Liquid crystal television spatial light modulators

Pattern recognition with an adaptive joint transform correlator

An adaptive joint transform correlator for real-time pattern recognition is presented. A reference image for the correlator is generated with a new iterative algorithm. The training algorithm is based on synthetic discriminant functions. The obtained reference image contains the information needed to reliably discriminate a target against known false objects and a cluttered background. Calibration lookup tables of all optoelectronics elements used are included in the design of the adaptive joint transform correlator. Two methods for the implementation of the proposed joint transform correlator in an optodigital setup are considered. Experimental results are provided and compared with those of computer simulations.

Multilevel phase- and amplitude-encoded modified-filter synthetic-discriminant-function filters

The performance of the modified-filter synthetic-discriminant-function (MfSDF) filter with multilevel phase and amplitude (MLAP) constraints is investigated with various in-plane rotated images from an in-class Bradley armored personnel carrier vehicle and an out-of-class Abram MI tank; this is of interest because of the commercial availability of liquid-crystal televisions, which are able to encode the gray-level amplitude and/or the discrete multilevel phase information. The evaluation is performed to explain better the image-distortion range that can be covered effectively by MLAP/MfSDF filters. The results show that the MLAP/MfSDF filter offers much-improved correlator system performance with a greater allowable image-distortion range while maintaining 100% discrimination between in-class and out-ofclass images; furthermore, it shows an improved ability to accommodate the input image noise when compared with the MfSDF filter with a binary phase-only constraint. Thus the MLAP/MfSDF can be implemented effectively by a hybrid optical/digital correlator system to track a vehicle or a tank dynamically as it moves along a random trajectory across the input field.

Generalized methodology for modeling and simulating optical interconnection networks using diffraction analysis

Research in the field of free-space optical interconnection networks has reached a point where simulators and other design tools are desirable for reducing development costs and for improving design time. Previously proposed methodologies have only been applicable to simple systems. Our goal was to develop a simulation methodology capable of evaluating the performance characteristics for a variety of different free-space networks under a range of different configurations and operating states. The proposed methodology operates by first establishing the optical signal powers at various locations in the network. These powers are developed through the simulation by diffraction analysis of the light propagation through the network. After this evaluation, characteristics such as bit-error rate, signal-to-noise ratio, and system bandwidth are calculated. Further, the simultaneous evaluation of this process for a set of component misalignments provides a measure of the alignment tolerance of a design. We discuss this simulation process in detail as well as provide models for different optical interconnection network components.

Phase-correlation techniques for quasi real-time measurement of deformations with digital speckle interferometry

A new approach of in-plane speckle-displacement analysis by binary phase-correlation evaluation is described. An optical implementation of the method based on digital speckle-pattern interferometry and one-focal-length correlator architecture that uses a liquid-crystal light valve is proposed.

Unipolar terminal-attractor-based neural associative memory with adaptive threshold and perfect convergence

A perfectly convergent unipolar neural associative-memory system based on nonlinear dynamical terminal attractors is presented. With adaptive setting of the threshold values for the dynamic iteration for the unipolar binary neuron states with terminal attractors, perfect convergence is achieved. This achievement and correct retrieval are demonstrated by computer simulation. The simulations are completed (1) by exhaustive tests with all of the possible combinations of stored and test vectors in small-scale networks and (2) by Monte Carlo simulations with randomly generated stored and test vectors in large-scale networks with an M/N ratio of 4 (M is the number of stored vectors; N is the number of neurons ≤ 256). An experiment with exclusive-oR logic operations with liquid-crystal-television spatial light modulators is used to show the feasibility of an optoelectronic implementation of the model. The behavior of terminal attractors in basins of energy space is illustrated by examples.

Optical implementation of terminal-attractor-based associative memory

For the purpose of reducing the spurious states in a Hopfield neural net for associative memory, we invoke terminal attractors. In achieving the optical implementation of the terminal-attractor-based associative memory (TABAM) as described in this paper, we first prove the existence of the terminal-attractor model with binary neuron representation. We then present several one- and two-dimensional optical architectures for the TABAM. Finally, as an example, we experimentally demonstrate an inner-product optical neural model using liquid-crystal spatial light modulators with engineering approximations and discuss how to apply the inner-product model to build a two-dimensional parallel processing TABAM.

Multitarget data association using an optical neural network

A neural network solution to the data association problem in multitarget tracking is presented. It uses position and velocity measurements of the targets over two consecutive time frames. A quadratic neural energy function, which is suitable for an optical processing implementation, results. Simulation resultsusing realistic target trajectories with target measurement noise including platform movement or jitter are presented. The results show that the network performs well when track data are corrupted by significant noise. Several possible optical neural network architectures to implement this algorithm are discussed, including a new all-optical matrix-vector multiplication approach. The matrix structure is employed to allow binary-ternary spatial light modulators to be used.

Redundant-interconnection interpattern-association neural network

We have shown that introducing interconnection redundancy can make a neural network more robust. We describe performances under noisy input and partial input that show that the optimum-redundant interconnection improves both the noise tolerance and the pattern discriminability. Simulated and experimental demonstrations are also provided.

Generalized filtering in acoustooptic systems using area modulation

We report on a technique for implementing general filtering operations in acoustooptic signal processing systems. We use a binary recording method called area modulation to reduce linearity problems associated with spatial light modulators that operate by controlling the amplitude of light. We present an analysis of this method, we report on experiments with an acoustooptic system to verify the analysis, using photographic film and a liquid crystal display to implement the area modulation masks, and we discuss the limitations of the technique.

Optical implementation of a quadratic associative memory by using the polarization-encoding process

As a higher-order associative memory of a neural network, the quadratic associative memory is implemented holographically by using the polarization-encoding process. The use of polarization encoding to represent sign in the quadratic associative memory tensor is made by twisted nematic liquid-crystal light modulators. Experimental results of retrieving the stored objects from an incomplete input are presented.