基于IMS移动终端的即时通信联系人列表管理器

本文主要介绍基于IMS移动终端的即时通信联系人管理器,该管理器主要由XML搜索器、XML解析器、XML管理模块、XCAP通信模块构成。分析联系人列表中联系人的XML片段的定长特性,设计了XML搜索器,实现了节点的快速查找。在XML管理器模块中,以节点路径和消息片段的方式提交联系人修改,减少XML消息传输的字节数,从而提高了消息的传输速率。     

Supervised classification of share price trends

Share price trends can be recognized by using data clustering methods. However, the accuracy of these methods may be rather low. This paper presents a novel supervised classification scheme for the recognition and prediction of share price trends. We first produce a smooth time series using zero-phase filtering and singular spectrum analysis from the original share price data. We train pattern classifiers using the classification results of both original and filtered time series and then use these classifiers to predict the future share price trends. Experiment results obtained from both synthetic data and real share prices show that the proposed method is effective and outperforms the well-known K-means clustering algorithm.     

1D correlation filter based class-dependence feature analysis for face recognition

In this paper, a novel one-dimensional correlation filter based class-dependence feature analysis (1D-CFA) method is presented for robust face recognition. Compared with original CFA that works in the two dimensional (2D) image space, 1D-CFA encodes the image data as vectors. In 1D-CFA, a new correlation filter called optimal extra-class origin output tradeoff filter (OEOTF), which is designed in the low-dimensional principal component analysis (PCA) subspace, is proposed for effective feature extraction. Experimental results on benchmark face databases, such as FERET, AR, and FRGC, show that OEOTF based 1D-CFA consistently outperforms other state-of-the-art face recognition methods. This demonstrates the effectiveness and robustness of the novel method.     

A factorization-based approach for articulated nonrigid shape, motion and kinematic chain recovery from video

Recovering articulated shape and motion, especially human body motion, from video is a challenging problem with a wide range of applications in medical study, sport analysis and animation, etc. Previous work on articulated motion recovery generally requires prior knowledge of the kinematic chain and usually does not concern the recovery of the articulated shape. The non-rigidity of some articulated part, e.g. human body motion with nonrigid facial motion, is completely ignored. We propose a factorization-based approach to recover the shape, motion and kinematic chain of an articulated object with nonrigid parts altogether directly from video sequences under a unified framework. The proposed approach is based on our modeling of the articulated non-rigid motion as a set of intersecting motion subspaces. A motion subspace is the linear subspace of the trajectories of an object. It can model a rigid or non-rigid motion. The intersection of two motion subspaces of linked parts models the motion of an articulated joint or axis. Our approach consists of algorithms for motion segmentation, kinematic chain building, and shape recovery. It handles outliers and can be automated. We test our approach through synthetic and real experiments and demonstrate how to recover articulated structure with non-rigid parts via a single-view camera without prior knowledge of its kinematic chain.     

Cracking the walnut: Using a computer game to impact cognition,emotion, and behavior of highly aggressive fifth grade students

Three fifth grade highly aggressive students participated in a study examining effects of an aggression intervention game. Using single-subject multiple baseline research design, the effect of the game was measured across three domains: attribution of peer intent (cognitive), emotional response (affective) and behavior response (behavioral). The results indicate that, while there were observable individual differences, the game had an immediate impact on attribution of peer behavior across all participants, had a less predictable impact on reported behavioral reactions, and had no discernable impact on reported emotional response. Theoretical and practical implications of the study are discussed.     

一种维类转换为维层次的方法

分析维层次的结构和定义对数据仓库聚集操作的重要作用,确定了某些维类也可能产生类似维层次的分析要求,提出维类转换为维层次的CDCDH方法.给出维类和维层次的定义,并进一步提出显性维层次和隐性维层次的定义.对于表现为维类的隐性维层次,则确定其向显性维层次转换的条件和转换的步骤.同时结合一个具体的实例,说明了CDCDH方法的实施过程.     

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.     

FT64并行系统上的EP和GEMM并行算法设计与实现

EP和GEMM是科学计算领域中常用的计算核心,并广泛应用于高性能计算机体系结构的性能评测.基于一种当今热门的体系结构--流体系结构,设计实现了这两个测试程序在FT64并行系统上的并行算法,并取得了很好的效果.     

基于改进模糊ISODATA算法的遥感影像非监督聚类研究

F-ISODATA是一种有效的遥感图像非监督聚类算法.但是,最优迭代次数很难设定;一般遥感图像的数据量大,若迭代误差限取极小值,分类也很难实现.本文以某次迭代中"合并"和"分裂"都为零为求最优分类数的迭代条件,而不是预先设定迭代次数;取最大和最小隶属度取代每一个隶属度为比对特征值,提高了分类速度和精度;利用等效转换研究隶属度矩阵的迭代误差变化规律,得出变化速度趋于稳定时为求解最优隶属度矩阵的智能迭代控制,减少人为事先干预.最后,进行实验分析,结果显示整个改进的算法提高了分类的智能化,整体效果较好.     

CircleSearch：流媒体VoD环境下的P2P搜索算法

在基于P2P的流媒体VoD环境下,搜索机制是影响服务质量的关键因素之一。该文提出一种新的基于环状网络的分布式自适应概率搜索算法——CircleSearch。它依据节点本体内容生成结构化的环状拓扑网络,保证算法的可扩展性、健壮性和分布性。自发组织基于本体距离和连接可靠性的small world覆盖网络,减小消息的转发次数,采用自适应搜索算法提高文件定位的准确性。仿真实验表明,CircleSearch以其高成功率、低服务器负载和很小的平均搜索长度实现媒体流传输的即时、稳定、可控和连续,为VoD服务提供QoS保证。