Gas identification with microelectronic gas sensor in presence of drift using robust GMM

Sofiane Brahim-Belhouari, Amine Bermak, Philip C H Chan

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

The pattern recognition problem for real life applications of gas identification is particularly challenging due to the small amount of data available and the temporal variability of the instrument mainly caused by drift. In this paper we present a gas identification approach based on class-conditional density estimation using Gaussian mixture models (GMM). A drift counteraction approach based on extracting robust feature using a simulated drift is proposed. The performance of the retrained GMM shows the effectiveness of the new approach in improving the classification performance in the presence of artificial drift.

Original languageEnglish
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume5
Publication statusPublished - 2004
Externally publishedYes

Fingerprint

Chemical sensors
microelectronics
Microelectronics
sensors
Gases
gases
Pattern recognition
pattern recognition

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Acoustics and Ultrasonics

Cite this

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