Biomedical subjects

Henry Leung

Publications and source records attributed to Henry Leung.

5 recordsLinked to original sources

PubMed2006

Nonlinear spatial-temporal prediction based on optimal fusion.

The problem of spatial-temporal signal processing and modeling has been of great interest in recent years. A new spatial-temporal prediction method is presented in this paper. An optimal fusion scheme based on fourth-order statistic is first employed to combine the received signals at different spatial domains. The fused signal is then used to construct a spatial-temporal predictor by a support vector machine. It is shown theoretically that the proposed method has an improved performance even in non-Gaussian environments. To demonstrate the practicality of this spatial-temporal predictor, we apply it to model real-life radar sea scattered signals. Experimental results show that the proposed method can provide a more accurate model for sea clutter than the conventional methods.

Algorithms↗

PubMed2005

Ergodic chaotic parameter modulation with application to digital image watermarking.

This paper presents a novel technique for image watermarking based on chaos theory. Chaotic parameter modulation (CPM) is employed to modulate the copyright information into the bifurcating parameter of a chaotic system. The system output is a wideband signal and is used as a watermark to be inserted into the host image. In the detection, a novel method based on the ergodic property of chaotic signal is developed to demodulate the embedded copyright information. Compared to previous works on blind watermarking, the proposed technique can effectively remove the interference from the host image and, thus, improve the detection performance dramatically. Simulation results show that the ergodic CPM approach is effective for image watermarking in terms of noise performance, robustness against attacks, and payload. In addition, its implementation is very simple and the computation speed is fast. Compared to holographic transform domain method and the conventional spread spectrum watermarking scheme, the proposed technique is shown to be superior.

Algorithms↗

PubMed2005

Blind equalization using a predictive radial basis function neural network.

In this paper, we propose a novel blind equalization approach based on radial basis function (RBF) neural networks. By exploiting the short-term predictability of the system input, a RBF neural net is used to predict the inverse filter output. It is shown here that when the prediction error of the RBF neural net is minimized, the coefficients of the inverse system are identical to those of the unknown system. To enhance the identification performance in noisy environments, the improved least square (ILS) method based on the concept of orthogonal distance to red the estimation bias caused by additive measurement noise is proposed here to perform the training. The convergence rate of the ILS learning is analyzed, and the asymptotic mean square error (MSE) of the proposed predictive RBF identification method is derived theoretically. Monte Carlo simulations show that the proposed method is effective for blind system identification. The new blind technique is then applied to two practical applications: equalization of real-life radar sea clutter collected at the east coast of Canada and deconvolution of real speech signals. In both cases, the proposed blind equalization technique is found to perform satisfactory even when the channel effects and measurement noise are strong.

Algorithms↗

PubMed2004

Time-varying synchronization of chaotic systems in the presence of system mismatch.

The problem of synchronization of two identical chaotic systems in the presence of system mismatch is investigated in this article. The instantaneous mean square error (E) of the unidirectionally coupled synchronization scheme is analyzed based on the Jacobian equation of the response system. It is shown that synchronization based on a constant coupling parameter does not produce satisfactory performance. A synchronization scheme is proposed here, and the time-varying coupling parameter sequence used in this new scheme is obtained by minimizing the instantaneous E. Numerical simulations show that the proposed time-varying synchronization method has smaller mean square synchronization error than the conventional approach based on using a constant coupling parameter.

Computer Simulation↗

PubMed2004

A maximum likelihood approach for image registration using control point and intensity.

Registration of multidate or multisensor images is an essential process in many image processing applications including remote sensing, medical image analysis, and computer vision. Control point (CP) and intensity are the two basic features used separately for image registration in the literature. In this paper, an exact maximum likelihood (EML) registration method, which combines both CP and intensity, is proposed for image alignment. The EML registration method maximizes the likelihood function based CP and intensity to estimate the registration parameters, including affine transformation and CP coordinates. The explicit formulas of the Cramer-Rao bound (CRB) are also derived for the proposed EML and conventional image registration algorithms. The performances of these image registration techniques are evaluated with the CRBs.

Algorithms↗