On research to determine reliable acoustic indicators for the type of stress present in speech, the majority of systems have concentrated on the statistics extracted from pitch contour, energy contour, wavelet based subband features and Teager-Energy-Operator (TEO) based feature parameters. These systems work mostly on pair-wise distinction between stress and neutral speech. Their performance decreases substantially when tested in multi-style detection among many stress categories. In this paper, a novel system is proposed using linear short time Log Frequency Power Coefficients (LFPC) and TEO based nonlinear LFPC features in both time and frequency domain. Five-state Hidden Markov Model (HMM) with continuous Gaussian mixture distribution is used. The stress classification ability of the system is tested using data from the SUSAS (Speech Under Simulated and Actual Stress) database to categorize five stress conditions individually. It is found that the performance of linear acoustic features LFPC is better than that of nonlinear TEO based LFPC feature parameters. Results show that with linear acoustic feature LFPC, average accuracy of 84% and the best accuracy of 95% can be achieved in the classification of the five categories. Results of test of the system under different signal-to-noise conditions show that the performance of the system does not degrade drastically with increase in noise. It is also observed that classification using nonlinear frequency domain LFPC features gives relatively higher accuracy than that using nonlinear time domain LFPC features.

This paper describes a low-power-supply 2-GHz CMOS up-converter. A current-mode mixing method using current adding and self-switching mixers is proposed for 1-V operation. The current-mode up-converter achieves conversion gain of 6.7 dB and linearity of 6.5-dBm OIP3 at 1 V. Balanced configuration and DC offset canceller reduce LO leakage below -40 dBc even with 20-mV Vth mismatches. The bias circuit of the IC is designed to maintain constant conversion gain for variation of temperature for practical usage. The measurement results indicate the proposed up-converter is applicable for future wireless systems.

Basic characteristics of a short-ended half-wavelength resonator made of a coplanar waveguide (CPW) and their applications to bandpass filters (BPFs) are discussed. The first part of this paper gives the essence for improving out-of-band characteristics of the BPF by describing the basic characteristics of a tap-coupled resonator. Secondly, a new BPF with attenuation poles using the short-ended half-wavelength CPW resonators is proposed and realized. It is confirmed that our methodology is useful for improving out-of-band characteristics of the BPF using the short-ended half-wavelength CPW resonators without complicated filter design.

In fractal image coding, for each range block, the best matching domain block is identified, and information from the best matching domains and range blocks are transmitted to the decoder for image reconstruction. In this paper, the similarity between range blocks and domain blocks is evaluated according to their centers of gravity. The number of searched domain blocks are reduced by limiting the candidates for the best matching domain blocks to those domain blocks whose similarity to the range block are high. Using simulation experiments, the number of candidates for the best matching domain blocks were reduced to about 10-23% of the current method. Thus, our proposed method had significantly reduced the number of searched domain blocks below the current method and at the same time it turns out that degradation of the reconstructed image was seldom observed.

We propose linearization techniques for MMIC amplifiers. The key points of these techniques are increased linearity of a newly-developed low-distortion MESFET (LD-FET) and maximized IP3 by combining the LD-FET with a high-gain depletion-mode MESFET (D-FET) with no increase in power consumption. The LD-FET is characterized by its unique channel dopant-profile prepared by a buried p-type ion-implantation and double n-type ion-implantations with high- and low-acceleration energies. This FET achieves flatter behavior in terms of mutual conductance (gm) compared with conventional MESFETs irrespective of changes in the gate bias voltage (Vgs). A self-alignment/selective ion-implantation process enables the LD-FET and D-FET to be fabricated simultaneously. This process encourages IP3 maximization of the multi-stage amplifier by appropriately combining the advantages of the two differently characterized MESFETs. We fabricated and tested a highly linearized two-stage MMIC amplifier utilizing the proposed techniques, and found that its third-order intermodulation ratio (IMR) performance was 8.7 dB better than that of conventional MMIC amplifiers at an input signal level of -20 dBm with no increase in current dissipation. The configuration constructed by using the proposed techniques equivalently reduces the current dissipation of the second stage to 1/2.72 times that of the conventional configuration, which requires a 2.72 times larger D-FET at the second stage to obtain an 8.7-dB IMR improvement. Furthermore, we were able to improve the IMR by 3.5 dB by optimizing the gate bias conditions for the LD-FET. These results confirm the validity of the proposed techniques.

Finite-state vector quantization (FSVQ) is a well-known block encoding technique for digital image compression at low bit rate application. In this paper, an improved feature map finite-state vector quantization (IFMFSVQ) algorithm using three-sided side-match prediction is proposed for image coding. The new three-sided side-match improves the prediction quality of input blocks. Precoded blocks are used to alleviate the error propagation of side-match. An edge threshold is used to classify the blocks into nonedge or edge blocks to improve bit rate performance. Furthermore, an adaptive method is also obtained. Experimental results reveal that the new IFMFSVQ reduces bit rate significantly maintaining the same subjective quality, as compared to the basic FMFSVQ method.

The objective of this paper is to evaluate the impacts of impulsive class-A noise, co-channel interference due to other piconet, Rician fading on the packet error rate (PER), and throughput performance in the Bluetooth scatternet. Simulation results illustrate the significant difference in performance between synchronous and asynchronous Bluetooth systems. The paper also provides the insights on how to design Bluetooth scatternet for minimal PER and maximum throughput performance.

Recently, neural networks (NNs) have been extensively applied to many signal processing problem due to their robust abilities to form complex decision regions. In particular, neural networks add flexibility to the design of equalizers for digital communication systems. Recurrent neural network (RNN) is a kind of neural network with one or more feedback loops, whereas self-organizing map (SOM) is characterized by the formation of a topographic map of the input patterns in which the spatial locations (i.e., coordinates) of the neurons in the lattice are indicative of intrinsic statistical features contained in the input patterns. In this paper, we propose a novel receiver structure by combining adaptive RNN equalizer with a SOM detector under serious ISI and nonlinear distortion in QAM system. According to the theoretical analysis and computer simulation results, the performance of the proposed scheme is shown to be quite effective in channel equalization under nonlinear distortion.

This paper proposes an idea for modeling aggregated TCP/IP traffic arriving at a bottleneck link by focusing on its scaling behavior. Here, the aggregated TCP/IP traffic means the IP packet traffic from many TCP connections sharing the bottleneck link. The model is constructed based on the outcomes of our previous works investigating how the TCP/IP networking mechanism affects the self-similar scaling behavior of the aggregated TCP/IP traffic in a LAN/WAN environment. The proposed traffic model has been examined from the perspective of application to network performance estimation. The examinations have shown that it models the scaling behavior and queueing behavior of actual traffic, though it neglects the interaction among TCP connections that compete with each other for the single bottleneck link bandwidth.

In this paper we treat a novel adaptation strength according to neighborhood ranking of self-organizing neural networks with the objective of avoiding the initial dependency of reference vectors, which is related to the strength in the neural-gas network suggested by Martinetz et al. The present approach exhibits the effectiveness in the average distortion compared to the conventional technique through numerical experiments. Furthermore the present approach is applied to image data and the validity in employing as an image coding system is examined.

This paper describes a QoS evaluation method for VoIP communications using a self-organizing neural network. Based on measurements in real environments, evaluation results confirmed that our method can effectively display total QoS level composed of several QoS-related factors such as PSQM+ and end-to-end delay.

An image halftoning technique that uses a simple GA has proven to be effective generating bi-level halftone images with quality higher than conventional techniques. Many devices are designed to handle more than two halftone levels and a GA based multi-level halftoning technique is desirable. In this paper we extend the bi-level halftoning technique to generate multi-level halftone images. Also we introduce an improved GA (GA-SRM) into the proposed multi-level halftoning technique. Experimental results show that the proposed technique can effectively generate high quality multi-level halftone images and that the inclusion of GA-SRM substantially contributes reducing memory usage and accelerating image generation.

This paper presents a new method of designing a dither matrix based on simulated annealing. An obtained dither matrix (halftone screen/mask) is appropriate for press printing. Because of several physical reasons, halftoning for press printing is more difficult than halftoning for electronic displays, or ink-jet printers. Even if stochastic dispersed-dot screening (so-called FM-screening) is one of the best solutions for halftoning, that is not appropriate for press printing. On the other hand, classical periodic clustered-dot screening (so-called AM-screening) is more important and is widely used even now. We recognize unfavorable quality of AM-screening, but we can not ignore its productive stability in printing section. The proposed halftone dither matrix has aperiodic clustered-dot pattern, and size of cluster can be controlled by a weighting parameter of a cost function. We will obtain a dither matrix which consists of clustered-dots. Some characteristics of the design algorithm and halftoned images are investigated in detail. As a result, the fact that an obtained dither matrix is superior to AM-screen and comparable to FM-screen in visual quality, and the matrix is comparable to AM-screen and superior to FM-screen in press printability is confirmed.

Studies on artificial neural network have been conducted for a long time, and its contribution has been shown in many fields. However, the application of neural networks in the real world domain is still a challenge, since nature does not always provide the required satisfactory conditions. One example is the class size imbalanced condition in which one class is heavily under-represented compared to another class. This condition is often found in the real world domain and presents several difficulties for algorithms that assume the balanced condition of the classes. In this paper, we propose a method for solving problems posed by imbalanced training sets by applying the modified large-scale neural network "CombNET-II. " CombNET-II consists of two types of neural networks. The first type is a one-layer vector quantization neural network to turn the problem into a more balanced condition. The second type consists of several modules of three-layered multilayer perceptron trained by backpropagation for finer classification. CombNET-II combines the two types of neural networks to solve the problem effectively within a reasonable time. The performance is then evaluated by turning the model into a practical application for a fog forecasting problem. Fog forecasting is an imbalanced training sets problem, since the probability of fog appearance in the observation location is very low. Fog events should be predicted every 30 minutes based on the observation of meteorological conditions. Our experiments showed that CombNET-II could achieve a high prediction rate compared to the k-nearest neighbor classifier and the three-layered multilayer perceptron trained with BP. Part of this research was presented in the 1999 Fog Forecasting Contest sponsored by Neurocomputing Technical Group of IEICE, Japan, and CombNET-II achieved the highest accuracy among the participants.

This paper discusses self-similarity in cell dwell time of a mobile terminal, the discovery of which was described in our previous paper, and its effects on teletraffic of mobile communication networks. We have evaluated various teletraffic statistics, such as cell dwell time and channel occupancy time, of a mobile terminal based on measurements of motion for various types of vehicles. Those results show that cell dwell time follows a long-tailed log-normal distribution rather than the exponential distribution that has been used for modeling. Here, we first elaborate on self-similarity in cell dwell time of various vehicles. We then evaluate self-similarity in channel occupancy time. For future mobile multimedia communication systems employing a micro-cell configuration, it is anticipated that data communication will be the main form of communication and that call holding time will be long. For such cases, we have shown that channel occupancy time will be greatly affected by the cell dwell time of the mobile terminal, and that self-similarity, a characteristic that is not seen in conventional systems, will consequently appear. We have also found that hand-off frequently fails as self-similarity in cell dwell time of a mobile terminal becomes stronger.

Self-ordered mesoporous silicate films from organic-inorganic compound materials are successfully fabricated into the surface photo voltage (SPV) type gas sensor device as a gas adsorption insulator layer. These kinds of gas sensors device exhibit NO gas sensing property dependent on their mesoporous film structure. We are succeeded in indication about a possibility of mesoporous silicate film for the SPV type gas sensor application.

In this paper, a novel type of neural networks called grey neural network (GNN) is proposed and applied to improve short term load forecasting (STLF) performance. This work is motivated by the following observations: First, the forecasting performance of neural network is affected by the randomness in STLF data. That is, poor performance results from large randomness and vice versa. Second, the grey first-order accumulated generating operation (1-AGO) is reported having randomness reduction property. By the observations, the GNN is proposed and expected to have better STLF performance. The GNN consists of grey 1-AGO, the piecewise linear neural network (PLNN), and grey first-order inverse accumulated generating operation (1-IAGO). Given a set of STLF data, the data is first converted by grey 1-AGO and then is put into the PLNN to perform forecasting. Finally, the predicted load of GNN is obtained through grey 1-IAGO. For comparison, the original STLF data is also put into the PLNN itself. With identical training conditions, the simulation results indicate that with various network structures the GNN, as expected, outperforms the PLNN itself in terms of mean squared error.

Most conventional studies on self-stabilization have been indifferent to the vulnerability under convergence. This paper investigates how mutual exclusion property can be achieved in self-stabilizing rings even for illegitimate configurations. We present a new method which uses a state with a large state space to detect faults. If some faults are detected, every process is reset and not given a privilege. Even if the reset values are different between processes, our protocol mimics the behavior of Dijkstra's unidirectional K-state protocol. Then we have a fast and safe mutual exclusion protocol. Simulation study also examines its performance.

The requirement to realize large-capacity, high-speed and guaranteed Quality of Service (QoS) communications in IP networks is a recent development. A technique to satisfy these requirements, Multi-Protocol Label Switching (MPLS) is the focus of this paper. In the future, it is expected that congestion and faults on a Label Switched Path (LSP) will seriously affect service contents because various applications are densely served in a large area. In MPLS, however, methods to solve these problems are not clear. Therefore, this study proposes a concrete traffic engineering method to avoid heavy congestion, and at the same time, endeavors to realize a fault-tolerant network by autonomous restoration, or self-healing.

Teletraffic characteristics of a mobile packet communication network, which supports mobile Internet, were quantitatively evaluated by using a terminal migration model in which the cell dwell time possesses self-similarity. I used a migration model in which the migration speed of the terminal is determined by the density of the dwell terminals in a cell (determined from measured vehicular mobility characteristics). The transmission rates per terminal in a cell were estimated as teletraffic on the mobile packet communication networks using this migration model. I found that when there is self-similarity in the terminal cell dwell time, communicating terminals may be concentrated in the cell and restricted for an indefinite period of time to using only a narrow bandwidth.