wu, Wo-Tak
Hall, Peter W.
Fairbanks, Jerrie V.
Nasibulin, Radik
Roveda, Janet M.
Affiliation
Univ Arizona, Dept Elect & Comp EngnUniv Arizona, Biomed Engn Grad Interdisciplinary Program
Issue Date
2017-05-18
Metadata
Show full item recordPublisher
SPIE-INT SOC OPTICAL ENGINEERINGCitation
Linda 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/10.1117/12.2263220Rights
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).Collection Information
This 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.Abstract
New 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.ISSN
0277-786XVersion
Final published versionae974a485f413a2113503eed53cd6c53
10.1117/12.2263220