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
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;
Journal_Title :
Science, Measurement and Technology, IEE Proceedings -
DOI :
10.1049/ip-smt:20030004