A comparative study of density models for gas identification using microelectronic gas sensor

S. Brahim-Belhouari, A. Bermak, Guangfen Wei, Philip Ching Ho Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

The aim of this paper is to compare the accuracy of a range of advanced density models for gas identification from sensor array signals. Density estimation is applied in the construction of classifiers through the use of Bayes rule. Experiments on real sensors' data has proved the effectiveness of the approach with an excellent classification performance. We compare the classification accuracy of four density models, Gaussian mixture models, generative topographic mapping, probabilistic PCA mixture and k nearest neighbors. On our gas sensors data, the best performance was achieved by the Gaussian mixture models.
Original languageEnglish
Title of host publicationProceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003
PublisherIEEE
Pages138-141
Number of pages4
ISBN (Electronic)0780382927, 9780780382923
DOIs
Publication statusPublished - 1 Jan 2003
Externally publishedYes
Event3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003 - Maritim Rhein/Main Hotel, Darmstadt, Germany
Duration: 14 Dec 200317 Dec 2003

Conference

Conference3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003
CountryGermany
CityDarmstadt
Period14/12/0317/12/03

Keywords

  • Brain modeling
  • Gas detectors
  • Linear discriminant analysis
  • Microelectronics
  • Nearest neighbor searches
  • Pattern recognition
  • Principal component analysis
  • Sensor arrays
  • Signal processing
  • Thin film sensors

ASJC Scopus subject areas

  • Signal Processing

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