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Proceedings Paper

Adjacent-event detection in intensified CCDs using a Hopfield neural network
Author(s): Tony Prud'homme
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Paper Abstract

Classical peak detection methods, used for event detection in intensified CCDs, do not permit the location of events in two adjacent pixels of a single frame, dramatically reducing the spatial resolution of the observations. To overcome this limitation, in the context of real time processing, a Hopfield neural network for event detection is proposed. A n2 neuron network is designed so that each neuron is associated with a corresponding pixel of a n X n window scanning the CCD frames. The n2 pixel values of the window are inputs to the network which evolves dynamically according to the propagation rules of the Hopfield model. At convergence, the activation state of each neuron is a binary value, 0 or 1, operating as an event flag for the associated pixel. The network parameters are determined by formulating the event detection problem as a signal decision problem, assuming a model of shape for the photon splashes in the detector. Digital implementation of the network has been studied and simulation results are presented.

Paper Details

Date Published: 1 April 1993
PDF: 10 pages
Proc. SPIE 1982, Photoelectronic Detection and Imaging: Technology and Applications '93, (1 April 1993); doi: 10.1117/12.142050
Show Author Affiliations
Tony Prud'homme, Instituto de Astrofisica de Canarias (Spain)

Published in SPIE Proceedings Vol. 1982:
Photoelectronic Detection and Imaging: Technology and Applications '93
LiWei Zhou, Editor(s)

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