Video summarization via minimum sparse reconstruction

The rapid growth of video data demands both effective and efficient video summarization methods so that users are empowered to quickly browse and comprehend a large amount of video content. In this paper, we formulate the video summarization task with a novel minimum sparse reconstruction (MSR) problem. That is, the original video sequence can be best reconstructed with as few selected keyframes as possible. Different from the recently proposed convex relaxation based sparse dictionary selection method, our proposed method utilizes the true sparse constraint L₀ norm, instead of the relaxed constraint _L2,1$L_{2,1}$ norm, such that keyframes are directly selected as a sparse dictionary that can well reconstruct all the video frames. An on-line version is further developed owing to the real-time efficiency of the proposed MSR principle. In addition, a percentage of reconstruction (POR) criterion is proposed to intuitively guide users in obtaining a summary with an appropriate length. Experimental results on two benchmark datasets with various types of videos demonstrate that the proposed methods outperform the state of the art.     

Joint relay selection and power allocation for amplify-and-forward two-path relaying networks

This paper presents a joint relay selection and power allocation scheme for amplify-and-forward two-path relaying networks,in which diferent relay nodes forward information symbols alternatively in adjacent time slots.Our approach is based on the maximization of the received signal-to-noise ratio under total power consumption by the transmission of the symbol.We show that in spite of inter-relay interferences,the maximization problem has a closed-form solution.Simulation results explicitly indicate that the performance of proposed approach outmatches the existing methods including equal power allocation and one-path relaying.     

Grid resource brokering algorithms enabling advance reservations and resource selection based on performance predictions

We present algorithms, methods, and software for a Grid resource manager, that performs resource brokering and job scheduling in production Grids. This decentralized broker selects computational resources based on actual job requirements, job characteristics, and information provided by the resources, with the aim to minimize the total time to delivery for the individual application. The total time to delivery includes the time for program execution, batch queue waiting, and transfer of executable and input/output data to and from the resource. The main features of the resource broker include two alternative approaches to advance reservations, resource selection algorithms based on computer benchmark results and network performance predictions, and a basic adaptation facility. The broker is implemented as a built-in component of a job submission client for the NorduGrid/ARC middleware.     

遗传算法与文本特征选择方法相结合的研究

对文本特征选择的传统方法进行了深入分析并对各自的优缺点进行了详细的对比;其次,通过利用遗传算法自身的寻优特点,把它与传统特征选择方法相结合,并得出了算法。最后,在对各自算法进行了分析,表明了在性能上有了明显的提高。     

MIMO-CDMA子选择系统设计及性能分析

多用户MIMO-CDMA是MIMO技术领域研究的最新成果,MIMO-CDMA系统可以显著地提高系统的容量和BER性能。但系统复杂度高,而天线选择技术可以大大降低MIMO-CDMA系统实现复杂度。设计了一种基于帧结构的MIMO-CDMA接收端子选择系统仿真平台,分析和比较了在MBER、MMI和MNP等不同选择准则条件下的系统性能,并对仿真结果进行了评价。     

协同设计环境中任务分解与调度的研究

在协同设计环境中,为了满足快速响应,低成本和高质量产品的设计需求,进一步提高产品的设计效率,提出了两级任务分解方法和双向选择优先的任务调度策略.借助计算机支持的协同设计技术,两级任务分解在协同设计联盟的抽象层次模型的基础上,采用了基于产品结构、特征服务和子任务目标属性的任务分解方法,从而降低了任务的复杂性.在此基础上,提出了双向选择优先的任务调度策略,以优化分布合作求解问题,并进行调度策略的仿真实验.仿真结果表明了该调度策略的优越性.     

基于模糊自适应免疫算法的非线性系统模型参数估计

复杂生产工艺中非线性系统的模型参数估计是系统建模优化问题中的难点, 为避免优化算法过早收敛于错误的参数估计值, 根据生物免疫机理和模糊逻辑原理提出了一种新颖的模糊自适应免疫算法, 该算法采用混沌超变异操作增强算法搜索能力, 并用免疫网络调节策略保持抗体群的多样性, 同时采用模糊逻辑调节算法参数以提高算法的自适应能力. 函数优化仿真结果表明其具有较好的收敛性能, 并能够克服早收敛问题. 最后将其成功应用于重油热解非线性模型参数估计中, 验证了该算法解决实际建模问题的可行性和有效性.     

基于VSM的自适应信息过滤系统的研究与设计

为了从网络动态信息流中获得感兴趣的内容或过滤掉无关的垃圾信息,设计了一个基于向量空间模型的自适应信息过滤系统;描述了该系统的结构和工作流程;对该系统实现的关键技术,包括文本表示、用户模板与过滤阈值初始化、特征项选取、自适应过滤算法、模板更新和阈值调整等进行了深入的分析和研究。     

高维数据环境下网络异常检测的改进否定选择算法

人工免疫中的否定选择算法目前已成功地应用于异常检测的低维数据集上,但在高维数据集上的效果不大理想。为了改善算法的性能,分析了现有否定选择算法在网络异常检测中的不足,对带变长检测器的否定选择算法进行了修改,提出了一种改进的否定选择算法,新算法中通过移动检测器控制检测器覆盖情况。仿真实验结果表明了改进算法在网络异常检测中高维数据集上的有效性,可以取得较高的检测率和较低的误报率。     

结合先天和适应性免疫的蠕虫检测免疫模型

现有的蠕虫检测方法大多通过关闭不安全的端口,切断感染主机与未感染主机之间通信等方法延缓蠕虫传播而达到将损害减少到最低程度的目的.实际上在实施这些方法时往往有许多障碍需要克服,其中的最大障碍就是存在错误检测率高的问题.现将免疫危险理论中的DCs(树突状细胞,Dendritic Cells)-T细胞协同机制用于蠕虫检测,其中DCs属于先天免疫系统细胞,T细胞属于适应性免疫系统细胞.本模型将蠕虫进程触发的系统调用序列当作抗原,将感染蠕虫导致的主机和网络异常当作危险信号.在该模型中,DCs负责危险信号的收集检测并提呈与该危险信号关联的抗原给T细胞检测器进行抗原结构检测.理论分析说明,这样的双重检测方法可以降低伪肯定率和伪否定率,并且记忆T细胞检测器的采用能使系统对类似蠕虫的再次感染反应更加迅速.