彩色组合编码条纹光栅轮廓术

研究了一种新的编码光栅投影三维轮廓术。其中投影光栅利用彩色空间红、绿、蓝三基色相互独立的特性,用彩色条纹对光栅进行编码,以白色条纹为起始位,后接红、绿、蓝（R、G、B）三种颜色的条纺组成一组。改变红、绿、蓝的排列顺序可使各组有不同的编码。根据排列组合原理,红、绿、蓝三色可有6种不同的排列方式。经过编码处理的光栅在保证测量精度不变的前提下,加大了高度测量的范围。由于每条纹只采用0和255两个状态,因此具有较强的抗干扰能力。测量的精度主要取决于图像的分辨率。     

Spectra edited by relative signs of homonuclear couplings of low abundance nuclei

The proposed homonuclear coupling sign edited (HCSE) experiment can detect signed homonuclear couplings between low abundant nuclei like ¹³C, ²⁹Si and ¹⁵N in linear spin systems, that is, in systems where two nuclei are coupled by the measured coupling, and one of them is coupled by a second coupling to a nucleus of different kind. The third nucleus is usually high abundant hydrogen. Two spectra are measured during the HCSE experiment. Their weighed sum and difference yield two other spectra, one containing peaks coupled only by positive measured couplings and the other having peaks coupled by negative measured couplings. The usual E.COSY‐type experiment requires all three couplings in the three spin system (triangular spin system) and not only two couplings as the HCSE experiment. The experiment was successfully tested on known carbon–carbon and silicon–silicon two bond couplings. A set of six simple siloxanes with |²J(Si‐O‐Si)| couplings ranging from 0.5 to 9.0 Hz was measured for the first time, and all the couplings were found to be positive. Copyright © 2012 John Wiley & Sons, Ltd.     

两类特殊图的逆符号边控制数

设G=(V,E)是一个图,对于图G的一个函数f:E→{-1,1},如果对任意e∈E(G),均有Σe′∈N[e]f(e′)≤1,则称f为图G的一个逆符号边控制函数.图G的逆符号边控制数γ′s(G)=max{Σe∈E(G)f(e)|f为图G的一个逆符号边控制函数}.在逆符号边控制数定义基础上,得到了所有轮图和扇图的逆符号边控制数.     

An image encryption scheme based on rotation matrix bit-level permutation and block diffusion

This paper proposes a novel image encryption scheme based on rotation matrix bit-level permutation and block diffusion. Firstly, divide plain image into non-overlapping 8 × 8 pixels blocks with a random matrix, then transform each block into an 8 × 8 × 8 three-dimensional (3-D) binary matrix, which has six directions just as a cube. Permutation is performed by multiplying the 3-D matrix by the rotation matrix that relies on plain image according to different direction. Secondly, use block diffusion to further change the statistical characteristics of the image after confusion. Experiment results and analysis show that the scheme can not only achieve a satisfactory security performance, but also have the suitability for a parallel mode and the robustness against noise in communication system.     

Sum of ranking differences for method discrimination and its validation: comparison of ranks with random numbers

This paper describes the theoretical background, algorithm and validation of a recently developed novel method of ranking based on the sum of ranking differences [TrAC Trends Anal. Chem. 2010; 29 : 101–109]. The ranking is intended to compare models, methods, analytical techniques, panel members, etc. and it is entirely general. First, the objects to be ranked are arranged in the rows and the variables (for example model results) in the columns of an input matrix. Then, the results of each model for each object are ranked in the order of increasing magnitude. The difference between the rank of the model results and the rank of the known, reference or standard results is then computed. (If the golden standard ranking is known the rank differences can be completed easily.) In the end, the absolute values of the differences are summed together for all models to be compared. The sum of ranking differences (SRD) arranges the models in a unique and unambiguous way. The closer the SRD value to zero (i.e. the closer the ranking to the golden standard), the better is the model. The proximity of SRD values shows similarity of the models, whereas large variation will imply dissimilarity. Generally, the average can be accepted as the golden standard in the absence of known or reference results, even if bias is also present in the model results in addition to random error. Validation of the SRD method can be carried out by using simulated random numbers for comparison (permutation test). A recursive algorithm calculates the discrete distribution for a small number of objects (n < 14), whereas the normal distribution is used as a reasonable approximation if the number of objects is large. The theoretical distribution is visualized for random numbers and can be used to identify SRD values for models that are far from being random. The ranking and validation procedures are called Sum of Ranking differences (SRD) and Comparison of Ranks by Random Numbers (CRNN), respectively. Copyright © 2010 John Wiley & Sons, Ltd.     

Comparative analysis of automotive paints by laser induced breakdown spectroscopy and nonparametric permutation tests

Laser-induced breakdown spectroscopy (LIBS) has been investigated for the discrimination of automobile paint samples. Paint samples from automobiles of different makes, models, and years were collected and separated into sets based on the color, presence or absence of effect pigments and the number of paint layers. Twelve LIBS spectra were obtained for each paint sample, each an average of a five single shot “drill down” spectra from consecutive laser ablations in the same spot on the sample. Analyses by a nonparametric permutation test and a parametric Wald test were performed to determine the extent of discrimination within each set of paint samples. The discrimination power and Type I error were assessed for each data analysis method. Conversion of the spectral intensity to a log-scale (base 10) resulted in a higher overall discrimination power while observing the same significance level. Working on the log-scale, the nonparametric permutation tests gave an overall 89.83% discrimination power with a size of Type I error being 4.44% at the nominal significance level of 5%. White paint samples, as a group, were the most difficult to differentiate with the power being only 86.56% followed by 95.83% for black paint samples. Parametric analysis of the data set produced lower discrimination (85.17%) with 3.33% Type I errors, which is not recommended for both theoretical and practical considerations. The nonparametric testing method is applicable across many analytical comparisons, with the specific application described here being the pairwise comparison of automotive paint samples.     

关于置换因子循环布尔矩阵半群（英文）

PMn（B）表示布尔代数B={0,1}上的所有n×n置换因子循环矩阵组成的集合.PMn（B）对于矩阵乘法成为一个半群.刻画了PMn（B）中的幂等元,并给出了半群PMn（B）中的Euler-Fermat定理.     

具有禁用子图的图的（全）符号控制数

给出了具有禁用子图的图的(全)符号控制数的一些下界.