A novel method for the primitive description of the multiprimitive texture is proposed. This method segments a texture by the watershed algorithm into fragments each of which contains one grain. The similar fragments are grouped by the cluster analysis in the feature space whose basis is the morphological size density. Each primitive is extracted as the grain of the central fragment in each cluster.

As we move toward the transition to the IPv6 next generation network environment, it is necessary to realize heterogeneous communications between IPv6 and IPv4 terminals without sacrificing any convenience or frameworks of current communication methods. Mechanisms that satisfy such requirements are called translators. This paper categorizes various translator mechanisms and clarifies their characteristics. As a result of analyses, this paper proposes a SOCKS-based IPv6/IPv4 Translator, and describes its design and implementation. Compared with other translator mechanisms, the SOCKS-based translator have small constraints and good characteristics. For example, it can integrate DNS name resolving procedures, which is an important mechanism for the transition. The translator has already been implemented and it has been proved that it can support typical communication services such as telnet, ftp, http, mail without any problems.

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.

This paper assesses the performance of the handoff algorithm based on distance and RSSI measurements in a multi-cellular environment by computer simulation. The algorithm performs a handoff if handoff initiation conditions, handoff possible conditions, and handoff selective conditions are met. The performance criteria are based on the average number of handoffs, the crossover points and the average number of outages. Numerical results are presented to demonstrate the feasibility of the algorithm. The performance of the distance-assisted handoff algorithm is compared with that of a conventional algorithm that utilizes signal strength alone. Overall, the distance-assisted algorithm exhibits higher performance in average number of handoffs and the crossover points, yet exhibits a higher number of outages on average than the conventional algorithm.

We propose and analyze a new efficient handoff scheme called Splitted-Rating Channel Scheme in UMTS networks, and we analyze the call level performance of splitted-rating channel scheme and then packet level performance of downlink traffic at UMTS circuit-switched networks. In order to reduce the blocking probability of originating calls and the forced termination probability of handoff calls, a splitted-rating channel scheme is applied to the multimedia UMTS networks. This multimedia network supports two classes of calls; narrowband call requiring one channel and wideband call requiring multiple channels. The channels in service for wideband call are splitted its channels for lending to originating call and handoff call according to threshold control policy. By assuming that arrivals of narrowband calls and arrivals of wideband calls are Poisson, we model the number of narrowband calls and the number of wideband calls in the one cell by Level Dependent Quasi-Birth-Death (QBD) process and obtain their joint stationary distribution. For packet level analysis, we first describe the downlink traffic from the base station to a mobile terminal in UMTS networks, and calculate the mean packet delay of a connected wideband call by using QBD analysis. Numerical examples show that our splitted-rating channel scheme reduces the blocking probability of originating call and the forced termination probability of handoff call with a little degradation of packet delay.

Carrying IP traffic over connection-oriented networks requires the use of bandwidth on demand schemes at gateways or network interfaces. A new virtual queue occupancy, which is more accurate than the classical one, is being proposed for IP/SONET bandwidth on demand. Based on the virtual queue occupancy, two enhanced periodic approaches for lossless services, LAVQ and LAVQL, are simulated and evaluated. Simulations show that LAVQ outperforms its counterpart LAQ in terms of bandwidth utilization. By curbing the queue occupancy fluctuation, LAVQL further promotes bandwidth utilization and conceals the influence of the system latency on delay jitter as well.

This paper describes a hybrid force-directed self-organizing neural network approach to printed circuit board (PCB) placement with consideration of electromagnetic compatibility (EMC). In most of the conventional PCB automatic placement algorithms, the only factor considered in the objective function is minimized total net length. However, for today's high speed and high density PCB, EMC compliance cannot be met by such single objective. To tackle this problem, the presented algorithm takes EMC into consideration, besides component overlap and minimized total net length. These factors are optimized by means of an adapted self-organizing map. Comparison of simulated placement results as well as actual measurements with commercial softwares confirms the effectiveness of the proposed method.

A number of schemes have been proposed for communication using chaos over the past years. Regardless of the exact modulation method used, the transmitted signal must go through a physical channel which undesirably introduces distortion to the signal and adds noise to it. The problem is particularly serious when coherent-based demodulation is used because the necessary process of chaos synchronization is difficult to implement in practice. This paper addresses the channel distortion problem and proposes a technique for channel equalization in chaos-based communication systems. The proposed equalization is realized by a modified recurrent neural network (RNN) incorporating a specific training (equalizing) algorithm. Computer simulations are used to demonstrate the performance of the proposed equalizer in chaos-based communication systems. The Henon map and Chua's circuit are used to generate chaotic signals. It is shown that the proposed RNN-based equalizer outperforms conventional equalizers.

The cooperative and competitive network suitable for circuit realization is presented, based on the network proposed by Amari and Arbib. To ensure WTA process, the output function of the original network is replaced with the piecewise linear function and supplying the inputs as pulse waveforms is obtained. In the SPICE simulations, it is confirmed that the network constructed by operational amplifiers attains WTA process, even if the scale of the network becomes large.

A new blind restoration method applying Real-coded genetic algorithm (RcGA) will be proposed, and this method will be proven valid for the blurred image restoration with unidentified degradation in the experiments. In this restoration method, the degraded and blurred image is going to get restricted to the images possible to be expressed in the point spread function (PSF), then the restoration filter for this degraded image, which is also the 2-dimentional inverse filter, will be searched among several points applying RcGA. The method will enable to seek efficiently among vast solution space consists of numeral coefficient filters. And perceiving the essential features of the spectrum in the frequency space, an evaluation function will be proposed. Also, it will be proposed to apply the Rolling-ball transform succeeding an appropriate Gaussian degrade function against the dual degraded image with blur convoluting impulse noise. By above stated features of this restoration method, it will enable to restore the degraded image closer to the original within a practical processing time. Computer simulations verify this method for image restoration problem when the factors causing image distortions are not identified.

Fuzzy value is a fuzzy set on interval [0,1], whose α-cuts are all closed intervals for α [0,1], i.e., fuzzy value is a fuzzy number on [0,1]. In this note, we introduce three kinds of metrics di (i=1,2,3) into fuzzy-valued space m[0,1] and consider continuity of fuzzy-valued operations on metric spaces (m[0,1], di) (i=1,2,3). The obtained results will provide some theoretical bases for numeral calculation of fuzzy-valued operations.

A simple system configuration was used to generate transform-limited optical pulses at 160 Gbit/s in the sub-picosecond range (625 fs). Pulse compression was achieved by broadening the spectrum using supercontinuum generation followed by a linear frequency chirping compensation.

In this paper, we describe a novel technique to extract a polyhedral description from panoramic range data of a scene taken by a panoramic laser range finder. First, we introduce a reasonable noise model of the range data acquired with a laser radar range finder, and derive a simple and efficient approximate solution of the optimal fitting of a local plane in the range data under the assumed noise model. Then, we compute the local surface normals using the proposed method and extract stable planar regions from the range data by using both the distribution information of local surface normals and their spatial information in the range image. Finally, we describe a method which builds a polyhedral description of the scene using the extracted stable planar regions of the panoramic range data with 360 field of view in a polar coordinate system. Experimental results on complex real range data show the effectiveness of the proposed method.

This paper gives a new sufficient condition for cellular neural networks with delay (DCNNs) to be completely stable. The result is a generalization of two existing stability conditions for DCNNs, and also contains a complete stability condition for standard CNNs as a special case. Our new sufficient condition does not require the uniqueness of equilibrium point of DCNNs and is independent of the length of delay.

This paper presents an efficient graph-based evolutionary optimization technique called Evolutionary Graph Generation (EGG) and its extension to a parallel version. A new version of parallel EGG system is based on a coarse-grained model of parallel processing and can synthesize heterogeneous networks of various different components efficiently. The potential capability of parallel EGG system is demonstrated through the design of current-mode logic circuits.

This paper investigates the behavior of one-dimensional discrete-time binary cellular neural networks with both the A- and B-templates and gives the necessary and sufficient conditions for the above network to be stable for unspecified fixed boundaries.

In this paper, we present the following schemes for a content-based image search: (1) A fast image search algorithm that can significantly reduce similarity calculation compared to a full comparison of every database image. (2) A compact image representation scheme that can describe the global/local information of the images and provide successful retrieval performance. For fast searches, a tree is constructed by successfully dividing nodes into the desired depth level by working from the root to the leaf nodes using the k-means algorithm. When the query is completed, we traverse the tree top-down by minimizing the route taken between the query image and node centroid until we meet the undivided nodes. Within undivided nodes, the algorithm of triangle inequality is used to find the images most similar to the query. For compact image representation, RGB color histogram features which are quantized into 16 bins each of the R, G, and B channels are used for global information. Dominant hue, saturation, and value which are extracted from the HSV joint histogram in the localized regions within the image are used for local information. These features are sufficiently compact to index image features in large database systems. For experiments on the retrieval efficiency, the use of the proposed method provided substantial performance benefits by reducing the image similarity calculation up to an average of a 96% and for experiments on the retrieval effectiveness, in the best case, it provide a 36.8% recall rate for a whale query image and a 100% precision rate for an eagle query image. The overall performance was a 20.0% recall rate and a 72.5% precision rate.

This paper presents a new Reinforcement Learning (RL) method, called "Labeling Q-learning (LQ-learning)," to solve the partially obervable Markov Decision Process (POMDP) problems. Recently, hierarchical RL methods are widely studied. However, they have the drawback that the learning time and memory are exhausted only for keeping the hierarchical structure, though they wouldn't be necessary. On the other hand, our LQ-learning has no hierarchical structure, but adopts a new type of internal memory mechanism. Namely, in the LQ-learning, the agent percepts the current state by pair of observation and its label, and then, the agent can distinguish states, which look as same, but obviously different, more exactly. So to speak, at each step t, we define a new type of perception of its environment õt=(ot,θt), where ot is conventional observation, and θt is the label attached to the observation ot. Then the classical RL-algorithm is used as if the pair (ot,θt) serves as a Markov state. This labeling is carried out by a Boolean variable, called "CHANGE," and a hash-like or mod function, called Labeling Function (LF). In order to demonstrate the efficiency of LQ-learning, we will apply it to "maze problems" in Grid-Worlds, used in many literatures as POMDP simulated environments. By using the LQ-learning, we can solve the maze problems without initial knowledge of environments.

Since the deployment of base stations (BS's) is far from optimum in 3-dimensional (3-D) space, i.e., the vertical baseline is relatively shorter than the planar baseline, the geometric degradation of precision of the altitude estimate is larger than that of the planar location. This paper considers the problem of 3-D range location and attempt to improve the altitude estimate. We first use a volume formula of tetrahedron to transform the range measurements to the volume measurements, then a novel pseudo-linear solution is proposed based on a linear relationship between the rectangular and the volume coordinates. Theory analysis and numerical examples are included to show the improved accuracy of the altitude estimate of mobile location. Finally, an improved estimate of 3-D mobile location is given by solving a set of augmented linear equations.

In this paper, we investigate the optimal assignment problem of cells in PCS (Personal Communication Service) to switches on a ATM (Asynchronous Transfer Mode) network. Given cells and switches on an ATM network (whose locations are fixed and known), the problem is to group cells into clusters and assign these clusters to switches in an optimum manner. This problem is modeled as a complex integer programming problem. Since finding an optimal solution of this problem is NP-hard, a heuristic solution model consists of three phases (Cell Pre-Partitioning Phase, Cell Exchanging Phase, and Cell Migrating Phase) is proposed. Experimental results show that Cell Exchanging and Cell Migrating Phases can really reduce total cost near 44% on average.