Since the radio spectrum is limited, future wireless system will have micro cellular architecture in order to provide multimedia traffic services on personal communicatons network. The small coverage area of micro cellular systems results in frequent hadnoffs and location updates. We focuse on network load and terminal power due to frequent location updates and propose a location tracking scheme using embedded-pager PCS terminals. The proposed scheme is expected to minimize theconsumption of PCS terminal powr and improve the receiving rate of callee because of the power saving mechanism and the embedded-pager PCS terminal. This paper proposes signaling flows among the network elements for the proposed location tracking scheme. The signaling flows are accomodated to not only the existed network architectures but alsothe ATM backbone network arcitecture. To verify the signaling flows, we used petri-Net model. The resutls show that the signaling flows are suitable to our proposed location tracking scheme.

In this paper, we propose a new cell switch fabric for increasing the performance of ATM switching systems. Proposed switching network consists of a sorting network and a routing network. Both of these are multistage networks where each stage performs a fixed permutation on the incoming lines, and then routes them through a clumn of 2x2 switching elements. It is designed for distributing inputs and parallel processing to reduce the hardware complexity and obtain high performance of switching network. The structure and the operation of th eswitching network aredescribed and the performanceof the switching network is anlyzed under uniform traffic models. In this result, though the size of proposed network is increased the large scale, it has always the same throughput as the that of genral output queueing system with N=2. So, it is found that our proposed network is appropriate for the high apeed and lrger size of ATM switching systems.

This paper is on a discrete controller order reduction with the closed-loop stability and performance guaranteed. to achieve this, after finding the solutionsof two lyapunov inequalities and balancing the full order controller system, we find the reudced order controlers using the balanced truncation (BT) and the balanced singular perturbation approximation (BSPA). When the solutions of the two lyapunov inequalities exist, it is shown that the resulting controllers guarantee the closed-loop stability, and .inf.-norm error bounds are derived for the closed-loop performance region for the BT and in low frequency region for the BSPA. Finally, a numerical example is given to illustrate the validity of the proposed method.

In this paper, we propsoe a new method of th ecommunication system for cooperative behaviors and works in collective autonomous mobile robots. A communication function among the collective robots is essential to intelligent cooperative works. In genral, global communication is effective for small number of robots. However when the number of robot goes on increasing, this becomes difficult to be realized because of limited communication capacity and increasing amount of information to handle. And also the problems such as communciation interfeence and improper message transmission occur. So we propose local communication system based on infrared sensor to realize the cooperative behavior among robots as the solution of above problem. It is possible to prevent overflow of information and exchange of complex information by fusion sign board model which transmits the information to unspecified robots and message passing model which communicate a specific robot. And we formularize optimal communication range by analysis of information propagation mechanism from the proposed comunication system. At last we verify the effectiveness of the proposed communication system from example of cooperative works.

In this paper, we psropose the feedback linearization technique for a nonlinear system using genetic algorithms (GAs) and fuzzy logic system. The proposed control scheme approximates the nonlinear term of a nonlinear system using the fuzzy logic system and computes the control input for cancelling the nonlinear term. Then in the fuzzy logic system, the number and shape of membership function of the premise aprt will be tuned to minimize the control error boundary using GAs. And the parameters of the consequence of fuzzy rule will be tuned by the adaptive laws based on lyapunov stability theory in order to guarantee the closed loop stability of control system. The evolution of fuzzy logic system is processed during the on-line adaptive control. The effectiveness of proposed method will be demonstrated by computer simulation of simple nonlinear sytem.

For the effective removal of artifacts and the extraction of an improved evoked potential response, we propose the averaging method usin gthe shrinkag eof wavelet coefficients. The wavelet analysis decomposes the measured evoked potentials into scale coefficients with low frequency components and wavelet coefficients with high ones as a resolution level, respectively. and in the course of synthesis evoked potentials, the presented method shrinks the wavelet coefficients, and then reproduces the evoked potentials, and lastly averages it. We measured visual evoked potentials to simulate the averaging method using the shrinkage of wavelet coefficients, and compared it with aveaged signal. As a result of simulations, the proposed method gets improved VEP about 0.2-1.6dB in comparison with the averaging method with daubechies wavelet in the resolution level four.

For the practical applications of the EIT technology, it is essential to reconstruct sttic images iwth a higher spatial resolution in a reasonalble amount of processing time. Using the conventional EIT static image reconstruction algorithms, however, the processing time increases exponential with poor convergence characteristics as we try to get a higher spatial resolution. In order to overcome this problem, we developed a recursive mesh grouping method based on the Fuzzy-GA like algorithm. Computational simulation using the well-known improve dewton-raphson method with the proposed recursive mesh grouping algorithm shows a promising result that we can significantly reduce the processing time in the reconstruction of EIT static images of a higher spatial resolution.

In this paper, a new stereo matching algorithm is proposed which uses th econstrainted optimization technique and the matching measures between the segments extracted from zero-crossing edges. The initial matching measures and average disparities are calculated by the features of segments on the searching window of the left and right images. The matching measure is calculated by applying an exponential function using the differences of slope, overlapped length and intensity. The coherency constraint is that neighbouring image points corresponding to the same object should have nearly the same disparities. The matching measures are iteratively updated by applying the coherency constraint. Simulation results on various images show that the proposed algorithm more acculately extracts the segment disparity.

In this paper an efficient morphologica algorithm for reducing gaussian and impulse noise in gray-scale image is presented. Based on the edge information the input image is partitioned into a flat region and an edge region, then different algorithms are selectively applied to each region. in case of impulse noise, MGR (morphologica grayscale reconstruction) algorithm with directional SE (structuring element) is applied to the flat region. For theedge region opening-closing (closing-opening) is used instead of dialation (erosion), so that the remaining noise around large objects can be removed. In case of gaussian noise, 5*5 OCCO(opening closing closing opening) and 3*3 DMF(directional morphological filter ) are used for the flat region and the edgeregion, respectively. In order to remove discontinuity at the edge boundary, the algorithm uses 3*3 OCCO around the edge region to reconstruct the final image. Experimetnal results have shown that the proposed algorithm achieves a high performance in terms of noise removal, detail preservation, and NMSE.

Image segmentation is teh important step in image infromation extraction for computer vison sytems. Fuzzy clustering methods have been used extensively in color image segmentation. Most analytic fuzzy clustering approaches are derived from the fuzzy c-means (FCM) algorithm. The FCM algorithm uses th eprobabilistic constraint that the memberships of a data point across classes sum to 1. However, the memberships resulting from the FCM do not always correspond to the intuitive concept of degree of belongingor compatibility. moreover, the FCM algorithm has considerable trouble above under noisy environments in the feature space. Recently, the possibilistic C-mean (PCM) for solving growing for color image segmentation. In the PCM, the membersip values may be interpreted as degrees of possibility of the data points belonging to the classes. So, the problems in the FCM can be solved by the PCM. The clustering results by just PCM are not smoothly bounded, and they often have holes. So, the region growing was used as a postprocessing. In our experiments, we illustrated that the proposed method is reasonable than the FCM in noisy enviironments.