一种基于多核嵌入式系统的TD-LTE同步校正方法

针对TD-LTE系统中同步性错误随机发生的问题,提出了一种通过ARM+DSP+FPGA平台的嵌入式系统实现帧号和原语的同步校正方法。该方法基于TD-LTE射频一致性测试仪表硬件平台,通过该嵌入式系统中ARM、DSP、FPGA间的协调工作,由GPMC模块通过帧号和原语两方面对系统同步性进行校正。在TD-LTE射频一致性测试仪表硬件平台中进行了验证,结果表明,该方法在实现帧号与子帧号同步校正的基础上明显提高了TD-LTE系统通信的稳定性。     

基于FPGA实现AES的侧信道碰撞攻击

为了解决攻击点在能量迹中具体位置的识别问题,在对侧信道碰撞攻击技术研究的基础上,提出了通过计算能量迹中每个采样点的方差来识别攻击点的方差检查技术。并利用基于相关系数的碰撞检测方法,对一种AES的FPGA实现实施了攻击。实验结果表明,方差检查技术可以有效地识别攻击点在能量迹中的具体位置。     

A talking profile to distinguish identical twins

Identical twins pose a great challenge to face recognition due to high similarities in their appearances. Motivated by the psychological findings that facial motion contains identity signatures and the observation that twins may look alike but behave differently, we develop a talking profile to use the identity signatures in the facial motion to distinguish between identical twins. The talking profile for a subject is defined as a collection of multiple types of usual face motions from the video. Given two talking profiles, we compute the similarities of the same type of face motion in both profiles and then perform the classification based on those similarities. To compute the similarity of each type of face motion, we give higher weights to more abnormal motions which are assumed to carry more identity signature information.     

有限长矩形压力容器的应力分析法

对有限长承受内压的矩形压力容器，本文提出了同时考虑矩形容器６个面相互作用的分析计算方法。用所编制的程序计算了一个实际的矩形容器，得到了满意的结果。     

用瞬时法测定液体导热系数

以珠状热敏电阻为加热元件,建立了用瞬时法测定液体导热系数的装置,在25℃下用乙二醇等7个标准样品,对该装置进行了标定,平均偏差为2%.为了核实装置和所得关系式的可靠性,又测定了13个已知导热系数的样品,多数偏差在2%以内,最大偏差为5%.单次测量的精度为0.2%.本文叙述了这一方法的原理,并对实验结果进行了讨论.     

Security-aware intermediate data placement strategy in scientific cloud workflows

Massive computation power and storage capacity of cloud computing systems allow scientists to deploy data-intensive applications without the infrastructure investment, where large application datasets can be stored in the cloud. Based on the pay-as-you-go model, data placement strategies have been developed to cost-effectively store large volumes of generated datasets in the scientific cloud workflows. As promising as it is, this paradigm also introduces many new challenges for data security when the users outsource sensitive data for sharing on the cloud servers, which are not within the same trusted domain as the data owners. This challenge is further complicated by the security constraints on the potential sensitive data for the scientific workflows in the cloud. To effectively address this problem, we propose a security-aware intermediate data placement strategy. First, we build a security overhead model to reasonably measure the security overheads incurred by the sensitive data. Second, we develop a data placement strategy to dynamically place the intermediate data for the scientific workflows. Finally, our experimental results show that our strategy can effectively improve the intermediate data security while ensuring the data transfer time during the execution of scientific workflows.     

具有负载分享的P2P IPTV重迭网络的设计

针对在IPTV服务中当网上收视人数大规模增长时网络带宽消耗及服务器负载大大提高的问题,提出了利用P2P(Peer-to-Peer)概念中的应用层群播所形成的重迭网络来解决IPTV负载分享的问题,并通过P2P中Chord路由算法,加速构建与寻找对应的群播代理人节点。实验证明将Chord与应用层群播代理人做有效结合,可以提供用户具有服务质量、扩充性与负载分享的全方位P2P IPTV服务。     

An improved ν-twin support vector machine

Twin support vector machine (TSVM) is regarded as a milestone in the development of the powerful SVM. It finds two nonparallel planes by resolving a pair of smaller-sized quadratic programming problems rather than a single large one, which makes the learning speed of TSVM approximately four times faster than that of the standard SVM. However, the empirical risk minimization principle is implemented in the TSVM, so it easily leads to the over-fitting problem and reduces the prediction accuracy of the classifier. ν-TSVM, as a variant of TSVM, also implements the empirical risk minimization principle. To enhance the generalization ability of the classifier, we propose an improved ν-TSVM by introducing a regularization term into the objective function, so there are two parts in the objective function, one of which is to maximize the margin between the two parallel hyper-planes, and the other one is to minimize the training errors of two classes of samples. Therefore the structural risk minimization principle is implemented in our improved ν-TSVM. Numerical experiments on one artificial dataset and nine benchmark datasets show that our improved ν-TSVM yields better generalization performance than SVM, ν-SVM, and ν-TSVM. Moreover, numerical experiments with different proportions of outliers demonstrate that our improved ν-TSVM is robust and stable. Finally, we apply our improved ν-TSVM to two BCI competition datasets, and also obtain better prediction accuracy.