基于可连Cell的无线传感器网络拓扑控制算法

在无线传感器网络的拓扑控制(TC)中,基于Cell的TC算法被认为是一类可以节省传感器节点能量并延长网络生命周期的方法,但是其需要较多的骨干网节点并且无法保证连通性.通过分析现今算法的内在局限性,提出了一种1-Con思想:当一个Cell的头节点被加入当前骨干网时,所有其可以连接的Cell使用该节点连入拓扑结构,然后此新骨干网递归地继续扩大.基于此思想,设计了一种基于可连Cell的拓扑控制算(CCTC),并从理论上证明:1)CCTC可以保证其所形成的拓扑结构维持网络连通;2)每一轮用于形成骨干网的工作节点非常少.CCTC的计算复杂度是线性的,空间复杂度和信息交换量都是常数量级.仿真实验同样显示,CCTC可以在提供良好鲁棒性和较少的消息交换的情况下,更有效地节省节点能耗并延长网络生命周期.     

基于统计的SVC层间编码快速模式决策算法

可分级视频编码提供了较为灵活的编码方式,但层间模式决策算法增加了编码器的计算复杂度。该文分析了基本层和增强层间模式分布的相关性,设计一种层间编码快速模式决策算法。实验结果表明,在PSNR平均仅降低0.04 dB、码率减少0.096%的前提下,该优化算法能使编码时间平均减少50.34%。     

基于耳廓参数与面部特征相结合的人耳识别

针对外耳轮廓特征与人脸五官的比例关系,提出一种基于耳廓几何参数与面部结构特征相结合的生物鉴别方法。该方法将外耳轮廓与人脸某些特征相结合,如特征向量中包括人耳宽度与两眼瞳孔之间的水平距离之比、人耳高度与眼睛和嘴之间的垂直距离之比。实验结果表明,该算法简单、可操作性强,对数据采集设备及采集条件要求较低,识别率较高。     

基于直接估计梯度思想的数据降维算法

高维非线性数据的降维处理对于计算机完成高复杂度的数据源分析是非常重要的。从拓扑学角度分析,维数约简的过程是挖掘嵌入在高维数据中的低维线性或非线性的流形。该文在局部嵌入思想的流形学习算法的基础上,提出直接估计梯度值的方法,从而达到局部线性误差逼近最小化,实现高维非线性数据的维数约简,并在Swiss roll曲线上采样测试取得了良好的降维效果。     

使用HTTP Tunneling技术实现穿透网络防火墙的通信

近年来,防火墙在网络中得到了广泛的应用.它能够比较有效地防范病毒与黑客的攻击,保护网络的安全.但是也带来了一些不利影响,使一些需要进行网络通信的应用软件系统不能正常工作.介绍一个解决方案,并且重点介绍通过HTTP Tunneling技术使用基于文本的HTTP协议传送二进制数据的方法.     

Dynamical characteristics of software trustworthiness and their evolutionary complexity

Developing trusted softwares has become an important trend and a natural choice in the development of software technology and applications, and software trustworthiness modeling has become a prerequisite and necessary means. To discuss and explain the basic scientific problems in software trustworthiness and to establish theoretical foundations for software trustworthiness measurement, combining the ideas of dynamical system study, this paper studies evolutionary laws of software trustworthiness and the dynamical mechanism under the effect of various internal and external factors, and proposes dynamical models for software trustworthiness, thus, software trustworthiness can be considered as the statistical characteristics of behaviors of software systems in the dynamical and open environment. By analyzing two simple examples, the paper explains the relationship between the limit evolutionary behaviors of software trustworthiness attributes and dynamical system characteristics, and interprets the dynamical characteristics of software trustworthiness and their evolutionary complexity. Supported partially by the National Basic Research Program of China (Grant No. 2005CB321900) and the National Natural Science Foundation of China (Grant No. 60473091)     

基于网络表的数据收集转发器的分析与设计

数据收集转发器是一种对表具进行连续、长时间、自动、实时监测,并进行分析、处理、存储、数据转发以及自动报警的中继设备。介绍了数据收集转发器的硬件结构,提出了数据收集转发器的设计方案,可以广泛应用于燃气表数据的远程无线集抄。     

基于模型预测控制的移动机器人轨迹跟踪

通过分析与建立全向移动机器人运动学与动力学模型,研究了移动机器人轨迹跟踪问题。在误差模型的线性化描述的基础上使用模型预测控制方法,在满足控制约束的条件下,将样周期内最小化目标函数的优化问题转换为二次规划问题的求解,有效减低了计算量。结果表明该方法对移动机器人的轨迹跟踪控制是有效可行的。     

Multivariable direct adaptive decoupling controller using multiple models and a case study

In this paper, a multivariable direct adaptive controller using multiple models without minimum phase assumption is presented to improve the transient response when the parameters of the system jump abruptly. The controller is composed of multiple fixed controller models, a free-running adaptive controller model and a re-initialized adaptive controller model. The fixed controller models are derived from the corresponding fixed system models directly. The adaptive controller models adopt the direct adaptive algorithm to reduce the design calculation. At every instant, the optimal controller is chosen out according to the switching index. The interaction of the system is viewed as the measured disturbance which is eliminated by the choice of the weighing polynomial matrix. The global convergence is obtained. Finally, several simulation examples in a wind tunnel experiment are given to show both effectiveness and practicality of the proposed method. The significance of the proposed method is that it is applicable to a non-minimum phase system, adopting direct adaptive algorithm to overcome the singularity problem during the matrix calculation and realizing decoupling control for a multivariable system. Supported by the National Natural Science Foundation of China (Grant Nos. 60504010, 60864004), the National High-Tech Research and Development Program of China (Grant No. 2008AA04Z129), the Key Project of Chinese Ministry of Education (Grant No. 208071), and the Natural Science Foundation of Jiangxi Province (Grant No. 0611006)     

Statistical learning makes the hybridization of particle swarm and differential evolution more efficient—A novel hybrid optimizer

This brief paper reports a hybrid algorithm we developed recently to solve the global optimization problems of multimodal functions, by combining the advantages of two powerful population-based metaheuristics—differential evolution (DE) and particle swarm optimization (PSO). In the hybrid denoted by DEPSO, each individual in one generation chooses its evolution method, DE or PSO, in a statistical learning way. The choice depends on the relative success ratio of the two methods in a previous learning period. The proposed DEPSO is compared with its PSO and DE parents, two advanced DE variants one of which is suggested by the originators of DE, two advanced PSO variants one of which is acknowledged as a recent standard by PSO community, and also a previous DEPSO. Benchmark tests demonstrate that the DEPSO is more competent for the global optimization of multimodal functions due to its high optimization quality. Supported by the National Natural Science Foundation of China (Grant No. 60374069), and the Foundation of the Key Laboratory of Complex Systems and Intelligent Science, Institute of Automation, Chinese Academy of Sciences (Grant No. 20060104)