In distributed environments, data replication increases availability and decreases communication cost. However, it is difficult to maintain consistency and availability of data if site failure occurs. When we use the conventional hierarchical quorum consensus(HQC) method in order to maintain the consistency of data, occurrence of site failures makes it harder to perform the operation on replicated data because of insufficient votes. The objective of this paper is to improve the possibility of retaining necessary votes by reconstructing the HQC structure, when the site failure occurs. Furthermore, we compare the modified HQC method with the conventional HQC and QC methods in terms of improvement of availability.

This paper describes a design and implementation of the DFR(Document Filing and Retrieval) system. one of applications of DOAM(Distributed Office Application Model) which is the international standard in ISO(International Standards Organization). On the basis of the international standard, the DFR system is implemented on SUN workstation and PC/386 with C language, and its implementation is verified by tracing the association descriptor and primitives of service elements when its operation is tested between client and server. The result of this study shows that the DFR system can be implemented on the basis of the international standard, and makes a contribution toward the establishment of functional standards for the DFR system.

Linear combination of weighted order statistic(LWOS) filters, which is an extension of stack filters, can represent any Boolean function(BF) or its extension. Which is called the extended BF(EBF). In this paper, we present a procedure for finding an LWOS filter of the simplest type from LWOS filters which are equivalent to a given BF or EBF. In addition, a property that is useful for implementing an LWOS filter is derived and an algorithm for LWOS filtering is presented.

In this paper, a new time-domain parameter extraction method for speech recognition is proposed. The suggested emthod is based on the fact that the local peak-to-peak interval, i.e., the interval between maxima and minima of speech waveform is closely related to the frequency component of the speech signal. The parameterization is achieved by a sort of filter bank technique in the time domain. To test the proposed parameter extraction emthod, an isolated word recognizer based on Vector Quantization and Hidden Markov Model was constructed. As a test material, 22 words spoken by ten males were used and the recognition rate of 92.9% was obtained. This result leads to the conclusion that the new parameter extraction method can be used for speech recognition system. Since the proposed method is processed in the time domain, the real-time parameter extraction can be implemented in the class of personal computer equipped onlu with an A/D converter without any DSP board.

In this paper, an observer for linear discrete systems with unknown inputs is presented. The suggested observer can estimate the system state vector and the unknown inputs simultaneously. As an extension of the observer, a new fault diagnosis observer for linear discrete systems with structured uncertainty is presented. The fault diagnosis observer can detect and identify the actuator and the sensor faults as well. The stability conditionsand the design methods of the each observers are presented and the usability of the observers is shown via numerical examples.

In this paper, a block based image approximation technique using the Self Affine System(SAS) from the fractal theory is suggested. Each block of an image is divided into 4 tiles and 4 affine mapping coefficients are found for each tile. To find the affine mapping cefficients that minimize the error between the affine transformed image block and the reconstructed image block, the matrix euation is solved by setting each partial differential coefficients to aero. And to ensure the convergence of coding block. 4 uniformly partitioned affine transformation is applied. Variable block size technique is employed in order to applynatural image reconstruction property of fractal image coding. Large blocks are used for encoding smooth backgrounds to yield high compression efficiency and texture and edge blocks are divided into smaller blocks to preserve the block detail. Affine mapping coefficinets are found for each block having 16$\times$16, 8$\times$8 or 4$\times$4 size. Each block is classified as shade, texture or edge. Average gray level is transmitted for shade bolcks, and coefficients are found for texture and edge blocks. Coefficients are quantized and only 16 bytes per block are transmitted. Using the proposed algorithm, the computational load increases linearly in proportion to image size. PSNR of 31.58dB is obtained as the result using 512$\times$512, 8 bits per pixel Lena image.

The objective of camera calibration is to determine the internal optical characteristics of camera and the three-dimensional position and orientation of camera with respect to the real world. Calibration procedure for computer vision should be automatical, accurate and applicable to general purpose cameras and lenses. In this paper, we present camera calibration method which meets the above requirements. The algorithm is based on the two-stage method which takes into account lens distortion in the second stage. In this paper, the overdetermined nonlinear system is established in terms of the constraints to all directions and our calibration algorithm is proposed which is constructed by using Marquardt iterations and our calibration algorithm is proposed which is constructed by using Marquardt iteration method in solving nonlinear equations. Experimental results indicate that lens distortion should be taken into consideration for the calibration of the general-purpose lens. With 24 calibration points acquired out of 512$\times$512 image, the proposed algorithm came up with average error of less than 1 pixel and showed a higher accuracy over the conventional two-stage method.

The conventional machine vision system which has uniform rectangular grid requires tremendous amount of computation for processing and analysing an image especially in 2-D image transfermations such as scaling, rotation and 3-D reconvery problem typical in robot application environment. In this study, the imaging system with nonuiformly distributed image sensors simulating human visual system, referred to as Ploar Exponential Grid(PEG), is compared with the existing conventional uniform rectangular grid system in terms of image resolution and computational complexity. By mimicking the geometric structure of the PEG sensor cell, we obtained PEG-like images using computer simulation. With the images obtained from the simulation, image resolution of the two systems are compared and some basic image processing tasks such as image scaling and rotation are implemented based on the PEG sensor system to examine its performance. Furthermore Fourier transform of PEG image is described and implemented in image analysis point of view. Also, the range and heading-angle measurement errors usually encountered in 3-D coordinates recovery with stereo camera system are claculated based on the PEG sensor system and compared with those obtained from the uniform rectangular grid system. In fact, the PEC imaging system not only reduces the computational requirements but also has scale and rotational invariance property in Fourier spectrum. Hence the PEG system has more suitable image coordinate system for image scaling, rotation, and image recognition problem. The range and heading-angle measurement errors with PEG system are less than those of uniform rectangular rectangular grid system in practical measurement range.

Fetal monitoring is a routine procedure to obtain a record of physiologic functions during pregnancy and labor. It is required to determine fetal heart frequency accurately. There are various types of fetal heart rate(FHR) determination and the most frequently applied method is transabdominal Doppler ultrasound. However, in the case of weak or noise corrupted Doppler ultrasound signals, conventional peak detections and the autocorrelation function method have many difficulties to determine FHR precisely. Also the autocorrelation function is effected by threshold level and window size. To solve these problems, the high resolution pitch determination algorinthm is introduced to detect FHR from Doppler ultrasound signals. This scheme digitally processes Doppler ultrasound signal for digital rectification, envelope detection, decimation and correlation calculation of two interconnected segments and then FHR is determined by its maximal value. Even in the case of a greatly smeared noise signal, this algorithm is able to search FHR more accurately than autocorrelation function by means of compensating FHR with a constant correlation threshold. This algorithm is simulated by 386-MATLAB on PC 486/DX and verified that it is superior to the autocorrelation function method.