Practical realizations of N^4 optical interconnects

<title>Portable holographic neural system for distortion-invariant pattern recognition and tracking in controlled environment</title>

“…In general, automated target recognition (ATR) systems contain several subsystems vital to successful target detection and identification. These subsystems include: (1) Preprocessor (2) Interest point locater An input image is acquired by a camera (visible or IR) or sensor (range finder, radar). In the case of images, the first step in the ATR is image enhancement and conditioning.…”

“…The main goal is to improve the image quality and to enhance the appearance of objects of interest and to perform image transformations to increase the probability of recognition. In the spatial domain these techniques include (1) morphological processing (2) low contrast edge enhancement It is seen that the electronic preprocessing time should be adapted to the speed of the LCTV displays used for the near term version of holographic neuro-processor. There are commercially available dedicated, PC-compatible high speed real-time processing systems, such as the Sharp GPB-1 image processor [3].…”

“…Because of the high degree of parallel processing necessary for real-time performance and the need for high interconnectivity and large memory capacity, an optical neural network approach to ATR fulfills more than the basic requirements for an ATR system and has the potential to outperform, or at a minimum supplement, an electronic neural network approach. Our neural network is based on inter-pattern association model and interconnection network is implemented using N4 [1][2] hologram recording In this paper computer simulation as well as optical implementation of the neural network is presented. Computer simulation is used to implement neural network with 64X64 neurons for rotation invariance and weak scale invariance in a controlled environment.…”

Compact holographic optical neural network system for real‐time pattern recognition

“…[3][4][5][6][7][8][9] The innate parallel processing and unique 3-D geometric capability of optics complements rather than competes with electronic systems by addressing problem domains that tax the capabilities of conventional electronic processors. [10][11][12] Because the parallel processing capability of optics permits these operations to take place at high speed, 2-D optical processors are well suited for these problems. Optical engines are more efficient than electronic systems because the parallel searching operation does not require backtracking through the knowledge base.…”

“…These holographic materials offer an ideal means of massively parallel 3-D interconnection for large-scale neural network implementations. 12,13 In this paper we report on the development of a compact HONN system containing from 1000 to 16,000 neurons for rotation-shift-scale-invariant pattern recognition.…”