Gaussian process regression for improving the performance of self-powered time-of-occurrence sensors

Liang Zhou, Kenji Aono, Shantanu Chakrabartty

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In our previous work, we had demonstrated a CMOS timer-injector integrated circuit for self-powered sensing of time-of-occurrence of mechanical events. While the sensor could achieve an improved time-stamping accuracy by averaging the output across over multiple channels, the mismatch between the channels made the calibration process cumbersome and time-consuming. In this paper, we propose the use of non-parametric machine learning techniques to achieve more robust and accurate event reconstruction. This is demonstrated using training and testing data that were obtained from fabricated prototypes on a 0.5-μm CMOS process; the model trained using Gaussian process regression can achieve an average recovery accuracy of 3.3% on testing data, which is comparable to the performance of using an averaging technique on calibrated injection results. The experimental results also validate that scalable performance can be achieved by employing more injection channels.

Original languageEnglish
Title of host publication2018 IEEE 61st International Midwest Symposium on Circuits and Systems, MWSCAS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages996-999
Number of pages4
ISBN (Electronic)9781538673928
DOIs
StatePublished - Jul 2 2018
Event61st IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2018 - Windsor, Canada
Duration: Aug 5 2018Aug 8 2018

Publication series

NameMidwest Symposium on Circuits and Systems
Volume2018-August
ISSN (Print)1548-3746

Conference

Conference61st IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2018
Country/TerritoryCanada
CityWindsor
Period08/5/1808/8/18

Fingerprint

Dive into the research topics of 'Gaussian process regression for improving the performance of self-powered time-of-occurrence sensors'. Together they form a unique fingerprint.

Cite this