多组分气体检测与识别技术进展

介绍了国内外气体检测技术的分类与发展现状,论述了当前主要气体检测技术的原理、用途及优缺点,并对不同的气体检测方法进行了比较,通过分析认为红外光谱气体检测技术具有检测精度高、检测范围广的特点,在气体检测领域,是一种发展前景极为广阔的新技术,因此应用与开发红外光谱气体检测技术应该会成为气体检测领域发展的一种趋势.     

RankVisu: Mapping from the neighborhood network

Most multidimensional scaling methods focus on the preservation of dissimilarities to map high dimensional items in a low-dimensional space. However, the mapping function usually does not consider the preservation of small dissimilarities as important, since the cost is small with respect to the preservation of large dissimilarities. As a consequence, an item's neighborhoods may be sacrificed for the benefit of the overall mapping. We have subsequently designed a mapping method devoted to the preservation of neighborhood ranks rather than their dissimilarities: RankVisu. A mapping of data is obtained in which neighborhood ranks are as close as possible according to the original space.A comparison with both metric and non-metric MDS highlights the pros (in particular, cluster enhancement) and cons of RankVisu.     

Landmark MDS ensemble

Landmark multidimensional scaling (LMDS) uses a subset of data (landmark points) to solve classical multidimensional scaling (MDS), where the scalability is increased but the approximation is noise-sensitive. In this paper we present an LMDS ensemble where we use a portion of the input in a piecewise manner to solve classical MDS, combining individual LMDS solutions which operate on different partitions of the input. Ground control points (GCPs) that are shared by partitions considered in the ensemble, allow us to align individual LMDS solutions in a common coordinate system through affine transformations. We incorporate priors into combining multiple LMDS solutions such that the weighted averaging by priors improves the noise-robustness of our method. Our LMDS ensemble is much less noise-sensitive while maintaining the scalability and the speed of LMDS. Experiments on synthetic data (noisy grid) and real-world data (similar image retrieval) confirm the high performance of the proposed LMDS ensemble.     

Topology-Invariant Similarity of Nonrigid Shapes

This paper explores the problem of similarity criteria between nonrigid shapes. Broadly speaking, such criteria are divided into intrinsic and extrinsic, the first referring to the metric structure of the object and the latter to how it is laid out in the Euclidean space. Both criteria have their advantages and disadvantages: extrinsic similarity is sensitive to nonrigid deformations, while intrinsic similarity is sensitive to topological noise. In this paper, we approach the problem from the perspective of metric geometry. We show that by unifying the extrinsic and intrinsic similarity criteria, it is possible to obtain a stronger topology-invariant similarity, suitable for comparing deformed shapes with different topology. We construct this new joint criterion as a tradeoff between the extrinsic and intrinsic similarity and use it as a set-valued distance. Numerical results demonstrate the efficiency of our approach in cases where using either extrinsic or intrinsic criteria alone would fail.     

应用MDS图的逻辑电路级设计--篮球30秒定时计时器的设计

介绍了用方法直观方便、规范和有章可循的MDS图方法设计小型数字系统“篮球30秒定时计时器”的具体过程。     

无线传感器网络中一种改进的分布式加权多维尺度定位算法

本文在无线传感器网络单跳定位误差分析的基础上,分析了多跳节点定位误差的特性,并据此提出针对分布式加权多维尺度定位(Distributed Weighted Multidimensional Scaling,dwMDS)的权值优化算法.在无法获知参考点确切误差的情况下,利用分析出来的克拉美劳下限代替参考点误差并与距离测量误差合并,更准确的反映了多跳定位中的点与点之间的误差,从而有助于设计更优化的权值.仿真结果表明,使用优化权值改进的算法得到的节点定位误差明显减小.     

一种简易的微机开发系统

本文介绍的开发系统由TRS-80微型机和TP801-280单板机组成,不需增加任何器件。软件开发在微机上进行,可以采用汇编、FORTRAN或二者混合编程。编译产生的目的文件可被装入和并行传送到单板机在线模拟或写EPROM。并介绍了系统连接和有关软件。最后以一个开发实例简述了系统的应用。     

Internet上电子邮件的安全机制

近年来，Internet用户已不满足于简单电子邮件．要求提供多用途的和安全的电子邮件，为此出现了MIME，PEM，PGP和MOSS，S/MIME，PGP／MIMF，MIMEWithMSP等多种方案、本文综述了这些方案，尤其对安全机制进行了详细的讨论。     

分组密码中MDS矩阵的实现方法效能分析

MDS矩阵具有良好的扩散特性,是设计分组密码扩散结构的一种重要手段,并且扩散结构的实现性能将直接影响分组密码的实现性能.文中首先介绍MDS矩阵及其特性,然后以AES中使用的MDS矩阵为例,对MDS矩阵的4种常用实现方法编程实现,并进行定量的效能分析,找到不同应用环境中最优实现方法,对MDS矩阵在其他分组密码算法中的应用具有一定的参考价值.