This paper discusses issues on the disign and implementation of real-time automatic speaker verification system, as well as the performance analysis of the implemented system. The system employs TI's TMS320C25 digital signal processor TMS320C25 and high speed SRAMs. The system is designed to be used stand-alone as well as via hand-shaking with IBM-PC. The speech parameters used for speaker verification are PARCOR and LPC-cepstrum coefficients, and the employed decision logics are those based on the generalized weighted distance comcept. The implemented system showed the performance of 5.3% error rate for the PARCOR coefficient, and 4.7% error rate for the LPG-cepstrum coefficient.

Bilinear models are attractive for adaptive filtering applications because they can approximate a large class of nonlinear systems adequately, and usually with considerable parsimony in the number of coefficients compared with Volterra models. But bilinear filters have stability problem because they involve nonlinear feedback. Adaptive algorithms for bilinear filters may be diverge and have poor convergence characteristics when input signal is large In this paper, necessary and sufficient condition for mean square stability of bilinear filters for given input signal statistics is briefly described, and the method obtaining the input bound to guarantee the stability of bilinear filters is presented. New RPEM algorithm, which does not diverge and has the superior convergence characteristics compared with the conventional RPEM algorithm when input signal is large, is derived by applying the time-varying Kalman filtering concept to the conventional RPEM algorithm.

In this paper, robust adaptive control algorithms which can be applied to unknown uncertain systems are suggested. Transform matrix for dividing states into "uncontrolled" states and "controlled" states and general searching procedure for the transform matrix which assign arbitrary n-1 eigen values for the uncontrolled subsystem of n-th orther single-input single-output systems of which state variables can be observable are also studied and utilized for the design of new-type controllers. We drived new-type control laws by using adaptive control theory and variable structure system and its stability is proved by using Lyapunov stability theory.

This paper deals with the modeling and scheduling for FMC(Flexible Manufacturing Cells). The FMC system composed of unit or multi cells is capable of improving productivity with flexibility for machine. However, the properties of multiple jobs and various alternatives results in tne dynamic states which make system management very complex. The extended Peti nets are used to represent for complex properties of FMC which performs short-term scheduling and dynamic operational scheduling. The hierarchical control structure and integlligent scheduling through expert module are adopted for efficiency of FMC operations. The computer simulation reveals that intelligent scheduling method is better than heuristics in various performance indices.

There is basic problem with the current evaluation method for character recognition systems. The current method evaluates the average recognition rate by applying the test data to the target system. The average recognition rate tells no more than and no less than the overall performance and it depends on the data. In this paper we propose a testing method which will analyze the target system and point out its strong points and weak points. This can be made possible through using the data which are generated cy distorting the standard character images according to a carefully controlled manner. This paper will describe how to automatically generate such distorted images. Also we will show the method is actually effective and useful by applying it to evaluating existing recognition algorithms.