Robust matrix 𝒟U‐stability analysis

In this paper, the problem of robust matrix root‐clustering is addressed. The studied matrices are subject to both polytopic and unstructured uncertainties. An original point is the large choice of clustering regions enabled by the proposed approach since these regions can be unions of possibly disjoint and non‐symmetric subregions of the complex plane. The precise purpose is, considering a specified polytope, to determine the greatest robustness bound on the unstructured uncertainty such that robust matrix root‐clustering is ensured. To reduce conservatism in the derivation of the bound, the reasoning relies on a framework based upon parameter‐dependent Lyapunov functions. The bound value is computed by solving an ?? ?? ? problem. Copyright © 2003 John Wiley & Sons, Ltd.     

Influences of variable scales and activation functions on the performances of multilayer feedforward neural networks

This paper gives insight into the methods about how to improve the learning capabilities of multilayer feedforward neural networks with linear basis functions in the case of limited number of patterns according to the basic principles of support vector machine (SVM), namely, about how to get the optimal separating hyperplanes. And furthermore, this paper analyses the characteristics of sigmoid-type activation functions, and investigates the influences of absolute sizes of variables on the convergence rate, classification ability and non-linear fitting accuracy of multilayer feedforward networks, and presents the way of how to select suitable activation functions. As a result, this proposed method effectively enhances the learning abilities of multilayer feedforward neural networks by introducing the sum-of-squares weight term into the networks’ error functions and appropriately enlarging the variable components with the help of the SVM theory. Finally, the effectiveness of the proposed method is verified through three classification examples as well as a non-linear mapping one.     

基于小波函数的数据拟合方法

本把小波函数引入离散数据拟合领域，将小波函数与数据拟合的常用方法－最小二乘法相结合，给出了一种新型的数据拟合工具，中详细讨论了该方法的处理论和实施步骤，由于小波函数具有良好的局部性质，该方法在提高似合精度方面具有传统方法不可替代的优点。     

Perceptual grouping of segmented regions in color images

Image segmentation is often the first yet important step of an image understanding system. However, general-purpose image segmentation algorithms that do not rely on specific object models still cannot produce perceptually coherent segmentation of regions at a level comparable to humans. Over-segmentation and under-segmentation have plagued the research community in spite of many significant advances in the field. Therefore, grouping of segmented region plays a significant role in bridging image segmentation and high-level image understanding. In this paper, we focused on non-purposive grouping (NPG), which is built on general expectations of a perceptually desirable segmentation as opposed to any object specific models, such that the grouping algorithm is applicable to any image understanding application. We propose a probabilistic model for the NPG problem by defining the regions as a Markov random field (MRF). A collection of energy functions is used to characterize desired single-region properties and pair-wise region properties. The single-region properties include region area, region convexity, region compactness, and color variances in one region. The pair-wise properties include color mean differences between two regions; edge strength along the shared boundary; color variance of the cross-boundary area; and contour continuity between two regions. The grouping process is implemented by a greedy method using a highest confidence first (HCF) principle. Experiments have been performed on hundreds of color photographic images to show the effectiveness of the grouping algorithm using a set of fixed parameters.     

A User-Aware Prefetching Mechanism for Video Streaming

The randomly and unpredictable user behaviors during a multimedia presentation may cause the long retrieval latency in the client–server connection. To accommodate the above problem, we propose a prefetching scheme that using the association rules from the data mining technique. The data mining technique can provide some priority information such as the support, confidence, and association rules which can be utilized for prefetching continuous media. Thus, using the data mining technique, the proposed prefetching policy can predict user behaviors and evaluate segments that may be accessed in near future. The proposed prefetching scheme was implemented and tested on synthetic data to estimate its effectiveness. Performance experiments show that the proposed prefetching scheme is effective in improving the latency reduction, even for small cache sizes.     

一种新型智能模糊控制器的研究

从模糊控制器量化因子入手，对其进行了深入的分析，提出了一种智能型的模糊控制器结构。其中，智能调节器使量化因子随控制不同阶段而动态变化。文中对智能调节器的设计过程作了介绍，并对两个被控对象分别用基本模糊控制器和基于智能调节器的模糊控制器的控制效果进行了分析比较。仿真例子证明了智能模糊控制器的有效性。     

基于径向基函数多步离散数据插值的人脸变形研究

文章介绍了如何把基于径向基函数多步离散数据插值的方法用在人脸变形技术上,该方法是在对脸部定义的特征点进行分层的基础上,和已知特征点位移的条件下,利用一种基于迭代的插值方法求出特征点周围的网格点位移,从而进行人脸变形。这种方法既保证了脸部局部区域变形的精确性和平滑性,又减少了运算的复杂度。在人脸动画方面该算法也得到了广泛的应用。     

On the representation of shift-invariant operators by transfer functions

We give simple sufficient conditions on a function space on (0, ∞) to ensure that all shift-invariant operators defined on it are represented by transfer functions. This enables us to extend the result of Weiss on L_p(0, ∞) for 1 p < ∞ (but not p = ∞) to more general situations.     

基于Haar函数的分布参数系统辨识

借助于正交函数序列对分布参数系统进行辨识，是一种妊之有效的辨识方法。     

Sorting helps for voronoi diagrams

It is well known that, using standard models of computation, Ω(n logn) time is required to build a Voronoi diagram forn point sites. This follows from the fact that a Voronoi diagram algorithm can be used to sort. However, if the sites are sorted before we start, can the Voronoi diagram be built any faster? We show that for certain interesting, although nonstandard, types of Voronoi diagrams, sorting helps. These nonstandard types of Voronoi diagrams use a convex distance function instead of the standard Euclidean distance. A convex distance function exists for any convex shape, but the distance functions based on polygons (especially triangles) lead to particularly efficient Voronoi diagram algorithms. Specifically, a Voronoi diagram using a convex distance function based on a triangle can be built inO (n log logn) time after initially sorting then sites twice. Convex distance functions based on other polygons require more initial sorting.