Neuromorphic implementation of a software-defined camera that can see through fire and smoke in real-time

Jae H. Cha; A. Lynn Abbott; Harold H. Szu; Jefferson Willey; Joseph Landa; Keith A. Krapels

doi:10.1117/12.2052021

19 June 2014 Neuromorphic implementation of a software-defined camera that can see through fire and smoke in real-time

Jae H. Cha, A. Lynn Abbott, Harold H. Szu, Jefferson Willey, Joseph Landa, Keith A. Krapels

Author Affiliations +

Proceedings Volume 9118, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XII; 911809 (2014) https://doi.org/10.1117/12.2052021
Event: SPIE Sensing Technology + Applications, 2014, Baltimore, MD, United States

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, and 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); https://doi.org/10.1117/12.2052021

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