@inproceedings{22895ca6a9fd484f9cf4a930b4ad28c9,
title = "Low power real-time data acquisition using compressive sensing",
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.",
keywords = "Adaptive Design for Information, Compressive sensing, low power, real-time data acquisition",
author = "Powers, {Linda S.} and Yiming Zhang and Kemeng Chen and Huiqing Pan and Wu, {Wo Tak} and Hall, {Peter W.} and Fairbanks, {Jerrie V.} and Radik Nasibulin and Roveda, {Janet M.}",
note = "Publisher Copyright: {\textcopyright} 2017 SPIE.; Micro- and Nanotechnology Sensors, Systems, and Applications IX 2017 ; Conference date: 09-04-2017 Through 13-04-2017",
year = "2017",
doi = "10.1117/12.2263220",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Dutta, {Achyut K.} and Islam, {M. Saif} and Thomas George",
booktitle = "Micro- and Nanotechnology Sensors, Systems, and Applications IX",
}