Computation of radiative image formation in isolated source and collimated irradiation problems

Due to the ray effect, it is not suitable to employ the discrete ordinates method to calculate the radiation field and the image-formation process in radiative problems with isolated radiative sources (such as point and line sources, isolated medium or boundary source). In this paper, a hybrid method, named Monte Carlo-discrete ordinates method (MCDOM) is developed. Firstly, the Monte Carlo method is used to calculate the emission process. Secondly, the discrete ordinates method is employed to calculate the scattering process, correspondingly, an alternative energy partitioning method is proposed to combine the above two conventional methods. Thirdly, the DOS+ISW algorithm (JQSRT, 2003, 78: 437-453) is used to calculate the image-formation process. Finally, the MCDOM is applied to computing the image formation of an endoscope, which was used to study the hydrodynamics of circulating fluidized beds (Powder Technology, 2001;114:71-83).     

Visualization and interferometry for cylindrical and spherical sample tomography and profilometry

A technique combining image processing and laser interferometry for visualizing and detecting the deformation of transparent cylindrical and spherical sample is proposed. This deformation includes geometric deformation such as volume transition in profilometry and physical deformation such as refractive index change in tomography. Phase contour lines are used for quantitative analysis and graphical representation of the deformation. This method allows us to visually detect the spatial variation of the deformation field and to evaluate the test quality such as misalignment of optical system. A theoretical analysis using phase contour map to characterize the deformation field is described in detail. A method using phase contour map to qualify the interferometric test is proposed. Analysis of test examples is carried out. Suggestions on using phase contour line method to ameliorate test system design are finally discussed.     

Algorithms for manipulation of level sets of nonparametric density estimates

Summary  We present algorithms for finding the level set tree of a multivariate density estimate. That is, we find the separated components of level sets of the estimate for a series of levels, gather information on the separated components, such as volume and barycenter, and present the information together with the tree structure of the separated components. The algorithm proceeds by first building a binary tree which partitions the support of the density estimate, followed by bottom-up travels of this tree during which we join those parts of the level sets which touch each other. As a byproduct we present an algorithm for evaluating a kernel estimate on a large multidimensional grid. Since we find the barycenters of the separated components of the level sets also for high levels, our method finds the locations of local extremes of the estimate. Writing of this article was financed by Deutsche Forschungsgemeinschaft under project MA1026/8-1.     

The analysis of silica suspensions atomization

The paper contains the results of experimental investigation of air–water and air–silica suspension atomization process in effervescent nozzles with internal mixing obtained by the use of the digital microphotography method. In experiments the different aqueous solutions of silica Aerosil 300 of different concentration have been used. The suspensions containing up to 0.04 (kg solid particles/kg solution) have Newtonian rheological properties. The observations were carried out at liquid flow rates changed from 0.0014 to 0.011 (kg/s) and gas flow rates from 0.00015 to 0.0065 (kg/s). It corresponded to gas to liquid mass ratios (GLR) values from 0.014 to 0.46. The analysis of photos shows that the droplets which have been formed during the liquid atomization have very different sizes. The differences between characteristics of effervescent atomization for water and suspensions used have not been observed. The present study confirmed the previous reports which suggested that the small particles added to solution do not change spray characteristics. The experimental results show that C_D and SMD are non-linear functions of GLR. Their values are decreasing rapidly as GLR is increased from zero to around 0.07 and thereafter decreasing at a slower rate with further increase in GLR. In the same point (GLR = 0.07) the value of α is maximal. The first regime is characteristic for bubbly flow. The second is typical of annular flow regime. Boundary between bubbly and annular flow regime is observed at GLR = 0.07 for investigated systems. The correlations for C_D and Sauter mean diameter were proposed. The results may be used for example to verify numerical models or comparisons with respect to similar atomization processes.     

干涉高光谱图像的空间域显示方法

成像光谱数据在空间域的显示,对与用户的图像解译和信息提取有着至关重要的作用。目前,对于成像光谱数据空间域显示方法的研究主要集中在光谱域数据立方体,针对干涉型成像光谱数据的研究很少。干涉型成像光谱数据的空间域图像显示通常采用的方法是将干涉数据反演,然后采用光谱域图像显示的方法进行显示。干涉型成像光谱数据反演至光谱域计算复杂,耗时长,这对于干涉型光谱数据的空间域实时显示提出了巨大挑战。文章提出了干涉型光谱成像数据的空间域图像实时显示方法,该方法采用不同的光程差权重实现了干涉数据立方体的灰度显示和真彩色显示,推荐了三组权重系数用于干涉数据立方体的显示。将传统并与经过光谱反演的空间域显示方法进行比较,结果表明,在相同空间域显示效果下,该显示方法可大大提高显示速度,并且显示时间随数据立方体的尺寸增长速度缓慢可以满足系统的实时性需要。     

采用栅格特征及可视化技术感知跟踪中目标的遮挡状态

针对监控视频中运动目标跟踪问题,提出了一种栅格划分的梯度朝向直方图特征对目标进行描述,并通过可视化技术来实时观测目标局部区域特征量的变化,通过变化来感知目标在运动中可能发生的遮挡等异常情况.利用感知到的状态自适应调整参加融合计算的子模板的个数,指导跟踪过程,以防止异常情况对跟踪结果的影响,实现了对视频目标准确、稳健的跟踪.实验结果表明,提出的目标局部特征能够较准确地感知目标及周围环境发生的变化,对于提高跟踪算法的鲁棒性和准确性具有积极的作用.     

Infovis and Statistical Graphics: Different Goals,Different Looks

The importance of graphical displays in statistical practice has been recognized sporadically in the statistical literature over the past century, with wider awareness following Tukey's Exploratory Data Analysis and Tufte's books in the succeeding decades. But statistical graphics still occupy an awkward in-between position: within statistics, exploratory and graphical methods represent a minor subfield and are not well integrated with larger themes of modeling and inference. Outside of statistics, infographics (also called information visualization or Infovis) are huge, but their purveyors and enthusiasts appear largely to be uninterested in statistical principles.

We present here a set of goals for graphical displays discussed primarily from the statistical point of view and discuss some inherent contradictions in these goals that may be impeding communication between the fields of statistics and Infovis. One of our constructive suggestions, to Infovis practitioners and statisticians alike, is to try not to cram into a single graph what can be better displayed in two or more. We recognize that we offer only one perspective and intend this article to be a starting point for a wide-ranging discussion among graphic designers, statisticians, and users of statistical methods. The purpose of this article is not to criticize but to explore the different goals that lead researchers in different fields to value different aspects of data visualization.     

Natural Selection: Interactive Subset Creation

Abstract

Visualization is a critical technology for understanding complex, data-rich systems. Effective visualizations make important features of the data immediately recognizable and enable the user to discover interesting and useful results by highlighting patterns. A key element of such systems is the ability to interact with displays of data by selecting a subset for further investigation. This operation is needed for use in linked views systems and in drill-down analysis. It is a common manipulation in many other systems and is as ubiquitous as selecting icons in a desktop graphical user interface (GUI). It is therefore surprising to note that little research has been done on how selection can be implemented. This article addresses this omission, presenting a taxonomy for selection mechanisms and discussing the interactions between branches of the taxonomy.     

Diagnosing Multivariate Outliers Detected by Robust Estimators

We propose a number of diagnostic methods that can be used whenever multiple outliers are identified by robust estimates for multivariate location and scatter. Their main purpose is visualization of the multivariate data to help determine whether the detected outliers (a) form separate clusters or (b) are isolated or randomly scattered (such as heavy tails compared with Gaussian). We make use of Mahalanobis distances and linear projections, to check for separation and to reveal additional aspects of the data structure. Several real data examples are analyzed, and artificial examples are used to illustrate the diagnostic power of the proposed plots.

Code to perform the diagnostics, datasets used as examples in the article and documention are available in the online supplements.