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Gray image compression by analog silicon retina based on code and graph theories

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2 Author(s)
Kanda, K. ; Dept. of Electr. & Electron. Eng., Sophia Univ., Tokyo, Japan ; Tanaka, M.

A new associative neural network (NN) is described which is constructed based on code and graph theories. This NN is called a SANNET (Sophia associative neural network). Each neuron is an adder unit in the analog NN (ANN) based on real field Rb. The SANNET has many features: no multiplier, sparsity, cellular structure, high concurrency, high speed, and secret communication. The ANN can be applied to the data compression for gray images respectively. The rate of information compression is given by (n/l)k where n, l and k represent the numbers of nodes, links and layers. The S/N rate in the reproduction image depends on the structure sparsity parameter δ=loopm/cutseta where cutseta represents the average number of links incident to each node, and on the mapping from the original pixel to links in SANNET. Simulation results for gray image compression are given

Published in:

Circuits and Systems, 1990., Proceedings of the 33rd Midwest Symposium on

Date of Conference:

12-14 Aug 1990