胶体中布朗粒子简谐束缚下准直高斯光束的动态光散射

在将散射粒子间的相互作用看作简谐力、入射光为准直高斯光束、散射光为理想平面光波的模型下研究了胶体溶液中布朗粒子的统计特征并求解了动态光散射的散射光电场强度的自相关函数；     

智能化红外焊缝检测仪的实时信号自相关分析

详细介绍了在改进红外焊缝检测仪的过程中实时信号的一种抗干扰方法－－自相关方法。重点离自相关方法的数学模型，软件框图及设计思想，并给出了实验结果。     

基于CITAF-HAF的立方调频信号参数估计

针对立方调频（Cubic Frequency Modulated,CFM）信号的参数估计问题,提出了一种基于高阶模糊函数（High order Ambiguity Function,HAF）和相参积累三阶自相关函数（Coherently Integrated Trilinear Autocorrelation Function,CITAF）的参数估计方法。利用HAF将立方相位信号降阶为二次调频（Quadratic Frequency Modulated,QFM）信号,再利用CITAF完成参数估计。由于CITAF能够在时域和时延域完成信号能量的二维相参积累,其实现过程利用复乘、傅里叶变换和加法操作即可完成,因此该方法能够提高参数估计的分辨率和抗噪声干扰能力,并保持较低的计算量。实验结果证实了该算法的有效性和性能上的优越性。     

路面不平度模拟及其可视化系统的研究

根据路面不平度、自相关函数及功率谱密度三者之问的关系，利用时域AR模型模拟了国标GB7031-86规定的各级路面不平度，并且在Visual C＋＋6．0环境下利用跨平台的虚拟现实集成开发包WTK9．0实现了各级路面不平度的可视化。     

基音周期估计中倍数检验算法的研究

讨论了基于自相关函数法基音周期估计过程中的倍数检验和校正问题。自相关函数法是语音信号处理中的一种重要方法,但往往会把真实基音周期的倍数误判为真实的基音周期。为了防止误判,提出了倍数检验算法。实验表明:使用该算法,提高了基音周期估计的准确性,改善了基音周期轨迹的平滑度。     

闪烁噪声的自相关函数及其表征

闪烁噪声是一种非平稳随机过程,其功率谱密度函数在频率低端（ｆ＝０）发散,无法直接利用Ｗｉｅｎｅｒ－Ｋｈｉｎｔｃｈｉｎｅ关系得到它的自相关函数,本文采用“降阶－积分”方法,得到了频率源中闪烁调频噪声的自相关函数的解析表达式。根据这一结果,分析了频率稳定度的表征原理,指出了频率稳定度表征的实质是将非平稳过程转化为平稳过程并求取时间平均。最后,通过时域分析得到了闪烁噪声的Ａｌｌａｎ方差表达式。     

View-based virtual learning and recognition of 3D object using view model obtained by motion-stereo

View-based approach for learning and recognition of 3D object and its pose detection was proved to be affective and efficient, except its high learning cost. In this research, we propose a virtual learning approach which generates learning samples of views of an object from its 3D view model obtained by motion-stereo method. From the generated learning sample views, features of high-order autocorrelation are extracted, and discriminant feature spaces for object recognition and pose detection are built. Recognition experiments on real objects are carried out to show the effectiveness of the proposed method. Caihua Wang, Ph.D.: He received his B.S. in mathematics and M.E. in electronic engineering from Renmin University of China, Beijing, China in 1983 and 1986, and his Ph. D. from Shizuoka University, Hamamatsu, Japan in 1996. He is a JST domestic fellow and is doing his post doctoral research at Electrotechnical Laboratory. His research interests are computer vision and image processing. He is a member of IEICE and IPSJ. Katsuhiko Sakaue, Ph.D.: He received the B.E., M.E., and Ph.D. degrees all in electronic engineering from University of Tokyo, in 1976, 1978 and 1981, respectively. In 1981, he joined the Electrotechnical Laboratory, Ministry of International Trade and Industry, and engaged in researches in image processing and computer vision. He received the Encouragement Prize in 1979 from IEICE, and the Paper Award in 1985 from Information.     

基于名义尺度的空间自相关指数测度方法研究*

从空间自相关的基本构成出发,提出了一种测度名义尺度的空间自相关指数——自邻接指数,给出了基于名义尺度下全局空间自相关和局部空间自相关的测度方法,并在ArcView平台下通过Avenue二次编程进行了算法实现。最后通过例子展示了自邻接指数在土地利用格局分析中发挥的重要作用。