移动通信中的无线信道指纹特征建模

为了探索无线信道的传播特征并建立相应的无线信道模型,针对无线信道的"指纹"特征建模及场景识别问题,提出基于无迹卡尔曼神经网络的信道建模方法.首先采用霍特林变换对信道传播数据进行预处理,实现数据从复数域到实数域的转换,然后利用主元分析对数据进行降维,得到降维数据后,采用无迹卡尔曼神经网络进行无线信道"指纹"特征建模,最后根据模型的输出值建立无线信道的评价指标,对测量的信道数据进行场景划分以及场景识别.仿真结果表明:无迹卡尔曼神经网络所建信道模型可准确对无线信道进行类别划分和场景识别.     

基于SOM网络的机场天气聚类分析

机场范围内的天气状况对机场运行有着较为显著的影响.然而,天气在发生时间、影响范围等方面的随机性给机场容量评估和流量调配带来一定的困难.运用SOM网络对2014年上海浦东国际机场的天气进行聚类分析,并运用DB指标和Silhouette指标对聚类结果进行评价.结果表明:SOM网络在机场天气聚类上有很好的精度与鲁棒性,2014年浦东机场的天气可按影响程度分为典型的五类,即:无影响、影响小、影响极大、影响大、影响中等.为实现航空天气聚类分析及空中交通流量管理自动化提供了有益参考.     

A dexterous feature selection artificial immune system algorithm for keystroke dynamics

The main problem in security protecting the computer or resources from intruders. The password and username are the most common means to provide security. But this method has many loop holes such as password sharing, shoulder surfing, brute-force attack, dictionary attack, guessing, and many more. Keystroke dynamics is one popular and inexpensive behavioral biometric technology, which identifies the authenticity of a user when the user is working via a keyboard. Keystroke features like dwell time and flight time of every user are evaluated in this paper by preprocessing techniques such as Hausdroff timing, mean, median, and standard deviation. The artificial immune system is used for feature selection, and comparison between preprocessing techniques is shown.     

Dempster–Shafer fusion of evidential pairwise Markov fields

Hidden Markov fields (HMFs) have been successfully used in many areas to take spatial information into account. In such models, the hidden process of interest X is a Markov field, that is to be estimated from an observable process Y. The possibility of such estimation is due to the fact that the conditional distribution of the hidden process with respect to the observed one remains Markovian. The latter property remains valid when the pairwise process $(X,Y)$ is Markov and such models, called pairwise Markov fields (PMFs), have been shown to offer larger modeling capabilities while exhibiting similar processing cost. Further extensions lead to a family of more general models called triplet Markov fields (TMFs) in which the triplet $(U,X,Y)$ is Markov where U is an underlying process that may have different meanings according to the application. A link has also been established between these models and the theory of evidence, opening new possibilities of achieving Dempster–Shafer fusion in Markov fields context. The aim of this paper is to propose a unifying general formalism allowing all conventional modeling and processing possibilities regarding information imprecision, sensor unreliability and data fusion in Markov fields context. The generality of the proposed formalism is shown theoretically through some illustrative examples dealing with image segmentation, and experimentally on hand-drawn and SAR images.     

结合NSDTCT和稀疏表示的遥感图像融合

为了提高全色图像与多光谱图像的融合质量,提出一种基于非下采样双树复轮廓波变换和稀疏表示的图像融合算法.对多光谱图像进行亮度-色度-饱和度变换,再对亮度分量和全色图像进行直方图匹配及亮度平滑滤波处理.利用非下采样双树复轮廓波变换处理亮度分量和全色图像,得到对应的高低频系数.对于低频系数,利用稀疏表示进行融合,采用空间频率和l1范数双指标取大的融合规则得到稀疏表示系数;对于高频系数,将改进的拉普拉斯能量和作为脉冲耦合神经网络的外部输入项,提出了改进的脉冲耦合神经网络的融合策略.最后进行非下采样双树复轮廓波逆变换和亮度-色度-饱和度逆变换得到融合结果.实验结果表明,该算法最大限度地保留光谱信息的同时可以提高空间分辨率,视觉效果及客观指标均优于经典的融合算法.     

基于案例和规则融合的民用客机故障诊断系统设计

目前很多工程应用中的飞机故障诊断方法,只考虑单一的基于案例或基于规则的诊断方法,故障诊断的效率有待提高。本文针对民用客机地面运营支持系统的特点和要求,将案例和规则融合的推理机制引入到飞机故障诊断系统的设计中。该方法采用规则推理为前导、案例推理后置补充的结合方式,最大化地利用飞机故障隔离手册知识和从维修记录中提取的案例知识,从而提高首次故障隔离效率、降低无效排故时间。以民用客机某系统的典型故障为例,阐述了该推理机制在故障诊断中的应用。通过历史飞行数据试验,验证了提出的基于案例与规则融合诊断方法的有效性,以此为基础设计的故障诊断方案可为航空公司构建更完善的专家系统提供参考。     

基于形态学空间特征的高光谱遥感图像分类方法

传统的高光谱图像分类主要是基于像素的光谱特征,在一定程度上忽略了高光谱遥感图像中像素之间的空间相关性。为了充分利用高光谱图像中的空间信息,提出了一种基于加权多结构元素无偏差形态学的空间特征提取方法,并基于形态学的多尺度特征和结构保持性提出了基于邻域的多尺度空间特征提取方法,得到了高光谱遥感图像的空间特征。对k-NN分类算法进行改进,提出了基于变精度粗糙集和重构误差的k-NN分类算法,实现了基于空间特征的高光谱遥感图像分类。在两个不同的高光谱遥感图像的实验验证了基于空间特征和改进k-NN分类算法的性能。     

制备大直径无气泡聚苯乙烯空心微球

 高品质聚苯乙烯（PS）空心微球是惯性约束聚变（ICF）实验用多层塑料微球靶的重要芯轴,常由乳液技术制备。针对由乳液技术制备的PS微球壳壁内容易形成气泡而且大直径微球制备困难的问题,实验研究了对壳壁内存在的气泡以及消除方法和制备大直径PS微球的制备技术。     

指纹图像的定量化区域矢量编码与识别

为了识别一幅图像 ,通常需要对它进行编码 ,提取特征信息。大体上可以归纳为两类方法 :统计方法和语言(或结构 )学方法。两类方法各有优缺点。第一类方法很少利用图像本身的结构关系 ,而第二类方法则没有考虑图像在环境中所受的噪声干扰。因此把二者结合起来 ,各取其长 ,是可取的途径。在统计方法的基础上 ,考虑了指纹的结构特点 ,按纹理方向进行分组 ,对它们实行定量化编码 ,并由此建立了方向矢量样本形式 ,给出了试验结果。这种方法的样本数据量小 ,构造过程简单 ,匹配和识别的信噪比高 ,不易产生错误匹配和判别。     

Optical Haar Wavelet for Extracting Edge Features along Arbitrary Directions

A novel circular Haar wavelet (CHW), which is obtained by a radially distributed polarization state, is proposed. Optical wavelet transforms using the proposed CHW are implemented by computer simulations. The proposed CHW is shown to have remarkable features: (1) it is useful for detecting outlines of input images; (2) one polarization component is available for extracting an edge feature along a required direction; and (3) unlike the previously proposed CHW (Z. He and S. Sato: Opt. Lett. 19 (1994) 686), this one can be applied to a multi-resolution edge extraction (Y. Sheng, D. Roberge and H. H. Szu: Opt. Eng. 31 (1992) 1840).