For a fuzzy control of manipulated variable so as to match a required output of a plant, tuning of fuzzy rules are necessary. For its purpose, various methods to tune their rules automatically have been proposed. In these method, some of them necessitate much time for its tuning, and the others are lacking in the generalization capability. In the fuzzy control by the steepest descent method, a use of piecewise linear membership functions (MSFs) has been proposed. In this algorithm, MSFs of the premise for each fuzzy rule are tuned having no relation to the other rules. Besides, only the MSFs corresponding to the given input and output data for the learning can be tuned efficiently. Comparing with the conventional triangular form and the Gaussian distribution of MSFs, an expansion of the expressiveness is indicated. As a result, for constructing the inference rules, the training cycles can be reduced in number and the generalization capability to express the behavior of a plant is expansible. An effectiveness of this algorithm is illustrated with an example of a parallel parking of an autonomous mobile robot.

This paper proposes a half-chip offset QPSK (Quadrature Phase Shift Keying) modulation CDMA (Code Division Multiple Access) scheme to allow the simple differential detection while realizing a compact spectrum in nonlinear channels for wireless LAN systems. The experimental results show the proposed scheme achieves excellent Pe (probability of error) performances in ACI (adjacent channel interference) and CCI (co-channel interference) environments. Moreover, by employing time diversity and high-coding-gain FEC (Forward Error Correction), the half-chip offset QPSK-CDMA scheme realizes an improvement of 3.0 dB (in terms of Eb/No at a Pe of 105) in Rician fading environments with a Doppler frequency fD of 10 Hz and a delay spread of 40 nsec.

The mean field theory has been recognized as offering an efficient computational framework in solving discrete optimization problems by neural networks. This paper gives a formulation based on the information geometry to the mean field theory, and makes clear from the information-theoretic point of view the meaning of the mean field theory as a method of approximating a given probability distribution. The geometrical interpretation of the phase transition observed in the mean field annealing is shown on the basis of this formulation. The discussion of the standard mean field theory is extended to introduce a more general computational framework, which we call the generalized mean field theory.

This paper consists of two parts. The first part is devoted to comparative study on handwritten ZIP code numeral recognition using seventeen typical feature vectors and seven statistical classifiers. This part is the counterpart of the sister paper Handwritten Postal Code Recognition by Neural Network - A Comparative Study" in this special issue. In the second part, a procedure for feature synthesis from the original feature vectors is studied. In order to reduce the dimensionality of the synthesized feature vector, the effect of the dimension reduction on classification accuracy is examined. The best synthesized feature vector of size 400 achieves remarkably higher recognition accuracy than any of the original feature vectors in recognition experiment using a large number of numeral samples collected from real postal ZIP codes.

This paper describes an on-line Kanji character recognition method that solves the one-to-one stroke correspondence problem with both the stroke-number and stroke-order variations common in cursive Japanese handwriting. We propose two kinds of complementary algorithms: one dissolves excessive mapping and the other dissolves deficient mapping. Their joint use realizes stable optimal stroke correspondence without combinatorial explosion. Also, three kinds of inter-stroke distances are devised to deal with stroke concatenation or splitting and heavy shape distortion. These new ideas greatly improve the stroke matching ability of the selective stroke linkage method reported earlier by the authors. In experiments, only a single reference pattern for each of 2,980 Kanji character categories is generated by using training data composed of 120 patterns written carefully with the correct stroke-number and stroke-order. Recognition tests are made using the training data and two kinds of test data in the square style and in the cursive style written by 36 different people; recognition rates of 99.5%, 97.6%, and 94.1% are obtained, respectively. Moreover, comparative results obtained by the current OCR technique as applied to bitmap patterns of on-line character data are presented. Finally, future work for enhancing the stroke matching approach to cursive Kanji character recognition is discussed.

This paper discusses visualization of temporal and spatial information in natural language descriptions (NLDs), focusing on the translation process of intermediate representations of NLDs to proper scenarios" and environments" for animations. First, the intermediate representations are shown according to the idea of actors. Actors and non-actors are represented as primitives of objects, whereas actions as those of events. Temporal and spatial constraints by a given NLD text are imposed upon the primitives. Then, the representations containing unknown temporal or spatial parameters --time and coordinates-- are translated into evaluation functions, where the unlikelihood of the deviations from the predicted temporal or spatial relations are estimated. Particularly, the functions concerning actor's movements contain both temporal and spatial parameters. Next, the sum of all the evaluation functions is minimized by a nonlinear optimization method. Thus, the most proper actors' time-table, or scenario, and non-actors' location-table, or environment, for visualization are obtained. Implementation and experiments show that both temporal and spatial information in NLDs are well connected through actors' movements for visualization.

The problem of genetic algorithm's efficiency has been attracting the attention of genetic algorithm community. Over the last decade, considerable researches have focused on improving genetic algorithm's performance. However, they are generally under the framework of natural evolutionary mechanism and the major genetic operators, crossover and mutation, are activated by the prior probabilities. An operator based on a prior probability possesses randomness, that is, the unexpected individuals are frequently operated, but the expected individuals are sometimes not operated. Moreover, as the evaluation function is the link between the genetic algorithm and the problem to be solved, the evaluation function provides the heuristic information for evolutionary search. Therefore, how to use this kind of heuristic information (present and past) is influential in the efficiency of evolutionary search. This paper, as an attempt, presents a eugenics-based genetic algorithm (EGA) -- a genetic algorithm that reflects the human's decision will (eugenics), and fully utilizes the heuristic information provided by the evaluation function for the decisions. In other words, EGA = evolutionary mechanisms + human's decision will + heuristic information. In EGA, the ideas of the positive eugenics and the negative eugenics are applied as the principle of selections and the selections are not activated by the prior probabilities but by the evaluation values of individuals. A method of genealogical chain-based selection for mutation is proposed, which avoids the blindness of stochastic mutation and the disruptive problem of mutation. A control strategy of reasonable competitions is proposed, which brings the effects of crossover and mutation into full play. Three examples, the minimum problem of a standard optimizing function--De Jong's test function F2, a typical combinatorial optimization problem--the traveling salesman problem, and a problem of identifying nonlinear system, are given to show the good performance of EGA.

Continuous nonlinearity" is stressed as a fundamental principle in pattern recognition including handprinted Kanji character recognition. Continuity" in template matching and spatial nonlinearity" in structural analysis should be unified toward deriving a higher level of recognition algorithm. At the same time, continuous nonlinearity in the temporal axis is important, as is the case of simultaneous processing of segmentation and recognition for touching characters. The above viewpoint is discussed in the following examples: nonlinear normalization, directional pattern matching, locally maximized similarity, relaxation matching, dynamic programming matching, segmentation of character string using dynamic programming, and exhaustive matching for character extraction on complex background.

Digital halftoning is a well-known technique in image processing to convert an image having several bits for brightness levels into a binary image consisting only of black and white dots. A great number of algorithms have been presented for this problem, some of which have only been evaluated just by comparison with human eyes. In this paper we formulate the digital halftoning problem as a combinatiorial problem which allows an exact solution with graph-theoretic tools. For this, we consider a d-dimensional grid of n := Nd pixels (d 1). For each pixel, we define a so-called k-neighborhood, k {0,...N - 1}, which is the set of at most (2k + 1)d pixels that can be reached from the current pixel in a distance of k. Now, in order to solve the digital halftoning problem, we are going to minimize the sum of distances of all k-neighborhoods between the original picture and the halftoned one. We show that the problem can be solved in linear time in the one-dimensional case while it looks hopeless to have a polynomial-time algorithm in higher dimension including the usual two-dimensional case. We present an exact algorithm for the one-dimensional case which runs in O(n) time if k is regarded to be a constant. For two-dimensional case we present fast approximation techniques based on space filling curves. An experimental comparison of several implementations of approximate algorithms proves that our algorithms are of practical interest.

The charge neutralized at a small gap discharge has been evaluated from measured electromagnetic fields by two methods. The small gap discharges simulate ESD events. The evaluated charge decreases rapidly as a step shape immediately in a moment of the discharge. The accumulated static charge and the risetime of the neutralization step increase with the gap length. When the gap length is 0.1mm, risetime and the initial static charge are about 0.3ns and 5.6nC, respectively.

A novel method of multichannel surface EMG processing has been developed to compensate for the distortion in bipolar surface EMG signals due to the movement of innervation zones. The distortion of bipolar surface EMG signals was mathematically described as a filtering function. A compensating technique for such distorted bipolar surface EMG signals was developed for the brachial biceps during dynamic contractions in which the muscle length and tension change. The technique is based on multichannel surface EMG measurement, a method for estimating the movement of an innervation zone, and the inverse filtering technique. As a result, the distorted EMG signals were compensated and transformed into nearly identical waveforms, independent of the movement of the innervation zone.

In this paper, we present some contributions to improve a previous work's approach presented for the segmentation of magnetic resonance images of the human brain, based on the unsupervised Hopfield neural network. We formulate the segmentation problem as a minimization of an energy function constructed with two terms, the cost-term as a sum of errors' squares, and the second term is a temporary noise added to the cost-term as an excitation to the network to escape from certain local minimums and be more close to the global minimum. Also, to ensure the convergence of the network and its utility in clinic with useful results, the minimization is achieved with a step function permitting the network to reach its stability corresponding to a local minimum close to the global minimum in a prespecified period of time. We present here our approach segmentations results of a patient data diagnosed with a metastatic tumor in the brain, and we compare them to those obtained based on, previous works using Hopfield neural networks, Boltzmann machine and the conventional ISODATA clustering technique.

Using fundamental device and circuits, we have realized three functions required for synaptic connections in self-organizing neural networks: long term memory of synaptic weights, fixed total amount of synaptic weights in a neuron, and lateral inhibition. The first two functions have been condensed into an optical adaptive device and circuits with floating gates. Lateral inhibition has been realized by a winner-take-all circuit and a following lateral excitatory connection circuit. We have fabricated these devices and circuits using CMOS technology and confirmed the three functions. In addition, topological mapping, which is essential for feature extraction, has been formed in a primitive network constructed with the fundamental device and circuits.

The synergistic effects obtained by adopting both space diversity reception and adaptive equalization play a very important role in circuit outage reduction. This paper quantitatively analyzes these synergistic effects when dispersive and flat fading occur simultaneously. Analytical results show that the synergistic effects are of the same magnitude as the adaptive equalizer improvement factor when only dispersive fading causes outage. The synergistic effects gradually disappear when noise is the predominant cause of outage.

A clique of a graph G(V,E) is a subset of V such that every pair of vertices is connected by an edge in E. Finding a maximum clique of an arbitrary graph is a well-known NP-complete problem. Recently, several polynomial time energy-descent optimization algorithms have been proposed for approximating the maximum clique problem, where they seek a solution by minimizing the energy function representing the constraints and the goal function. In this paper, we propose the binary neural network as an efficient synchronous energy-descent optimization algorithm. Through two types of random graphs, we compare the performance of four promising energy-descent optimization algorithms. The simulation results show that RaCLIQUE, the modified Boltzmann machine algorithm, is the best asynchronous algorithm for random graphs, while the binary neural network is the best one for k random cliques graphs.

In this paper, we propose a new FPGA design algorithm, Maple-opt, in which technology mapping, placement, and global routing are executed so that the delay of each critical signal path in an input circuit is within a specified upper bound imposed on it. The basic algorithm of Maple-opt is top-down hi-erarchical bi-partitioning of regions. Technology mapping onto logic-blocks of FPGAs, their placement, and global routing are determined simulatenously in each hierarchical process. This simultaneity leads to less congested layout for routing. In addition to that, Maple-opt computes a lower bound of delay for each path with a constraint value and determines critical paths based on the difference between the lower bound and the constraint value dynamically in each hierarchical process. Two delay reduction processes are executed for the critical paths; one is routing delay reduction and the other is logic-block delay reduction. Routing delay reduction is realized such that, when bi-partitioning a region, each constrained path is assigned to one subregion. Logic-block delay reduction is realized such that each constrained path is mapped onto fewer logic-blocks. Experimental results for some benchmark circuits show its efficiency and effectiveness.

This paper proposes an algorithm for the design of FIR digital filters whose coefficients have CSD representations. The total number of nonzero digits is specified. A set of filters whose frequency responses have less than or equal to a given Chebyshev error have their coefficients in a convex polyhedron in the Euclid space. The proposed algorithm searches points where a coefficient is maximum or minimum in the convex polyhedron by using linear programing. These points are connected whih the origin to make a convex cone. Then the algorithm evaluates CSD points near these edges of the cone. Moving along these edges means the scaling of frequency responses. The point where the frequency response is the best among all the candidates under the condition of specified total number of nonzero digits is selected as the solution. Several techniques are used to reduce the calculation time. Design examples show that the proposed method can design better frequency responses than the conventional methods.

The scaling laws between the design rules and the smallest sizes and numbers of particles capable of causing pattern defects and scrapping dies in semiconductor device manufacturing are described. Simulation with electromagnetic waveguide model indicates the possibility that particles, the sizes of which are of comparable order or even smaller than the wavelength of the lithography irradiation sources, are capable of causing pattern defects. For example, in the future 0.25 µm-design-rule era, the critical sizes of Si, Al, and SiO2 particles are simulated as 120 nm 120 nm, 120 nm 120 nm, and 560 nm 560 nm, respectively, in the case of 0.7 µm-thick chemically-amplified positive photoresist with 47 nm-thick top anti-reflective coating films. Future giga-scale integration era is also predicted.

This paper describes a CDMA cellular system based on adaptive interference cancellation (CDMA-AIC) with a large capacity. In the CDMA-AIC, each base station employs a single-user type adaptive interference canceller (AIC), which consists of a fractionally chip-spaced code-orthogonalizing filter (COF) and a coherent detector. The AIC adaptively removes power-dominant multiple-access interferences (MAIs) in the cellular system, regardless of whether they are intra-cell interferences or inter-cell interferences, without any information about them, such as spreading codes, signal received timings and channel parameters. Evaluation under the multiple-cell environment demonstrates that the reverse link capacity of the CDMA-AIC with QPSK modulation is 3.6 times as large as the capacity of the CDMA without MAI cancellation. Further, the capacity is less sensitive to transmission power control errors than that of the conventional CDMA systems.

This paper reports experimental results of a media synchronization mechanism which was proposed by the authors, focusing on the graceful recovery scheme. The proposed method consists of intra-stream and inter-stream synchronization mechanisms. The inter-stream synchronization control is performed after the intra-stream synchronization control over each media unit (MU) such as a video frame. Then, whether the intra-stream synchronization is still maintained or not is checked. In the experimental system, video and voice stored in a source workstation are transferred to a destination workstation via an FDDI network, and then they are synchronized and outputted at the destination (i.e., lip-synch). At the transmission of each MU, we simulate network delay jitters by generating a pseudo-delay which is exponentially distributed. Using the system, we have confirmed the validity of the mechanism. We also clarify how to set the threshold and parameter values defined in the mechanism by evaluating mean square error and average MU rate or by subjective assessment. Furthermore, we demonstrate that the intra-stream synchronization control for each streams in addition to the inter-stream control is necessary for high quality synchronization.