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Proceedings Paper

Feature-level signal processing for near-real-time odor identification
Author(s): Thaddeus A. Roppel; Mary Lou Padgett; Joakim T. A. Waldemark; Denise M. Wilson
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Paper Abstract

Rapid detection and classification of odor is of particular interest in applications such as manufacturing of consumer items, food processing, drug and explosives detection, and battlefield situation assessment. Various detection and classification techniques are under investigation so that end users can have access to useful information from odor sensor arrays in near-real-time. Feature-level data clustering and classification techniques are proposed that are (1) parallelizable to permit efficient hardware implementation, (2) adaptable to readily incorporate new data classes, (3) capable of gracefully handling outlier data points and failed sensor conditions, and (4) can provide confidence intervals and/or a traceable decision record along with each classification to permit validation and verification. Results from using specific techniques will be presented and compared. The techniques studied include principal components analysis, automated outlier determination, radial basis functions (RBF), multi-layer perceptrons (MLP), and pulse-coupled neural networks (PCNN). The results reported here are based on data from a testbed in which a gas sensor array is exposed to odor samples on a continuous basis. We have reported previously that more detailed and faster discrimination can be obtained by using sensor transient response in addition to steady state response. As the size of the data set grows we are able to more accurately model performance of a sensor array under realistic conditions.

Paper Details

Date Published: 4 September 1998
PDF: 12 pages
Proc. SPIE 3392, Detection and Remediation Technologies for Mines and Minelike Targets III, (4 September 1998); doi: 10.1117/12.324220
Show Author Affiliations
Thaddeus A. Roppel, Auburn Univ. (United States)
Mary Lou Padgett, Auburn Univ. (United States)
Joakim T. A. Waldemark, Umea Univ. (Sweden)
Denise M. Wilson, Univ. of Kentucky/Lexington (United States)


Published in SPIE Proceedings Vol. 3392:
Detection and Remediation Technologies for Mines and Minelike Targets III
Abinash C. Dubey; James F. Harvey; J. Thomas Broach, Editor(s)

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