Optical Implementation of Entropy Network Mapped from Binary Feature Tree

Abstract

An entropy network mapped from a binary feature tree is described and compared with the Hamming net. Implementation of the entropy network includes extracting features from an input and making the decision according to these features. The feature extraction in the first layer of the entropy network is implemented by an optical method. Optical implementation with an incoherent shadow casting correlation system is proposed, and experimental demonstrations are given. The processing capability of the proposed optical system is discussed.