The conformance test is a methodology which verifies whether the Implementation Under Test (IUT) conforms to its standard specification or not. In this paper, first, the Conformance Test System(CTS) is implemented in prototype including verification test type, black-box test strategy, conformance test class, remote test structure, and IUT which is described by the ISO Estelle among Formal Description Techniques. Second, through this prototype the proposed system is experimented. The prototype is composed of two modules. The first module is the Estelle-to-Pascal preprocessor which converts the Estelle program to the corresponding Pascal program because the experimental IUT was described using the unexecutable code form of Estelle before being tested by the TD(Test Driver). The TD, the second module, is based on the master-slave concept and plays important role is CTS. The TD acts as the master with the IUTd being the slave. The prototype system is implemented using Turbo-C, Turbo-Pascal and Turbo-Assembly.

This paper analyzes the properties of such algorithm that corresponds to the LMS algorithm with additional update terms, parameterized by the scalar factors $\alpha$ and $\beta$, and presents its structure. The analysis of convergence leads to complex eigenvalues of the transition matrix for the mean weight vector. Regions in which the algorithm becomes stable are demonstrated. The computational cmomplexities of MLMS algorithms are compared with those of MADF, sign and the conventional LMS algorithms. In application of the system identification the second order momentum MLMS algorithm has faster convergence speed than LMS and the first order MLMS algorithms.

The validity of the application of the second-order Born equation to the ultrasound tomography algorithm is studied by comparing the scattered fields computed using the first-order Born equation, the first-order Rytove equation, and the second-order Born equation. The second-order Born equation turns out to provide more desirable results than the other two equations for a certain group of test objects. Phantom images with resolutions upto 1 pixel$\times$1 pixel are satisfactorily reconstructed using the second-order Born equation. It is shown that when the view angle is limited, good resonstruction results are also obtained using multi-frequency incident fields.

Using Multi-Dimentional Scaling(MDS) method, this paper analyzes the differences of acoustic properties of Korean phonemes projected as distances on a plan space. The phonemes were extracted from mono-syllables frequently occurring in daily conversation. From the distances between vowels we found that the distances between vowels /∂/ and /w/, /o/ and /u/, and vowels /$\varepsilon$/ and /e/ were relatively too short separate automatically. From the analysis of consonants. we found short distances between 1) phonemes in each phoneme group, 2) nasals and liquid /r/ of work initial, 3) nasal /n,m/ and liquid /l/ of word finals. But nasals, liquids and plosives of word initials had enough distances to be separated from those of word finals in automatic recogniation.

This paper presents a decentralized adaptive controller design for a robot manipulator to track the given desired trajectory in the joint space. The controller is of distributed structure and does not require the complex manipulator dynamic model, thereby it is computationally very efficient. Each joint is independently controlled by a PID feedback part and a velocity-acceleration feedforward part. Simulation results for a two-link direct drive manipulator conform that the proposed decentralized scheme is feasible.

A numerical trajectory planning method for path-constrained trajectory planning is proposed which ensures collision-free and time-optimal motions for two robotic manipulators with limited actuator torques and velocities. For each robot, physical constraints of the robots such as limited torques or limited rotational velocities of the actuators are converted to the constraints on velocity and acceleration along the path, which is described by a scalar variable denoting the traveled distance from starting point. Collision region is determined on the coordination space according to the kinematic structures and the geometry of the paths of the robots. An Extended Coordination Space is then constructed` an element of the space determines the postures and the velocities of the robots, and all the constraints described before are transformed to some constraints on the behaviour of the coordination-velocity curves in the space. A dynamic programming technique is them provided with on the discretized Extended Coordination Space to derive a collision-free and time-optimal trajectory pair. Numerical example is included.

The sum-of-product type MVL (Multiple-valued logic) functions can be directly transformed into the exclusive-sum-of-literal-product(ESOLP) type MVL functions with a substitution of the OR operator with the exclusive-OR(XOR) operator. This paper presents an algorithm that can reduce the number of minterms for the purpose of minimizing the hardware size and the complexity of the circuit in the realization of ESOLP-type MVL functions. In Boolean algebra, the joinable true minterms can form the cube, and if some cubes form a cube-chain with adjacent cubes by the insertion of false cubes(or, false minterms), then the created cube-chain can become a large cube which includes previous cubes. As a result of the cube grouping, the number of minterms can be reduced artificially. Since ESOLP-type MVL functions take the MIN/XOR structure, a XOR circuit and a four-valued MIN/XOR dynamic-CMOS PLA circuit is designed for the realization of the minimized functions, and PSPICE simulation results have been also presented for the validation of the proposed algorithm.

The economical detection of dual-tone multifrequency(DTMF) signals is a important when developing cost-effective telecommunication equipment. This paper propose that the modified M-channel Goertzel algorithm can reduces the real-time processing and memory waste. By using this M. G. algorithm, it will compare and evaluate to the performance of the digital DTMF receivers using the G. A.

In this paper, an algorithm for recovering the 3-D shape from a single shaded image is proposed. In the proposed algorithm, by using the relation between the height and surface gradient (p, q), a set of linear equations is derived from the linearized reflectance function. Then the 3-D surface is recovered by employing the conjugate gradient technique. In order to improve the convergence speed of the solution, we also employ the hierarchical basis function and multiresolution images in the algorithm. A method for determining the regularization parameter, which is determined by trial and error in the conventional approach, is also introduced. In addition, the proposed algorithm attempts to recover the 3-D surface without requiring the boundary conditions, making it suitable for a real-time implementation. Simulation results for real image as well as synthetic image are provided to demonstrate the performance of the proposed algorithm.

In this paper we propose a diffusion neural network model. In this model, each excitatory and inhibitory neuron has the capability of diffusing external excitations. We show that this model can be used for the detection of intensity changes of an input image. The relations between the diffusion coefficient, the iteration number of diffusion, and the detected spatial frequency are analyzed. The calculation time is reduced than that of a LOG(a Laplacian of a Gaussian) method.

HVS based transform coding techniques for still images take the lowest spatial frequency and luminance contrast sensitivity as the most crucial factors in determining the image quality perceived by human eye. Only the frequency weighting has been main issue. The proposed Weighted Discrete Cosine Transform (WDCT) has the lowest frequency of about 4 [cyc/deg] in 8$\times$8 subblock with the contrast-weighted DC coefficient. The error criteria have been rederived to adapt the contrast weighting for performance evaluation. The proposed scheme has produced higher quality images compared to the unweighted and other schemes.

In this paper, a new method of character recognition is proposed. It uses density information, in addition to positional and directional information generally used, to recognize a character. Four directional feature primitives are extracted from the thinning templates on the observation that the output of the templates have directional property in general. A simple and fast feature extraction scheme is possible. Features are organized from recursive nonary tree(N-tree) that corresponds to normalized character area. Each node of the N-tree has four directional features that are sum of the features of it's nine sub-nodes. Every feature primitive from the templates are added to the corresponding leaf and then summed to the upper nodes successively. Recognition can be accomplished by using appropriate feature level of N-tree. Also, effectiveness of each node's feature vector was tested by experiment. A method to implement the proposed feature vector organization algorithm into hardware is proposed as well. The third generation node, which is 4$\times$4, is used as a unit processing element to extract features, and it was implemented in hardware. As a result, we could observe that it is possible to extract feature vector for real-time processing.

In this paper, we propose an edge detection scheme for noisy images based on the co-occurrence matrix. In the proposed scheme based on the step edge model, the gray level information is simply converted into a bit-map, i.e., the uniform and boundary regions of an image are transformed into a binary pattern by using the local mean. In this binary bit-map pattern, 0 and 1 densely distributed near the boundary region while they are randomly distributed in the uniform region. To detect the boundary region, the co-occurrence matrix on the bit-map is introduced. The effectiveness of the proposed scheme is shown via a quantitative performance comparison to the conventional edge detection methods and the simulation results for noisy images are also presented.

In this paper we propose a fuzzy-neural network(FNN) which includes both advantages of the fuzzy logic and the neural network. The basic idea of the FNN is to realize the fuzzy rule-base and the process of reasoning by neural network and to make the corresponding parameters be expressed by the connection weights of neural network. After constructing the FNN, a novel controller consisting of a conventional P-controller and a FNN is explained. In this control scheme, the rule-base of a FNN are automatically generated by error back-propagation algorithm. Also the parallel connection of the P-controller and the FNN can guarantee the stability of a plant at initial stage before the rules are completely created. Finally the effectiveness of the proposed strategy will be verified by computer simulations using a 2 degree of freedom robot manipulator.

This paper presents the word recognition method using a neural-fuzzy pattern matching, in order to make a proper speech pattern for a spectrum sequence and to improve a recognition rate. In this method, a frequency variation is reduced by generating binary spectrum patterns through associative memory using a neural network, and a time variation is decreased by measuring the simillarity using a fuzzy pattern matching. For this method using binary spectrum patterns and logic algebraic operations to measure the simillarity, memory capacity and computation requirements are far less than those of DTW using a conventional distortion measure. To show the validity of the recognition performance for this method, word recognition experiments are carried out using 28 DDD city names and compared with DTW and a fuzzy pattern matching. The results show that our presented method is more excellent in the recognition performance than the other methods.