Shyang Chang

A new criterion for automatic multilevel thresholding

A new criterion for multilevel thresholding is proposed. The criterion is based on the consideration of two factors. The first one is the discrepancy between the thresholded and original images and the second one is the number of bits required to represent the thresholded image. Based on a new maximum correlation criterion for bilevel thresholding, the discrepancy is defined and then a cost function that takes both factors into account is proposed for multilevel thresholding. By minimizing the cost function, the classification number that the gray-levels should be classified and the threshold values can be determined automatically. In addition, the cost function is proven to possess a unique minimum under very mild conditions. Computational analyses indicate that the number of required mathematical operations in the implementation of our algorithm is much less than that of maximum entropy criterion. Finally, simulation results are included to demonstrate their effectiveness.

Statistical change detection with moments under time-varying illumination

In this paper, an illumination-independent statistical change detection method is proposed. The proposed method consists of two parts. First, based on our defined circular shift moments, structural changes can be distinguished from those due to time-varying illumination in the noise-free case. Moreover, the amount of computation is less than that of the shading model method. Second, in the light of the characteristics of the defined moments, a statistical decision rule is also proposed to cope with the effects of noise. The change detection problem can be treated as one of hypothesis testing. Critical values can be chosen according to the desired level of significance. Experimental results indicate that the proposed method detects changes accurately in the time-varying illumination case.

A generalized depth estimation algorithm with a single image

A depth estimation algorithm proposed by A.P. Pentland (1987) is generalized. In the proposed algorithm, the raw image data in the vicinity of the edge is used to estimate the depth from defocus. Since no differentiation operation on the image data is required before the optimization process, the method is less sensitive to the noise disturbance of measurements. Furthermore, the edge orientation that was critical in Pentlands approach will not be required in the case. This algorithm is then applied to synthetic images containing various amounts of noise to test its performance. Experimental results indicate that the depth estimation errors are kept within 5% of true values on the average when it is applied to real images. >

An adaptive learning algorithm for principal component analysis

Principal component analysis (PCA) is one of the most general purpose feature extraction methods. A variety of learning algorithms for PCA has been proposed. Many conventional algorithms, however, will either diverge or converge very slowly if learning rate parameters are not properly chosen. In this paper, an adaptive learning algorithm (ALA) for PCA is proposed. By adaptively selecting the learning rate parameters, we show that the m weight vectors in the ALA converge to the first m principle component vectors with almost the same rates. Comparing with the Sangers generalized Hebbian algorithm (GHA), the ALA can quickly find the desired principal component vectors while the GHA fails to do so. Finally, simulation results are also included to illustrate the effectiveness of the ALA.

A video tracking system with adaptive predictors

Abstract A novel video tracking system with adaptive predictors is developed. In order to locate the moving target efficiently, a Hadamard transform locator is proposed. The accuracy of the target location is usually influenced by noise, hence an effective morphological noise filter is invoked to remove it. Moreover, the recursive least square adaptive predictor (RLSAP) is adopted to predict robustly the future location of the target. After successful integration of the aforementioned subsystems, the moving target is always kept in the center of the observing window. Experimental results are given to demonstrate the effectiveness of this proposed system.

Studies of detrusor-sphincter synergia and dyssynergia during micturition in rats via fractional Brownian motion

In normal humans, the bladder is supposed to empty by a synergistic contraction of the detrusor and relaxation of the sphincter during micturition. By contrast, suprasacral spinal cord injury (SCI) patients usually will have both of them contract simultaneously and result in the so-called detrusor-sphincter dyssynergia. In this study, the cystometrograms of the detrusor and the electromyograms of external urethral sphincter of intact and SCI female Wistar rats are used to investigate the detrosor-sphincter synergia and dyssynergia during micturition. Due to the statistical self-similarity of the waveforms, the cystometrogram is modeled as discrete-time fractional Brownian motion and the electromyogram as discrete-time fractional Gaussian noise. Fractal dimensions of both of them are calculated and used as indices in the investigation of synergia. Results indicate that, for intact rats, the sphincter muscle is not only active but also recruited in a synchronous fashion with the detrusor. Furthermore, clear-cut measure using the fractal dimensions can be used to diagnose detrusor-sphincter synergia and dyssynergia.

Tracking control of DC motors via an improved chattering alleviation control

An improved version of the chattering alleviation control (CAC) augmented with boundary layers is proposed. It preserves all the features of CAC and exhibits three additional advantageous properties. First, the resultant control law is continuous instead of the discontinuous function proposed in CAC. Second, the chattering level is smaller than that of CAC. Third, the parameters in the boundary layer approach can be determined quantitatively via this algorithm. The resultant system via this nonlinear feedback control law is proved to be asymptotically stable, which is then applied to the tracking control of DC motors. Experimental results indicate that the high-speed tracking of the desired trajectory can be achieved. In addition, the applied armature voltage contains fewer higher frequency components as compared to other variable structure systems control. >

A fast estimation algorithm on the Hurst parameter of discrete-time fractional Brownian motion

The purpose of this paper is to demonstrate that the discrete-time fractional Gaussian noise (DFGN) is a regular process. Based on this property, a fast algorithm with low computational cost is proposed to estimate the Hurst parameter H, which is an important parameter in fractional Brownian motion (FBM). This algorithm is robust under amplitude shift, invariant to time shift, and unaffected by a scaling factor in power spectral density (PSD). Finally, the computational complexity is also investigated.

Neural networks for routing of communication networks with unreliable components

A new neural network model, Routron, which can handle dependent component failures of communication networks, is proposed. We prove that the proposed Routron has a stable solution. Moreover, useful upper and lower bounds for the design parameters are derived to help select them in implementations. Simulation results are included to illustrate the effectiveness of the algorithm.

The meridian system and mechanism of acupuncture—A comparative review. Part 1: The meridian system

In traditional Chinese medicine (TCM), acupuncture has been used to heal various diseases and physiologic malfunctions in clinical practice for more than 2500 years. Due to its efficacy, acupuncture has been recommended by the World Health Organization in 1980 as an effective alternative therapy for 43 different disorders. Over the past few decades, various theories of the meridian system and mechanisms have been proposed to explain how acupuncture might work. Most of these mechanisms, however, cannot yet explain conclusively why acupuncture is efficacious in treating so many different diseases. A plausible mechanism has been unavailable until recently. This is the first of a three-part series that aims to provide a comparative review of the aforementioned topics. Part 1 reviews the current indications for acupuncture, basic concepts of TCM, and the essence of the meridian system. To establish a mathematically rigorous framework of TCM, the chaotic wave theory of fractal continuum is proposed. This theory is then applied to characterize the essence of the meridian system. Parts 2 and 3 will review the possible mechanisms of acupuncture analgesia and acupuncture therapies, respectively, based on biochemical, bioelectromagnetic, chaotic wave, and neurophysiologic approaches. It is sincerely hoped that this series of review articles can promote an understanding of the meridian system and acupuncture mechanisms to help patients in a logical and passionate way.