Preparation of Porous Silver Nanowires-Melamine Formaldehyde Hybrid Composite Materials and Their Electromagnetic Shielding and Flame-Retardant Properties

Along with the booming development of communication technology and electronic equipment, higher requirements of flame-retardant and EMI shielding performances for electromagnetic interference (EMI) shielding materials are put forward. Herein, the ultralight and porous silver nanowires (AgNWs)-melamine formaldehyde (MF) hybrid composite with unique micro-/nanostructure is developed by a facile dip-coating method, which uses the AgNWs as 1D conductive coating and MF foam (MF foam) as 3D skeleton template. Benefiting from the unique porous micro-/nanostructure, the resultant hybrid composite displays low density, excellent EMI shielding performances, and superior flame-retardant property. The EMI shielding effectiveness (SE) and specific EMI SE (SSEt) of the hybrid composite in X-band (8.2–12.4 GHz) can be up to 77 dB and 26971.4 dB cm⁻² g⁻¹, respectively. At the same time, the hybrid composite also passes the vertical burning test and shows an increased LOI value of 40.6%. The combination of flame-retardant and EMI shielding performances for EMI shielding materials makes the AgNWs-MF hybrid composite great application potential in civil and military fields. This work provides a new guide for the design of multifunctional high-performance EMI shielding materials.     

基于SIMD指令的柔性物体并行碰撞检测

复杂场景中柔性物体间的碰撞检测依然难以满足交互设计的要求.为了提高处理速度,文中给出了一种充分利用现代CPU的并行处理能力的碰撞检测算法.算法基于两方面的并行处理:即基于SIMD指令的指令级并行处理和基于多线程的任务级并行处理.算法给出了一种针对SIMD指令特别优化的k-DOP模型--SIMD-DOP,从理论上分析了该包围盒的高效性,并与常规的16-DOP和24-DOP进行了运行效率对比.通过使用SIMD-DOP同时在多核间进行负载均衡,算法获得了优化的并行加速.文中算法已经在一台16核工作站上针对一组复杂测试场景进行了验证.     

基于支持向量机的文本隐写分析

通过对自然文本统计模型和特性的分析,指出隐藏消息后可能对文本统计特性带来的变化,提出基子支持向量机的通用检测算法。将文本的5个基本统计特征量作为分类特征向量,采用支持向量机对自然文本和载密文本进行有效分类检测。实验结果证明,该算法具有较好的适用性和可靠性。     

基于Citrix的异地软件共享系统的设计与实现

针对国家科技基础条件平台中大型仪器、设备等共享的需求,提出基于思杰（Citrix）的异地软件共享解决方案,其中包括系统原理、设计与实现的描述。经过用户试用,证明该方案可行,有助于提高大型软件的利用率,减少大型软件的购买费用,符合科技基础条件平台资源共享与整合的建设原则。     

区分服务网络中SCTP协议的改进

针对双窗口算法难以判断包丢弃类型的问题,提出一种新的包丢弃类型判断方法,对SCTP协议的确认块进行扩展,从确认块中获取包丢弃类型信息。在NS2上进行仿真实验,实验结果表明,改进后的SCTP协议在吞吐率方面有显著提高。     

基于参数重要度的多元时间序列相似性查询

针对多元时间序列的相似性查询问题,给出参数重要度的定义,提出一种基于参数重要度的候选集查询方法。通过对多元时间序列的SVD分解,将奇异值向量和特征矩阵作为多元序列的特征,基于线性空间中的坐标变换原理构造2个多元时间序列的相似性度量模型,实现在候选集上的精确匹配并获得最终的结果集。对飞行数据的相似性查询实验验证了该方法的有效性。     

基于视频三音子的双模态语料自动选取算法

为实现可视语音合成,建立符合条件的双模态语料库,提出双模态语料自动选取算法。根据视频中唇部发音特征,对已有的三音子模型归类,形成视频三音子,在其基础上从原始语料中自动选取语料,利用评估函数对原始语料中的句子打分。与其他双模态语料库相比,该语料库在覆盖率等指标上有较大改进,为实现具有真实感的可视语音合成奠定基础。     

Wireless channel extraction analyzing based on graph theory

Wireless channels comprise various signal characteristics that correspond to different features. This research applies digital signal processing to first excavate and categorize various features found in the channel data. Then, borrowing from graph theory, fast clustering analysis and decision tree modeling are introduced to identify unique “fingerprint” characteristics. Finally, two scenarios were tested using artificial neural networks to identify and verify their applicability in different geographical locations.     

Deriving evidence theoretical functions in multivariate data spaces: a systematic approach.

The mathematical theory of evidence is a generalization of the Bayesian theory of probability. It is one of the primary tools for knowledge representation and uncertainty and probabilistic reasoning and has found many applications. Using this theory to solve a specific problem is critically dependent on the availability of a mass function (or basic belief assignment). In this paper, we consider the important problem of how to systematically derive mass functions from the common multivariate data spaces and also the ensuing problem of how to compute the various forms of belief function efficiently. We also consider how such a systematic approach can be used in practical pattern recognition problems. More specifically, we propose a novel method in which a mass function can be systematically derived from multivariate data and present new methods that exploit the algebraic structure of a multivariate data space to compute various belief functions including the belief, plausibility, and commonality functions in polynomial-time. We further consider the use of commonality as an equality check. We also develop a plausibility-based classifier. Experiments show that the equality checker and the classifier are comparable to state-of-the-art algorithms.