Stabilization of One‐Dimensional Schrödinger Equation with Variable Coefficient under Delayed Boundary Output Feedback

This article considers stabilization of a one‐dimensional Schrödinger equation with variable coefficient and boundary observation which suffers from an arbitrary given time delay. We design an observer and predictor to stabilize the system. The state is estimated in the time span where the observation is available, and also predicted in the time interval where the observation is not available. It is shown that the estimated state feedback stabilizes the system exponentially. A numerical simulation is presented to illustrate the effect of the stabilizing controller.     

SoC测试访问机制和测试壳的蚁群联合优化

针对系统级芯片(SoC)测试壳优化和测试访问机制的测试总线划分问题,提出了基于蚁群算法的SoC Wrapper/TAM联合优化方法.构造蚁群算法时首先进行IP核的测试壳优化,用于缩短最长扫描链长度,减少单个IP核的测试时间;在此基础上进行TAM结构的蚁群优化,通过算法迭代逼近测试总线的最优划分,从而缩短SoC测试时间.对ITC2002基准SoC电路进行实验的结果表明,该方法能有效地解决SoC测试优化问题.     

P2P流媒体点播系统中的缓存管理方案的研究和实现

视频点播系统的缓存管理对系统的性能有重要的影响.回顾了现有的缓存策略,分析了P2P网络和传统中心服务器网络缓存策略的不同,然后提出一种适应P2P流媒体点播系统的缓存管理算法.给出了新型的算法描述,并构建原型系统进行性能对比测试.测试结果表明系统骨干节点的资源利用率和普通节点节目的播放质量和流畅性都得到了提高,证实了算法在P2P点播系统中的可行性和有效性.     

基于GMA的教育资源网格监控研究

实现全面资源共享、充分提高资源利用率是教育资源网格的主要研究目标,为了实现该目标,设计了ERGM(educa-tion resource grid monitor)监控模型,它具有采集的信息全面、良好的可扩展性和容错性等优点.通过它可以及时采集资源的状态信息为资源调度系统提供依据,使资源的利用更加合理,很好地满足了教育资源网格监控的需求.     

PSO算法在含一阶导数变分问题中的应用

变分问题是一个研究泛函极值的经典数学问题,寻求变分问题的直接解法具有重要的理论和现实意义.鉴于PSO算法在极值问题中的广泛应用,利用分段Hermite插值.建立了求解含一阶导数的变分问题优化模型,构造出了适应度函数,从而使得PSO算法成功应用到变分问题的求解当中.数值实验结果表明了方法的可行性,同时也拓展了PSO算法的应用领域.     

合成孔径雷达1553B通信接口设计与实现

介绍某型号合成孔径雷达系统的1553B总线通信接口设计方案与工程实现;方案采用国内自主研发的4M/s速率1553B总线接口芯片,PC/104工控机与FPGA结合,通过硬件系统设计和软件编程,实现对1553B总线数据收发和雷达部分辅助数据的提供,实现项目总体对1553B总线通信接口的要求。实际应用情况表明,该通信接口运行稳定、可靠,具有一定的实用性和良好的应用前景。     

Stereo-Based Stochastic Mapping for Robust Speech Recognition

We present a stochastic mapping technique for robust speech recognition that uses stereo data. The idea is based on constructing a Gaussian mixture model for the joint distribution of the clean and noisy features and using this distribution to predict the clean speech during testing. The proposed mapping is called stereo-based stochastic mapping (SSM). Two different estimators are considered. One is iterative and is based on the maximum a posteriori (MAP) criterion while the other uses the minimum mean square error (MMSE) criterion. The resulting estimators are effectively a mixture of linear transforms weighted by component posteriors, and the parameters of the linear transformations are derived from the joint distribution. Compared to the uncompensated result, the proposed method results in 45% relative improvement in word error rate (WER) for digit recognition in the car. In the same setting, SSM outperforms SPLICE and gives similar results to MMSE compensation of Huang A 66% relative improvement in word error rate (WER) is observed when applied in conjunction with multistyle training (MST) for large vocabulary English speech recognition in a real environment. Also, the combination of the proposed mapping with CMLLR leads to about 38% relative improvement in performance compared to CMLLR alone for real field data.     

An adaptive interference alignment scheme based on the dynamic selection of desired transmitters for unmanned ship network

Wireless Networks - In the unmanned ship networking scenario, the position of the unmanned ship changes continuously, leading to the result that the desired transmitters and the interference...     

Joint Classification and Regression for Visual Tracking with Fully Convolutional Siamese Networks

International Journal of Computer Vision - Visual tracking of generic objects is one of the fundamental but challenging problems in computer vision. Here, we propose a novel fully convolutional...