In this paper, a new description of a separable-denominator (S-D) two-dimensional (2-D) transfer matrix is proposed, and its realization is considered. Some of this problem had been considered for the transfer matrices whose elements are two-variables rational functions. We shall propose a 2-D transfer matrix whose inputs-outputs relation is represented by a ratio of two-variables polynomial matrices, and present an algorithm to obtain a 2-D state-space model from it. Next, it is shown that the description proposed in this paper is always minimally realizable. And, we shall present a method of obtaining the description proposed in this paper from a S-D 2-D rational transfer matrix.

An optical array imaging system is presented with basic experimental results. First, a remote object is illuminated with laser light at an angle and the reflected light is detected with an array sensor after interfering it with the reference light. This process is repeated by changing the illumination angle to collect a set of fringe patterns, which are A/D converted and stored in a harddisk in a computer. Then, the data are processed on a computer, first, to estimate the complex-amplitude object wave fields, second, to derive the eigenvector with the maximum eigenvalue for the correlation of the estimated object fields, and finally, to form an image of the object. The derivation of the eigenvector follows an iterative algorithm, which can be interpreted as the process of repeating backward wave propagation of the field between the two apertures illuminating and detecting laser light. The eigenvector field can be expected to backpropagate to focus at a point on the object with the maximum coefficient of reflection, so that a beam-steering operation is applied to the eigenvector to form an image of the object. The method uses only the information of the array data and the lateral spacings of the receiving array (CCD) elements. Hence, the method can give good images of objects even if the reference light is uncollimated with an unknown distorted wavefront, and even if the illuminating angles are imprecise in three dimensions. Basic experimental results clearly show the usefulness of the method.

Extended form of interpolatory approximation is presented for tne n-dimensional (n-D) signals whose generalized spectrums have weighted norms smaller than a given positive number. The presented approximation has the minimum measure of approximation error among all the linear and the nonlinear approximations using the same generalized sample values.

In general, a two-dimensional display is defined by two orthogonal unit vectors. In developing the display, discriminant analysis has a shortcoming that the extracted axes are not orthogonal in general. First, in order to overcome the shortcoming, we propose discriminant analysis which provides an orthonormal system in the transformed space. The transformation preserves the discriminatory ability in terms of the Fisher criterion. Second, we present a necessary and sufficient condition that discriminant analysis in the original space provides an orthonormal system. Finally, we investigate the relationship between orthogonal discriminant analysis and the Karhunen-Loeve expansion in the original space.

We have developed an advanced tool for dimensioning circuit-switched networks, called CNEP (Circuit-Switched Network Evaluation Program) , for effective design of digital networks. CNEP features a high-reliability network structure (node dispersion, double homing, etc) , both-way circuit operation, and circuit modularity (or big module size), all of which are critical for digital networks. CNEP also solves other dimensioning problems such as the cost difference between existing and newly installed circuits, and handles multi-hour traffic conditions, dynamic routing, and multiple-switching-unit nodes. Operations Research techniques are applied to produce exact and heuristic algorithms for these problems. Algorithms with good time-performance trade-off characteristics are chosen for CNEP.

The optimal coding strategy for signal detection in the correlated gaussian noise is established for the distributed sensors system with essentially zero transmission rate constraint. Specifically, we are able to obtain the same performance as in the situation of no restriction on rate from each sensor terminal to the fusion center. This simple result contrasts with the previous ad hoc studies containing many unnatural assumptions such as the independence of noises contaminating received signal at each sensor. For the design of optimal coder, we can use the classical Levinson-Wiggins-Robinson fast algorithm for block Toeplitz matrix to evaluate the necessary weight vector for the maximum-likelihood detection.

Topographic maps begin to be recognized as one of the major computational structures underlying neural computation in the brain. They provide dimension-reducing projections between feature spaces that seem to be established and maintained under the participation of selforganizing, adaptive processes. In this contribution, we investigate how well the structure of such maps can be replicated by simple adaptive processes of the kind proposed by Kohonen. We will particularly address the important issue, how the dimensionality of the input space affects the spatial organization of the resulting map.

A theoretical conjecture on fractal dimensions of a dendrite distribution in neural networks is presented on the basis of the dendrite tree model. It is shown that the fractal dimensions obtained by the model are consistent with the recent experimental data.

Dynamic behavior of a distributed parameter system described by the one-dimensional wave equation with a nonlinear boundary condition is examined in detail using a graphical method proposed by Witt on a digital computer. The bifurcation diagram, homoclinic orbit and one-dimensional map are obtained and examined. Results using an analog simulator are introduced and compared with that of the graphical method. The discrepancy between these results is considered, and from the comparison among the bifurcation diagrams obtained by the graphical method, it is denoted that the energy dissipation in the system considerably restrains the chaotic state in the bifurcation process.

In two-dimensional simulation of thin-base RHET, we combined three different simulation methods--the Schrödinger equation, the Monte Carlo simulation, and two-dimensional device simulation within a drift and diffusion model. We found that, in the thin-base RHET, the potential distribution differs from that expected from the thick-base RHET. In the thin-base RHET, the potential of the intrinsic base region does not equal that of the base electrode because the intrinsic base region is depleted and the negative emitter voltage (VEB0) raises the potential of both the intrinsic base and the nondoped region under the intrinsic base. There are also modified by the collector voltage. We also show emitter current-voltage characteristics, transfer ratio, and transit time calculated using this method and compare them with results for the one-dimensional case.