This paper presents an effective synchronization algorithm. It is different from the existing synchronization methods by inserting appropriate synchronization instructions between statements according to different kinds of data dependencies. The overhead caused by too many synchronization instructions in a loop can be a critical problem. Synchronization optimization is a method which discriminates and eliminates the redundant synchronization instructions in a loop. In this paper, a new synchronization optimization algorithm is developed, and performance analysis using simulation on the UNIX operating system is carried out.

This paper considers and implementation of the memory system which provides simultaneous access to pq image points of block(p$\times$q), horizontal vector(1$\times$pq)and/vertical vector(pq$\times$1) in 2-dimension image array, where p and q are design parameters. This memory system consists of an address calculation circuit, address routing circuit, data routing circuit, module selection circuit and m memory modules where m>qp. The address calculation circuit computes pq addresses in parallel by using the difference of addresses among image points. Extra module assignment circuit is not used by improving module selection circuit with routhing circuit. By using Verilog-XL logic simulator, we verify the correctness of the memory system and estimate the performance. The implemented system provides simultaneous access to 16 image points and is 6 times faster than conventional memory system.

Generally, When we calculate notch angle to eliminate some selected harmonics using PWM inverter, we put the previously analysed notch angle formed by look-up table into memory, or perform the program to claculate notch angle iteratively with Fourier series. But, these methods are very difficult to control the system in real-time. Now, in this paper, we propose a new method to calculate notch angle using Walsh series, design real-time logic circuits which can be applied in 3 phase circuits and make one chip to reduce complexity and size of circuits using VLSI design technique.

This paper proposes a numerical method of motion planning for manipulators using Foruier series. For a redundant manipulator, we predetermine the trajectories of redundant joints in terms of the Nth partial sum of the fourier series. then the optimal coefficients of the fourier series are searched by the Powell's method. For a nonredundant or redundant manipulator, CS02T-continuous smooth joint trajectory for a point-to-point task can be obtained while considering the frequency response. We apply the proposed method to the 3-link planar manipulator and the PUMA 560 manipulator. To show the validity of the proposed method, we analyze solutions by the Fast Fourier Transform (FFT). Also, several features are discussed to obtain an optimal solution.

For a precise manipulator control in the presence of environmental uncertainties, it has long been recognized that the robot should be controlled in a task-referenced space. In this respect, an effective visual servo control system for robot manipulators based on neural networks is proposed. In the proposed control system, a Backpropagation neural network is used first to learn the mapping relationship between the robot's joint space and the video image space. However, in the real control loop, this network is not used in itself, but its first and second derivatives are used to generate servo commands for the robot. Second, and Adaline neural network is used to identify the approximately linear dynamics of the robot and also to generate the proper joint torque commands. Computer simulation has been performed demonstrating the proposed method's superior performance. Futrhermore, the proposed scheme can be effectively utilized in a robot skill acquisition system where the robot can be taught by watching a human behavioral task.

This paper considers uncertain systems with closed-loop poles in a polygonal region. A method is presented which is applicable to computing the perturbation of a pole-located region due to parameter uncertainties. A method is also proposed to calculate the bound on parameter uncertainties which allow the closed-loop poles to remain in a specified region. They provide useful robustness measures on the closed-loop poles of uncertain systems.

This paper presents a decentralized model reference adaptive control scheme for an interconnected linear system composed of a number of single-input single-output subsystems in which outgoing interactions pass through the measurement channel and are subjected to bounded external disturbances. The scheme can treat the unknown strength of interactions as well as uncertainties in subsystem dynamics, and allows for the case when the relative degree of each decoulped subsystem does not exceed two.

In this paper, we proposed coordinator description and ambiguity on the hybrid controller for position/force control of robot manipulator. When the hybrid controller is desiged based on the PID control conception, the parameter sharing problem must be considered. However, selection problem of coordinate system on n-DOF robot manipulator control is unsolved. Moreover, contact force on object and change of shape make another problems. And it is very difficult to figure out the accurate mathematical model of manipulator on account of ambiguity and nonlinearity of actuator. Therfore, we design a new hybrid controller, FPID(Fuzzy PID). For verifying the validity of the controller, we tried computer simulation of this system. As a result, we can get remarkable improvement of overdamping and overshooting. Also we can solve compicance problem effectively. Furthermore, ambiguity problem is solved by adding control knowledge based compensator. So robust controller can be acheived, too.

Pattern Recognition is one of the fundamental areas of computer vision. The recognition of patterns with varying size and severe defects is especially important. However, it is known that the conventional algorithms such as GHT or structural approaches have limitations in speed and accuracy. In this paper, in order to avoid above-mentioned problems, we propose a new recognition algorithm which exploits the histogram of contour directions and the sampled spot matching method. While the former provides little influence against size variation, the latter has strong immunity to noise and defects. We applied those proposed algorithms for the recognition of numbers extracted from the car number plates and shapes of aircraft. Experimental result shows that it is possible to solve above-mentioned problems by complementary uses of those two suggested algorithms. The contour directional histogram method resulted in high-speed of average 0.013 sec/char and 0.1 sec/aircraft-image on IBM-386. The accuracy of recognition is as high as 99%. Sampled spot matching method has less speed than the former one, however, it showed fairly strong immunity to noise and defects.

In this paper, a multiresolution edge detction algorithm for speckle images is proposed. Due to the signal dependency of speckle images, the number of edge points detected depends on the local average intensity. Thus the edge detection method independent of the average intensity is required to detect properly real significant changes in an original signal. In the proposed method, candidate area is first selected based on the statistical propeties of speckle images,i.e., based on the busyness measure such as the CoV(coefficient of variation) and the difference between the real and theoretical CDF(cumulative density function). Then the real edges are extracted in a multiresolution environment. Computer simulation with test images shows that the proposed method reduces significantly false edges in relatively homogeneous areas while detects fine details properly.

In this paper, we propose a neural network model for speech recognition. The model consists of feature extraction parts and recognition parts. The interconnection model based on ${\Delta}^2$G operator was used for frequency analysis. Two features, global feature and local feature, were extracted from this model. Recognition parts consist of global grouping stage and local grouping stage. When the input pattern was coded by slope method, the recognition rate of speakers, A and B, was 100%. When the test was performed with the data of 9 speakers, the recognition rate of 91.4% was obtained.

In this paper, an integrated document input/output system is developed which constructs the graphic document from a text file, converts the document into encoded facsimile data, and also recognizes printed/handwritten alphanumerics and Korean characters in a facsimile or graphic document. For an output system, we develop the method which generates bit-map patterns from the document consisting of the KSC5601 and ASCII codes. The binary graphic image, if necessary, is encoded by the G3 coding scheme for facsimile transmission. For a user friendly input system for documents consisting of alphanumerics and Korean characters obtained from a facsimile or scanner, we propose a document recognition algirithm utilizing several special features(partial projection, cross point, and distance features) and the membership function of the fuzzy set theory. In summary, we develop an integrated document input/output system and its performance is demonstrated via computer simulation.