Mathematical topographical correction of XPS images using multivariate statistical methods

For rough heterogeneous samples, the contrast observed in XPS images may result from both changes in elemental or chemical composition and sample topography. Background image acquisition and subtraction are frequently utilized to minimize topographical effects so that images represent concentration variations in the sample. This procedure may significantly increase the data acquisition time. Multivariate statistical methods can assist in resolving topographical and chemical information from multispectral XPS images. Principal component analysis (PCA) is one method for identification of the highest correlation/variation between the images. Topography, which is common to all of the images, will be resolved in the first most significant component. The score of this component contains spatial information about the topography of the surface, whereas the loading is a quantitative representation of the topography contribution to each elemental/chemical image. The simple‐to‐use self‐modelling mixture analysis (Simplisma) method is a pure variable method that searches for the source of most differences in the data and therefore has the potential to distinguish between chemical and topographical phases in images. The mathematical background correction scheme is developed and validated by comparing results to the experimental background correction for samples with differing degrees of topography. Copyright © 2004 John Wiley & Sons, Ltd.     

Confidence regions in a multivariate regression model with constraints II

The multivariate model, where not only parameters of the mean value of the observation matrix, but also some other parameters occur in constraints, is considered in the paper. Some basic inference is presented under the condition that the covariance matrix is either unknown, or partially unknown, or known. Supported by the grant of the Council of Czech Republic MSM 6 198 959 214.     

基于矩阵初等变换的n元函数极值的快速判别法

给出了 n元函数极值的一个充分条件 ,并结合矩阵的初等变换建立了 n元函数极值的一种快速判别法 ,最后给出了一个例子     

On interpolation by radial polynomials

A lemma of Micchelli's, concerning radial polynomials and weighted sums of point evaluations, is shown to hold for arbitrary linear functionals, as is Schaback's more recent extension of this lemma and Schaback's result concerning interpolation by radial polynomials. Schaback's interpolant is explored. Happy 60th and beyond, Charlie! Mathematics subject classifications (2000) 41A05, 41A6.     

响应矩阵分析方法在BEPC储存环上的应用

由于储存环中各种元件误差的存在, 机器的实际运行模式与设计模式有一定的偏差. 目前广泛开展的响应矩阵方法研究, 可以分析出磁铁元件以及束流位置测量元件的误差, 使束流基本参数得到校正. 介绍了用响应矩阵分析方法, 在BEPC储存环上进行的局部轨道校正的实验研究, 以及BEPC储存环束流参数校正的模拟研究.     

X光底片在位相对标定

本文描述了X光底片在位相对标定技术。其原理是使X射线谱经阶梯形吸收滤片透射后,对X光底片曝光,测量底片的曝光量。文中给出了标定方法和数据处理方法,而且也给出了在X光激光实验中得到的Kodak AA5底片的特性曲线。     

用最小电离的质子刻度铅–闪烁光纤夹层电磁量能器

研究利用最小电离的质子和已知能量的电子来刻度铅–闪烁光纤夹层电磁量能器.对最小电离粒子输出响应进行光纤衰减修正后,利用最小电离质子的输出响应对量能器测量单元进行标定,然后对不同入射能量的电子事例进行能量重建,重建能量与入射能量有很好的线性关系.本文还对最小电离的质子与μ子对量能器测量单元的输出响应进行了比较,发现二者在误差范围内是一致的.     

同输入M/M/∞排队群的联合队长过程

本提出一个偏微分方程方法，用这一方法研究同输入M/M／∞排队群中的联合队长分布。在任意初始条件下，给出了瞬时联合队长分布的多元母函数，也讨论了稳态队长的联合分布及各排队系统之间的相关性。     

Implementation and experimental study on fast object modeling based on multiple structured stripes

In this paper, a practical structured light-based system for fast object modeling and reconstruction is reported. The system consists of an off-the-shelf digital projector, a video camera and a turntable. The system calibration is implemented accurately and automatically by taking a single image of an illuminated calibration template. The multiple color light stripes emitted by the projector adopt only three colors and the color can be modified easily according to the surface texture of the object. This makes the system widely applicable in many situations, and the assumption about color neutrality, which is a limitation to other similar systems, can be largely relaxed. The registration and merging algorithm is greatly simplified by employing a turntable. The validity and high accuracy of the method are demonstrated by extensive real experiments.     

A class of multivariate skew-normal models

The existing model for multivariate skew normal data does not cohere with the joint distribution of a random sample from a univariate skew normal distribution. This incoherence causes awkward interpretation for data analysis in practice, especially in the development of the sampling distribution theory. In this paper, we propose a refined model that is coherent with the joint distribution of the univariate skew normal random sample, for multivariate skew normal data. The proposed model extends and strengthens the multivariate skew model described in Azzalini (1985,Scandinavian Journal of Statistics,12, 171–178). We present a stochastic representation for the newly proposed model, and discuss a bivariate setting, which confirms that the newly proposed model is more plausible than the one given by Azzalini and Dalla Valle (1996,Biometrika,83, 715–726).