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Proceedings Paper

Large-scale holographic neuron system for multispectral sensor fusion and high-speed signal processing
Author(s): Thomas Taiwei Lu; Freddie Shing-Hong Lin; Andrew A. Kostrzewski; Jeremy M. Lerner
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Paper Abstract

One-dimensional (1D) and 2D sensor signals returned from the mass spectrometers, x-ray spectral analyzers, CT scanners, or vapor detectors present a major challenge for the detection and identification of illegal substances. Prompt and accurate identification and classification of detected signatures demands extremely high computation power and requires sophisticated signal processing. The paper presents the development of a real-time multispectral analysis system that performs high-speed neural operations and sensor fusion for feature extraction and trace identification. The system utilizes the technology of large-scale holographic optical neural networks being developed and demonstrated by Physical Optics Corporation (POC). This technology is based on fully parallel optical processing of spectral information to produce parallel spectral pattern recognition. In addition, POC's processing algorithm has demonstrated the ability to extract spectral information from extremely noisy backgrounds. This translates into very high instrument sensitivity.

Paper Details

Date Published: 1 February 1994
PDF: 10 pages
Proc. SPIE 2093, Substance Identification Analytics, (1 February 1994); doi: 10.1117/12.172518
Show Author Affiliations
Thomas Taiwei Lu, Physical Optics Corp. (United States)
Freddie Shing-Hong Lin, Physical Optics Corp. (United States)
Andrew A. Kostrzewski, Physical Optics Corp. (United States)
Jeremy M. Lerner, Physical Optics Corp. (United States)


Published in SPIE Proceedings Vol. 2093:
Substance Identification Analytics
James L. Flanagan; Richard J. Mammone; Albert E. Brandenstein; Edward Roy Pike; Stelios C. A. Thomopoulos; Marie-Paule Boyer; H. K. Huang; Osman M. Ratib, Editor(s)

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