Analysis of terahertz spectral images of explosives and bioagents using trained neural networks

Filipe Oliveira; Robert Barat; Brian Schulkin; Feng Huang; John F. Federici; Dale Gary; David Zimdars

doi:10.1117/12.542648

8 September 2004 Analysis of terahertz spectral images of explosives and bioagents using trained neural networks

Filipe Oliveira, Robert Barat, Brian Schulkin, Feng Huang, John F. Federici, Dale Gary, David Zimdars

Proceedings Volume 5411, Terahertz for Military and Security Applications II; (2004) https://doi.org/10.1117/12.542648
Event: Defense and Security, 2004, Orlando, Florida, United States

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.

Citation Download Citation

Filipe Oliveira, Robert Barat, Brian Schulkin, Feng Huang, John F. Federici, Dale Gary, and David Zimdars "Analysis of terahertz spectral images of explosives and bioagents using trained neural networks", Proc. SPIE 5411, Terahertz for Military and Security Applications II, (8 September 2004); https://doi.org/10.1117/12.542648

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available