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Proceedings Paper

Improved chemical identification from sensor arrays using intelligent algorithms
Author(s): Thaddeus A. Roppel; Denise M. Wilson
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Paper Abstract

Intelligent signal processing algorithms are shown to improve identification rates significantly in chemical sensor arrays. This paper focuses on the use of independently derived sensor status information to modify the processing of sensor array data by using a fast, easily-implemented "best-match" approach to filling in missing sensor data. Most fault conditions of interest (e.g., stuck high, stuck low, sudden jumps, excess noise, etc.) can be detected relatively simply by adjunct data processing, or by on-board circuitry. The objective then is to devise, implement, and test methods for using this information to improve the identification rates in the presence of faulted sensors. In one typical example studied, utilizing separately derived, a-priori knowledge about the health of the sensors in the array improved the chemical identification rate by an artificial neural network from below 10 percent correct to over 99 percent correct. While this study focuses experimentally on chemical sensor arrays, the results are readily extensible to other types of sensor platforms.

Paper Details

Date Published: 26 February 2001
PDF: 7 pages
Proc. SPIE 4205, Advanced Environmental and Chemical Sensing Technology, (26 February 2001); doi: 10.1117/12.417459
Show Author Affiliations
Thaddeus A. Roppel, Auburn Univ. (United States)
Denise M. Wilson, Univ. of Washington (United States)

Published in SPIE Proceedings Vol. 4205:
Advanced Environmental and Chemical Sensing Technology
Tuan Vo-Dinh; Stephanus Buettgenbach, Editor(s)

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