Precoloring extension on unit interval graphs

In the precoloring extension problem a graph is given with some of the vertices having preassigned colors and it has to be decided whether this coloring can be extended to a proper k-coloring of the graph. Answering an open question of Hujter and Tuza [Precoloring extension. III. Classes of perfect graphs, Combin. Probab. Comput. 5 (1) (1996) 35-56], we show that the precoloring extension problem is NP-complete on unit interval graphs.     

Numerical control of kohonen neural network for scattered data approximation

Surface reconstruction from scattered data using Kohonen neural network is presented in this paper. The network produces a topologically predefined grid from the unordered data which can be applied as a rough approximation of the input set or as a base surface for further process. The quality and computing time of the approximation can be controlled by numerical parameters. As a further application, ruled surface is produced from a set of unordered lines by the network. AMS subject classification 68U07, 65D17, 68T20     

η-INVARIANT AND CHERN-SIMONS CURRENT

The author presents an alternate proof of the Bismut-Zhang localization formula of ηinvariants, when the target manifold is a sphere, by using ideas of mod k index theory instead of the difficult analytic localization techniques of Bismut-Lebeau. As a consequence, it is shown that the R/Z part of the aualytically defined η invariant of Atiyah-Patodi-Singer for a Dirac operator on an odd dimensional closed spin manifold can be expressed purely geometrically through a stable Chern-Simons current on a higher dimensional sphere. As a preliminary application, the author discusses the relation with the Atiyah-Patodi-Singer R/Z index theorem for unitary flat vector bundles,and proves an R refinement in the case where the Dirac operator is replaced by the Signature operator.     

一种基于时域和频域特征的图像拼接方法

1 引言图像拼接是指将具有部分景物重合的图像进行无缝拼合,生成一张具有较宽视角的高分辨率图像的技术．现有的图像拼接技术主要有两类:基于区域的方法和基于特征的方法．基于区域的方法[1]通常是取参考图像中某一窗口中的点作为特征模板,建立描述     

Building nonredundant adaptive wavelets by update lifting

In this paper, we propose a technique for building adaptive wavelets by means of an extension of the lifting scheme. Our scheme comprises an adaptive update lifting step and a fixed prediction lifting step. The adaptivity consists hereof that the system can choose between two different update filters, and that this choice is triggered by the local gradient of the original signal. If the gradient is large (in some seminorm sense) it chooses one filter, if it is small the other. We derive necessary and sufficient conditions for the invertibility of such an adaptive system for various scenarios. Furthermore, we present some examples to illustrate our theoretical results.     

Shannon sampling II: Connections to learning theory

We continue our study [S. Smale, D.X. Zhou, Shannon sampling and function reconstruction from point values, Bull. Amer. Math. Soc. 41 (2004) 279–305] of Shannon sampling and function reconstruction. In this paper, the error analysis is improved. Then we show how our approach can be applied to learning theory: a functional analysis framework is presented; dimension independent probability estimates are given not only for the error in the L² spaces, but also for the error in the reproducing kernel Hilbert space where the learning algorithm is performed. Covering number arguments are replaced by estimates of integral operators.     

On the \Gamma-limit of the Mumford-Shah functional

We study by means of -convergence the asymptotics of the rescaled Mumford-Shah functional

激光光栅显微图像检测技术及其应用

介绍一种可以用来测量曲面试件表面应分量（εｚ,εθ,εｚθ）的激光光栅显微图像检测技术（ＩＧＭ）,它能实现从小应变到大应变全过程的高精度定量测量。我们将这一技术成功地应用在铁基形状记忆合金（ＦＳＭＡ）管接头的结构设计中,在这项研究中,首先在管接头结构表面制作了变形的载体－位相型干涉光栅,由结构表面产生的衍射光通宵我学系统后形成新的干涉光栅,我们利用数字图像系统接收了逆相变前后各自的干涉图像,采用带     

网格数字图像相关方法测量位移场的研究

本文将数字图像相关方法与古老的网格法相结合,给出了一种测量面内位移场的新方法——网格数字图像相关方法,实现了高精度的测量,精度可达0.02亚像素．在该方法中网格点采用圆标记点,使该方法特别适用于小线应变位移场的研测．本文运用Windows下的新的图像采集方法测量物体位移场,突破了Dos系统下对内存操作的限制,并使每一帧图像的像素数由512×512增至768×576,提高了图形的分辨率．     

Deformation measurements by digital image correlation: Implementation of a second-order displacement gradient

This paper outlines the procedure for refining the digital image correlation (DIC) method by implementing a second-order approximation of the displacement gradients. The second-order approximation allows the DIC method to directly measure both the first- and second-order displacement gradients resulting from nonlinear deformation. Thirteen unknown parameters, consisting of the components of displacement, the first- and second-order displacement gradients and the gray-scale value offset, are determined through optimization of a correlation coefficient. The previous DIC method assumes that the local deformation in a subset of pixels is represented by a first-order Taylor series approximation for the displacement gradient terms, so actual deformations consisting of higher order displacement gradients tend to distort the infinitesimal strain measurements. By refining the method to measure both the first- and second-order displacement gradients, more accurate strain measurements can be achieved in large-deformation situations where second-order deformations are also present. In most cases, the new refinements allow the DIC method to maintain an accuracy of ±0.0002 for the first-order displacement gradients and to reach ±0.0002 per pixel for the second-order displacement gradients.