Gaussian pre-filtering for uncertainty minimization in digital image correlation using numerically-designed speckle patterns

This paper discusses the effect of pre-processing image blurring on the uncertainty of two-dimensional digital image correlation (DIC) measurements for the specific case of numerically-designed speckle patterns having particles with well-defined and consistent shape, size and spacing. Such patterns are more suitable for large measurement surfaces on large-scale specimens than traditional spray-painted random patterns without well-defined particles. The methodology consists of numerical simulations where Gaussian digital filters with varying standard deviation are applied to a reference speckle pattern. To simplify the pattern application process for large areas and increase contrast to reduce measurement uncertainty, the speckle shape, mean size and on-center spacing were selected to be representative of numerically-designed patterns that can be applied on large surfaces through different techniques (e.g., spray-painting through stencils). Such “designer patterns” are characterized by well-defined regions of non-zero frequency content and non-zero peaks, and are fundamentally different from typical spray-painted patterns whose frequency content exhibits near-zero peaks. The effect of blurring filters is examined for constant, linear, quadratic and cubic displacement fields. Maximum strains between ±250 and ±20,000 µε are simulated, thus covering a relevant range for structural materials subjected to service and ultimate stresses. The robustness of the simulation procedure is verified experimentally using a physical speckle pattern subjected to constant displacements. The stability of the relation between standard deviation of the Gaussian filter and measurement uncertainty is assessed for linear displacement fields at varying image noise levels, subset size, and frequency content of the speckle pattern. It is shown that bias error as well as measurement uncertainty are minimized through Gaussian pre-filtering. This finding does not apply to typical spray-painted patterns without well-defined particles, for which image blurring is only beneficial in reducing bias errors.     

Weight distributions of a class of cyclic codes with arbitrary number of nonzeros in quadratic case

Cyclic codes are an important class of linear codes, whose weight distribution have been extensively studied. So far, most of previous results obtained were for cyclic codes with no more than three nonzeros. Recently, the authors of [37] constructed a class of cyclic codes with arbitrary number of nonzeros, and computed the weight distribution for several cases. In this paper, we determine the weight distribution for a new family of such codes. This is achieved by introducing certain new methods, such as the theory of Jacobi sums over finite fields and subtle treatment of some complicated combinatorial identities.     

Prediction of an outcome using NETwork Clusters (NET-C)

Birth weight is a key consequence of environmental exposures and metabolic alterations and can influence lifelong health. While a number of methods have been used to examine associations of trace element (including essential nutrients and toxic metals) concentrations or metabolite concentrations with a health outcome, birth weight, studies evaluating how the coexistence of these factors impacts birth weight are extremely limited. Here, we present a novel algorithm NETwork Clusters (NET-C), to improve the prediction of outcome by considering the interactions of features in the network and then apply this method to predict birth weight by jointly modelling trace element and cord blood metabolite data. Specifically, by using trace element and/or metabolite subnetworks as groups, we apply group lasso to estimate birth weight. We conducted statistical simulation studies to examine how both sample size and correlations between grouped features and the outcome affect prediction performance. We showed that in terms of prediction error, our proposed method outperformed other methods such as (a) group lasso with groups defined by hierarchical clustering, (b) random forest regression and (c) neural networks. We applied our method to data ascertained as part of the New Hampshire Birth Cohort Study on trace elements, metabolites and birth outcomes, adjusting for other covariates such as maternal body mass index (BMI) and enrollment age. Our proposed method can be applied to a variety of similarly structured high-dimensional datasets to predict health outcomes.     

Scale-space point spread function based framework to boost infrared target detection algorithms

Small target detection is one of the major concern in the development of infrared surveillance systems. Detection algorithms based on Gaussian target modeling have attracted most attention from researchers in this field. However, the lack of accurate target modeling limits the performance of this type of infrared small target detection algorithms. In this paper, signal to clutter ratio (SCR) improvement mechanism based on the matched filter is described in detail and effect of Point Spread Function (PSF) on the intensity and spatial distribution of the target pixels is clarified comprehensively. In the following, a new parametric model for small infrared targets is developed based on the PSF of imaging system which can be considered as a matched filter. Based on this model, a new framework to boost model-based infrared target detection algorithms is presented. In order to show the performance of this new framework, the proposed model is adopted in Laplacian scale-space algorithms which is a well-known algorithm in the small infrared target detection field. Simulation results show that the proposed framework has better detection performance in comparison with the Gaussian one and improves the overall performance of IRST system. By analyzing the performance of the proposed algorithm based on this new framework in a quantitative manner, this new framework shows at least 20% improvement in the output SCR values in comparison with Laplacian of Gaussian (LoG) algorithm.     

各向同性光纤中拉曼增益对光脉冲自陡峭的影响

运用数学解析法导出了关于拉曼增益与自陡峭综合效应的光脉冲传输方程,在此基础上引入洛伦兹模型将拉曼增益整合到非线性系数中来研究光脉冲中拉曼增益对自陡峭效应的作用,重点分析了高斯脉冲在各向同性光纤中传播时,拉曼增益对其自陡峭效应具体影响方式,结果表明拉曼增益会减弱自陡峭中后沿偏移程度,减小脉冲展宽,但不会影响其峰值大小.     

光阑对偏振高斯-谢尔模型光束偏振特性的影响（英文）

根据广义惠更斯-菲涅尔原理和Collins公式,基于复高斯函数展开法,推导出椭圆偏振的高斯-谢尔模型光束经过矩形光阑衍射后的交叉谱密度公式,结合斯托克斯矢量理论推导了椭圆偏振高斯-谢尔模型光束在接收平面的光强、偏振度、方位角和椭圆度的表达式,数值分析了光阑的孔径遮拦比对光强、偏振度及方位角和椭圆度的影响.结果表明,光阑的孔径遮拦比在近场区对经过光阑后椭圆偏振高斯-谢尔模型光束的光强和偏振特性有显著影响;随着传输距离的增大,光强和偏振特性受孔径遮拦比的影响减小,光强和偏振特性变化平稳.     

激光通过散射介质的聚焦

基于角谱衍射公式和散射介质的圆形高斯分布模型,数值模拟光通过散射介质的聚焦.通过反馈优化算法实现散射光束的波前整形,在目标位置处形成一个很亮的聚焦光斑.改变聚焦目标位置后,可实现任意位置处的单点或多点聚焦.讨论了空间光调制器上的总调制单元个数、每个调制单元相位准确度与光强增长因子的关系.结果表明:目标位置处的光强随着总像素单元数的增加而线性地增强,并且随着调制单元的相位准确度的增加而增强.     

基于CCD对高斯光束进行实时判断及特性参数的测量

主要使用等距四点采光测量法,用CCD进行采样,电脑编程处理来判断、分析和研究高斯光束的光强在空间的分布情况——传播特性。并利用计算机可以实时判断激光器输出是否为高斯光束,如是高斯光束,还可以实验测得高斯光束的径向强度分布、束径以及扩散角。     

Optimum step-stress accelerated degradation test for Wiener degradation process under constraints

To assess a product's reliability for subsequent managerial decisions such as designing an extended warranty policy and developing a maintenance schedule, Accelerated Degradation Test (ADT) has been used to obtain reliability information in a timely manner. In particular, Step-Stress ADT (SSADT) is one of the most commonly used stress loadings for shortening test duration and reducing the required sample size. Although it was demonstrated in many previous studies that the optimum SSADT plan is actually a simple SSADT plan using only two stress levels, most of these results were obtained numerically on a case-by-case basis. In this paper, we formally prove that, under the Wiener degradation model with a drift parameter being a linear function of the (transformed) stress level, a multi-level SSADT plan will degenerate to a simple SSADT plan under many commonly used optimization criteria and some practical constraints. We also show that, under our model assumptions, any SSADT plan with more than two distinct stress levels cannot be optimal. These results are useful for searching for an optimum SSADT plan, since one needs to focus only on simple SSADTs. A numerical example is presented to compare the efficiency of the proposed optimum simple SSADT plans and a SSADT plan proposed by a previous study. In addition, a simulation study is conducted for investigating the efficiency of the proposed SSADT plans when the sample size is small.     

Exponentially modified Gaussian relevance to the distributions of translocation events in nanopore-based single molecule detection

Nanopore technique plays an important role in single molecule detection, which illuminates the properties of an individual molecule by analyzing the blockage durations and currents. However, the traditional exponential function is lack of efficiency to describe the distributions of blockage durations in nanopore experiments. Herein, we introduced an exponentially modified Gaussian （EMG） function to fit the duration histograms of both simulated events and experimental events. In comparison with the traditional exponential function, our results demonstrated that the EMG provides a better fit while covers the entire range of the distributions. In particular, the fitted parameters of EMG could be directly used to discriminate the sequence length of the oligonucleotides at single molecule level.