Generation of reconfigurable interconnections with a two-dimensional acousto-optic deflector.

We detail the technical considerations for producing reconfigurable spot patterns with an integrated two-dimensional acousto-optic deflector. Practical limitations concerning the generation of high-density interconnect arrays are described. Experimental results for 13 × 13, 21 × 21, and 50 × 50 spot arrays are presented.

Design and performance optimization of fiber optic adaptive filters.

There is a great need for easy-to-fabricate and versatile fiber optic signal processing systems in which optical fibers are used for the delay and storage of wideband guided lightwave signals. We describe the design of the least-mean-square algorithm-based fiber optic adaptive filters for processing guided lightwave signals in real time. Fiber optic adaptive filters can learn to change their parameters or to process a set of characteristics of the input signal. In our realization we employ as few electronic devices as possible and use optical computation to utilize the advantages of optics in the processing speed, parallelism, and interconnection. Many schemes for optical adaptive filtering of electronic signals are available in the literature. The new optical adaptive filters described in this paper are for optical processing of guided lightwave signals, not electronic signals. We analyzed the convergence or learning characteristics of the adaptive filtering process as a function of the filter parameters and the fiber optic hardware errors. From this analysis we found that the effects of the optical round-off errors and noise can be reduced, and the learning speed can be comparatively increased in our design through an optimal selection of the filter parameters. A general knowledge of the fiber optic hardware, the statistics of the lightwave signal, and the desired goal of the adaptive processing are enough for this optimum selection of the parameters. Detailed computer simulations validate the theoretical results of performance optimization.

Improved arithmetic Fourier transform algorithm.

A new, improved version of the arithmetic Fourier transform algorithm is presented. This algorithm computes the Fourierc oefficientso f continuous-times ignals by using the number-theoretic technique of Mobius inversion. The improved algorithm can calculate all the Fourier coefficients including the dc component. It also requires a smaller number of delays and arithmetic operations than the standard arithmetic Fourier transform algorithm.

Matrix preconditioning: a robust operation for optical linear algebra processors.

Analog electrooptical processors are best suited for applications demanding high computational throughput with tolerance for inaccuracies. Matrix preconditioning is one such application. Matrix preconditioning is a preprocessing step for reducing the condition number of a matrix and is used extensively with gradient algorithms for increasing the rate of convergence and improving the accuracy of the solution. In this paper, we describe a simple parallel algorithm for matrix preconditioning, which can be implemented efficiently on a pipelined optical linear algebra processor. From the results of our numerical experiments we show that the efficacy of the preconditioning algorithm is affected very little by the errors of the optical system.