In this paper load redistribution algorithm to allow fault-tolerance by redistributing the workload of n failure nodes to the remaining good nodes in distributed systems are investigated. To evaluate the efficiency of the algorithms a simulation model of algorithms is developed using SLAM II simulation language. The job arrival rate service rate failure and repair rate of nodes and communication delay time due to load migraion are used as parameters. The result of the simulation shows that the job arrival rate failure and repair rate of nodes do not affected on the relative efficiency of algorithms. If the communication delay time is greater than average job processing time algorithm B is better. Otherwise algorithm C is superior to the others.

The matching probability P(ο/$\lambda$), of the signal sequence(ο) observed for a finite time interval with a HMM (Hidden Markov Model $\lambda$) indicates the probability that signal comes from the given model. By utilizing the fact that the probability represents matching score of the observed signal with the model we can recognize an unknown signal pattern by comparing the magnitudes of the matching probabilities with respect to the known models. Because the algorithm however uses floating point variables during the computing process hardware implementation of the algorithm requires floating point units. This paper proposes an integer algorithm which uses positive integer numbers rather than float point ones to compute the matching probability so that we can economically realize the algorithm into hardware. The algorithm makes the model parameters integer numbers by multiplying positive constants and prevents from divergence of data through the normalization of variables at each step. The final equation of matching probability is composed of constant terms and a variable term which contains logarithm operations. A scheme to make the log conversion table smaller is also presented. To analyze the qualitive characteristics of the proposed algorithm we attatch simulation result performed on two groups of 10 hypothetic models respectively and inspect the statistical properties with repect to the model order the magnitude of scaling constants and the effect of the observation length.

The lossless bounded real(LBR) two-pair cascade structure is one of the exiting low-sensitivity digital filter structures such as wave digital filters(WDFs) orthogonal filters. They are known to have the same structures which are composed of canonic building blocks interconnected to each other. The LBR two-pair cascade filters amount to describing in a unified manner the existing canonic low-sensitivity filters in terms of transfer matrices and chain matrices. However the existing structures have somewhat degraded low-sensitivity performance because they include dependent parameters within their structures. In this paper we propose a filter structure called “two-type-interlaced(TTI) structure.” eliminating such problem completely. This structures can be viewed as the WDFs of analog ladder circuits. As ladder circuits are obtained by cascading Brune sections and merging neighboring inductors or capacitors. so TTI structures at e obtained by cascading Type 3 LBR two-pairs and merging neighboring Type 1 LBR two-pairs. Next, a test procedure called “LBR test” is also presented in this paper. which determines whether of not the quantized TTI structure is stable . If it is unstable we can fine-tune the quantized parameters to make the overall structure stable. Therefore we can solve the dependent parameter problem completely with TTI structure along with LBR test. test.

This paper presents an improved interpolation algorithm which enables to find a unit function in $H^{\infty}$. From the proposed algorithm the interpolation problem on the infinity point with multiplicity can be solved. This is based on the DPL algorithm the acquired unit function has low order and can be directly applied to strong/simultaneous stabilization problem in control systems. Finally, we verify that poles of transfer function of closed-loop system exist in stable region while investgating internal stability.

In this paper a state reconstruction filter for linear discrete-time stochastic systems with unknown inputs and noises is presented. The suggested filter can estimate the system state vector and the unknown inputs simultaneously As an extension of the filter a fault diagnosis filter for linear discrete-time stochastic systems with unknown inputs and noises is presented for each filters the optimal gain determination methods which minimize the variance of the state reconstruction errorare presented. Finally the usability of the filtersis shown via numerical examples.

The dynamic feedback is well-known to be much more powerful tool in control than the static one. This paper deals with the dynamic feedback linearization of the nonlinear systems which are not (static) feedback linearizable. The dynamic feedback linearization problem is however too difficult to solve at momemt. Thus we introduce a restricted class of the dynamic feedback (pure integrators followed by the static feedback) which is often used to study the problems using dynamic feedback and obtain the necessary and sufficient conditions of the linearization problem using this class of the dynamic feedback.

In this paper we present an algorithmic technique for determining a feedback compensator which will stabilize the interval dynamic system specifically the robust regulator design for interval plants. The approach taken here is to allow the system parameters to live within prescribed intervals then design a dynamic feedback compensator which guarantees closed-loop system stable. The main contribution of this paper is the idea of introducting a "simplified Kharitonov`s results" for low order polynomials to search for suitable compensator parameters in the compensator parammeter space to make the uncertain system robust. We also design the robust regulator which will $D_{\phi}$ -stabilize (have the closed-loop poles in the left sector only) the dynamic interval system while having good performance. the numerical examples are given to show the substantially improved robustness which results from our approach.

In this paper a hierarchical stereo matching algorithm to obtain the disparity in wavelet transformed domain by using locally adaptive window and weights is proposed. The pyramidal structure obtained by wavelet transform is used to solve the loss of information which the conventional Gaussian or Laplacian pyramid have. The wavelet transformed images are decomposed into the blurred image the horizontal edges the vertical edges and the diagonal edges. The similarity between each wavelet channel of left and right image determines the relative importance of each primitive and make the algorithm perform the area-based and feature-based matching adaptively. The wavelet transform can extract the features that have the dense resolution as well as can avoid the duplication or loss of information. Meanwhile the variable window that needs to obtain precise and stable estimation of correspondense is decided adaptively from the disparities estimated in coarse resolution and LL(low-low) channel of wavelet transformed stereo image. Also a new relaxation algorithm that can reduce the false match without the blurring of the disparity edge is proposed. The experimental results for various images show that the proposed algorithm has good perfpormance even if the images used in experiments have the unfavorable conditions.

Object-oriented analysis-synthesis coding subdivides each image of a sequence into moving objects and compensates the motion of each object. Thus it can reconstruct real motion better than conventional motion-compensated coding techniques at very-low-bit-rates. It uses a mapping parameter technique for estimating motion information of each object. Since a mapping parameter technique uses gradient operators it is sensitive to redundant details and noise. To accurately determine mapping parameters, we propose a new analysis method using integral projections for estimation of gradient values. Also to reconstruct correctly the local motion the proposed algorithm divides an image into segmented objects each of which having uniform motion information while the conventional one assumes a large object having the same motion information. Computer simulation results with several test sequences show that the proposed image analysis method in object-oriented analysis-synthesis coding shows better performance than the conventional one.

In this paper an image compression technique based on fractal theory using iterated contractive transformation is analysed and an improved image coder is suggested. Existing methods used the classifier proposed by Ramamurthi and Gersho which utilize the properties of neighboring pixels in the spatial domain. In this paper DCT-based classification is applied to 512$\times$512 images and PSNR improvement of 0.4~2.7 dB is obtained at lower bit rate over conventional algorithms. In addition the effect of varying the domain block size and quantization step size of the luminance shift parameter on the compression ratio and the image quality is compared and analysed.

We proposed new field/frame mode selection quantization and buffer control strategy for interlaced digital HDTV image coding system. The field/frame mode selection is performed based on the mean square error and number of generated bits for each superblock. The quantization factor for each superblock is determined by the characteristics of human visual system and buffer status. The statistical characteristics of the number of generated bits for basis block and the prediction of buffer status are used for buffer control. Simulation results show that the proposed field/frame mode selection and rate buffer control. Simulation results show that the proposed field/frame mode selection and rate buffer control strategy have good subjective image quality and have stable buffer status.

In this paper we propose a new method for the recognition of 2-D partially occluded objects. The border of the object is transformed to a curve in arc length-accumulated interior angle plane. The transformed curve of an image is partitioned so that each segment is bounded by the concave interior angles. In order to tolerate shape distortion due to the polygonal approximation of the boundary of the object a group of feature points of the input image are matched with those of model views. The estimation method for positions and orientations of the identified objects objects is presented.

In this paper an adaptive moving method of the search region tracking the motion is proposed. The search region in BMA is determined by the capability of hardware implementation and the degree of motion. But once determined nothing can be changed during coding procedure. In this paper we predict the level of motion of the current block using motion vectors of previous frames without overhead information and change the location of the search region according to the level of the motion predicted. In short the proposed method can be archieved the dsirable effect such that the size of search region gets large when the motion is large. Results of experiments show that prediction efficiency has been improved by using adaptive moving method resulting in reduced prediction error in the blocks with large motion.

In this paper,we propose a knowledge-based system for locating Destination Address Block(DAB) by analyzing the structure of Korean envelope images. In the proposed system the preprocessing steps such as adaptive binarization connected component extraction and deskewing are carried out first for the effective structure analysis of the envelope image. Then DAB containing address name and zipcode parts of the input envelope image is extracted by an iterative procedure based on the knowledge acquired from the statistical feature analysis of the various envelope images. Most of the system for slocating address blocks on envelopes have extracted DAB by segmenting an envelope image into several candidate blocks followed by selecting one among the candidate blocks. Because it is very difficult to segment a Korean envelope image into several blocks due to the specific writing habits that the addresses on the envelope are written in close proximity to each other the proposed iterative procedure determines DAB by splitting or merging the connected components and verifies the determined DAB without segmentation and selection. Experiments with a great number of the live envelopes provided from Seoul Mail Center in Koorea were carried out. The results reveal that the proposed system is very effective for address block location on Korean envelopes.

The performance of the improvement method of spatial resolution for satellite images based on the generalized inverse matrix is superior to the conventional methods. But, this method calculates the coefficient values for extracting the spatial information from the relation between a small pixel and large pixels. Accordingly it has the problem of remaining the blocky patterns at the result image. In this paper, a new generalized inverse matrix method is proposed which is different in the calculation method of coefficient values for extracting the spatial information. In this proposed metod, it calculates the coefficient values for extracting the spatial information from the relation between a small pixel and small pixels. Consequently it can improve the spatial resolution more efficiently without remaining the blocky patterns at the result image. The effectiveness of the proposed method is varified by simulation experiments with real TM image data.

The wide dynamic range and severely attenuated contrast in mediastinal area appearing in typical chest radiographs have often caused difficulties in effective visualization and diagnosis of lung diseases. This paper proposes a new adaptive image enhancement technique which potentially solves this problem and there by improves observer performance through image processing. In the proposed method image processing is applied to the chest radiograph with different processing parameters for the lung field and mediastinum adaptively since there are much differences in anatomical and imaging properties between these two regions. To achieve this the chest radiograph is divided into the lung and mediastinum by gray level thresholding using the cumulative histogram and the dynamic range compression and local contrast enhancement are carried out selectively in the mediastinal region. Thereafter a gray scale transformation is performed considering the JND(just noticeable difference) characteristic for effective image displa. The processed images showed apparenty improved contrast in mediastinum and maintained moderate brightness in the lung field. No artifact could be observed. In the visibility evaluation experiment with 5 radiologists the processed images with better visibility was observed for the 5 important anatomical structures in the thorax.

Implantable biotelemetry systems are indispensable tools not only in animal research but also in clinical medicine as such systems enable the acquisition of otherwise unavailable physiological data. We present the manufacture of CMOS IC and its system for implantable multichannel biotelemeter system. The internal circuits of this system are designed not only to achieve as multiple functions and low power dissipation as possible but also to enable continuous measurement of physiological data. Its main functions are to enable continuous measurement of physiological data and to accomplish on-off power swiching of an implantable battery by receiving appropriate commanc signals from an external circuit. The implantable circuits of this system are designed and fabricated on a single silicon chip using $1.5\mu$m n-well CMOS process technology. The total power dissipation of implantable circuits for a continuous operation was 6.7mW and for a stand-by operation was 15.2$\mu$ W. This system used together with approriate sensors is expected to contribute to clinical medicine telemetry system of measuring and wireless transmitting such significant physiological parameters as pressure pH and temperature.

A new method for enhancing blurred images using fuzzy logic concepts is proposed. Blurred images contain blurred boundaries which make it difficult to detect edges and segment areas in images. In order to sharpen blurred edges local contrast information of an image and erosion/dilation properties of local min/max operations are used in which local min/max operations are fuzzy logic operations. so that given images are transformed to fuzzy images and then these operations are applied on them. In this method the sharpening operation can be iteratively applied to the image to get better deblurring effect and gray-scale "salt-and-pepper" noises are suppressed. the efficiency of our algorithm is demonstrated through experimental results obtained with artificially-made blurred images and real blurred images.

Gupta defined deadlocks in the blocks world and proved that decision making problem in the blocks world is NP-Complete. The types of deadlocks are classified and the properties of deadlocks are analyzed in this paper. The heuristic planner algorithm for the limited table-capacity blocks world using both the inverse position of the block and properties of deadlocks is designed and its performance is also evaluated.

We proposed so-called AFHR (adaptable fuzzy hyper-resolution principle) that can manipulate uncertain knowledge and tune the range of resolution. The AFHR can make a rangable resolution to execute an efficient resolution and can represent linguistic truth values. In this paper, we introduce new concepts of law of contrary, meaningless range level for truth values and strict degree of adaptable resolution. We show that the differences of AFHR and existing fuzzy resolution. Finally we prove completeness of AFHR.

In this paper we develope a hybrid learning rule to improve the robustness of multi-layer Perceptions. In most neural networks the activation of a neuron is deternined by a nonlinear transformation of the weighted sum of inputs to the neurons. Investigating the behaviour of activations of hidden layer neurons a new learning algorithm is developed for improved robustness for multi-layer Perceptrons. Unlike other methods which reduce the network complexity by putting restrictions on synaptic weights our method based on error-backpropagation increases the complexity of the underlying proplem by imposing it saturation requirement on hidden layer neurons. We also found that the additional gradient-descent term for the requirement corresponds to the Hebbian rule and our algorithm incorporates the Hebbian learning rule into the error back-propagation rule. Computer simulation demonstrates fast learning convergence as well as improved robustness for classification and hetero-association of patterns.

Sinusoidal current tracking inverters have substantial advantages in high performance acdrive systems and various control strategies for the inverter have been proposed by several researchers. This paper develops a sinusoidal current tracking inverter with neural networks. The neural network are trained to follow a set of reference current waveforms by erro back propagation algorithm and the trained neural networks are applied to the current control. We compare neural networks method with conventional current control methods (fixed band and sinusiidal band hystersis methods) and simulation results are presented.