Wen Jyi Hwang

A supervisory fuzzy neural network control system for tracking periodic inputs

A supervisory fuzzy neural network (FNN) control system is designed to track periodic reference inputs in this study. The control system is composed of a permanent magnet (PM) synchronous servo motor drive with a supervisory FNN position controller. The supervisory FNN controller comprises a supervisory controller, which is designed to stabilize the system states around a defined bound region and an FNN sliding-mode controller, which combines the advantages of the sliding-mode control with robust characteristics and the FNN with online learning ability. The theoretical and stability analyses of the supervisory FNN controller are discussed in detail. Simulation and experimental results show that the proposed control system is robust with regard to plant parameter variations and external load disturbance. Moreover, the advantages of the proposed control system are indicated in comparison with the sliding-mode control system.

Scalable medical data compression and transmission using wavelet transform for telemedicine applications

In this paper, a novel medical data compression algorithm, termed layered set partitioning in hierarchical trees (LSPIHT) algorithm, is presented for telemedicine applications. In the LSPIHT, the encoded bit streams are divided into a number of layers for transmission and reconstruction. Starting from the base layer, by accumulating bit streams up to different enhancement layers, we can reconstruct medical data with various signal-to-noise ratios (SNRs) and/or resolutions. Receivers with distinct specifications can then share the same source encoder to reduce the complexity of telecommunication networks for telemedicine applications. Numerical results show that, besides having low network complexity, the LSPIHT attains better rate-distortion performance as compared with other algorithms for encoding medical data.

Fuzzy channel-optimized vector quantization

A novel fuzzy clustering algorithm for the design of channel-optimized source coding systems is presented in this letter. The algorithm, termed fuzzy channel-optimized vector quantizer (FCOVQ) design algorithm, optimizes the vector quantizer (VQ) design using a fuzzy clustering process in which the index crossover probabilities imposed by a noisy channel are taken into account. The fuzzy clustering process effectively enhances the robustness of the performance of VQ to channel noise without reducing the quantization accuracy. Numerical results demonstrate that the FCOVQ algorithm outperforms existing VQ algorithms under noisy channel conditions for both Gauss-Markov sources and still image data.

A novel entropy-constrained competitive learning algorithm for vector quantization

Abstract In this paper, we present a novel competitive learning algorithm for the design of a variable-rate vector quantizer (VQ). The algorithm, termed entropy-constrained competitive learning (ECCL) algorithm, can achieve a near-optimal performance subject to the average rate constraint. Simulation results show that, under the same average rate, the ECCL algorithm enjoys a better performance than that of the existing competitive learning algorithms. Moreover, the ECCL algorithm outperforms the entropy-constrained vector quantizer (ECVQ) (Chou et al., IEEE Trans. Acoust. Speech Signal Process. 37 (1989) 31–42) design algorithm under the same rate constraint and initial codewords. The ECCL algorithm is also more insensitive to the selection of initial codewords as compared with the ECVQ algorithm. Therefore, the ECCL algorithm can be an effective alternative to the existing variable-rate VQ design algorithms for the applications of signal compression.

A novel competitive learning algorithm for the parametric classification with Gaussian distributions

Abstract A competitive learning algorithm for the parametric classification of Gaussian sources is presented in this letter. The algorithm iteratively estimates the mean and prior probability of each class during the training. Bayes rule is then used for classification based on the estimated information. Simulation results show that the proposed algorithm outperforms k -means and LVQ algorithms for the parametric classification.

Robust transmission based on variable-rate error control and genetic programming

This paper presents a novel variable-rate error control design algorithm matched to full-search vector quantizers (VQs) for robust transmission. In the algorithm, different locations of binary strings obtained from VQ encoders are protected by channel codes with different protection levels. The degree of protection at each location is determined by a genetic programming technique minimizing the end-to-end average distortion of transmission systems. The technique outperforms the equal error protection method. Moreover, as compared with a full search algorithm for optimal unequal error protection, our technique attains comparable performance with significantly lower computational complexities.

A fast vector quantization encoding method using wavelet transform

Abstract A new fast codeword search algorithm for vector quantizers (VQ) is presented in this paper. This algorithm uses the pyramid structure of the codewords to accelerate the encoding process. The pyramid structure is obtained using the wavelet transform. This algorithm is able to reduce the codeword search time without sacrificing the performance and storage complexity of the VQs. Simulation results show that this algorithm is well-suited for the VQs with high vector dimension and/or large codebook size.

A novel fuzzy entropy-constrained competitive learning algorithm for image coding

Abstract A novel variable-rate vector quantizer (VQ) design algorithm using both fuzzy and competitive learning technique is presented. The algorithm enjoys better rate-distortion performance than that of other existing fuzzy clustering and competitive learning algorithms. In addition, the learning algorithm is less sensitive to the selection of initial reproduction vectors. Therefore, the algorithm can be an effective alternative to the existing variable-rate VQ algorithms for signal compression.

Genetic fuzzy entropy‐constrained vector quantization

Abstract A novel variable‐rate vector quantizer (VQ) design algorithm using both genetic and fuzzy clustering techniques is presented. The algorithm, termed genetic fuzzy entropy‐constrained VQ (GFECVQ) design algorithm, has a superior rate‐distortion performance than that of the existing variable‐rate VQ design algorithms. The algorithm utilizes fuzzy clustering technique to enhance the rate‐distortion performance for the VQ design. In addition, a novel genetic algorithm is employed to ensure the robustness of the performance against the selection of initial parameters. Simulation results demonstrate that the FECVQ can be an effective alternative for the design of variable‐rate VQs.

Multiplication-free fast codeword search algorithm using Haar transform with squared-distance measure

Abstract This letter presents novel multiplication-free fast codeword search algorithms for encoding of vector quantizers (VQs) based on squared-distance measure. The algorithms accomplish fast codeword search by performing the partial distance search (PDS) in the wavelet domain. To eliminate the requirement for multiplication, simple Haar wavelet is used so that the wavelet coefficients of codewords are finite precision numbers. The computation of squared distance for PDS can therefore be effectively realized using additions. To further enhance the computational efficiency of the algorithms, the addition-based squared-distance computation is decomposed into a number of stages. The PDS process is then extended to these stages to reduce the addition complexity of the algorithm. In addition, by performing PDS over smaller number of stages, lower computational complexity can be obtained at the expense of slightly higher average distortion for encoding. Simulation results show that our algorithms are very effective for the encoding of VQs, where both low computational complexity and average distortion are desired.