19 June 2014 Neuromorphic implementation of a software-defined camera that can see through fire and smoke in real-time
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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.
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Jae H. Cha, A. Lynn Abbott, Harold H. Szu, Jefferson Willey, Joseph Landa, Keith A. Krapels, "Neuromorphic implementation of a software-defined camera that can see through fire and smoke in real-time", Proc. SPIE 9118, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII, 911809 (19 June 2014); doi: 10.1117/12.2052021; https://doi.org/10.1117/12.2052021
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