Yeung Sam Hung

H/sub /spl infin// control for networked systems with random communication delays

This note is concerned with a new controller design problem for networked systems with random communication delays. Two kinds of random delays are simultaneously considered: i) from the controller to the plant, and ii) from the sensor to the controller, via a limited bandwidth communication channel. The random delays are modeled as a linear function of the stochastic variable satisfying Bernoulli random binary distribution. The observer-based controller is designed to exponentially stabilize the networked system in the sense of mean square, and also achieve the prescribed H/sub /spl infin// disturbance attenuation level. The addressed controller design problem is transformed to an auxiliary convex optimization problem, which can be solved by a linear matrix inequality (LMI) approach. An illustrative example is provided to show the applicability of the proposed method.

Robust Kalman filtering for discrete time-varying uncertain systems with multiplicative noises

In this paper, a robust finite-horizon Kalman filter is designed for discrete time-varying uncertain systems with both additive and multiplicative noises. The system under consideration is subject to both deterministic and stochastic uncertainties. Sufficient conditions for the filter to guarantee an optimized upper bound on the state estimation error variance for admissible uncertainties are established in terms of two discrete Riccati difference equations. A numerical example is given to show the applicability of the presented method.

Distributed

In this paper, the distributed H∞ filtering problem is addressed for a class of polynomial nonlinear stochastic systems in sensor networks. For a Lyapunov function candidate whose entries are polynomials, we calculate its first- and second-order derivatives in order to facilitate the use of Itôs differential rule. Then, a sufficient condition for the existence of a feasible solution to the addressed distributed H∞ filtering problem is derived in terms of parameter-dependent linear matrix inequalities (PDLMIs). For computational convenience, these PDLMIs are further converted into a set of sums of squares that can be solved effectively by using the semidefinite programming technique. Finally, a numerical simulation example is provided to demonstrate the effectiveness and applicability of the proposed design approach.

H_{\infty}

Electroencephalographic gamma band oscillations (GBOs) induced over the human primary somatosensory cortex (SI) by nociceptive stimuli have been hypothesized to reflect cortical processing involved directly in pain perception, because their magnitude correlates with pain intensity. However, as stimuli perceived as more painful are also more salient, an alternative interpretation of this correlation is that GBOs reflect unspecific stimulus-triggered attentional processing. In fact, this is suggested by recent observations that other features of the electroencephalographic (EEG) response correlate with pain perception when stimuli are presented in isolation, but not when their saliency is reduced by repetition. Here, by delivering trains of three nociceptive stimuli at a constant 1 s interval, and using different energies to elicit graded pain intensities, we demonstrate that GBOs recorded over SI always predict the subjective pain intensity, even when saliency is reduced by repetition. These results provide evidence for a close relationship between GBOs and the cortical activity subserving pain perception.

Filtering for Polynomial Nonlinear Stochastic Systems in Sensor Networks

This paper presents a hierarchical approach for fast and robust ellipse extraction from images. At the lowest level, the image is described as a set of edge pixels, from which line segments are extracted. Then, line segments that are potential candidates of elliptic arcs are linked to form arc segments according to connectivity and curvature relations. After that, arc segments that belong to the same ellipse are grouped together. Finally, a robust statistical method, namely RANSAC, is applied to fit ellipses. This method does not need a high dimensional parameter space like Hough transform based algorithms, and so it reduces the computation and memory requirements. Experiments on both synthetic and real images demonstrate that the proposed method has excellent performance in handling occlusion and overlapping ellipses.

Gamma-Band Oscillations in the Primary Somatosensory Cortex—A Direct and Obligatory Correlate of Subjective Pain Intensity

Filters and wrappers are two prevailing approaches for gene selection in microarray data analysis. Filters make use of statistical properties of each gene to represent its discriminating power between different classes. The computation is fast but the predictions are inaccurate. Wrappers make use of a chosen classifier to select genes by maximizing classification accuracy, but the computation burden is formidable. Filters and wrappers have been combined in previous studies to maximize the classification accuracy for a chosen classifier with respect to a filtered set of genes. The drawback of this single-filter-single-wrapper (SFSW) approach is that the classification accuracy is dependent on the choice of specific filter and wrapper. In this paper, a multiple-filter-multiple-wrapper (MFMW) approach is proposed that makes use of multiple filters and multiple wrappers to improve the accuracy and robustness of the classification, and to identify potential biomarker genes. Experiments based on six benchmark data sets show that the MFMW approach outperforms SFSW models (generated by all combinations of filters and wrappers used in the corresponding MFMW model) in all cases and for all six data sets. Some of MFMW-selected genes have been confirmed to be biomarkers or contribute to the development of particular cancers by other studies.

A Hierarchical Approach for Fast and Robust Ellipse Extraction

This paper addresses the problem of establishing robust stability of uncertain genetic networks with sum regulatory functions. Specifically, we first consider uncertain genetic networks where the regulation occurs at the transcriptional level, and we derive a sufficient condition for robust stability by introducing a bounding set of the uncertain nonlinearity. We hence show that this condition can be formulated as a convex optimization through polynomial Lyapunov functions and polynomial descriptions of the bounding set by exploiting the square matricial representation (SMR) of polynomials which allows to establish whether a polynomial is a sum of squares (SOS) via a linear matrix inequality (LMI). Then, we propose a method for computing a family of bounding sets by means of convex optimizations. It is worthwhile to remark that these results are derived in spite of the fact that the variable equilibrium point cannot be computed as being the solution of a system of parameter-dependent nonlinear equations, and is hence unknown. Lastly, the proposed approach is extended to models where the regulation occurs at different levels and both mRNA and protein dynamics are nonlinear.

A Multiple-Filter-Multiple-Wrapper Approach to Gene Selection and Microarray Data Classification

Visual servoing consists of steering a robot from an initial to a desired location by exploiting the information provided by visual sensors. This paper deals with the problem of realizing visual servoing for robot manipulators taking into account constraints such as visibility, workspace (that is obstacle avoidance), and joint constraints, while minimizing a cost function such as spanned image area, trajectory length, and curvature. To solve this problem, a new path-planning scheme is proposed. First, a robust object reconstruction is computed from visual measurements which allows one to obtain feasible image trajectories. Second, the rotation path is parameterized through an extension of the Euler parameters that yields an equivalent expression of the rotation matrix as a quadratic function of unconstrained variables, hence, largely simplifying standard parameterizations which involve transcendental functions. Then, polynomials of arbitrary degree are used to complete the parametrization and formulate the desired constraints and costs as a general optimization problem. The optimal trajectory is followed by tracking the image trajectory with an IBVS controller combined with repulsive potential fields in order to fulfill the constraints in real conditions.

Stability analysis of uncertain genetic sum regulatory networks

MOTIVATION Recently developed network component analysis (NCA) approach is promising for gene regulatory network reconstruction from microarray data. The existing NCA algorithm is an iterative method which has two potential limitations: computational instability and multiple local solutions. The subsequently developed NCA-r algorithm with Tikhonov regularization can help solve the first issue but cannot completely handle the second one. Here we develop a novel Fast Network Component Analysis (FastNCA) algorithm which has an analytical solution that is much faster and does not have the above limitations. RESULTS Firstly FastNCA is compared to NCA and NCA-r using synthetic data. The reconstruction of FastNCA is more accurate than that of NCA-r and comparable to that of properly converged NCA. FastNCA is not sensitive to the correlation among the input signals, while its performance does degrade a little but not as dramatically as that of NCA. Like NCA, FastNCA is not very sensitive to small inaccuracies in a priori information on the network topology. FastNCA is about several tens times faster than NCA and several hundreds times faster than NCA-r. Then, the method is applied to real yeast cell-cycle microarray data. The activities of the estimated cell-cycle regulators by FastNCA and NCA-r are compared to the semi-quantitative results obtained independently by Lee et al. (2002). It is shown here that there is a greater agreement between the results of FastNCA and Lees, which is represented by the ratio 23/33, than that between the results of NCA-r and Lees, which is 14/33. AVAILABILITY Software and supplementary materials are available from http://www.eee.hku.hk/~cqchang/FastNCA.htm

Global Path-Planning for Constrained and Optimal Visual Servoing

The problem of depth-from-motion using a monocular image sequence is considered. A pixel-based model is developed for direct depth estimation within a Kalman filtering framework. A method is proposed for incorporating local surface structure into the Kalman filter. Experimental results are provided to illustrate the effect of structural information on depth estimation.