欠采样对LFM脉冲压缩的影响

研究了对LFM信号进行正交I/Q采样时,采样率对脉冲压缩处理的影响。理论分析证明:在欠采样情况下,脉冲压缩后会出现成对回波,因而系统的多目标分辨能力受到影响;由于信号带宽的损失,脉压输出回波信号的主瓣展宽,距离分辨力下降;另外,主瓣幅度或相位还会受到调制。最后给出了仿真的结果。     

分形图像压缩的进展

扼要综述了几何学新理论分形（Ｆｒａｃｔａｌ）用于静止图像编码与运动图像编码的原理与研究进展。     

随钻声波测井数据压缩的小波神经网络法

针对当前石油工程声波随钻测井中大量测井数据需要在井下实时压缩存储的迫切要求,结合声波测井数据的特点,提出了一种基于小波神经网络的数据压缩算法,并利用共轭梯度法优化网络参数。实验结果表明,该算法具有参数收敛速度快、数据压缩比高的特点,能够满足随钻声波数据高速实时处理的需要。     

Carlitz-Klamkin不等式的指数推广及其应用

将几何不等式中的 Carlitz-Klamkin 不等式作了指数推广,讨论了推广结果的一些应用,提出了三个有关的猜想。     

快速神经网络无损压缩方法研究

传统的人工神经网络数据编码算法需要离线训练且编码速度慢,因此通常多用于专用有损编码领域如声音、图像编码等,在无损数据编码领域应用较少。针对这种现状,该文详细地研究了最大熵统计模型和神经网络算法各自的特点,提出了一种基于最大熵原理的神经网络概率预测模型并结合自适应算术编码来进行数据压缩,具有精简的网络结构的自适应在线学习算法。试验表明,该算法在压缩率上可以优于目前流行的压缩算法Limpel-Zip(zip,gzip),并且在运行时间和所需空间性能上同PPM和Burrows Wheeler算法相比也是颇具竞争力的。该算法实现为多输入和单输出的两层神经网络,用已编码比特的学习结果作为待编码比特的工作参数,符合数据上下文相关约束的特点,提高了预测精度,并节约了编码时间。     

基于FastICA的高光谱图像压缩技术

提出了一种基于快速独立分量分析(FastICA)的高光谱图像压缩算法。首先引入虚拟维数算法估计图像中的目标端元个数,进而提取出感兴趣的目标端元矢量,并初始化快速独立分量分析的混合矩阵;利用最小噪声分量变换对原始数据进行降维,从降维后的主分量中提取独立分量;对独立分量进行恒虚警率检测与形态学滤波,实现目标分割。对高光谱图像进行谱间Karhunen-Loeve变换,利用比例位移法对感兴趣目标的小波系数进行提升,最后对各主分量进行最优码率的SPIHT压缩。实验结果表明,该方法在获得较高压缩性能的同时能够有效地保留感兴趣的目标。     

H.264在会议电视业务中的应用

本文首先介绍了会议电视业务的概念以及未来的发展方向,然后重点讲述了H.264图像压缩编码技术,对H.264技术优势进行介绍,并对H.264技术以及采用了H.264技术的会议电视业务未来的应用前景进行了分析.     

利用有色浓淡斑点进行三维空间位置测试

提出有色方向符号匹配法,提出有色浓淡斑点,并利用顶点处配置了有色浓淡斑点的三角形标示进行空间位置测试。     

Longitudinal network models and permutation-uniform Markov chains

Consider longitudinal networks whose edges turn on and off according to a discrete-time Markov chain with exponential-family transition probabilities. We characterize when their joint distributions are also exponential families with the same parameter, improving data reduction. Further we show that the permutation-uniform subclass of these chains permit interpretation as an independent, identically distributed sequence on the same state space. We then apply these ideas to temporal exponential random graph models, for which permutation uniformity is well suited, and discuss mean-parameter convergence, dyadic independence, and exchangeability. Our framework facilitates our introducing a new network model; simplifies analysis of some network and autoregressive models from the literature, including by permitting closed-form expressions for maximum likelihood estimates for some models; and facilitates applying standard tools to longitudinal-network Markov chains from either asymptotics or single-observation exponential random graph models.     

Leaving Home and Living Alone: An Historical Perspective

This paper provides a temporal and spatial perspective on a variety of household forms in present-day Europe. Compared with the situation in pre-industrial England, many more people now live on their own but there are some surprising continuities in household forms. Notably, pre-industrial households were no more likely than present-day ones to include distant relatives, and the recent rise in the proportion of one-person families has simply returned the position to that produced by early widowhood in the seventeenth century. Nor has the general increase during recent decades in the proportion of one-person households reduced the variation within Europe in the frequency of living alone which remains much less likely in southern and parts of eastern Europe than in western Europe and Scandinavia. A more thorough comparative exercise is hampered by inconsistencies in the design of tables used to illustrate household types in different countries, and it is suggested that a standard set of tables should be agreed and produced for different national populations.