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Proceedings Paper

Analysis of fast gas-chromatographic signals with artificial neural systems
Author(s): Douglas A. Palmer; Eugene K. Achter; David Lieb
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Paper Abstract

Thermedics Detection's explosive detectors use fast gas chromatograph analyzers to identify key components in explosives. The analyzer produces a time series of measurements which identify mobility through the columns. This time series of measurements appears as a spectrum of values with peaks corresponding to certain substances, explosive and otherwise. The analytical task of the system is to isolate the signal peaks from the detection noise, background, pedestals and peaks from extraneous substances. A uniquely modified back- propagation neural network (non-linear adaptive filter) was developed to perform the signal analysis. The unique feature of this signal analysis system was the analysis to train the network to provide only signal amplitudes but, additionally, a measure of confidence in the derived amplitudes with respect to the simulated interferents, random noise and peak time jitter included in the training. Alarms can then be set according to confidence of detection.

Paper Details

Date Published: 1 April 1993
PDF: 11 pages
Proc. SPIE 1824, Applications of Signal and Image Processing in Explosives Detection Systems, (1 April 1993); doi: 10.1117/12.142889
Show Author Affiliations
Douglas A. Palmer, ThermoTrex Corp. (United States)
Eugene K. Achter, Thermedics Detection, Inc. (United States)
David Lieb, Thermedics Detection, Inc. (United States)

Published in SPIE Proceedings Vol. 1824:
Applications of Signal and Image Processing in Explosives Detection Systems
James M. Connelly; Shiu M. Cheung, Editor(s)

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