Although production systems are widely used in artificial intelligence (AI) applications, they are seen to have certain disadvantages in terms of their need for special purpose assistance software to build and execute their knowledge-bases (KB), and in the fact that they will not run on any operating system (platform dependency). Furthermore, for AI applications such as learning assistance systems, there is a strong requirement for a self-adaptive function enabling a flexible change in the service contents provided, according to the user. Against such a background, a Java based production system (JPS) featuring no requirement for special purpose assistance software and no platform dependency, is proposed. Furthermore, a new self-adaptive Java production system (A-JPS) is proposed to realize the "user adaptation" requirement mentioned above. Its key characteristic is the combination of JPS with a Causal-network (CN) for obtaining a "user profile". In addition, the execution time of the JPS was studied using several benchmark problems with the aim of comparing the effectiveness of different matching algorithms in their recognize-act cycles as well as comparing their performance to that of traditional procedural programs for different problem types. Moreover, the effectiveness of the user adaptation function of the A-JPS was studied for the case of a CN with a general DAG structure, using the experimental KB of a learning assistance system.

Integrated implementation of RF front-end components has been shown to posses many benefits. Furthermore, it presents a new way of approaching RF design. This paper will discuss the recent developments by the author's group in the field of RF front-end technology. This will include stand-alone RF front-end components such as a self-heterodyne mixer as well as more functional front-end circuitry such as digital beamformer arrays, retrodirective arrays and an array error calibration scheme.

We study the statistical multiplexing performance of self-similar traffic. We consider that input streams have different QoS (Quality of Service) requirements such as loss and delay jitter. By applying the FBM (fractal Brownian motion) model, we present methods of estimating the effective bandwidth of aggregated traffic. We performed simulations to evaluate the QoS performances and the bandwidths required to satisfy them. The comparison between the estimation and the simulation confirms that the estimation could give rough data of the effective bandwidth. Finally, we analyze the bandwidth gain with priority multiplexing against non-prioritized multiplexing and suggest how to get better performance with the right configuration of QoS parameters.

This work explores generative models of handwritten digit images using natural elastic nets. The analysis aims to extract global features as well as distributed local features of handwritten digits. These features are expected to form a basis that is significant for discriminant analysis of handwritten digits and related analysis of character images or natural images.

A directional audible sound can be generated by amplitude-modulated (AM) into ultrasound wave from a parametric array. To synthesize audio signals produced by the self-demodulation effect of the AM sound wave, a quasi-linear analytical solution, which describes the nonlinear wave propagation, is developed for fast numerical evaluation. The radiated sound field is expressed as the superposition of Gaussian Beams. Numerical results are presented for a rectangular parametric loudspeaker, which are in good agreement with the experimental data published previously.

Malignant melanoma is a skin cancer that can be mistaken as a harmless mole in the early stages and is curable only in these early stages. Therefore, dermatologists use a microscope that shows the pigment structures of the skin to classify suspicious skin lesions as malignant or benign. This microscope is called "dermoscope." However, even when using a dermoscope a malignant skin lesion can be mistaken as benign or vice versa. Therefore, it seems desirable to analyze dermoscopic images by computer to classify the skin lesion. Before a dermoscopic image can be classified, it should be segmented into regions of the same color. For this purpose, we propose a segmentation method that automatically determines the number of colors by optimizing a cluster validity index. Cluster validity indices can be used to determine how accurately a partition represents the "natural" clusters of a data set. Therefore, cluster validity indices can also be applied to evaluate how accurately a color image is segmented. First the RGB image is transformed into the L*u*v* color space, in which Euclidean vector distances correspond to differences of visible colors. The pixels of the L*u*v* image are used to train a self-organizing map. After completion of the training a genetic algorithm groups the neurons of the self-organizing map into clusters using fuzzy c-means. The genetic algorithm searches for a partition that optimizes a fuzzy cluster validity index. The image is segmented by assigning each pixel of the L*u*v* image to the nearest neighbor among the cluster centers found by the genetic algorithm. A set of dermoscopic images is segmented using the method proposed in this research and the images are classified based on color statistics and textural features. The results indicate that the method proposed in this research is effective for the segmentation of dermoscopic images.

In this paper, the magnetic field properties around household appliances are investigated with the single coil model and equivalent source model, which are used as main source models in the European standard EN50366 (CENELEC). The accuracy of the field properties is conducted for the coil model (defined in the EN50366), by comparing with the results of the equivalent source model, which allow the reproduction of the complicated inhomogeneous magnetic field around the appliance with full generality (i.e. supports three dimensional vector fields).

A simple millimeter-wave quasi-maximal-ratio-combin-ing antenna diversity system based on the millimeter-wave self-heterodyne transmission technique is described. The millimeter-wave self-heterodyne transmission technique is useful for developing millimeter-wave systems with enhanced characteristics in regard to system miniaturization, development and fabrication cost, and the frequency stability of the signal transmission. We also show that applying this technique with an antenna diversity receiver configuration can easily solve a problem peculiar to millimeter-wave systems--the fact that the transmission link always requires a line-of-sight path--without requiring hardware designed with millimeter-scale precision. In this paper, we theoretically analyze the operating principle of a combining antenna diversity system based on the millimeter-wave self-heterodyne transmission technique. We further prove that we can obtain a diversity gain in accordance with that of a maximal-ratio combining diversity system without resorting to any complicated control of the received signal envelope and phase. Our experiments using the simplest two-branch diversity structure have validated the operating principle derived in our theoretical analysis. Our results show that a received CNR improvement of 3 dB is obtained as a diversity gain. We also demonstrate that circuit precision corresponding to the wavelength of the intermediate frequency, rather than to the millimeter wavelength, is sufficient to obtain the diversity effect when we control the signal phase or delay in combining the received signals.

This paper addresses the issue of reconfiguring multi-layer neural networks implemented in single or multiple VLSI chips. The ability to adaptively reconfigure network configuration for a given application, considering the presence of faulty neurons, is a very valuable feature in a large scale neural network. In addition, it has become necessary to achieve systems that can automatically reconfigure a network and acquire optimal weights without any help from host computers. However, self-reconfigurable architectures for neural networks have not been studied sufficiently. In this paper, we propose an architecture for a self-reconfigurable multi-layer neural network employing both reconfiguration with spare neurons and weight training by GAs. This proposal offers the combined advantages of low hardware overhead for adding spare neurons and fast weight training time. To show the possibility of self-reconfigurable neural networks, the prototype system has been implemented on a field programmable gate array.

In a high-rate indoor wireless personal communication system, the delay spread due to multi-path propagation results in intersymbol interference which can significantly increase the transmission bit error rate (BER). The technique most commonly used for combating the intersymbol interference and frequency-selective fading found in communications channels is the adaptive equalization. In this paper, we propose a novel neural detector based on self-organizing map (SOM) to improve the system performance of the receiver. In the proposed scheme, the SOM is used as an adaptive detector of equalizer, which updates the decision levels to follow the received faded signal. To adapt the proposed scheme to the time-varying channel, we use the Euclidean distance, which will be updated automatically according to the received faded signal, as an adaptive radius to define the neighborhood of the winning neuron of the SOM algorithm. Simulations on a 16 QAM system show that the receiver using the proposed neural detector has a significantly better BER performance than the traditional receiver.

This paper describes an analysis of IP-network traffic in terms of the time variation of self-similarity. To get a comprehensive view in analyzing the degree of long-range dependence (LRD) of IP-network traffic, this paper used a self-organizing map, which provides a way to map high-dimensional data onto a low-dimensional domain. Also, in the LRD-based analysis, this paper employed detrended fluctuation analysis (DFA), which is applicable to the analysis of long-range power-law correlations or LRD in non-stationary time-series signals. In applying this method to traffic analysis, this paper performed two kinds of traffic measurement: one based on IP-network traffic flowing into NTT Musashino R&D center (Tokyo, Japan) from the Internet and the other based on IP-network traffic flowing through at an interface point between an access provider (Tokyo, Japan) and the Internet. Based on sequential measurements of IP-network traffic, this paper derived corresponding values for the LRD-related parameter α of measured traffic. As a result, we found that the characteristic of self-similarity seen in the measured traffic fluctuated over time, with different time variation patterns for two measurement locations. In training the self-organizing map, this paper used three parameters: two α values for different plot ranges, and Shannon-based entropy, which reflects the degree of concentration of measured time-series data. We visually confirmed that the traffic data could be projected onto the map in accordance with the traffic properties, resulting in a combined depiction of the effects of the degree of LRD and network utilization rates. The proposed method can deal with multi-dimensional parameters, projecting its results onto a two-dimensional space in which the projected data positions give us an effective depiction of network conditions at different times.

This paper presents a compensating method based on Self-Organizing Map (SOM) for nonlinear distortion, which is caused by high-power amplifier (HPA) in 16-QAM-OFDM system. OFDM signals are sensitive to nonlinear distortions and different methods are studied to solve them. In the proposed scheme, the correction is done at the receiver by a SOM algorithm. Simulations are carried out considering an additive white Gaussian (AWG) transmission channel. Simulation results show that the SOM algorithm brings perceptible gains in a complete 16-QAM-OFDM system.

High-frequency characteristics of SiGe heterojunction bipolar transistors with different emitter sizes are studied based on pulsed measurements. Because the self-heating effect in transistors will enhance the Kirk effect, as the devices operate in high current region, the measured cutoff frequency and maximum oscillation frequency decrease with measurement time in the pulsed duration. By analyzing the equivalent small-signal device parameters, we know the reduction of cutoff frequency and maximum oscillation frequency is attributed to the reduction of transconductance and the increase of junction capacitances for fixed base-emitter voltage, while it is only attributed to the degradation of transconductance for fixed collector current. Besides, the degradation of high-frequency performance due to self-heating effect would be improved with the layout design combining narrow emitter finger and parallel-interconnected subcells structure.

Measuring and modeling network traffic is of key importance for the traffic engineering of IP networks, due to the growing diversity of multimedia applications and the need to efficiently support QoS differentiation in the network. Several recent measurements have shown that Internet traffic may incorporate long-range dependence and self-similar characteristics, which can have significant impact on network performance. Self-similar traffic shows variability over many time scales, and this behavior must be taken into account for accurate prediction of network performance. In this paper, we propose a new parameter fitting procedure for a superposition of Markov Modulated Poisson Processes (MMPPs), which is able to capture self-similarity over a range of time scales. The fitting procedure matches the complete distribution of the arrival process at each time scale of interest. We evaluate the procedure by comparing the Hurst parameter, the probability mass function at each time scale, and the queuing behavior (as assessed by the loss probability and average waiting time), corresponding to measured traffic traces and to traces synthesized according to the proposed model. We consider three measured traffic traces, all exhibiting self-similar behavior: the well-known pOct Bellcore trace, a trace of aggregated IP WAN traffic, and a trace corresponding to the popular file sharing application Kazaa. Our results show that the proposed fitting procedure is able to match closely the distribution over the time scales present in data, leading to an accurate prediction of the queuing behavior.

This letter presents a significant property of the mapping parameters that play a central role to represent a given signal in Fractal Interpolation Functions (FIF). Thanks to our theoretical analysis, it is derived that the mapping parameters required to represent a given signal are also applicable to represent the upsampled signal of a given one. Furthermore, the upsampled signal obtained by using the property represents the self-affine property more distinctly than the given signal. Experiments show the validity and usefulness of the significant property.

In this paper, we propose a self-nonself recognition algorithm based on positive and negative selection used in the developing process of T cells. The anomaly detection algorithm based on negative selection is a representative model among self-recognition method and it has been applied to computer immune systems in recent years. In biological immune systems, T cells are produced through both positive and negative selection. Positive selection is the process used to determine a MHC receptor that recognizes self-molecules. Negative selection is the process used to determine an antigen receptor that recognizes antigens, or nonself cells. In this paper, we propose a self-recognition algorithm based on the positive selection and also propose a fusion algorithm based on both positive and negative selection. To verify the effectiveness of the proposed system, we show simulation results for detecting some infected data obtained from cell changes and string changes in the self-file.

Self-stabilization is a theoretical framework of non-masking fault-tolerant distributed algorithms. In this paper, we investigate the Steiner tree problem in distributed systems, and propose a self-stabilizing heuristic solution to the problem. Our algorithm is constructed by four layered modules (sub-algorithms): construction of a shortest path forest, transformation of the network, construction of a minimum spanning tree, and pruning unnecessary links and processes. Competitiveness is 2(1-1/l), where l is the number of leaves of optimal solution.

In this paper, a new grouping method for Group Technology using Self-Organizing Map (SOM) is proposed. The purpose of our study is to divide machines in a factory into any number of cells so that the machines in each cell can process a similar set of parts to increase productivity. A main feature of our method is to specify not only the number of the cells but also the maximum and minimum numbers of machines in a cell. Some experimental results show effectiveness of our proposed algorithm.

We have developed a novel self-alignment process using the surface tension of the liquid resin for assembly of electronic and optoelectronic devices. Due to their characteristics of low surface tension, however, the parametric design guidelines are necessary for resin self-alignment capability. In this paper, a shape prediction mathematical model and a numerical method are developed. The developed system is capable of achieving the liquid joint geometry and the parametric design for self-alignment capability. The influences of geometric parameters such as liquid volume, component weight, pad radius, liquid surface tension on the shape of liquid joint are investigated. Furthermore, the parametric design guidelines considered the process-related practical matters of misalignment level, distribution of the supplied liquid volumes and coplanarity deviation includes difference of the height between the pads are provided.

Field Configurable Self-assembly is a novel programmable force field based heterogeneous integration technology. Herein, we demonstrate application of the method to rapid, parallel assembly of similar and dissimilar sub-200 µm GaAs-based light emitting diodes at silicon chip substrates. We also show that the method is compatible with post-process collective wiring techniques for fully planar hybrid integration of active devices.