In this paper, we present simulation algorithms among enhanced mesh models. The enhanced mesh models here include reconfigurable mesh and mesh with multiple broadcasting. A reconfigurable mesh (RM) is a processor array that consists of processors arranged to a 2-dimensional grid with a reconfigurable bus system. The bus system can be used to dynamically obtain various interconnection patterns among the processors during the execution of programs. A horizontal-vertical RM (HV-RM) is obtained from the general RM model, by restricting the network topology it can take to the ones in which each bus segment must be along row or column. A mesh with multiple broadcasting (MWMB) is an enhanced mesh, which has additional broadcasting buses endowed to every row and column. We present two algorithms:1) an algorithm that simulates a HV-RM of size nn time-optimally in θ(n) time on a MWMB of size nn, and 2) an algorithm that simulates a RM of size nn in θ(log2 n) time on a HV-RM of size nn. Both algorithms use a constant number of storage in each processor. Furthermore, we show that a RM of size nn can be simulated in θ((n/m)2 log n log m) time on a HV-RM of size mm, in θ ((n/m)2 m log n log m) time on a MWMB of size mm (m < n). These simulations use θ((n/m)2) storage in each processor, which is optimal.

In pattern recognition using neural networks, it is very difficult for researchers or users to design optimal neural network architecture for a specific task. It is possible for any kinds of neural network architectures to obtain a certain measure of recognition ratio. It is, however, difficult to get an optimal neural network architecture for a specific task analytically in the recognition ratio and effectiveness of training. In this paper, an evolutional method of training and designing feedforward neural networks is proposed. In the proposed method, a neural network is defined as one individual and neural networks whose architectures are same as one species. These networks are evaluated by normalized M. S. E. (Mean Square Error) which presents a performance of a network for training patterns. Then, their architectures evolve according to an evolution rule proposed here. Architectures of neural networks, in other words, species, are evaluated by another measurement of criteria compared with the criteria of individuals. The criteria assess the most superior individual in the species and the speed of evolution of the species. The species are increased or decreased in population size according to the criteria. The evolution rule generates a little bit different architectures of neural network from superior species. The proposed method, therefore, can generate variety of architectures of neural networks. The designing and training neural networks which performs simple 3 3 and 4 4 pixels which include vertical, horizontal and oblique lines classifications and Handwritten KATAKANA recognitions are presented. The efficiency of proposed method is also discussed.

This paper describes the expanded generalized method of characteristics (GMC) in order to handle large linear interconnect networks. The conventional GMC is applied to modeling each of transmission lines. Therefore, this method is not suitable to deal with large linear networks containing many transmission lines. Here, we propose the expanded GMC method to overcome this problem. This method computes a characteristic impedance and a new propagation function of the large linear networks containing many transmission lines. Furthermore the wave propagation delay is removed from the new wave propagation function using delay evaluation technique. Finally, it is shown that the present method enables the efficient and accurate simulation of the transmission line networks.

It is known that AND-EXOR two-level networks obtained by AND-EXOR expressions with positive literals are easily testable. They are based on the single-rail-input logic, and require (n+4) tests to detect their single stuck-at faults, where n is the number of the input variables. We present three-level networks obtained from single-rail-input OR-AND-EXOR expressions and propose a more easily testable realization than the AND-EXOR networks. The realization is an OR-AND-EXOR network which limits the fan-in of the AND and OR gates to n/r and r respectively, where r is a constant (1 r n). We show that only (r+n/r) tests are required to detect the single stuck-at faults by adding r extra variables to the network.

The fact that there exists a core sinusoidal oscillator at the heart of Saito's double-screw hysteresis chaotic oscillator is demonstrated. By applying Bruton's transformation to the active linear part of the circuit, which is shown to be a classical LC-R negative resistor sinusoidal oscillator, an inductorless realization based on a frequency-dependent negative resistor (FDNR) is obtained. The LC-R sinusoidal oscillator is replaced by an FDNR-R oscillator. Furthermore, we show that chaotic behaviour can be preserved when a simple minimum component 2R-2C sinusoidal oscillator is used. Two different realizations of the non-monotone current-controlled hysteresis resistor, one of which is completely passive, are investigated. Experimental results of selected circuits, PSpice and numerical simulations are included.

In order to lead an ongoing software project to success, it is important to flexibly control its dynamically-changing software process. However, it is generally impossible not only to exactly pre-define the production process but also to prescribe the process change process (meta-process). To solve the problem, we have focused on communication between the project staff through which process change requests presented by individuals can be immediately shared, designed, verified, validated and implemented. This paper proposes a communication model which can represent a wide variety of communication states between the project manager and developers discussing how to implement process change requests. The communication model has been derived by investigating the sort of process change requests and, based on the model, we have implemented a cooperative process planning system (called CooPs). CooPs is a communication environment designed for software projects and supports information sharing for discussing the process change requests. By using CooPs, the software project can flexibly deal with not only expected change requests but also unexpected ones. To evaluate the applicability of the communication model and the capabilities of CooPs, we have conducted an experiment which is an application of CooPs to the ISPW6 example problem. This paper describes the concepts of CooPs, the system implementation, and the experiment.

In multi-media systems, the type of interactive communication channels is found almost everywhere and plays an important role, as well as the type of unilateral communication channels. In this report, we shall construct a fluctuation theory based on the concept of set-valued mappings, suitable for evaluation, control and operation of interactive communication channels in multi-media systems, complicated and diversified on large scales. Fundamental conditions for availability of such channels are clarified in a form of fixed point theorem for system of set-valued mappings.

The current-voltage characteristics of a single electron transistor (SET) in the resonant transport mode are investigated. In the future when SET devices are applied to integrated electronics, the quantum effect will seriously modify their characteristics in ultra-small geometry. The current will be dominated by the resonant transport through narrow energy levels in the dot. The simple case of a two-level system is analyzed and the transport mechanism is clarified. The transport property at low temperatures (higher than the Kondo temperature) in the low tunneling rate limit is discussed, and a current map where current values are classified in the gate bias-drain bias plane is provided. It was shown that the dynamic aspect of electron flow seriously influences the current value.

A new vector-matching circuit technology has been developed aiming at compact implementation of maximum likelihood search engine for neuron-MOS associative processor. The new matching cell developed in this work possessed the template information in the form of an analog mask ROM and calculates the absolute value of difference between the template vector and the input vector components. The analog-mask ROM merged matching cell is composed of only five transistors to be compared with our earlier-version memory separated matching cell of 13 transistors. In addition, the undesirable cell-to-cell data interference through the common floating node ("boot-strap effect") has been eliminated without using power-consuming current source loads in source followers. As a result, dc-current-free matching cell operation has been established, making it possible to build a low-power, high-density search engine. Test circuits were fabricated by a 0.8-µm double-polysilicon double-metal n-well CMOS process, and the circuit operation has been experimentally verified.

A review of research, covering about 50 years, about random phenomena in nonlinear dynamical systems and the problems of modeling such phenomena using real (as contrasted to abstract, axiomatic) mathematics. Private views of the author concerning personalities and events.

The MPEG video coding is the most widely used video coding standard which usually generates variable bitrate (VBR) data streams. Although ATM can deliver VBR traffic, the burst traffic still has the possibility to be dropped due to network congestion. The cell loss can be minimized by using an enforced rate control method. However, the quality of the reproduced video may be sacrificed due to insufficient peak rate available. In this work, we propose an end-to-end quality adaptation mechanism for MPEG traffic over ATM. The adaptive quality control (AQC) scheme allocates a certain number of coding bits to each video frame based on the network condition and the type of next frame. More bits may be allocated if the network condition, represented by the connection-level, is good or the next frame is B-frame that usually consumes fewer bits. A high connection-level allows a relatively large number of tagged cells, which are non-guaranteed in delivery, for video frames with high peak rates. The connection-level adjustment unit at the encoder end adjusts the connection-level based on the message of the network condition from the quality monitoring unit at decoder. The simulation results show that the AQC system can effectively utilize the channel bandwidth as well as maintain satisfactory video quality in various network conditions.

Feature selection is an integral part of any pattern recognition system. Removal of redundant features improves the efficiency of a classifier as well as cut down the cost of future feature extraction. Recently neural network classifiers have become extremely popular compared to their counterparts from statistical theory. Some works on the use of artificial neural network as a feature selector have already been reported. In this work a simple feature selection algorithm has been proposed in which a fractal neural network, a modified version of multilayer perceptron, has been used as a feature selector. Experiments have been done with IRIS and SONAR data set by simulation. Results suggest that the algorithm with the fractal network architecture works well for removal of redundant informations as tested by classification rate. The fractal neural network takes lesser training time than the conventional multilayer perceptron for its lower connectivity while its performance is comparable to the multilayer perceptron. The ease of hardware implementation is also an attractive point in designing feature selector with fractal neural network.

In digital modulation for mobile radio telephone services frequency modulation with continuous phase with small modulation indices (MSK/GMSK) is sometimes used. Extension of the synchronization subsystems' pulling band in a coherent receiver and reducing synchronization delay is important for the mobile communication. At this moment there are only two possible synchronization schemes for the coherent MSK/GMSK receiver: Costas and de Buda's. This paper presents a new method (a possible alternative to both of them) where the frequency discriminator with decoupled carrier and bit synchronizing subsystem are combined to handle the task. For comparison, this paper also describes performances of the Costas carrier recovery scheme, which is widely employed for MSK/GMSK coherent demodulation. Discrimination and fluctuation characteristics for frequency, phase, and symbol delay synchronization subsystems are shown and the BER degradation from the conventional Costas scheme is calculated. This paper demonstrates with simulation results that the proposed scheme improves RF carrier acquisition performances, and at the same time, for large signal-to-noise ratios (SNR's) provides similar or better tracking performances than the Costas one. While limited to higher SNR ratios, the proposed synchronization scheme is suitable for many applications and can be implemented with simpler circuitry, well suited to integrated circuit implementation.

This paper explores a possibility of constructing massively parallel molecular computing systems using molecular electronic devices called enzyme transistors. The enzyme transistor is, in a sense, an artificial catalyst which selects a specific substrate molecule and transforms it into a specific product. Using this primitive function, various active continuous media for signal transfer/processing can be realized. Prominent examples discussed in this paper are: (i) Turing pattern formation and (ii) excitable wave propagation in a two-dimensional enzyme transistor array. This paper demonstrates the potential of enzyme transistors for creating reaction-diffusion dynamics that performs useful computations in a massively parallel fashion.

This paper describes an efficient simulator for state transition analysis of multivalued continuous-time neural networks, where the multivalued transfer function of neuron is regarded as a stepwise constant one. Use of stepwise constant method enables to analyse the state transition of the network without solving explicitly the differential equations. This method also enables to select the optimal timestep in numerical integration. The proposed method is implemented on the simulator and applied to the general neural network analysis. Furthermore, this is compared with the conventional simulators. Finally, it is shown that our simulator is drastically faster and more practical than the conventional simulators.

We report relations between invariant manifolds of saddle orbits (Lyapunov family) around a saddle-center equilibrium point and lowest periodic orbits on the two degree of freedom swing equation system. The system consists of two generators operating onto an infinite bus. In this system, a stable equilibrium point represents the normal operation state, and to understand its basin structure is important in connection with practical situations. The Lyapunov families appear under conservative conditions and their invariant manifolds constitute separatrices between trapped and divergent motions. These separatrices continuously deform and become basin boundaries, if changing the system to dissipative one, so that to investigate those manifolds is meaningful. While, in the field of two degree of freedom motions, systems with saddle loops to a saddle-center are well studied, and existence of transverse homoclinic structure of separatrix manifolds is reported. However our investigating system has no such loops. It is interesting what separatrix structure exists without trivial saddle loops. In this report, we focus on above invariant manifolds and lowest periodic orbits which are foliated for the Hamiltonian level.

Recent developments in computer technology allow us to analyze all the data in a huge database. Data mining is to analyze all the data in such a database and to obtain useful information for database users. One of the well-studied problems in data mining is the search for meaningful association rules in a market basket database which contains massive amounts of transactions. One way to find meaningful association rules is to find all the large itemsets first, and then to find meaningful association rules from the large itemsets. Although a number of algorithms for computing all the large itemsets have been proposed, the computational complexity of them is scarcely disscussed. In this paper, we show that it is NP-complete to decide whether there exists a large itemset that has a given cardinality. Also, we propose subclasses of databases in which all the meaningful association rules can be computed in time polynomial of the size of a database.

Recently, many on-line control methods of partially observed discrete event systems(DES's) have been proposed. This paper proposes an algorithm for on-line control based on a supervisor under complete observation. It is shown that DES's controlled by the proposed on-line controller generate maximally controllable and observable sublanguages which include the supremal normal sublanguages. Moreover, computational complexity of the proposed algorithm is polynomial with respect to the numbers of the unobservable events and the state of the supervisor under complete observation.

We analyze the dynamics of self-organizing cortical maps under the influence of external stimuli. We show that if the map is a contraction, then the system has a unique equilibrium which is globally asymptotically stable; consequently the system acts as a stable encoder of external input stimuli. The system converges to a fixed point representing the steady-state of the neural activity which has as an upper bound the superposition of the spatial integrals of the weight function between neighboring neurons and the stimulus autocorrelation function. The proposed theory also includes nontrivial interesting solutions.

This paper treats the systematic design of chaos generators which are capable of generating continuous-time signals with prescribed probability density function and power density spectra. For a specific signal model a statistical analysis is performed such that the inverse problem, i. e. the calculation of the model parameters from prescribed signal characteristics, can be solved. Finally from the obtained model parameters and the model structure the signal generating system is constructed. The approach is illustrated by several examples.