运动目标检测与跟踪的DSP实现

研究了运动目标检测与跟踪的DSP（DigitalSignalProcessor）实现算法,以形心跟踪算法为整个处理系统的核心。采用目标形心跟踪算法,通过目标分割阶段的目标标记,如目标面积、周长、形心位置等信息的提取建立目标跟踪波门,实现目标的连续跟踪,并将此算法移植到sEED—VPM642硬件平台,实验结果表明能够达到预定目标。此外,为了克服形心算法的准确性和实时性缺陷,采用粒子滤波对算法进行必要的扩展,从MATLAB的仿真结果看,除个别采样点存在误差较大的情况,真实值曲线与粒子滤波跟踪曲线拟合较好。     

Distributed match-making

In many distributed computing environments, processes are concurrently executed by nodes in a store- and-forward communication network. Distributed control issues as diverse as name server, mutual exclusion, and replicated data management involve making matches between such processes. We propose a formal problem called distributed match-making as the generic paradigm. Algorithms for distributed match-making are developed and the complexity is investigated in terms of messages and in terms of storage needed. Lower bounds on the complexity of distributed match-making are established. Optimal algorithms, or nearly optimal algorithms, are given for particular network topologies.The work of the second author was supported in part by the Office of Naval Research under Contract N00014-85-K-0168, by the Office of Army Research under Contract DAAG29-84-K-0058, by the National Science Foundation under Grant DCR-83-02391, and by the Defence Advanced Research Projects Agency (DARPA) under Contract N00014-83-K-0125. Current address of both authors: CWI, Kruislaan 413, 1098 SJ Amsterdam, The Netherlands.     

电火花线切割加工中实体数据格式的程序转换

针对电火花线切割编程系统绘制、编辑复杂曲线图形、文字功能差以及数据文件兼容功能不强的弱点，本文提出一种综合解决方案。利用AutoCAD强大的图形能力，基于对其图形实体数据表的分析，对AutoLISP程序提取实体数据，并自动转换成线切割自动编程系统所需的数据文件。该方法可使电火花线切割系统完成复杂图形、字体及具有尖角造型实体的加工，实践证明有较大的实用意义。     

论"美丑对照"与"以丑为美"的文艺美学观

本文运用比较的方法,论述了雨果的“美丑对照原则”与波德莱尔“以丑为美”理论的异同,并以他们各自的创作实践说明了这两种理论的运用都没有改变事物（描写的对象）的本质,只是在一定程度上改变了事物的原有面貌;它们都是对客观事物的具体反映和直接把握,是艺术家对人性善和美的异曲同工的追求,为人们提供了全面的艺术范例和审美观念。     

Embedded devices for supply chain applications: Towards hardware integration of disparate technologies

The emergence of the RFID technology and its application to supply chain processes has in particular led to the creation of such standards as the EPCglobal’s model of supply networks as a tool for materializing intra- and inter-enterprise visibility of resources and products, collaboration and integration. Among other critical uses, RFID has been deployed by supply networks to improve asset utilization, effectively combat counterfeiting, and advance targeted product recalls. However, new affordable and deployable technologies and microsensors have recently appeared and keep maturing. This paper discusses the needs and the possibilities that exist for leveraging these technologies and sensors with RFID to guarantee continuous and seamless visibility of all assets (fixed and mobile resources and field personnel) of smart enterprises, thereby expanding and complementing the roles of RFID. It examines the design challenges for the integration of these technologies for advanced logistics operations at the level of product classes or their instances. It then outlines our development of an embedded microsystem that combines RFID, GPRS, GPS and environmental sensors for applications in logistics. The prototyped microsystem demonstrated the feasibility of the multi-sensor integration paradigm that the paper proposes.     

Recommending suitable learning scenarios according to learners’ preferences: An improved swarm based approach

The paper presents a new approach for recommending suitable learning paths for different learners groups. Selection of the learning path is considered as recommendations to choosing and combining the sequences of learning objects (LOs) according to learners’ preferences. Learning path can be selected by applying artificial intelligence techniques, e.g. a swarm intelligence model. If we modify and/or change some LOs in the learning path, we should rearrange the alignment of new and old LOs and reallocate pheromones to achieve effective learning recommendations. To solve this problem, a new method based on the ant colony optimisation algorithm and adaptation of the solution to the changing optimum is proposed. A simulation process with a dynamic change of learning paths when new LOs are inserted was chosen to verify the method proposed. The paper contributes with the following new developments: (1) an approach of dynamic learning paths selection based on swarm intelligence, and (2) a modified ant colony optimisation algorithm for learning paths selection. The elaborated approach effectively assist learners by helping them to reach most suitable LOs according to their preferences, and tutors – by helping them to monitor, refine, and improve e-learning modules and courses according to the learners’ behaviour.     

解决组合公钥共谋攻击和密钥碰撞的新方法

以解决组合公钥体制中共谋攻击和密钥碰撞问题为目的。首先,针对线性共谋攻击,提出了一种新的构造种子矩阵的方法,使得种子密钥和大于基点加法群的阶数,从而使密钥之间不能相互线性表示。其次在密钥的生产过程中,引入系数破坏了层不同和层互斥不同的关系,为解决选择共谋攻击提供了一种有效的方法,同时增强了抵御随机共谋攻击的能力。最后,在密钥产生的流程中,通过公钥对比来避免密钥碰撞,为解决密钥碰撞问题提出了一种新方法。     

智能电网的安全可控性

计算机与信息技术在电力系统中的广泛应用提高了电网的自动化、智能化水平,同时也将传统IT领域的众多安全隐患引入了电网。智能电网是社会域、信息域、物理域多域交互、渗透形成的大规模新型融合网络,其安全威胁具有多域渗透、跨域攻击的特点。文中描述了智能电网基本安全需求及其与传统IT安全需求的不同;分析了智能电网中多域渗透攻击并且对信息-物理安全威胁进行分类;基于分域防护、多域协同、边界防护的思想提出了智能电网多域协同安全防护模型。     

Discriminative multi-task objects tracking with active feature selection and drift correction

In this paper, we propose a discriminative multi-task objects tracking method with active feature selection and drift correction. The developed method formulates object tracking in a particle filter framework as multi-Task discriminative tracking. As opposed to generative methods that handle particles separately, the proposed method learns the representation of all the particles jointly and the corresponding coefficients are similar. The tracking algorithm starts from the active feature selection scheme, which adaptively chooses suitable number of discriminative features from the tracked target and background in the dynamic environment. Based on the selected feature space, the discriminative dictionary is constructed and updated dynamically. Only a few of them are used to represent all the particles at each frame. In other words, all the particles share the same dictionary templates and their representations are obtained jointly by discriminative multi-task learning. The particle that has the highest similarity with the dictionary templates is selected as the next tracked target state. This jointly sparsity and discriminative learning can exploit the relationship between particles and improve tracking performance. To alleviate the visual drift problem encountered in object tracking, a two-stage particle filtering algorithm is proposed to complete drift correction and exploit both the ground truth information of the first frame and observations obtained online from the current frame. Experimental evaluations on challenging sequences demonstrate the effectiveness, accuracy and robustness of the proposed tracker in comparison with state-of-the-art algorithms.