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Multi-layer holographic bifurcative neural network system for real-time adaptive EOS data analysisOptical data processing techniques have the inherent advantage of high data throughout, low weight and low power requirements. These features are particularly desirable for onboard spacecraft in-situ real-time data analysis and data compression applications. the proposed multi-layer optical holographic neural net pattern recognition technique will utilize the nonlinear photorefractive devices for real-time adaptive learning to classify input data content and recognize unexpected features. Information can be stored either in analog or digital form in a nonlinear photofractive device. The recording can be accomplished in time scales ranging from milliseconds to microseconds. When a system consisting of these devices is organized in a multi-layer structure, a feedforward neural net with bifurcating data classification capability is formed. The interdisciplinary research will involve the collaboration with top digital computer architecture experts at the University of Southern California.
Document ID
19950027812
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Liu, Hua-Kuang
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Huang, K. S.
(University of Southern California Los Angeles, CA., United States)
Diep, J.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
September 6, 2013
Publication Date
January 1, 1993
Publication Information
Publication: Colorado Univ., Applied Information Systems Research Program (AISRP) Workshop 3 Meeting Proceedings
Subject Category
Earth Resources And Remote Sensing
Accession Number
95N34233
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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