射频仿真多辐射中心方案设计及幅相误差分析

现有的射频仿真系统在单通道中同一时间只能模拟一个辐射中心,因此难以对多目标与分布式杂波进行仿真。通过改进原有馈电电路并调整开关矩阵与阵列天线的连接顺序,现提出了一套新型的多辐射中心仿真系统的设计方案。新方案能够在单通道单周期中模拟两个辐射中心,为在经费有限、通道数量较少的条件下,模拟多目标和分布式杂波提供了技术基础。为了验证新方案的可行性,利用计算机仿真分析了新型方案引入的幅、相误差对辐射中心位置精度的影响,得出了几种情况下位置误差的统计分布。仿真结果表明,虽然新型方案中的位置误差相对更大些,但其辐射中心的位置精度仍然达到了仿真系统的技术要求,新型方案是可行的。     

一种改进的基于DCT变换的语音增强算法

研究语音识别问题,变换域分析是语音增强中最常用的方法,采用离散余弦变换(DCT)来描述带噪语音信号的频域特性,并构造拉普拉斯-高斯参数模型(Laplacian-Gaussian)来表示带噪语音信号的分布,可改善增强效果并减少误差.在参数估计上采用了最大似然估计法(ML),并结合谱相减估计法对拉普拉斯模型参数作了进一步优化,最后给出了检测语音信号存在的条件.仿真实验结果表明,算法在用于处理含有F16、Pink、Babble及高斯白噪声的语音信号时,与其它基于DCT变换的算法相比,取得了更好的增强效果.     

基于MOSS-Project的项目管理研究和系统设计

为了实现项目管理信息化,在用MOSS—Project搭建的信息系统上进行项目管理研究。论述研究过程和研究成果,阐述应用研究成果来设计项目管理信息系统,以实现项目管理信息化的目标。     

一种基于ActiveX和Web Service的电力系统WebGIS的设计与实现

Web GIS是当前GIS应用领域的一个研究热点,通过Web GIS克服了传统桌面GIS应用局限,使得GIS应用具有真正的信息共享,巨大的扩展空间。提出了一种基于ActiveX控件和Web Service的Web GIS实现方案,并详细叙述了该方案各部分实现的关键技术。与传统的Web GIS结构相比,文章提出了一种胖客户端的实现,提高了用户的体验。     

基于Snort的IPv6网络入侵检测系统的设计与实现

随着IPv6的部署和网络技术的发展,一些基于新型攻击方式的网络入侵的事件也开始出现.本文基于Snort构架,设计了一个针对IPv6体系的入侵检测系统,对系统所属的各功能模块进行了分析,提出了详细的设计方案.     

分布式视频编码技术的研究

分布式视频编码建立在20世纪70年代提出的相关信源编码理论的基础上,即Slepian.Wolf和Wyner-Ziv分布式编码信息论原理,与传统视频编码技术相比,分布式视频编码从原理到实现方法上是全新的。它是基于解码端获得边信息（Side Information）的有损编码,这能够降低编码端的复杂度,而把大量的运算转移到解码端。     

基于神经网络控制的星载微波辐射计

本文提出一种采用神经网络控制技术的消除星载全功率微波辐射计系统增益和等效本机噪声波动的方法。此方法通过基于神经网络的控制系统,控制微波辐射计的系统增益与参考电压,使微波辐射计在轨运行期间系统参数保持不变。系统参数通过地面实验获得,因此在轨运行期间不再需要星上两点定标中的低温基准源,减少了低温定标天线等物理设备。     

Using time-stamp to improve the security of a chaotic maps-based key agreement protocol

Based on the semi-group property of Chebyshev chaotic map and time-stamp, we propose an enhanced chaotic maps-based key agreement protocol, which is more secure than the original one and can resist the replaying attack.     

Image categorization: Graph edit distance+edge direction histogram

This paper presents a novel algorithm for computing graph edit distance (GED) in image categorization. This algorithm is purely structural, i.e., it needs only connectivity structure of the graph and does not draw on node or edge attributes. There are two major contributions: (1) Introducing edge direction histogram (EDH) to characterize shape features of images. It is shown that GED can be employed as distance of EDHs. This algorithm is completely independent on cost function which is difficult to be defined exactly. (2) Computing distance of EDHs with earth mover distance (EMD) which takes neighborhood bins into account so as to compute distance of EDHs correctly. A set of experiments demonstrate that the newly presented algorithm is available for classifying and clustering images and is immune to the planar rotation of images. Compared with GED from spectral seriation, our algorithm can capture the structure change of graphs better and consume 12.79% time used by the former one. The average classification rate is 5% and average clustering rate is 25% higher than the spectral seriation method.     

Bayesian analysis of multivariate nominal measures using multivariate multinomial probit models

The multinomial probit model has emerged as a useful framework for modeling nominal categorical data, but extending such models to multivariate measures presents computational challenges. Following a Bayesian paradigm, we use a Markov chain Monte Carlo (MCMC) method to analyze multivariate nominal measures through multivariate multinomial probit models. As with a univariate version of the model, identification of model parameters requires restrictions on the covariance matrix of the latent variables that are introduced to define the probit specification. To sample the covariance matrix with restrictions within the MCMC procedure, we use a parameter-extended Metropolis-Hastings algorithm that incorporates artificial variance parameters to transform the problem into a set of simpler tasks including sampling an unrestricted covariance matrix. The parameter-extended algorithm also allows for flexible prior distributions on covariance matrices. The prior specification in the method described here generalizes earlier approaches to analyzing univariate nominal data, and the multivariate correlation structure in the method described here generalizes the autoregressive structure proposed in previous multiperiod multinomial probit models. Our methodology is illustrated through a simulated example and an application to a cancer-control study aiming to achieve early detection of breast cancer.