Abstract
We present a reconfigurable optical implementation of an acousto-optic algebra processor, based on a calomel (single crystal mercurous chloride) matrix-vector multiplier. Two successful applications are demonstrated: neural network training and curve detection. In the first, a perceptron learns two-input Boolean functions. In the second, a connectionist model of the Hough transform is generalized to handle arbitrary curves. Experiments for line detection and circle detection are performed, and the Hough transform's throughput is solely limited by the update latency of the acousto-optic unit.
Keywords: acousto-optics, optical processor, optical neural network, Hough transform, calomel.
|
|
Copyright 1999 Society of Photo-Optical Instrumentation Engineers.
This paper was published in the Journal of Optical Engineering volume 38 and is made available as an electronic reprint with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
|
© SPIE.
