Information-theoretic Approaches Based on Sequential Monte Carlo to Collaborative Distributed Sensors for Mobile Robot Localization

We consider the Sequential Monte Carlo (SMC) method for Bayesian inference applied to the problem of information-theoretic distributed sensor collaboration in complex environments. The robot kinematics and sensor observation under consideration are described by nonlinear models. The exact solution to this problem is prohibitively complex due to the nonlinear nature of the system. The SMC method is, therefore, employed to track the probabilistic kinematics of the robot and to make the corresponding Bayesian estimates and predictions. To meet the specific requirements inherent in distributed sensors, such as low-communication consumption and collaborative information processing, we propose a novel SMC solution that makes use of the particle filter technique for data fusion, and the density tree representation of the a posterior distribution for information exchange between sensor nodes. Meanwhile, an efficient numerical method is proposed for approximating the information utility in sensor selection. A further experiment, obtained with a real robot in an indoor environment, illustrates that under the SMC framework, the optimal sensor selection and collaboration can be implemented naturally, and significant improvement in localization accuracy is achieved when compared to conventional methods using all sensors.     

Detection of the boundary-layer separation point based on the flexible sensor arrays

Microelectromechanical systems plays an important role for studying gaseous or liquid fluid flows experimentally due to the dimensions of the device on the μm-scale. For aerodynamic applications, the detection of the location of the separation point of the boundary layer is very important, which is used for active flow control and smart skin adaptive control system. This paper presents the design and integration methods of a flexible polymide substrate micro thermal sensor array, sensor array driver circuit, dynamic data acquisition circuit, and criterion algorithm of the boundary layer separation point. The above algorithms were used to analyze the NACA0012 wind tunnel test data at flow conditions of the velocity 40 m/s and AOA 16°. The result demonstrated the above system can fulfill the dynamic and real-time of the adaptive control system.     

渐近跟踪鲁棒控制的混沌同步

对混沌系统同步控制中的控制器进行了研究,因其实际应用受限大等因素而要求系统具有较好的鲁棒性.把渐近跟踪作为系统同步的推广,从控制方法的角度设计一个混沌同步控制器,运用极点配置法求出控制器的增益参数并从控制理论上证明了该思路的可行性.通过对Lorenz系统的数值仿真试验,验证了该方法在控制混沌同步方面的有效性.为混沌控制中设计耐受性高的控制器提供了依据.     

基于社区分析的网络分割

在并行计算中,网络分割的质量会极大地影响并行计算的效率,然而在网络分割时并没有一个统一的质量评价指标,因此网络分割评价指标的定义以及最佳网络分割方法的构造,成为大规模网络中并行计算的一个核心问题.根据对影响并行计算效率的各种因素的分析,给出了一个网络分割评价指数的定义,并利用并行最短路径的计算验证了该评价指数的定义的正确性;并且基于社区分析,给出了一个全新的网络分割方法,理论分析表明:该网络分割方法是最优的.     

一种内网端点安全系统的设计

当前很多企业部署了内部网络,并将内网作为信息发布和共享的平台,确保内网安全和端点安全的要求与日俱增,随着攻击工具和手段的日趋复杂多样,传统的防范手段已不能保证内网安全和端点安全。文章针对端点安全,研究了对非安全网络端点的识别和隔离方法,提出了一种内网端点安全系统的设计方案,基于现有网络设备和标准协议,可实现入侵检测和访问控制的功能,成本较低、结构简单、管理方便。     

C++ builder增强图像质量的若干方法

介绍了增强图像质量的若干方法,包括图像平滑处理、图像锐化处理以及Windows API函数SetStretchBltMode的调用,阐述了其原理、算法及该原理在Windows平台上以C++ Builder为开发工具的具体程序实现.     

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)     

Network traffic classification based on ensemble learning and co-training

Classification of network traffic is the essential step for many network researches. However,with the rapid evolution of Internet applications the effectiveness of the port-based or payload-based identifi-cation approaches has been greatly diminished in recent years. And many researchers begin to turn their attentions to an alternative machine learning based method. This paper presents a novel machine learning-based classification model,which combines ensemble learning paradigm with co-training tech-niques. Compared to previous approaches,most of which only employed single classifier,multiple clas-sifiers and semi-supervised learning are applied in our method and it mainly helps to overcome three shortcomings:limited flow accuracy rate,weak adaptability and huge demand of labeled training set. In this paper,statistical characteristics of IP flows are extracted from the packet level traces to establish the feature set,then the classification model is created and tested and the empirical results prove its feasibility and effectiveness.     

Java程序到类图和AISD的转换方法研究

论述了Java类、UML类图和过程蓝图AISD的基本概念,详细介绍了Java程序和类图、抽象实现结构图的映射关系及转换方法,使抽象模型能全面地表达所对应的程序代码.     

高层次数据流的多电压低功耗综合方法

为了有效地进行算法行为描述到寄存器传输级结构描述的转换,提出一种同时考虑功能单元功耗、互连功耗和电压转换功耗的模型和基于网络流的低功耗设计方法.首先对给定的数据流图进行单电压高层次综合,然后对单电压综合结果迭代地进行多电压调整;提取每次迭代时需要调整的网络流子图,对该子图运行最小费用最大流增量算法.该方法充分利用前面迭代中得到的优化解,避免了对整个网络流的重复计算.实验结果表明,文中方法在互连功耗、电压转换功耗和总功耗等方面均有较大优化.