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Examination of stochastic and ordered methods to select optical filters for discrimination between chemical vibrational absorption bands
Author(s): Kevin J. Major; Jasbinder S. Sanghera; Thomas C. Hutchens; Menelaos K. Poutous; Ishwar D. Aggarwal; Kenneth J. Ewing
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Paper Abstract

Recent developments have shown that an optical filter-based sensing approach, inspired by human color vision, is capable of high-confidence discrimination between chemicals with similar infrared vibrational absorption bands. A key design point for this technique lies in the selection of the optical filters, which provide good discrimination between chemicals. Filter selection is also intrinsically tied to the classification method employed for the discrimination itself. Thus, it is imperative that the classification method or methods to be used are well understood and that mathematical means exist to compare the discrimination results provided by independent sets of optical filters. To meet this challenge, we are examining means to assign cost values to each set of optical filters for a given associated classification method. In this effort, the cost value used is the volume formed by three unique discrimination vectors. This method is developed from machine learning approaches, which define cost functions for stochastic optimization routines. We discuss multiple computational methods to discriminate between chemicals with similar infrared vibrational absorption bands using unique infrared (IR) tristimulus values for each chemical. These IR-tri-stimulus values are determined by the interaction with the chemical absorption bands and three individual optical IR band-pass filters. Methods to determine the associated cost for various selections of these IR band-pass filters and associated mathematical operations are described and compared for the computational methods. We discuss the methods employed to select the IR optical filters and discuss how the flexibility of this approach demonstrates the power of this biomimetic sensing method.

Paper Details

Date Published: 17 May 2019
PDF: 8 pages
Proc. SPIE 11010, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XX, 1101017 (17 May 2019); doi: 10.1117/12.2518238
Show Author Affiliations
Kevin J. Major, U.S. Naval Research Lab. (United States)
Jasbinder S. Sanghera, U.S. Naval Research Lab. (United States)
Thomas C. Hutchens, The Univ. of North Carolina at Charlotte (United States)
Menelaos K. Poutous, The Univ. of North Carolina at Charlotte (United States)
Ishwar D. Aggarwal, The Univ. of North Carolina at Charlotte (United States)
KeyW Corp. (United States)
Kenneth J. Ewing, U.S. Naval Research Lab. (United States)


Published in SPIE Proceedings Vol. 11010:
Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XX
Jason A. Guicheteau; Chris R. Howle, Editor(s)

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