Computerized design and generation of space-variant holographic filters. 1: System design considerations and applications of space-variant filters to image processing

Optical Computing: A 60-Year Adventure

Optical computing is a very interesting 60-year old field of research. This paper gives a brief historical review of the life of optical computing from the early days until today. Optical computing generated a lot of enthusiasm in the sixties with major breakthroughs opening a large number of perspectives. The period between 1980 and 2000 could be called the golden age with numerous new technologies and innovating optical processors designed and constructed for real applications. Today the field of optical computing is not ready to die, it has evolved and its results benefit to new research topics such as nanooptics, biophotonics, or communication systems.

Dynamic digital photorefractive memory for optoelectronic neural network learning modules

Neural network modules based on page-oriented dynamic digital photorefractive memory are described. The modules can implement two different interconnection organizations, fan-out and fan-in, depending on their target network applications. Neural network learning is realized by the real-time memory update of dynamic digital photorefractive memory. Physical separation of subvolumes in the page-oriented photorefractive memory architecture contributes to the low cross talk and high diffraction efficiency of the stored interconnection weights. Digitally encoded interconnection weights ensure high accuracy, providing superior neural network system scalability. Module scalability and feedforward throughput have been investigated based on photorefractive memory geometry and the photodetector power requirements. The following four approaches to extend module scalability are discussed: partial optical summation, semiparallel feedforward operation, time partitioning, and interconnection matrix partitioning. Learning capabilities of the system are investigated in terms of required interconnection primitives for implementing learning processes and three memory-update schemes. The experimental results of Perceptron learning network implementation with 900 input neurons with digital 6-bit accuracy are reported.

Space-variant optical interconnection through the use of computer-generated holograms

A space-variant optical interconnection through the use of computer-generated holograms is proposed, and specific configurations to increase the number of parallel channels are analyzed. To this end, the well-known method based on a matrix composed of subholograms is applied. The field diffracted by each channel (assumed to be square apertures) is calculated through the angular-spectrum technique, and the resulting fields are suitably superimposed to obtain a hologram matrix with a reduced bandwidth. Results show that a compact transmissive planar configuration can be handled; in particular, the small interconnect distance between the array planes and the hologram yields a limited system volume.

Node-based reconfigurable volume interconnections. 1. Principles and optical design

We present a general-purpose three-dimensional interconnection network that models various parallel operations between two data planes. This volume interconnection system exhibits reconfigurable capabilities because of parallel and externally weighted interconnection modules, called nodes. We propose a generic optical implementation based on the cascading of two planar hologram arrays, coupled with a bistable optically addressed spatial light modulator. The role of this component is discussed in terms of energy regeneration and spatial cross-talk limitation. As an example, a binary matrix-matrix multiplier is implemented that uses a ferroelectric liquid-crystal light valve.

High-speed acousto-optic mapping modulator for the generalized Hough transform

We describe a high-speed acousto-optic Hough-transform mapping modulator. This mapping modulator generates any θ slice of the straight line Hough-transform and one-dimensional slices of generalized Hough transforms (e.g., for circles and ellipses). We derive the mapping functions for the acousto-optic modulator for both circle and ellipse Hough transforms and show simulations of generalized Hough transforms using both functions. We also describe how the mapping modulators can compute Hough transforms for nonanalytically describable inputs.

Hough-transform system with optical anamorphic preprocessing and digital postprocessing

A modification of the optical Hough transformer [Appl. Opt. 25, 2734-2738 (1986)] is proposed. An input image is optically preprocessed to obtain an anamorphic magnified image. The anamorphic magnification leads to improved accuracy of the output plane parameter theta determination by means of a simple version of a Hough transformer. Then, the output signal is digitally postprocessed to obtain subpixel resolution in the rho parameter dimension. A description of the method and the experimental results is presented.

Design and analysis of fixed planar holographic interconnects for optical neural networks

A neural-network architecture of multifaceted planar interconnection holograms and optoelectronic neurons is analyzed. Various computer-generated hologram techniques are analyzed and tested for their ability to produce an interconnection hologram with high-accuracy interconnects and high diffraction efficiency. A new technique is developed by using the Gerchberg-Saxton algorithm, followed by a random-search error minimization that produces the highest interconnect accuracy and the highest diffraction efficiency of the techniques tested. Analysis of the system shows that the hologram has the capacity to connect 5000 neuron outputs to 5000 neuron inputs with bipolar synapses and that the encoded synaptic weights have an accuracy of ~5 bits. A simple feedback system is constructed and demonstrated.

Detection of a general ellipse by an optical Hough transform

A method to detect five parameters of an ellipse with an optical Hough transform is described. The method employs the Hough transform for detection of a straight line and for the one-dimensional analysis of the resultant parameter domain. This technique is also applied for detection of four parameters of a straight line segment (the two coordinates of the center, the length, and the orientation) by representing it as a particular case of an ellipse with one axis of zero length.