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Proceedings Paper

Neuromorphic implementation of a software-defined camera that can see through fire and smoke in real-time
Author(s): Jae H. Cha; A. Lynn Abbott; Harold H. Szu; Jefferson Willey; Joseph Landa; Keith A. Krapels
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Paper Abstract

Software-defined Cameras (SDC) based on Boltzmann’s molecular thermodynamics can “see” through visually-degraded fields such as fire, fog, and dust in some situations. This capability is possible by means of unsupervised learning implemented on a neuromorphic algo-tecture. This paper describes the SDC algorithm design strategy with respect to nontrivial solutions, stability, and accuracy. An example neuromorphic learning algorithm is presented along with unsupervised learning stopping criteria.

Paper Details

Date Published: 19 June 2014
PDF: 10 pages
Proc. SPIE 9118, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII, 911809 (19 June 2014); doi: 10.1117/12.2052021
Show Author Affiliations
Jae H. Cha, Virginia Polytechnic Institute and State Univ. (United States)
A. Lynn Abbott, Virginia Polytechnic Institute and State Univ. (United States)
Harold H. Szu, The Catholic Univ. of America (United States)
Jefferson Willey, The Catholic Univ. of America (United States)
Joseph Landa, The Catholic Univ. of America (United States)
Keith A. Krapels, The Univ. of Memphis (United States)


Published in SPIE Proceedings Vol. 9118:
Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII
Harold H. Szu; Liyi Dai, Editor(s)

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