A new traffic engineering and operation of ATM networks is described, which features adaptive virtual path (VP) bandwidth control and VP network reconfiguration capabilities. We call this operation system resilient self-sizing operation. By making full use of self-sizing network (SSN) capabilities, we can operate an ATM network efficiently and keep high robustness against traffic demand fluctuation and network failures, while reducing operating costs. In a multimedia environment, the multimedia services and unpredictability of traffic demand make network traffic management a very challenging problem. SSNs, which are defined as ATM networks with self-sizing traffic engineering and operation capability are expected to overcome these difficulties. This paper proposes VP network operation methods of self-sizing networks for high flexibility and survivability. The VP network operation is composed of adaptive VP bandwidth control to absorb changes in traffic demand, VP rerouting control to recover from failures, and VP network reconfiguration control to optimize the network. The combination of these controls can achieve good performance in flexibility and survivability.

We discuss the concept of coding over the ring of integers modulo m. This method of coding finds its origin in the early work by Varshamov and Tenengolz. We first give a definition of the codes followed by some general properties. We derive specific code constructions and show computer-search results. We conclude with applications in 8-phase modulation and peak-shift correction in magnetic recording systems.

The required building blocks of CMOS fuzzy chips capable of performing as adaptive fuzzy systems are described in this paper. The building blocks are designed with mixed-signal current-mode cells that contain low-resolution A/D and D/A converters based on current mirrors. These cells provide the chip with an analog-digital programming interface. They also perform as computing elements of the fuzzy inference engine that calculate the output signal in either analog or digital formats, thus easing communication of the chip with digital processing environments and analog actuators. Experimental results of a 9-rule prototype integrated in a 2. 4-µm CMOS process are included. It has a digital interface to program the antecedents and consequents and a mixed-signal output interface. The proposed design approach enables the CMOS realization of low-cost and high-inference fuzzy systems able to cope with complex processes through adaptation. This is illustrated with simulated results of an application to the on-line identification of a nonlinear dynamical plant.

This paper is concerned with the validation of simple turning points of two-point boundary value problems of nonlinear ordinary differential equations. Usually it is hard to validate approximate solutions of turning points numerically because of it's singularity. In this paper, it is pointed out that applying the infinite dimensional Krawcyzk-based interval validation method to enlarged system, the existence of simple turning points can be verified. Taking an example, the result of validation is also presented.

The hippocampus is thought to play an important role in the transformation from short-term memory into long-term memory, which is called consolidation. The physiological phenomenon of synaptic change called LTP or LTD has been studied as a basic mechanism for learning and memory. The neural network mechanism of the consolidation, however, is not clarified yet. The authors' approach is to construct information processing theory in learning and memory, which can explain the physiological data and behavioral data. This paper proposes a dynamical hippocampal model which can store and recall spatial input patterns. The authors assume that the primary functions of hippocampus are to store episodic information of sensory signals and to keep them for a while until the neocortex stores them as a long-term memory. On the basis of the hippocampal architecture and hypothetical synaptic dynamics of LTP/LTD, the authors construct a hippocampal model. This model considers: (1) divergent connections, (2) the synaptic dynamics of LTP and LTD based on pre- and postsynaptic coincidence, and (3) propagation of LTD. Computer simulations show that this model can store and recall its input spatial pattern by self-organizing closed activating pathways. By the backward propagation of LTD, the synaptic pathway for a specific spatial input pattern can be selected among the divergent closed connections. In addition, the output pattern also suggests that this model is sensitive to the temporal timing of input signals. This timing sensitivity suggests the applicability to spatio-temporal input patterns of this model. Future extensions of this model are also discussed.

This paper gives two kinds of functions for which Uesaka's Conjecture, stating that the globally optimum (not a local minimum) of a quadratic function F(x)=-(1/2)xtAx in the n-dimensional hypercube may be obtained by solving a differential equation, holds true, where n denotes the dimension of the vector x. Uesaka stated in his paper that he proved the conjecture only for n=2. This corresponds to a very special case of this paper. The results of this paper suggest that the conjecture really holds for a wide class of quadratic functions and therefore support the conjecture partially.

Synchronization between two fully stretching piecewise affine Markov maps in the usual master-slave configuration has been proven to be possible in some interesting 2-dimensional and 3-dimensional cases. Aim of this contribution is to make a further step in the study of this phenomenon by showing that, if the two systems synchronize, the probability of having a certain synchronization time is bounded from above by an exponentially vanishing distribution. This result gives some formal ground to the numerical evidence shown in [2].

Conventional approaches to neural network training do not consider possibility of selecting training samples dynamically during the learning phase. Neural network is simply presented with the complete training set at each iteration of the learning. The learning can then become very costly for large data sets. Huge redundancy of data samples may lead to the ill-conditioned training problem. Ill-conditioning during the training causes rank-deficiencies of error and Jacobean matrices, which results in slower convergence speed, or in the worst case, the failure of the algorithm to progress. Rank-deficiencies of essential matrices can be avoided by an appropriate selection of training exemplars at each iteration of training. This article presents underlying theoretical grounds for dynamic sample selection (DSS), that is mechanism enabling to select a subset of training set at each iteration. Theoretical material is first presented for general objective functions, and then for the objective functions satisfying the Lipschitz continuity condition. Furthermore, implementation specifics of DSS to first order line search techniques are theoretically described.

Computational expensiveness of the training techniques, due to the extensiveness of the data set, is among the most important factors in machine learning and neural networks. Oversized data set may cause rank-deficiencies of Jacobean matrix which plays essential role in training techniques. Then the training becomes not only computationally expensive but also ineffective. In [1] the authors introduced the theoretical grounds for dynamic sample selection having a potential of eliminating rank-deficiencies. This study addresses the implementation issues of the dynamic sample selection based on the theoretical material presented in [1]. The authors propose a sample selection algorithm implementable into an arbitrary optimization technique. An ability of the algorithm to select a proper set of samples at each iteration of the training has been observed to be very beneficial as indicated by several experiments. Recently proposed approaches to sample selection work reasonably well if pattern-weight ratio is close to 1. Small improvements can be detected also at the values of the pattern-weight ratio equal to 2 or 3. The dynamic sample selection approach, presented in this article, can increase the convergence speed of first order optimization techniques, used for training MLP networks, even at the value of the pattern-weight ratio (E-FP) as high as 15 and possibly even more.

Two families of rapidly synchronizable spreading codes are compared using the same component codes. The influence of component code choice is also discussed. It is concluded that correlation, code-division multiple-access (CDMA) and information security (measured by the value of linear complexity) properties of Kronecker sequences are considerably better than those of Combination sequences. Combination sequences cannot be recommended for CDMA use unless the number of active users is few. CDMA performance of Kronecker sequences is almost comparable with that of linear pseudonoise (PN) code families of equal length when a Gold or Kasami code is used as the innermost code and the Barker code is used as the outermost code to guarantee satisfactory correlation and CDMA properties. Kronecker sequences possess a considerably higher value of linear complexity than those of the corresponding non-linear Geffe and majority logic type combination sequences. This implies they are highly non-linear codes due to the Kronecker product construction method. It is also observed that the Geffe type Boolean combiner resulted in better correlation and CDMA performance than with majority logic. The use of the purely linear exclusive-or combiner for considerable reduction of code synchronization time is not found recommendable although it results in good CDMA performance.

In this study, a coupled chaotic circuits network is realized by real circuit elements. By using a simple circuit converting generating spatial patterns to digital signal, irregular self-switching phenomenon of the appearing patterns can be observed as real physical phenomenon.

This paper proposes a dual port color palette SRAM using a single bit line cell. Since the single bit line cell consists of fewer bit lines and transistors than standard dual port cells, it is able to reduce the area. However, the cell has had a problem in writing a high level. The port swap architecture solves the problem without any special mechanism such as a boot strap. In the architecture, each of two bit lines is assigned to the read/write MPU port and the read only pixel port, respectively. When writing a low level, the MPU port uses pre-assigned bit line. On the other hand, when writing a high level, the MPU port uses the bit line assigned to the pixel port by a swap operation. During the swapping, the pixel port continues the read operation by using the bit line assigned to the MPU port. A color palette using this architecture is fabricated with a 0. 5 µm CMOS process technology. The memory cell size reduces by up to 43% compared with standard dual port cells. The color palette is able to supply the pixel data at 300 MHz at the supply voltage of 3.3 V. This speed is enough to support the practical highest resolution monitors in the world.

This paper describes recent standardization activities on optical networking and relevant issues which are conducted in ITU-T. The organization is first described, then, several recommendations both which are already recommended and expected to be completed in a couple of years.

In this paper, the application of an unsupervised parallel approach called the Fuzzy Hopfield Neural Network (FHNN) for vector qunatization in image compression is proposed. The main purpose is to embed fuzzy reasoning strategy into neural networks so that on-line learning and parallel implementation for codebook design are feasible. The object is to cast a clustering problem as a minimization process where the criterion for the optimum vector qunatization is chosen as the minimization of the average distortion between training vectors. In order to generate feasible results, a fuzzy reasoning strategy is included in the Hopfield neural network to eliminate the need of finding weighting factors in the energy function that is formulated and based on a basic concept commonly used in pattern classification, called the "within-class scatter matrix" principle. The suggested fuzzy reasoning strategy has been proven to allow the network to learn more effectively than the conventional Hopfield neural network. The FHNN based on the within-class scatter matrix shows the promising results in comparison with the c-means and fuzzy c-means algorithms.

We introduce an image contour clustering method based on a multiscale image representation and its application to image compression. Multiscale gradient planes are obtained from the mean squared sum of 2D wavelet transform of an image. The decay on the multiscale gradient planes across scales depends on the Lipshitz exponent. Since the Lipshitz exponent indicates the spatial differentiability of an image, the multiscale gradient planes represent smoothness or sharpness around edges on image contours. We apply vector quatization to the multiscale gradient planes at contours, and cluster the contours in terms of represntative vectors in VQ. Since the multiscale gradient planes indicate the Lipshitz exponents, the image contours are clustered according to its gradients and Lipshitz exponents. Moreover, we present an image recovery algorithm to the multiscale gradient planes, and we achieve the skech-based image compression by the vector quantization on the multiscale gradient planes.

This paper proposes a bitstream scaling technique for MPEG video for the purpose of media synchronizations. The proposed scaling technique can reduce the frame rate as well as the bit rate of an MPEG data sequence to fit them to the values specified by a synchronization system. The advantage of the proposed technique over existing scaling methods is that it is considering not only the performance of synchronization but also the picture quality of the resulting sequences. To further improve the quality of sequences scaled by the proposed method, this paper also proposes an MPEG encoding technique which sets some of the parameters suitable for the scaling. An experiment using these techniques in an actual media synchronization system has illustrated the usefulness of the proposed approach.

This paper describes the design of polarization insensitive InP-based arrayed waveguide gratings (AWGs), and the characteristics of fabricated devices. The use of a deep-ridge waveguide structure made the fabrication of compact polarization-insensitive AWGs possible. As a result, a low crosstalk (-30 dB) 8-channel AWG and a large-scale (64 channel) AWG with 50 GHz channel spacing could be fabricated. An integrated circuit containing an 8-channel AWG with photodetectors is also described.

High- and low-reflection Bragg gratings with a flat-top spectral response free from ripples are proposed. Add/drop filters are created based on gratings photoinduced on planar waveguides by using the new design schemes. The measured spectral responses for the high and low reflection gratings are in good agreement with the calculated ones, and show the flat-top spectral responses.

We have developed a multiple quantum well (MQW) electroabsorption (EA) modulator for wavelength-division multiplexing (WDM) switching systems. The fabricated MQW EA gate has low polarization and wavelength-dependent loss and high extinction ratio within the wavelength range of 1545 to 1560 nm. And by using this gate ultra-high-speed switching is achieved for WDM signals. Moreover, we optimize the EA gate for the full gain-band of an erbium-doped fiber amplifier (EDFA)(1535 to 1560 nm). This EA gate provides low polarization-dependent loss, higher extinction ratio, and high saturation input power in the wider wavelength range. These MQW EA gates will play an important role in future WDM switching systems.

We studied 2 types of polarization insensitive semiconductor optical amplifier (SOA) gates for use in wavelength division multiplexing (WDM) applications: 1) a low operation current SOA gate with a small and square bulk active region but without spot-size converters and 2) a multi channel SOA gate array with tapered waveguide spot-size converters (SS-SOA) on both sides. The low operation current SOA gate provided a very low current for fiber-to-fiber loss-less operation (5. 4-7. 0 mA) and a high extinction ratio (>30 dB) over a wide wavelength range (1530-1580 nm). For multi channel array assembling, the SS is indispensable. The 4-channel SS-SOA gate array was assembled on a planar lightwave circuit (PLC) platform for the first time. The gain characteristics of each channel were very similar and a low fiber-to-fiber loss-less current of 33 mA and a high extinction ratio of nearly 40 dB were achieved in all channels. The polarization dependence was less than 1 dB. Using the fully packaged 4-channel hybrid gate array module (a 4 channel SS-SOA on PLC platform), an ultra-wide-band (1530-1600 nm) high speed wavelength selector was successfully demonstrated. Both rise- and fall-times were less than 1 ns, which makes the wavelength selector suitable for high-speed optical packet switching. Electrical and optical interference between channels were negligible.