Shared buffer ATM switches have been attractive since they can achieve a superior performance in terms of cell loss ratio and throughput with a relatively small buffer size. Shared multi-buffer structures have also been considered by several researchers to enhance the access speed of the cell memory for a large switch. High quality services, however, cannot be provided without reliable operation at each module comprising the ATM switches. In this paper, we present a novel on-line error monitoring technique for shared-buffer ATM switches. The technique detects almost all of the functional errors that could occur in the ATM switches. Moreover, it can detect errors with small hardware overhead and negligible time overhead. An early detection of functional errors in ATM switches could not only reduce the wasted bandwidth due to the transmission of erroneous cells, but greatly enhance the recovery time.

In this letter, the novel mapping network named self-organizing relationship (SOR) network, which can approximate the desired I/O relationship by employing the modified Kohonen's learning law, is proposed. In the modified Kohonen's learning law, the weight vectors are updated to be attracted to or repulsed from the input vector.

We successfully fabricated plastic ferrules and split alignment sleeves for single-mode fiber-optic connectors by the injection molding process. Liquid crystalline polymer (LCP) was used as the molding material for the ferrule. We introduced an eccentricity control mechanism into the ferrule mold and realized an eccentricity of less than 1 µm. As the molding material for the sleeve, thermosetting epoxy resin was used. Suitable mechanical properties were realized by employing appropriate dimensional design and the molding process. The optical characteristics of a system combining these plastic components are compatible with single-mode SC-type connectors and are also stable under hot and humid conditions.

We analyze the dispersion-managed optical transmission system for the non-return-to-zero (NRZ) pulse format. First, we investigate the physical image of dispersion management by computing small-signal-based transfer functions, and summarize the dependence of transmission performance on system parameters. Next, the Q-map is computed numerically to design long-distance large-capacity dispersion-managed transmission systems for a single channel in a more detailed manner. It is shown that the third-order dispersion of fibers negatively influences transmission performance, and third-order dispersion compensation is proved to be an effective method for extending the transmission distance of high bit-rate systems. Utilizing these results, guidelines can be derived for the optimal design of long-distance large-capacity NRZ transmission systems.

In this paper, a new feature which is characterized by the extrema density of 2-D wavelet frames estimated at the output of the corresponding filter bank is proposed for texture segmentation. With and without feature selection, the discrimination ability of features based on pyramidal and tree-structured decompositions are comparatively studied using the extrema density, energy, and entropy as features, respectively. These comparisons are demonstrated with separable and non-separable wavelets. With the three-, four-, and five-category textured images from Brodatz album, it is observed that most performances with feature selection improve significantly than those without feature selection. In addition, the experimental results show that the extrema density-based measure performs best among the three types of features investigated. A Min-Min method based on genetic algorithms, which is a novel approach with the spatial separation criterion (SPC) as the evaluation function is presented to evaluate the segmentation performance of each subset of selected features. In this work, the SPC is defined as the Euclidean distance within class divided by the Euclidean distance between classes in the spatial domain. It is shown that with feature selection the tree-structured wavelet decomposition based on non-separable wavelet frames has better performances than the tree-structured wavelet decomposition based on separable wavelet frames and pyramidal decomposition based on separable and non-separable wavelet frames in the experiments. Finally, we compare to the segmentation results evaluated with the templates of the textured images and verify the effectiveness of the proposed criterion. Moreover, it is proved that the discriminatory characteristics of features do spread over all subbands from the feature selection vector.

Classification of the new and used bills using the spectral patterns of raw time-series acoustic data (observation data) poses some difficulty. This is the fact that the observation data include not only a bill sound, but also some motor sound and noise by a transaction machine. We have already reported the method using adaptive digital filters (ADFs) to eliminate the motor sound and noise. In this paper, we propose an advanced technique to eliminate it by the neural networks (NNs). Only a bill sound is extracted from observation data using prediction ability of the NNs. Classification processing of the new and used bills is performed by using the spectral data obtained from the result of the ADFs and the NNs. Effectiveness of the proposed method using the NNs is illustrated in comparison with former results using ADFs.

This paper describes a spatial spectral subtraction method by using the complementary beamforming microphone array to enhance noisy speech signals for speech recognition. The complementary beamforming is based on two types of beamformers designed to obtain complementary directivity patterns with respect to each other. In this paper, it is shown that the nonlinear subtraction processing with complementary beamforming can result in a kind of the spectral subtraction without the need for speech pause detection. In addition, the optimization algorithm for the directivity pattern is also described. To evaluate the effectiveness, speech enhancement experiments and speech recognition experiments are performed based on computer simulations under both stationary and nonstationary noise conditions. In comparison with the optimized conventional delay-and-sum (DS) array, it is shown that: (1) the proposed array improves the signal-to-noise ratio (SNR) of degraded speech by about 2 dB and performs more than 20% better in word recognition rates under the conditions that the white Gaussian noise with the input SNR of -5 or -10 dB is used, (2) the proposed array performs more than 5% better in word recognition rates under the nonstationary noise conditions. Also, it is shown that these improvements of the proposed array are same as or superior to those of the conventional spectral subtraction method cascaded with the DS array.

This paper is concerned with blind identification of a nonminimum phase transfer function model. By over-sampling the output at a higher rate than the input, it is shown that its input-output relation can be described by a single input multiple output model (SIMO) with a common denominator polynomial. Based on the model expression, we present an algorithm to estimate numerator polynomials and common denominator polynomial in a blind manner. Furthermore, identifiability of the proposed scheme is clarified, and some numerical results are given for demonstrating its effectiveness.

In this paper we propose an algorithm for generating maximum weight independent sets in a circle graph, that is, for putting out all maximum weight independent sets one by one without duplication. The time complexity is O(n3 + β ), where n is the number of vertices, β output size, i. e. , the sum of the cardinalities of the output sets. It is shown that the same approach can be applied for spider graphs and for circular-arc overlap graphs.

Technical advances in the development of portable computers and wireless communications enable users to take part in distributed computing even while moving. The resulting environment is subject to be constrained by the mobility of users and the nature of the cordless medium. In this paper we propose a commit protocol for providing low-powered mobile hosts with two phase commit service which is a powerful technique to implement atomic actions in distributed systems, with some important aspects such as low power consumption, efficient mobility management, subject oriented service binding and effective disconnection handling to well adapt to a mobile computing environment.

Modern micro-architectures employ superscalar techniques to enhance system performance. Since the superscalar microprocessors must fetch at least one instruction cache line at a time to support high issue rate and large amount speculative executions. There are cases that multiple branches are often encountered in one cycle. And in practical implementation this would cause serious problem while there are variable number of instruction addresses that look up the Branch Target Buffer simultaneously. In this paper, we propose a Range Associative Branch Target Buffer (RABTB) that can recognize and predict multiple branches in the same instruction cache line for a wide-issue micro-architecture. Several configurations of the RABTB are simulated and compared using the SPECint95 benchmarks. We show that with a reasonable size of prediction scope, branch prediction can be improved by supporting multiple / up to 8 branch predictions in one cache line in one cycle. Our simulation results show that the optimal RABTB should be 2048 entry, 8-column range-associate and 8-entry modified ring buffer architecture using PAs prediction algorithm. It has an average 5.2 IPC_f and branch penalty per branch of 0.54 cycles. This is almost two times better than a mechanism that makes prediction only on the first encountered branch.

A nonlinear time series predictor was proposed, in which a nonlinear sub-predictor (NSP) and a linear sub-predictor (LSP) are combined in a cascade form. This model is called "hybrid predictor" here. The nonlinearity analysis method of the input time series was also proposed to estimate the network size. We have considered the nonlinear prediction problem as a pattern mapping one. A multi-layer neural network, which consists of sigmoidal hidden neurons and a single linear output neuron, has been employed as a nonlinear sub-predictor. Since the NSP includes nonlinear functions, it can predict the nonlinearity of the input time series. However, the prediction is not complete in some cases. Therefore, the NSP prediction error is further compensated for by employing a linear sub-predictor after the NSP. In this paper, the prediction mechanism and a role of the NSP and the LSP are theoretically and experimentally analyzed. The role of the NSP is to predict the nonlinear and some part of the linear property of the time series. The LSP works to predict the NSP prediction error. Furthermore, predictability of the hybrid predictor for noisy time series is investigated. The sigmoidal functions used in the NSP can suppress the noise effects by using their saturation regions. Computer simulations, using several kinds of nonlinear time series and other conventional predictor models, are demonstrated. The theoretical analysis of the predictor mechanism is confirmed through these simulations. Furthermore, predictability is improved by slightly expanding or shifting the input potential of the hidden neurons toward the saturation regions in the learning process.

This paper proposes a fast encoding algorithm for iterated function system (IFS) coding of gray-level homogeneous fractal images. In order to realize IFS coding of high order fractal images, it is necessary to solve a set of simultaneous equations with many unknowns. Solving the simultaneous equations using a multi-dimensional, numerical root-finding method is however very time consuming. As preprocessing of numerical computation, the proposed algorithm employs univariate polynomial manipulation, which requires less computation time than multivariate polynomial manipulation. Moreover, the symmetry of the simultaneous equations with respect to the displacement coefficients enables us to derive an equation with a single unknown from the simultaneous equations using univariate polynomial manipulation. An experimental result is presented to illustrate that the encoding time of the proposed algorithm is about 5 seconds on a personal computer with a 400 MHz Pentium II processor.

Recently, Kundur and Hatzinakos showed that a linear restoration filter designed by using the almost obvious a priori knowledge on the original image, such as (i) nonnegativity of the true image and (ii) the smallest rectangle encompassing the original object, can realize a remarkable performance for a blind image deconvolution problem. In this paper, we propose a new set-theoretic blind image deconvolution scheme based on a recently developed convex projection technique called Hybrid Steepest Descent Method (HSDM), where some partial information can be utilized set-theoretically by parallel projections onto convex sets while the others are incorporated in a cost function to be minimized by a steepest descent method. Numerical comparisons with the standard set-theoretic scheme based on POCS illustrate the effectiveness of the proposed scheme.

It is an important problem in signal processing, system realization and system identification to find linear discrete-time systems which are consistent with given covariance parameters. This problem is formulated as a problem of finding discrete-time positive real functions which interpolate given covariance parameters. Various investigations have yielded several significant solutions to the problem, while there remains an important open problem concerning the McMillan degree. In this paper, we use more general input-output characteristics than covariance parameters and consider finding discrete-time positive real matrix functions which interpolate such characteristics. The input-output characteristics are given by the coefficients of the Taylor series at some complex points in the open unit disk. Thus our problem is a generalization of the interpolation problem of covariance parameters. We reduce the problem to a directional interpolation problem with a constraint and develop the solution by a state-space based new approach. The main results consist of the necessary and sufficient condition for the existence of the discrete-time positive real matrix function which interpolates the given characteristics and has a limited McMillan degree, and a parameterization of all such functions. These are a contribution to the open problem and a generalization of the previous result.

We have developed a low-latency, error-correcting-code-(ECC-)adaptable skew-compensation technique, which is needed for high-speed and long-distance parallel optical interconnections. A new frame-coding technique called shuffled mB1C encoding, which requires no clock-rate conversion circuit and no data buffering, and a new skew-measurement method which is suitable for ECC adaptation have been developed for the compensation. Full-digital skew-compensation circuits using these new techniques were able to compensate for a two-clock-cycle skew, even when one transmission channel was removed. The maximum latency for skew compensation was only five clock cycles.

We have developed a low-latency, error-correcting-code-(ECC-)adaptable skew-compensation technique, which is needed for high-speed and long-distance parallel optical interconnections. A new frame-coding technique called shuffled mB1C encoding, which requires no clock-rate conversion circuit and no data buffering, and a new skew-measurement method which is suitable for ECC adaptation have been developed for the compensation. Full-digital skew-compensation circuits using these new techniques were able to compensate for a two-clock-cycle skew, even when one transmission channel was removed. The maximum latency for skew compensation was only five clock cycles.

We analyze the dispersion-managed optical transmission system for the non-return-to-zero (NRZ) pulse format. First, we investigate the physical image of dispersion management by computing small-signal-based transfer functions, and summarize the dependence of transmission performance on system parameters. Next, the Q-map is computed numerically to design long-distance large-capacity dispersion-managed transmission systems for a single channel in a more detailed manner. It is shown that the third-order dispersion of fibers negatively influences transmission performance, and third-order dispersion compensation is proved to be an effective method for extending the transmission distance of high bit-rate systems. Utilizing these results, guidelines can be derived for the optimal design of long-distance large-capacity NRZ transmission systems.

This paper discusses global area optical transport ring networks using wavelength division multiplexing (WDM) technologies and proposes a novel optical add/drop multiplexer (OADM) architecture suitable for such an application field. Study on the requirements of a global area ring application elucidates the appropriate ring/protection architecture as the path switched bi-directional ring. The proposed OADM architecture has flexibility in terms of path provisioning and scalability. We conclude that the proposed OADM can effectively configure the large-scale path switched bi-directional rings.

This paper discusses global area optical transport ring networks using wavelength division multiplexing (WDM) technologies and proposes a novel optical add/drop multiplexer (OADM) architecture suitable for such an application field. Study on the requirements of a global area ring application elucidates the appropriate ring/protection architecture as the path switched bi-directional ring. The proposed OADM architecture has flexibility in terms of path provisioning and scalability. We conclude that the proposed OADM can effectively configure the large-scale path switched bi-directional rings.