• DocumentCode
    1175677
  • Title

    Identification of CO and NO2 using a thermally resistive microsensor and support vector machine

  • Author

    Al-Khalifa, S. ; Maldonado-Bascon, S. ; Gardner, J.W.

  • Author_Institution
    Sch. of Eng., Univ. of Warwick, Coventry, UK
  • Volume
    150
  • Issue
    1
  • fYear
    2003
  • Firstpage
    11
  • Lastpage
    14
  • Abstract
    Levels of carbon monoxide and nitrogen dioxide in air are currently monitored using two different thick-film resistive gas sensors. The resultant high power consumption of thick-film-based gas sensors is problematic for portable multi-gas monitors. The use of a single low-power thermally-modulated resistive gas sensor to monitor simultaneously both gases is reported. The silicon micromachined substrate not only reduces the DC power consumption to 100 mW at 300°C but also permits AC temperature modulation through a small thermal mass. Uniquely, a support vector machine is employed to classify the wavelet coefficients of the AC resistive signal. This simple method permits the rapid classification of CO/NO2 gas mixtures with a high level of confidence (94% or better) using just one low-power gas microsensor. Thus demonstrating the potential application of a single low-power thermally-modulated resistive gas sensor in portable multi-gas monitors.
  • Keywords
    carbon compounds; gas sensors; learning automata; low-power electronics; microsensors; nitrogen compounds; portable instruments; signal classification; wavelet transforms; 100 mW; 300 degC; AC temperature modulation; CO; CO/NO2 gas mixture; DC power consumption; NO2; Si; low-power thermally-modulated thick-film resistive gas sensor; portable multi-gas monitor; signal classification; silicon micromachined substrate; support vector machine; thermally resistive microsensor; wavelet coefficients;
  • fLanguage
    English
  • Journal_Title
    Science, Measurement and Technology, IEE Proceedings -
  • Publisher
    iet
  • ISSN
    1350-2344
  • Type

    jour

  • DOI
    10.1049/ip-smt:20030004
  • Filename
    1192342