Shing-Tai Pan

Robust Kalman Filter Synthesis for Uncertain Multiple Time-Delay Stochastic Systems

The problem of robust Kalman filter synthesis is considered in this present study for discrete multiple time-delay stochastic systems with parametric and noise uncertainties. A discrete multiple time-delay uncertain stochastic system can be transformed into another uncertain stochastic system with no delay by properly defining state variables. Minimax theory and Bellman-Gronwall lemma are employed on the basis of the upper norm-bounds of parametric uncertainties and noise uncertainties. A robust criterion can consequently be derived which guarantees the asymptotic stability of the uncertain stochastic system. Designed procedures are finally elaborated upon with an illustrative example.

Evolutionary Computation on Programmable Robust IIR Filter Pole-Placement Design

This paper explores the pole-placement design problem of a robust stable infinite-impulse-response (IIR) filter to attenuate or eliminate the undesired measurement noise and proposes a strategy based on an adaptive differential evolution (ADE) algorithm to design a filter. The results are compared to the results of other popular evolutionary algorithms, e.g., particle swarm optimization (PSO), genetic algorithm (GA), and improved genetic algorithm (IGA). The stability robustness for an IIR filter will be achieved by placing all poles inside a disk D(α, r) contained in the unit disk, in which α is the center, and r is the radius of the disk. This investigation first uses a robust stability criterion, called the D(α, r)-stability criterion, to ensure that digital filter poles lie inside a disk D(α, r). The proposed strategy checks the criterion during differential evolution (DE) and adaptively adjusts the DE parameters, depending on the current DE performance. This paper also introduces two design examples of a bandpass IIR filter and a low-pass IIR filter for the measurement of a speech signal. These examples show that the proposed strategy performance based on the proposed ADE is better than designs based on PSO, GA, and IGA. Finally, this paper implements an IIR filter on the field-programmable gate array (FPGA) chip to verify the designed filter performance in practical electronic devices and uses speech signals as an input signal to the FPGA chip to verify that the measurement noise of the speech signal is attenuated by the designed IIR filter.

A canonic-signed-digit coded genetic algorithm for designing finite impulse response digital filter

In this paper, the genetic algorithm (GA) based on Canonic Signed Digit (CSD) code was used to find the optimum design of a finite impulse response digital filter (FIR). By using the characteristics of the CSD structure, the circuit was able to be simplified and also the calculation speed was raised to increase the hardwares efficiency. However, CSD structure cannot be guaranteed by a general GA after the evolution of chromosomes. Thus in this research an algorithm was proposed which the CSD structure can be maintained. A CSD coded GA was used to the evolution of chromosome to reduce the time wasted by trials and errors during the evolution and then to accelerate the training speed. In this paper, a new hybrid code for the filter coefficients was proposed to improve the precision of the coefficient of FIR. An example is shown in this paper to verify the efficiency of the proposed algorithm.

A transition-constrained discrete hidden Markov model for automatic sleep staging

BackgroundApproximately one-third of the human lifespan is spent sleeping. To diagnose sleep problems, all-night polysomnographic (PSG) recordings including electroencephalograms (EEGs), electrooculograms (EOGs) and electromyograms (EMGs), are usually acquired from the patient and scored by a well-trained expert according to Rechtschaffen & Kales (R&K) rules. Visual sleep scoring is a time-consuming and subjective process. Therefore, the development of an automatic sleep scoring method is desirable.MethodThe EEG, EOG and EMG signals from twenty subjects were measured. In addition to selecting sleep characteristics based on the 1968 R&K rules, features utilized in other research were collected. Thirteen features were utilized including temporal and spectrum analyses of the EEG, EOG and EMG signals, and a total of 158 hours of sleep data were recorded. Ten subjects were used to train the Discrete Hidden Markov Model (DHMM), and the remaining ten were tested by the trained DHMM for recognition. Furthermore, the 2-fold cross validation was performed during this experiment.ResultsOverall agreement between the expert and the results presented is 85.29%. With the exception of S1, the sensitivities of each stage were more than 81%. The most accurate stage was SWS (94.9%), and the least-accurately classified stage was S1 (<34%). In the majority of cases, S1 was classified as Wake (21%), S2 (33%) or REM sleep (12%), consistent with previous studies. However, the total time of S1 in the 20 all-night sleep recordings was less than 4%.ConclusionThe results of the experiments demonstrate that the proposed method significantly enhances the recognition rate when compared with prior studies.

An FPGA-Based Embedded Robust Speech Recognition System Designed by Combining Empirical Mode Decomposition and a Genetic Algorithm

A field-programmable gate array (FPGA)-based robust speech measurement and recognition system is the focus of this paper, and the environmental noise problem is its main concern. To accelerate the recognition speed of the FPGA-based speech recognition system, the discrete hidden Markov model is used here to lessen the computation burden inherent in speech recognition. Furthermore, the empirical mode decomposition is used to decompose the measured speech signal contaminated by noise into several intrinsic mode functions (IMFs). The IMFs are then weighted and summed to reconstruct the original clean speech signal. Unlike previous research, in which IMFs were selected by trial and error for specific applications, the weights for each IMF are designed by the genetic algorithm to obtain an optimal solution. The experimental results in this paper reveal that this method achieves a better speech recognition rate for speech subject to various environmental noises. Moreover, this paper also explores the hardware realization of the designed speech measurement and recognition systems on an FPGA-based embedded system with the System-On-a-Chip (SOC) architecture. Since the central-processing-unit core adopted in the SOC has limited computation ability, this paper uses the integer fast Fourier transform (FFT) to replace the floating-point FFT to speed up the computation for capturing speech features through a mel-frequency cepstrum coefficient. The result is a significant reduction in the calculation time without influencing the speech recognition rate. It can be seen from the experiments in this paper that the performance of the implemented hardware is significantly better than that of existing research.

Design of Robust D-Stable IIR Filters Using Genetic Algorithms With Embedded Stability Criterion

This paper proposes a novel evolution strategy for a genetic algorithm (GA). This new algorithm is then applied to design robust D(alpha,r)-stable infinite-impulse-response (IIR) filters. Unlike existing research on designing IIR filters by using GA, in which the stability of IIR filters is tested by trial and error after the evolution of each generation of a GA, the stability criterion in this paper is embedded within the evolution of each generation. Consequently, the stability of this system can be guaranteed without the need for any other checks of the stability criterion in the evolution of each generation. Numerical experimental results are discussed to illustrate the soundness of the proposed evolution strategy. The robustness of the IIR filters is achieved by ensuring that all poles of the filters are located inside a disk D(alpha,r) contained in the unit circle, in which alpha is the center, r is the radius of the disk and IalphaI +r < 1 . So, in this paper, a D(alpha,r)-stability criterion will be first derived and then embedded in the GA for the design of robust IIR filters. Finally, two examples will be presented to show that the designed filters remain D(alpha,r)-stable during the evolution of the GA and will provide satisfactory results.

Particle Swarm Optimization on D-stable IIR filter design

This paper explores the design of robust stable digital filter by the Particle Swarm Optimization (PSO) algorithm. The results are compared to the design by Genetic Algorithm (GA). We first derive a robust stability criterion which will be used to ensure the D(α, r) -stability of the digital filter in the evolution of DE. Finally, we will compare the result designed by DE with that designed by GA. It will be found that the performance of the PSO is better that that of GA in the design example of this paper.

Differential Evolution Algorithm on Robust IIR Filter Design and Implementation

This paper explores the design of robust stable digital filter by the differential evolution (DE) algorithm. The results are compared to the design by genetic algorithm (GA). We first derive a robust stability criterion which will be used to ensure the D(alpha,r)-stability of the digital filter in the evolution of DE. Finally, we will compare the result designed by DE with that designed by GA. It will be found that the performance of the DE is better that that of GA in the design example of this paper.

D-stability bound analysis for discrete multiparameter singularly perturbed systems

The D-stability (i.e., the stability in the sense that all the poles of a system are lying inside the disk D(/spl alpha/,r)) problem for discrete multiparameter singularly perturbed systems is considered in this brief. A two-stage method is first developed to analyze the stability relationship between the discrete multiparameter singularly perturbed systems and their corresponding reduced systems. An upper bound of the singular perturbation parameters is then derived such that the D-stability of the reduced systems implies that of the original systems, provided that the singular perturbation parameters are small enough to be within this bound. This fact enables us to investigate D-stability of the original systems by establishing that of their corresponding reduced systems.

Stability Analysis for a Class of Uncertain Discrete Singularly Perturbed Systems With Multiple Time Delays

In this paper, the robust stability problem for a class of nominally stable uncertain discrete singularly perturbed linear systems with multiple time delays is considered. A stability criterion for the slow and fast subsystems is first derived. A delay-dependent criterion is then proposed to guarantee the robust stability of the system subject to norm-bounded perturbations. A numerical example is provided to illustrate our main results.