The self-organization rule of planar neural networks has been proposed for learning of 2D distributions. However, it cannot be applied to 3D objects. In this paper, we propose a new model for global representation of the 3D objects. Based on this model, global topology reserving self-organization is achieved using parallel local competitive learning rule such as Kohonen's maps. The proposed model is able to represent the objects distributively and easily accommodate local features.

The problem of local minima is inevitable when solving combinatorial optimization problems by conventional methods such as the Hopfield network, relying on the minimization of an objective function E(X). Such a problem arises from the search mechanism in which only the local information about the objective function E(X) is used. In this paper we propose a new approach called the Single Minimum Method (SMM) which uses the global information in searching for the solutions to combinatorial optimization problems. In this approach, we add a function -TS(X) to the original objective function E(X) to construct the function F(X)=E(X)-TS(X) which has only one minimum, one which can be easily found by any general gradiet method including the Hopfield network. Based on an analogy between thermodynamic systems and neural networks, it is shown that the global information about the original objective function E(X) is included in the single minimum of the function F(X) and can be used for finding the global minimum of the objective function E(X). In order to show how to apply the Single Minimum Method to a combinatorial optimization problem we give an algorithm for the TSP problem based on our method. The simulation results show that the algorithm can almost always find the shortest or near shortest paths. Finally, a modified SMM, which has some great advantages for hardware implementation, is also given.

This paper presents an optimization method for pseudo-Kronecker expressions of p-valued input two-valued output functions by using multi-place decision diagrams for p2 and p4. A conventional method using extended truth tables requires memory of O (3n) to simplify an n-variable expression, and is only practical for functions of up to n14 variables when p2. The method presented here utilizes multi-place decision diagrams, and can optimize considerably larger problems. Experimental results for up to n39 variables are shown.

The paper considers multiple-valued logic systems having the property that the ambiguity of the system increases as the ambiguity of each component increases. The partial-ordering relation with respect to ambiguity with the greatest element 1/2 and minimal elements 0, 1 or simply the ambiguity relation is introduced in the set of truth values V {0, 1/ (p1), , 1/2, , (p2) / (p1), 1}. A-monotonic p-valued logic functions are defined as p-valued logic functions monotonic with respect to the ambiguity relation. A necessary and sufficient condition for A-monotonic p-valued logic functions is presented along with the proofs, and their logic formulae using unary operators defined in the ambiguity relation are given. Some discussions on the extension of theories to other partial-ordering relations are also given.

This paper presents Bi-CMOS current-mode multiple valued logic circuit with 1.5 V supply voltage. This circuit is composed of current mirror, threshold detector and current source. This circuit has advantages such as high accuracy, high speed, high density and low supply voltage. So, it is possible to realize high-radix multiple valued logic circuit. As an other application of the proposed circuit, a processing unit of fuzzy inference is given. This circuit operates with high speed and high accuracy. The circuit simulation of the proposed circuit has been performed using SPICE2 program.

Generally it is said that a fuzzy control system has fault tolerant properties, but it is not clearly studied. In this paper, first, the influence of faults in fuzzy control systems is examined. Errors given by fault simulation are not negligible. However, no fault detecting method is applied in the realized fuzzy control systems. Then a fault-checking method to detect faults is proposed in this paper.

In the direct product space of a complete metric linear space X and its related space Y, a fuzzy mapping G is introduced as an operator by which we can define a projective fuzzy set G(x,y) for any xX and yY. An original system is represented by a completely continuous operator f(x)Y, e.g., in the form x=λ(f(x)), (λ is a linear operator), and a nondeterministic or fuzzy fluctuation induced into the original system is represented by a generalized form of system equation xβG(x,f(x)). By establishing a new fixed point theorem for the fuzzy mapping G, the existence and evaluation problems of solution are discussed for this generalized equation. The analysis developed here for the fluctuation problem goes beyond the scope of the perturbation theory.

Surface reconstruction and visualization from sparse and incomplete surface data is a fundamental problem and has received growing attention in both computer vision and graphics. This paper presents a computational scheme for realistic visualization of free-formed surfaces from 3D range images. The novelty of this scheme is that by integrating computer vision and computer graphics techniques, we dynamically construct a mesh representation of the arbitrary view of the surfaces, from a view-invariant shape description obtained from 3D range images. We outline the principle of this scheme and describle the frame work of a graphical reconstruction model, we call arbitrarily oriented meshes', which is developed based on differential geometry. The experimental results on real range data of human faces are shown.

In this work, we introduce a fuzzy inference in conventional backpropagation learning algorithm, for networks of neuron like units. This procedure repeatedly adjusts the learning parameters and leads the system to converge at the earliest possible time. This technique is appropriate in a sense that optimum learning parameters are being applied in every learning cycle automatically, whereas the conventional backpropagation doesn't contain any well-defined rule regarding the proper determination of the value of learning parameters.

Gradient-based methods for the computation of the velocity from image sequences assume that the velocity field varies smoothly over image. This creates difficulties at regions where the image intensity changes abruptly such as the occluding contours or region boundaries. In this letter, we propose a method to overcome these difficulties by incorporating the information of discontinuities in image intensity into a standard local optimization method. The presented method is applied to the synthetic and real images. The results show that the velocity field computed by the proposed method is less blurred at region boundaries than that of the standard method.

The three layer neural network (TLNN) is treated, where the nonlinearity of a neuron is of signum. First we propose an expression of the discriminant function of the TLNN, which is called a linear-homogeneous expression. This expression allows the differentiation in spite of the signum property of the neuron. Subsequently a learning algorithm is proposed based on the linear-homogeneous form. The algorithm is an error-correction procedure, which gives a mathematical foundation to heuristic error-correction learnings described in various literatures.

The minimum transferable linewidth by X-ray is derived using waveguide analysis. The minimum width is determined by the refractive index of the absorber and does not depend on the X-ray wavelength. Therefore there is an optimum mask aperture size which provides the minimum linewidth. By using Au as the absorber, 8 nm linewidth is attainable.

Formal verification has become an increasing prominent technique towards establishing the correctness of hardware designs. We present a framework to specifying and verifying the design of systolic architectures. Our approach allows users to represent systolic arrays in Z specification language and to justify the design semi-automatically using the verifier. Z is a notation based on typed set theory and enriched by a schema calculus. We describe how a systolic array for matrix-vector multiplication can be specified and justified with respect to its algorithm.

In this paper, we consider a class of the languages that have (constant round) perfect zero-knowledge interactive proofs without assuming any complexity assumptions. Especially, we investigate the interactive protocol with the restricted prover who runs in probabilistic polynomial time and knows the complete factorization as a trapdoor information of the integer associated with the input. We give a condition of the existence of constant round perfect zero-knowledge interactive proofs without assuming any complexity assumptions. The bit commitment based on the quadratic residuosity has an important role in our protocol and the simulation is based on the technique developed by Bellare, Micali, and Ostrovsky in Ref. (9), so call double running process. However, the proof of perfect zero-knowledgeness needs a more powerful simulation technique. Our simulation extracts more knowledge, the complete factorization of the integer associated with the input, from a (cheating) verifier than Bellare-Micali-Ostrovsky's simulation does. Furthermore, our main result implies that Blum integer has a five move perfect zero-knowledge interactive proof without assuming any complexity assumptions. (All previous known zero-knowledge protocols for Blum integer required either unproven cryptographic assumptions or unbounded number of rounds of message exchange.)

In a display system with a line-of-gaze (LOG) controller, it is difficult to make the directions and motions of a LOG-controlled object coincide as closely as possible in the display with the user's intended LOG-directions and motions. This is because LOG behavior is not only smooth, but also saccadic due to the problem of involuntary eye movement. This article introduces a flexible on-line LOG-control scheme to realize nearly perfect LOG operation. Using a mesh-wise cursor pattern, the first visual experiment elucidates subjectively that a Kalman Filter (KF) for smoothing and predicting is effective in filtering out macro-saccadic changes of the LOG and in predicting sudden changes of the saccade while movement is in progress. It must be assumed that the LOG trajectory can be described by a linear position-velocity-acceleration approximation of Sklansky Model (SM). Furthermore, the second experiment uses a four-point pattern and simulations to scrutinize the two physical properties of velocity and direction-changes of the LOG in order to quantitatively and efficiently resolve "moving" and "gazing". In order to greatly reduce the number of LOG-small-position changes while gazing, the proposed Gaze-Holding algorithm (GH) with a gaze-potential function is combined with the KF. This algorithm allows the occurrence frequency of the micro-saccade to be reduced from approximately 25 Hz to 1 or 2 Hz. This great reduction in the frequency of the LOG-controlled object moves is necessary to achieve the user's desired LOG-response while gazing. Almost perfect LOG control is accomplished by the on-line SM+KF+GH scheme while either gazing or moving. A menu-selection task was conducted to verify the effectiveness of the proposed on-line LOG-control method.

A distributed processing system is described, which is dedicated to multilayer SOG routing. The system is constructed of global and detailed routers, each based on different rip-up and rerouting procedures, so as to be run on a computer network composed of a number of workstations. Several implementation results attained for five-layer SOG are also shown to reveal the practicability of the system.

An adaptive algorithm is presented for fuzzy clustering of data. Partitioning is fuzzified by addition of an entropy term to objective functions. The proposed method produces more convex membership functions than those given by the fuzzy c-means algorithm.

An accurate numerical solution is presented for the electromagnetic scattering from a double strip grating, where the strip planes are each supported by a dielectric slab. This structure is a model of polarization diplexers. The direction of propagation and the polarization of the incident plane wave are arbitrary. We derive a set of singular integral equations and solve it by the moment method, where the Chebyshev polynomials are successfully used as the basis and the testing functions. By numerical computations we examine the dependence of the diplexing properties on grating parameters in detail. The cross-polarization characteristics at skew incidence are also referred. From these results we construct an algorithm for the design of polarization diplexers.

This article analyzes the property of the fully interconnected neural networks as a method of solving combinatorial optimization problems in general. In particular, in order to escape local minimums in this model, we analyze theoretically the relation between the diagonal elements of the connection matrix and the stability of the networks. It is shown that the position of the global minimum point of the energy function on the hyper sphere in n dimensional space is given by the eigen vector corresponding the maximum eigen value of the connection matrix. Then it is shown that the diagonal elements of the connection matrix can be improved without loss of generality. The equilibrium points of the improved networks are classified according to their properties, and their stability is investigated. In order to show that the change of the diagonal elements improves the potential for the global minimum search, computer simulations are carried out by using the theoretical values. In according to the simulation result on 10 neurons, the success rate to get the optimum solution is 97.5%. The result shows that the improvement of the diagonal elements has potential for minimum search.

Low-sensitivity digital filters are required for accurate signal processing. Among many low-sensitivity digital filters, a method using complex allpass circuits is well-known. In this paper, a new synthesis of complex allpass circuits is proposed. The proposed synthesis can be realized more easily either only in the z-domain or in the s-domain than conventional methods. The key concept for the synthesis is based on the factorization of lossless scattering matrices. Complex allpass circuits are interpreted as lossless digital two-port circuits, whose scattering matrices are factored. Furthermore, in the cases of Butterworth, Chebyshev and inverse Chebyshev responses, the explicit formulae for multiplier coefficients are derived, which enable us to synthesize the objective circuits directly from the specifications in the s-domain. Finally design examples verify the effectiveness of the proposed method.