In the beginning of the new century, many information appliance (IA) products will replace traditional electronic appliances to help people in smart, efficient, and low-cost ways. These successful products must be capable of communicating multimedia information, which is embedded into the electronic appliances with high integration, innovation, and power-throughput tradeoff. In this paper, we develop a codesign procedure to analyze, compare, and emulate the multimedia communication applications to find the candidate implementations under different criteria. The experimental results demonstrate that in general, memory technology dominates the optimal tradeoff and ALU improvements impact greatly on particular applications. The results also show that the proposed procedure is effective and quite efficient.

CAM (Content Addressable Memory) units are generally designed so that they can be applied to variety of application programs. However, if a particular application runs on CAM units, some functions in CAM units may be often used and other functions may never be used. We consider that appropriate design for CAM units is required depending on the requirements for a given application program. This paper proposes a CAM processor synthesis system based on behavioral descriptions. The input of the system is an application program written in C including CAM functions, and its output is hardware descriptions of a synthesized processor and a binary code executed on it. Since the system determines functions in CAM units and synthesizes a CAM processor depending on the requirements of an application program, we expect that a synthesized CAM processor can execute the application program with small processor area and delay. Experimental results demonstrate its efficiency and effectiveness.

Numerous scientific and engineering fields extensively utilize optimization techniques for finding appropriate parameter values of models. Various optimization methods are available for practical use. The optimization algorithms are classified primarily due to the rates of convergence. Unfortunately, it is often the case in practice that the particular optimization method with specified convergence rates performs substantially differently on diverse optimization tasks. Theoretical classification of convergence rates then lacks its relevance in the context of the practical optimization. It is therefore desirable to formulate a novel classification framework relevant to the theoretical concept of convergence rates as well as to the practical optimization. This article introduces such classification framework. The proposed classification framework enables specification of optimization techniques and optimization tasks. It also underlies its inherent relationship to the convergence rates. Novel classification framework is applied to categorizing the tasks of optimizing polynomials and the problem of training multilayer perceptron neural networks.

Coding and modulation tradeoffs for limiter-discriminator based CPM transceivers are examined in Rayleigh, fast fading environments. Comparisons are made on the basis of a fixed bandwidth to information rate ratio, so coded schemes and uncoded schemes can be compared fairly. It is shown that using the proper balance of modulation and coding is important to achieve good performance. It is found that combining bandwidth efficient modulation with convolutional coding achieves better performance than trellis coded modulation (TCM). The increase in performance as the code complexity is increased is also found to be larger for convolutional coding than for TCM.

Topographical changes induced by optical near-field around photo-irradiated nanoparticles were attained using a pulsed laser with a large peak power as a light source. The arrayed structure of nanoparticles was transcribed on urethane-urea azo copolymer film as dent structure. The experiments by the pulsed laser of different wavelength showed that the topographical change was caused by the light absorption. The dent diameter and the dent depth changed depending on the diameter of nanoparticles.

In this paper, the LVQ (Learning Vector Quantization) model and its variants are regarded as the clustering tools to discriminate the natural seismic events (earthquakes) from the artificial ones (nuclear explosions). The study is based on the six spectral features of the P-wave spectra computed from the short period teleseismic recordings. The conventional LVQ proposed by Kohenen and also the Fuzzy LVQ (FLVQ) models proposed by Sakuraba and Bezdek are all tested on a set of 26 earthquakes and 24 nuclear explosions using the leave-one-out testing strategy. The primary experimental results have shown that the shapes, the number and also the overlaps of the clusters play an important role in seismic classification. The results also showed how an improper feature space partitioning would strongly weaken both the clustering and recognition phases. To improve the numerical results, a new combined FLVQ algorithm is employed in this paper. The algorithm is composed of two nested sub-algorithms. The inner sub-algorithm tries to generate a well-defined fuzzy partitioning with the fuzzy reference vectors in the feature space. To achieve this goal, a cost function is defined as a function of the number, the shapes and also the overlaps of the fuzzy reference vectors. The update rule tries to minimize this cost function in a stepwise learning algorithm. On the other hand, the outer sub-algorithm tries to find an optimum value for the number of the clusters, in each step. For this optimization in the outer loop, we have used two different criteria. In the first criterion, the newly defined "fuzzy entropy" is used while in the second criterion, a performance index is employed by generalizing the Huntsberger formula for the learning rate, using the concept of fuzzy distance. The experimental results of the new model show a promising improvement in the error rate, an acceptable convergence time, and also more flexibility in boundary decision making.

In this paper we present an analysis based on the indirect Lyapunov criteria, that is used to study the convergence of an infinite impulse response (IIR) adaptive digital filter (ADF) based on estimation of the allpass system. The analysis is then extended to investigate the necessity of directly estimating the transfer level of the unknown system. We consider two cases of modeling the ADF. In the first system, the allpass section of the ADF estimates only the real poles of the unknown system while in the second system, both real and complex poles the allpass section are estimated. From the analysis and computer simulation, we realize that the poles of the ADF converge selectively to the poles of the unknown system, depending on the sign of the step size of adaptation. Using these results we proposed a new method to control the convergence of the poles the IIR ADF based on estimation of the allpass system.

A per-connection end-to-end call admission control (CAC) problem is solved in this paper to allocate network resources to an input session to guarantee its quality of service (Qos) requirements. In conjunction with the solution of the CAC problem, a traffic descriptor is proposed to describe the loss rate and the delay bound Qos requirements of the connection to be set up as well as the statistical characteristics of the associated input traffic which is modeled as a linear mean function plus a (zero-mean) fractional Brownian motion. The information in the traffic descriptor is sufficient to determine the allocation of channel bandwidth and buffer space to the input traffic in a network which employs leaky bucket shapers and scheduling algorithms to guarantee the Qos requirements. The CAC problem is solved by an iterative algorithm of which there are two stages in each iteration: one is responsible for the search of a candidate end-to-end routing path and the other for the verification of the legitimacy of this candidate path to meet the Qos requirements and for the allocation of resources in such a legitimate path.

Formal description techniques (FDTs) such as VDM, Z, LOTOS, etc are powerful to develop safety-critical systems since they have strict semantics and mathematical reasoning basis. However, they have no methods or guides how to construct specifications unlike specification and design methods such as Object-Oriented Modeling and Technique (OMT), and that makes it difficult for practitioners to compose formal specifications. One of the solutions is to connect formal description techniques with some existing methods. This paper discusses a technique how to integrate FDTs with specification and design methods such as OMT so that we can have new methods to support writing formal specifications. The integration mechanism is based on transformation rules of specification documents produced following methods into the descriptions written in formal description techniques. The transformation rules specify the correspondences on two meta models; of methods and of formal description techniques, and are described as graph rewriting rules. As an example, we pick up OMT as a method and LOTOS as a FDT and define the transformation rule on their meta models.

The quality of an architectural design of a software system has a great influence on achieving non-functional requirements to the system, so formal evaluation and validation techniques to designed architectures are necessary in the early phase of development processes. In this paper, we present a technique for describing software architectures formally based on Coloured Petri Nets (CPNs) and a technique for reusing architectural constituents. Architectural descriptions are essentially written with a CPN language, so that the evaluation and analysis on the architectural descriptions can be made in architectural design phrase. We extract reusable architectural parts from standard architecture styles and architectural patterns so that a designer can construct an architecture by only retrieving the parts and combine them. We also designed the language for describing the combination of the architectural parts. To show the effectiveness of our techniques, we illustrate how a blackboard architecture can be composed of reusable parts and be simulated on a CPN tool (Design/CPN).

The linear complementarity problem (LCP) is one of the most widely studied mathematical programming problems. The theory of LCP can be extended to oriented matroids which are combinatorial abstractions of linear subspaces of Euclidean spaces. This paper briefly surveys the LCP, oriented matroids and algorithms for the LCP on oriented matroids.

A synthesizable HDL generation method for pipelined processors is proposed. By using the proposed method, data-path and control logic descriptions of a target processor is generated from a clock based instruction set specification. From the experimental results, feasibility of the proposed method is evaluated and the amount of processor design time was drastically reduced than that of conventional RT level manual design in HDL.

In this paper, we propose an analytic method for dimensioning traffic descriptors at the leaky bucket-based UPC for VBR MPEG video traffic on ATM networks. We analytically derived cell violation probabilities at the UPC by using a proposed scene-based video traffic model, and then we showed that it was possible to select sets of traffic descriptors that produce the required violation probability. In two example video traces, the numerical results showed that our proposed traffic descriptor dimensioning method well approximated the simulation-based traffic control results of the real video traces. In cases where an effective bandwidth allocation method based on the ON/OFF model was used for the call admission control in the networks, we compared the allocated effective bandwidth to each set of traffic descriptors that produced zero UPC losses.

A procedure for radar target discrimination is presented in this paper. The scheme includes an enhancement of late-time noisy scattering data based on a proposed signal processing algorithm and a decision procedure using previously known resonance annihilation filters. The signal processing stage is specifically adapted to scattering signals and makes use of the results of the singularity expansion method. It is based on a signal reconstruction using the SVD of a data matrix with a suitable choice of the number of singular vectors employed. To justify the inclusion of this stage, this procedure is shown to maintain the signal characteristics necessary to identify the scattered response. Simulation results clearly reveal a significant improvement due to the inclusion of the proposed stage. This improvement becomes especially important when the noise level is high or the targets to be discriminated (five regular polygonal loops) have a similar geometry.

This paper addresses the problem of learning Bayesian belief networks (BBN) based on the minimum description length (MDL) principle. First, we give a formula of description length based on which the MDL-based procedure learns a BBN. Secondly, we point out that the difference between the MDL-based and Cooper and Herskovits procedures is essentially in the priors rather than in the approaches (MDL and Bayesian), and recommend a class of priors from which the formula is obtained. Finally, we show as a merit of using the formula that a modified version of the Chow and Liu algorithm is obtained. The modified algorithm finds a set of trees rather than a spanning tree based on the MDL principle.

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.

The ATM Forum specifies several fairness criteria, thus the scheduling mechanisms should allocate enough bandwidth to each connection to achieve one of such fairness criteria. However, two fairness criteria (MCR plus equal share, maximum of MCR or Max-Min share) cannot be achieved by conventional scheduling mechanisms. In this letter, we have developed new scheduling mechanisms that achieve these fairness criteria. We also present simulation results to show that our mechanisms can allocate bandwidth fairly.

This paper presents a heuristic algorithm that minimizes the delay of the given circuit through a two-pass cell selection in cell-based design. First, we introduce a new graph, called candidate web, which conveniently represents all cell combinations available for the implementation of the given circuit. We, then, present an efficient method to obtain a tentative set of optimal cells, while estimating the delay of the longest path between each cell and the primary output on the candidate web. In this step, multiple cells are allowed to bind the same logic gate. Finally, we describe how the proposed approach actually selects the optimal cells from the tentative set, which would minimize the circuit delay. Experimental results on a set of benchmarks show that the proposed approach is effective and efficient in minimizing the delay of the given circuit.

In this paper, the generalized learning vector quantization (GLVQ) algorithm is applied to design a hand-written Chinese character recognition system. The system proposed herein consists of two modules, feature transformation and recognizer. The feature transformation module is designed to extract discriminative features to enhance the recognition performance. The initial feature transformation matrix is obtained by using Fisher's linear discriminant (FLD) function. A template matching with minimum distance criterion recognizer is used and each character is represented by one reference template. These reference templates and the elements of the feature transformation matrix are trained by using the generalized learning vector quantization algorithm. In the experiments, 540100 (5401 100) hand-written Chinese character samples are used to build the recognition system and the other 540100 (5401 100) samples are used to do the open test. A good performance of 92.18 % accuracy is achieved by proposed system.

In this paper, we discuss the problem of active training data selection for improving the generalization capability of a neural network. We look at the learning problem from a function approximation perspective and formalize it as an inverse problem. Based on this framework, we analytically derive a method of choosing a training data set optimized with respect to the Wiener optimization criterion. The final result uses the apriori correlation information on the original function ensemble to devise an efficient sampling scheme which, when used in conjunction with the learning scheme described here, is shown to result in optimal generalization. This result is substantiated through a simulated example and a learning problem in high dimensional function space.