A COMPARATIVE-STUDY OF SIGNAL-PROCESSING TECHNIQUES FOR CLUSTERING MICROSENSOR DATA (A FIRST STEP TOWARDS AN ARTIFICIAL NOSE)

Microsensor technology has progressed to the point where it is now fea sible to place several hundred sensors on a computer chip. Such a sens or array can potentially be used in many applications including detect ing hazardous chemical emissions, food processsing, and fire detection . This paper addresses an important aspect involved in microsensor app lications, namely how the sensor signals are processed. The problem tr eated involves classifying whether a sensed signal is generated by one of four chemicals. Two broad approaches to processing the sensor sign als are discussed, one based on classical signal processing approaches , and one based on a model of how the olfactory system in animals func tions. The classical approaches used include: Gram Schmidt orthogonali zation, fast Fourier transforms, and Haar wavelets. For the experiment al signals treated, the classical approaches give superior results com pared to those produced by the olfactory model. (C) 1997 Elsevier Scie nce S.A.