A blind source separation based micro gas sensor array modeling method

Guangfen Wei, Zhenan Tang, Philip Ching Ho Chan, Jun Yu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Blind Source Separation (BSS) has been a strong method to extract the unknown independent source signals from sensor measurements which are unknown combinations of the source signals. In this paper, a BSS based modeling method is proposed and analyzed for a micro gas sensor array, which is fabricated with surface micromachining technology and is applied to detect the gas mixture of CO and CH4. Two widely used BSS methods-Independent Component Analysis (ICA) and Nonlinear Principal Component Analysis (NLPCA) are applied to obtain the gas concentration signals. The analyzing results demonstrate that BSS is an efficient way to extract the components which corresponding to the gas concentration signals.
Original languageEnglish
Pages (from-to)696-701
Number of pages6
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3173
Publication statusPublished - 1 Dec 2004
Externally publishedYes

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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