Input-output decoupling is well-known to be effective in the control of the nonlinear systems. This paper points out some problems of this technique in applications, and obtains a new digital input-output decoupling feedback law by using the inherent structure of the system. The effectiveness (accuracy) of our new control technique are confirmed by simple computer simulations. Finally, a digital compensator is also designed. The problems we study are of importance in the field of nonlinear control of robots, aerospace navigation, and vehicular control. The methodology to be employed involves both algebraic and geometric aspects of the systems.

This note presents the robust root clustering problem of interval systems whose characteristic equation might be given as either a family of interval polynomials or a family of polytopes. Corresponding to damping ratio and robustness margin approximately, we consider a certain class of D-region such as parabola, left-hyperbola, and ellipse in complex plane. Then a simpler D-stability criteria using rational function mapping is presented and prove. Without .lambda. or .omega. sweeping calculation, the absolute criteria for robust D-stability can be determined.

A mobile robot in a nuclear power plant is usually needed to equip the ability of going up and down stairs for a some kind of inspection. For this purpose, it is necessary for the mobile robot to figure out the size of stairs laid on a navigation path to gurantee robot's moving freely. In this paper, to measure the size of stairs existing in front of a mobile robot we designed the stair size recognition unit which can measure the stair's height and width using an ultrasonic sensor and/or a CCD camera. Also to obtain higher reliability of ultrasonic sensing data we proposed the horizontal sensing method. On the assupmtions that the mobile robot generates a trajectory while ascending stairs, we simulated it on a IBM compatible computer. The result showed that the suggested method satisfied our purpose. In a stair size estimation, the detected stair's height error was about .+-.3mm, and width was about .+-.5mm.

In this paper the optimized overlap driving scheme for improving the reduction problem of the operating voltage range occured by the overlap driving scheme proposed precedently and increasing the contrast ratio of screen image in the simple matrix LCDs is proposed. The characteristic estimation of the proposed method was performed in a condition that the number of scan electrodes was 120 and the threshold voltage of LC pixel was 2V and the overlap rate of scan signal was varied from 0% to 40% . As a result of estimation compared with the overlap driving scheme proposed precedently, this new method was certified as a method which it could increase the operating voltage range of the LC pixel by 16% in 20% overlap condition and it's operating voltage range was also increased very much with the increase of the overlap rate. Consequently this newly proposed method was certified as a method which it could maintain the improvement effect of the operating characteristics obtained by the overlap driving scheme proposed precedently with the big improvement in the contrast ratio of screen image.

Modified equivalent lumped element model for square spiral inductors have been derived. This model shows more accurate analysis performance than conventional models, and gives reliable design parameters. And this model is made through comparison among distributed multiple coupled line, numerical analysis and experimental data.

In this paper, a method has been proposed for the fully automatic detection of left ventricular endocardial boundary in B-mode short axis echocardiography without manual intervention by human operator. The proposed method makes use of the weighted model that approximates to endocardium and incomplete edge information for echocardiography. Therefore, this method is more effective than boundary detection by only edge information. The implementation of this method is as follows. First, the proposed algorithms are used in order to detect the approximate boundary, then a weighted model with the approximate boundary is constructed. Finally, the cavity center of the left ventricle performing the Hough transform with the weighted model and edge image can be found automatically, and then the endocardial boundary using detected center, original image, weighted model, and edge image can be detected. validations of this method with experimental results on echo image of dog's heart and clinical echocardiography is verified.

In this paper, we propose a robust estimation of camera parameters from image sequence for high quality video composition. We first establish correspondence of feature points between consecutive image fields. After the establishment, we formulate a nonlinear least-square data fitting problem. When the image sequence contains moving objects, and/or when the correspondence establishment is not successful for some feature points, we get bad observations, outliers. They should be properly eliminated for a good estimation. Thus, we propose an iterative algorithm for rejecting the outliers and fitting the camera parameters alternatively. We show the validity of the proposed method using computer generated data sets and real image sequeces.

In this paper, we describe hardware implementation of VSB (Vestigial SideBand) mo-dulation equalization systems for HDTV (High Definition TeleVision). By modifying an adaptive equalization algorithm, we propose a hardware architecture with a low hardware cost and the performance close to floating-point operations. We also employ the pipeline concept to reduce the hardware cost. The effectiveness of the proposed hardware architecture is de- monstrated through computer simulation and the optimization result of VHDL circuit descriptions.

In this paper, we transformed the bilinear filter into an equivalent linear multichannel filter and derived QR decomposition based recursive least squares algorithms for bilinear lattice filters. We also defined order update relation of the forward and the backward input vectors by using the channel decomposition. The forward and the backward data matrices were defined by using the forward and the backward input vectors and orthogonalized with the QR decomposition. we can obtain the lattice equations of the bilinear filters by using the channel decomposition. we can be derived the lattice equations of the bilinear filters using this decomposition process which are the same as the lattice equations derived by Baik, we can use the coefficient transformation algorithm proposed by Baik. We derived the equation error and the output error algorithm of the QRD based RLS bilinear lattice algorithm. Also, we evaluated the performance of the proposed algorithms through the system identification of the bilinear system.

In this paper, we suggest the implementation of a DGPS system using two low cost commercial C/A code GPS modules and modems and its efficient operational techniques to provide DGPS service which guarantees the position accuracy of better than 10 meters for more users. The proposed DGPS system can be implemented easil at low cost because it needs a GPS module and a modem for each reference station and user. The reference station makes plans of the receiving schedule from the satellite set at each period and then provides the correction data for various satellite sets in a period. The main contribution of this paper is that users can utilize the correction data continuously and efficiently through the recursive least squares lattice filtering method. Experimental results show the position accuracy of better than 10 meters using the suggested DGPS system in almost real time.

In this paper, we describe a modularized neuroclassifier for enhancing the recognition accuracy of mixed printed and handwritten numerals. This classifier combines four modularized subclassifiers using multi-layer perceptron module. The input of each subclassifier is comprised of a group of specialized feature sets. On applying this method to combining several subclassifiers for unconstrained handwritten numerals, the experimental result shows that the performance of individual subclassifier can be improved. In winner-take-all voting method, the result of subclassifier having the highest RF value is selected as the output. The generality of this classifier is tested with 1,080 printed and 3,000 handwritten numerals that was not shown in training the neural networks. Experimental results show 98.2% recognition rate. The typical recognition test with a threshold value(RF=1.5) has shown 97% recognition, 1% substitution and 2% rejection rates.

This paper presents a new learning method for a noise tolerant Fuzzy ART. In the conventional Fuzzy ART, the top-down and bottom-up weight vectors have the same value. They are updated by a fuzzy AND operation between the input vector and the current value of the top-down or bottom- up weight vectors. However, it can not prevent the abrupt change of the weight vector and can not achieve good performance for a noisy input vector. To solve the problems, we updated using the weighted sum of the input vector and the current value of the top-down vector. To achieve stability, the bottom-up weight vector is updated using the fuzzy AND operation between the newly learned top-down vector and the current value of the bottom-up vector. Computer simulations show that the proposed method prominently resolves the category proliferation problem without increasing the training epoch for stabilization in noisy environments.