基于可信计算的Ad hoc网络认证方案

针对在Ad hoc网络平台完整性证明方面存在的缺陷,基于可信计算技术提出一种认证方案。节点间通过直接匿名认证协议进行认证,利用平台配置寄存器值和存储测量日志进行平台证明,确认当前平台状态以及完整性,确保通信过程中节点平台的可信,消除了因平台完整性破坏带来的安全隐患,并且满足Ad hoc网络的动态拓扑和自组织特性。     

基于S-CIELAB空间的彩色图像分割

提出一种基于S-CIELAB颜色空间的彩色图像分割算法。在人类视觉彩色传递模型的基础上,将原始的RGB图像转换到S-CIELAB空间,运用均值漂移算法对图像进行分割。实验结果表明,该算法能模拟人类视觉模糊特性,得到与人类视觉非常接近的分割结果。对于被高斯噪声严重污染的彩色图像,该算法也能有效地进行分割。     

高边界精度和抗噪的均值漂移图像分割算法

Mean shift(均值漂移)算法核函数窗口的带宽目前仍没有一个统一的确定标准.对整体图像根据Canny算子提取的边缘方向信息分成3类子图,一类是规则边缘子图,由规则边缘像素组成;第2类为非规则边缘子图,由边界方向变化剧烈的边缘像素组成;第3类是非边缘子图,由区域内部平坦区域和噪声区域组成.规则边缘子图和非边缘子图采用大窗口使区域内部更为平滑,并使噪声区域达到更高的抗噪性能,非规则边缘子图用小窗口可保持更高的边界精度.实验采用金属断口图像进行分割,结果表明,针对不同子图采用不同核函数带宽的方法使分割后的金属断口图像边界更准确,抗噪性能也更强.     

复小波包变换域混合统计模型图像降噪算法

该方法利用四树复小波包变换具有的移不变性、良好的方向选择性和对高频信号的细致分析能力等特点, 把含噪图像分解成低频逼近子图和若干高频方向子图; 在保留低频逼近子图复系数不变的同时, 利用复系数层间相关性的强弱把高频方向子图分为主要类和次要类. 对主要类和次要类复系数分别进一步采用非高斯双变量模型和零均值高斯分布模型进行噪声抑制. 实验结果表明, 无论是峰值信噪比(PSNR)指标, 还是在视觉效果上, 本文方 法的去噪性能均好于传统的双树复小波变换去噪、四树复小波包变换去噪和小波域高斯尺度混合模型去噪, 在有效抑制噪声的同时, 具有很好的图像边缘和细节保护能力.     

Exponential stability and exponential stabilization of singularly perturbed stochastic systems with time‐varying delay

In this paper, the problems of exponential stability and exponential stabilization for linear singularly perturbed stochastic systems with time‐varying delay are investigated. First, an appropriate Lyapunov functional is introduced to establish an improved delay‐dependent stability criterion. By applying free‐weighting matrix technique and by equivalently eliminating time‐varying delay through the idea of convex combination, a less conservative sufficient condition for exponential stability in mean square is obtained in terms of ε‐dependent linear matrix inequalities (LMIs). It is shown that if this set of LMIs for ε=0 are feasible then the system is exponentially stable in mean square for sufficiently small ε?0. Furthermore, it is shown that if a certain matrix variable in this set of LMIs is chosen to be a special form and the resulting LMIs are feasible for ε=0, then the system is ε‐uniformly exponentially stable for all sufficiently small ε?0. Based on the stability criteria, an ε‐independent state‐feedback controller that stabilizes the system for sufficiently small ε?0 is derived. Finally, numerical examples are presented, which show our results are effective and useful. Copyright © 2010 John Wiley & Sons, Ltd.     

Robust exponential stability conditions for retarded systems with Lipschitz nonlinear stochastic perturbations

This paper investigates robust mean‐square exponential stability of a class of uncertain stochastic state‐delayed systems with Lipschitz nonlinear stochastic perturbation. Based on Lyapunov–Krasovskii functional (LKF) method and free‐weighting matrix technique, some new delay‐dependent stability conditions are established in terms of linear matrix inequalities (LMIs). In order to reduce the conservatism, (1) the delay is divided into several segments, i.e. the delay decomposition method is applied; (2) cross terms estimation is avoided; (3) some information of the cross terms relationships which has not been involved in Reference (IET Control Theory Appl. 2008; 2(11):966–973) is considered. Moreover, from the mathematical point of view, the results obtained by free‐weighting matrix technique can be equivalently re‐formulated by simpler ones without involving any additional free matrix variables. The effectiveness of the method is demonstrated by numerical examples. Copyright © 2010 John Wiley & Sons, Ltd.     

结合粒子滤波与Mean shift的高速运动目标跟踪

Mean shift跟踪算法能够有效跟踪视频序列中的各种运动目标,但是该算法无法准确地跟踪视频中高速运动目标.通过分析mean shift算法的原理,指出mean shift对高速运动目标跟踪失效的原因,提出一种基于mean shift的粒子滤波跟踪的新算法.通过实验比较,该算法能改善了Mean shift算法对高速运动目标的效果,并且在存在干扰目标的情况下具备良好的跟踪效果.     

多窗口目标跟踪算法

目标在复杂环境下运动,特征会受到光照、摄像机角度等因素影响,并且目标可能会被自身其他部分或者场景物体遮挡,为此,提出一种多窗口概念的视频目标跟踪算法,基本思想就是将目标用多个不同的窗口表示,每一个窗口对应一个跟踪器.这些窗口之间可以存在重叠,也可以包含被跟踪目标上下文信息,如在人脸跟踪中,有些跟踪窗口可以包含颈部信息、上衣颜色信息.然后利用这些窗口之间的位置关系以及在后继跟踪过程中的相似度来估计目标的真实位置.通过实验可以看出,提出的多窗口均值移动跟踪算法可以提高系统在遮挡、光照变化等复杂条件下的跟踪性能.     

基于蚁群优化的图像边缘检测算法

为提高图像边缘检测的精度与抗噪性能,提出一种基于蚁群优化的图像边缘检测算法.将图像像素梯度值和像素圆形邻域统计均值的相对差共同作为蚁群的启发信息,引导蚁群搜索图像边缘.实验结果表明,该算法能最大限度地保留边缘细节,并能抑制噪声和纹理,具有较好的鲁棒性.     

The complex local mean decomposition

The local mean decomposition (LMD) has been recently developed for the analysis of time series which have nonlinearity and nonstationarity. The smoothed local mean of the LMD surpasses the cubic spline method used by the empirical mode decomposition (EMD) to extract amplitude and frequency modulated components. To process complex-valued data, we propose complex LMD, a natural and generic extension to the complex domain of the original LMD algorithm. It is shown that complex LMD extracts the frequency modulated rotation and envelope components. Simulations on both artificial and real-world complex-valued signals support the analysis.