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Proceedings Paper

Analysis of terahertz spectral images of explosives and bioagents using trained neural networks
Author(s): Filipe Oliveira; Robert Barat; Brian Schulkin; Feng Huang; John F. Federici; Dale Gary; David Zimdars
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Paper Abstract

A non-invasive means to detect and characterize concealed agents of mass destruction in near real-time with a wide field-of-view is under development. The method employs spatial interferometric imaging of the characteristic transmission or reflection frequency spectrum in the Terahertz range. However, the successful (i.e. low false alarm rate) analysis of such images will depend on correct distinction of the true agent from non-lethal background signals. Neural networks are being trained to successfully distinguish images of explosives and bioagents from images of harmless items. Artificial neural networks are mathematical devices for modeling complex, non-linear relationships. Both multilayer perceptron and radial basis function neural network architectures are used to analyze these spectral images. Positive identifications are generally made, though, neural network performance does deteriorate with reduction in frequency information. Internal tolerances within the identification process can affect the outcome.

Paper Details

Date Published: 8 September 2004
PDF: 6 pages
Proc. SPIE 5411, Terahertz for Military and Security Applications II, (8 September 2004); doi: 10.1117/12.542648
Show Author Affiliations
Filipe Oliveira, New Jersey Institute of Technology (United States)
Robert Barat, New Jersey Institute of Technology (United States)
Brian Schulkin, New Jersey Institute of Technology (United States)
Feng Huang, New Jersey Institute of Technology (United States)
John F. Federici, New Jersey Institute of Technology (United States)
Dale Gary, New Jersey Institute of Technology (United States)
David Zimdars, Picometrix, Inc. (United States)


Published in SPIE Proceedings Vol. 5411:
Terahertz for Military and Security Applications II
R. Jennifer Hwu; Dwight L. Woolard, Editor(s)

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