Selective gas detection using conductivity-based MEMS resonator and machine learning

Wagner B. Lenz, Usman Yaqoob, Rodrigo T. Rocha, Mohammad I. Younis

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

Abstract

This work demonstrates multiple gases identification using a heated MEMS resonator and machine learning. The working principle of the gas sensor is based on the cooling/heating effect of the injected gases on the electrothermally actuated micro beam. As a case study, we demonstrate the concept using two analytes: Acetone and Helium. Machine learning algorithms and Principal Component Analysis are employed to classify each gas with its specific concentration level. The results show that a 100% accuracy rate is achieved for the identification of the different analytes with their concentration levels.

Original languageEnglish (US)
Title of host publication2022 IEEE Sensors, SENSORS 2022 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665484640
DOIs
StatePublished - 2022
Event2022 IEEE Sensors Conference, SENSORS 2022 - Dallas, United States
Duration: Oct 30 2022Nov 2 2022

Publication series

NameProceedings of IEEE Sensors
Volume2022-October
ISSN (Print)1930-0395
ISSN (Electronic)2168-9229

Conference

Conference2022 IEEE Sensors Conference, SENSORS 2022
Country/TerritoryUnited States
CityDallas
Period10/30/2211/2/22

Keywords

  • Classification
  • Data processing
  • Machine Learning
  • Smart sensing

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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