Illustrative stream surfaces

Stream surfaces are an intuitive approach to represent 3D vector fields. In many cases, however, they are challenging objects to visualize and to understand, due to a high degree of self-occlusion. Despite the need for adequate rendering methods, little work has been done so far in this important research area. In this paper, we present an illustrative rendering strategy for stream surfaces. In our approach, we apply various rendering techniques, which are inspired by the traditional flow illustrations drawn by Dallmann and Abraham \& Shaw in the early 1980s. Among these techniques are contour lines and halftoning to show the overall surface shape. Flow direction as well as singularities on the stream surface are depicted by illustrative surface streamlines. ;To go beyond reproducing static text book images, we provide several interaction features, such as movable cuts and slabs allowing an interactive exploration of the flow and insights into subjacent structures, e.g., the inner windings of vortex breakdown bubbles. These methods take only the parameterized stream surface as input, require no further preprocessing, and can be freely combined by the user. We explain the design, GPU-implementation, and combination of the different illustrative rendering and interaction methods and demonstrate the potential of our approach by applying it to stream surfaces from various flow simulations. ;     

加速体绘制技术

根据体绘制成像的各个操作环节，对体绘制的加速技术进行了较全面且系统的介绍，包括色彩合成、光线与数据的求交、插值计算、排序及视见变换、对体绘制的基于硬件及系统方面的加速技术（如并行体绘制和漫游体绘制）也进行了一些讨论，在实际应用中，只有将各种加速技术进行有机地结合才能充分发挥体绘制的可视化作用。     

Analyzing complex FTMS simulations: a case study in high-level visualization of ion motions

Current practice in particle visualization renders particle position data directly onto the screen as points or glyphs. Using a camera placed at a fixed position, particle motions can be visualized by rendering trajectories or by animations. Applying such direct techniques to large, time dependent particle data sets often results in cluttered images in which the dynamic properties of the underlying system are difficult to interpret. In this case study we take an alternative approach to the visualization of ion motions. Instead of rendering ion position data directly, we first extract meaningful motion information from the ion position data and then map this information onto geometric primitives. Our goal is to produce high-level visualizations that reflect the physicists' way of thinking about ion dynamics. Parameterized geometric icons are defined to encode motion information of clusters of related ions. In addition, a parameterized camera control mechanism is used to analyze relative instead of only absolute ion motions. We apply the techniques to simulations of Fourier transform mass spectrometry (FTMS) experiments. The data produced by such simulations can amount to 5 10(4) ions and 10(5) timesteps. This paper discusses the requirements, design and informal evaluation of the implemented system.     

An efficient and scalable parallel algorithm for out-of-core isosurface extraction and rendering

We consider the problem of isosurface extraction and rendering for large scale time-varying data. Such data sets have been appearing at an increasing rate especially from physics-based simulations, and can range in size from hundreds of gigabytes to tens of terabytes. Isosurface extraction and rendering is one of the most widely used visualization techniques to explore and analyze such data sets. A common strategy for isosurface extraction involves the determination of the so-called active cells followed by a triangulation of these cells based on linear interpolation, and ending with a rendering of the triangular mesh. We develop a new simple indexing scheme for out-of-core processing of large scale data sets, which enables the identification of the active cells extremely quickly, using more compact indexing structure and more effective bulk data movement than previous schemes. Moreover, our scheme leads to an efficient and scalable implementation on multiprocessor environments in which each processor has access to its own local disk. In particular, our parallel algorithm provably achieves load balancing across the processors independent of the isovalue, with almost no overhead in the total amount of work relative to the sequential algorithm. We conduct a large number of experimental tests on the University of Maryland Visualization Cluster using the Richtmyer–Meshkov instability data set, and obtain results that consistently validate the efficiency and the scalability of our algorithm.     

基于微机环境的三维数据场多等值面快速显示算法

直接体绘制技术能够利用半透明效果显示三维数据场，提供了比等值面绘制方法更为丰富的信息，但是，由于数据场中所有体素都参与了图象生成过程，使得该技术的计算开销昂贵，远远无法达到交互式操作的要求。事实上，如果用边界表示法来表示三维数据场，就可以利用三维空间连续性来大幅度缩短绘制时间。边界表示法只关心有值面穿过的边界体元，用Ｏ内存单元来表示大小的原始数据场，从而产生大规模数据压缩。本文在此基础上提出一种基     

Transform coding for hardware-accelerated volume rendering

Hardware-accelerated volume rendering using the GPU is now the standard approach for real-time volume rendering, although limited graphics memory can present a problem when rendering large volume data sets. Volumetric compression in which the decompression is coupled to rendering has been shown to be an effective solution to this problem; however, most existing techniques were developed in the context of software volume rendering, and all but the simplest approaches are prohibitive in a real-time hardware-accelerated volume rendering context. In this paper we present a novel block-based transform coding scheme designed specifically with real-time volume rendering in mind, such that the decompression is fast without sacrificing compression quality. This is made possible by consolidating the inverse transform with dequantization in such a way as to allow most of the reprojection to be precomputed. Furthermore, we take advantage of the freedom afforded by off-line compression in order to optimize the encoding as much as possible while hiding this complexity from the decoder. In this context we develop a new block classification scheme which allows us to preserve perceptually important features in the compression. The result of this work is an asymmetric transform coding scheme that allows very large volumes to be compressed and then decompressed in real-time while rendering on the GPU.     

The modified embedded-atom method interatomic potentials and recent progress in atomistic simulations

Atomistic simulations such as molecular dynamics and Monte Carlo are widely used for understanding the material behavior at a more fundamental level, e.g., at the atomic level. However, there still exist limitations in the variety of material systems, specimen size and simulation time. This article briefly outlines the formalism and performance of the second nearest-neighbor modified embedded-atom method, an interatomic potential formalism applicable to a wide range of materials systems. Recent progresses made to overcome the inherent size and time limitations of atomistic simulations are also introduced along with the challenges still remaining in extending their applicability. Finally, the authors release all the potential parameter sets for elements and alloy systems, and relevant homemade atomistic simulation codes based on the interatomic potential formalism with a user guide.     

Adaptive Surface Visualization of Vessels with Animated Blood Flow

The investigation of hemodynamic information for the assessment of cardiovascular diseases (CVDs) gained importance in recent years. Improved flow measuring modalities and computational fluid dynamics (CFD) simulations yield in reliable blood flow information. For a visual exploration of the flow information, domain experts are used to investigate the flow information combined with its enclosed vessel anatomy. Since the flow is spatially embedded in the surrounding vessel surface, occlusion problems have to be resolved. A visual reduction of the vessel surface that still provides important anatomical features is required. We accomplish this by applying an adaptive surface visualization inspired by the suggestive contour measure. Furthermore, an illustration is employed to highlight the animated pathlines and to emphasize nearby surface regions. Our approach combines several visualization techniques to improve the perception of surface shape and depth. Thereby, we ensure appropriate visibility of the embedded flow information, which can be depicted with established or advanced flow visualization techniques. We apply our approach to cerebral aneurysms and aortas with simulated and measured blood flow. An informal user feedback with nine domain experts, we confirm the advantages of our approach compared with existing methods, e.g. semi‐transparent surface rendering. Additionally, we assessed the applicability and usefulness of the pathline animation with highlighting nearby surface regions.     

Dynamical study of metallic clusters using the statistical method of time series clustering

We perform common neighbor analysis on the long-time series data generated by isothermal Brownian-type molecular dynamics simulations to study the thermal and dynamical properties of metallic clusters. In our common neighbor analysis, we introduce the common neighbor label (CNL) which is a group of atoms of a smaller size (than the cluster) designated by four numeric digits. The CNL thus describes topologically smaller size atomic configurations and is associated an abundance value which is the number of “degenerate” four digits all of which characterize the same CNL. When the cluster is in its lowest energy state, it has a fixed number of CNLs and hence abundances. At nonzero temperatures, the cluster undergoes different kinds of atomic activities such as vibrations, migrational relocation, permutational and topological isomer transitions, etc. depending on its lowest energy structure. As a result, the abundances of CNLs at zero temperature will change and new CNLs with their respective new abundances are created. To understand the temperature dependence of the CNL dynamics, and hence shed light on the cluster dynamics itself, we employ a novel method of statistical time series analysis. In this method, we perform statistical clustering at two time scales. First, we examine, at given temperature, the signs of abundance changes at a short-time scale, and assign CNLs to two short-time clusters. Quasi-periodic features can be seen in the time evolution of these short-time clusters, based on which we choose a long-time scale to compute the long-time correlations between CNL pairs. We then exploit the separation of correlation levels seen in these long-time correlations to extract strongly-correlated collections of CNLs, which we will identify as effective variables for the long-time cluster dynamics. It is found that certain effective variables show subtleties in their temperature dependences and these thermal traits bear a delicate relation to prepeaks and main peaks seen in clusters Ag₁₄, Cu₁₄ and Cu₁₃Au₁. We therefore infer from the temperature changes of effective variables and locate the temperatures at which these prepeaks and principal peaks appear, and they are evaluated by comparing with those deduced from the specific heat data.     

Membrane protein structure quality in molecular dynamics simulation

Our goal was to assess the relationship between membrane protein quality, output from protein quality checkers and output from molecular dynamics (MD) simulations. Membrane transport proteins are essential for a wide range of cellular processes. Structural features of integral membrane proteins are still under-explored due to experimental limitations in structure determination. Computational techniques can be used to exploit biochemical and medium resolution structural data, as well as sequence homology to known structures, and enable us to explore the structure-function relationships in several transmembrane proteins. The quality of the models produced is vitally important to obtain reliable predictions. An examination of the relationship between model stability in molecular dynamics (MD) simulations derived from RMSD (root mean squared deviation) and structure quality assessment from various protein quality checkers was undertaken. The results were compared to membrane protein structures, solved at various resolution, by either X-ray or electron diffraction techniques. The checking programs could predict the potential success of MD in making functional conclusions. MD stability was shown to be a good indicator for the quality of structures. The quality was also shown to be dependent on the resolution at which the structures were determined.     

Coherent Culling and Shading for Large Molecular Dynamics Visualization

Molecular dynamics simulations are a principal tool for studying molecular systems. Such simulations are used to investigate molecular structure, dynamics, and thermodynamical properties, as well as a replacement for, or complement to, costly and dangerous experiments. With the increasing availability of computational power the resulting data sets are becoming increasingly larger, and benchmarks indicate that the interactive visualization on desktop computers poses a challenge when rendering substantially more than millions of glyphs. Trading visual quality for rendering performance is a common approach when interactivity has to be guaranteed. In this paper we address both problems and present a method for high‐quality visualization of massive molecular dynamics data sets. We employ several optimization strategies on different levels of granularity, such as data quantization, data caching in video memory, and a two‐level occlusion culling strategy: coarse culling via hardware occlusion queries and a vertex‐level culling using maximum depth mipmaps. To ensure optimal image quality we employ GPU raycasting and deferred shading with smooth normal vector generation. We demonstrate that our method allows us to interactively render data sets containing tens of millions of high‐quality glyphs.     

MaterialVis: Material visualization tool using direct volume and surface rendering techniques

Visualization of the materials is an indispensable part of their structural analysis. We developed a visualization tool for amorphous as well as crystalline structures, called MaterialVis. Unlike the existing tools, MaterialVis represents material structures as a volume and a surface manifold, in addition to plain atomic coordinates. Both amorphous and crystalline structures exhibit topological features as well as various defects. MaterialVis provides a wide range of functionality to visualize such topological structures and crystal defects interactively. Direct volume rendering techniques are used to visualize the volumetric features of materials, such as crystal defects, which are responsible for the distinct fingerprints of a specific sample. In addition, the tool provides surface visualization to extract hidden topological features within the material. Together with the rich set of parameters and options to control the visualization, MaterialVis allows users to visualize various aspects of materials very efficiently as generated by modern analytical techniques such as the Atom Probe Tomography.     

Analysis of Long Molecular Dynamics Simulations Using Interactive Focus+Context Visualization

Analyzing molecular dynamics (MD) simulations is a key aspect to understand protein dynamics and function. With increasing computational power, it is now possible to generate very long and complex simulations, which are cumbersome to explore using traditional 3D animations of protein movements. Guided by requirements derived from multiple focus groups with protein engineering experts, we designed and developed a novel interactive visual analysis approach for long and crowded MD simulations. In this approach, we link a dynamic 3D focus+context visualization with a 2D chart of time series data to guide the detection and navigation towards important spatio‐temporal events. The 3D visualization renders elements of interest in more detail and increases the temporal resolution dependent on the time series data or the spatial region of interest. In case studies with different MD simulation data sets and research questions, we found that the proposed visual analysis approach facilitates exploratory analysis to generate, confirm, or reject hypotheses about causalities. Finally, we derived design guidelines for interactive visual analysis of complex MD simulation data.     

A practical privacy-preserving targeted advertising scheme for IPTV users

In this work, we present a privacy-preserving scheme for targeted advertising via the Internet Protocol TV (IPTV). The scheme uses a communication model involving a collection of subscribers, a content provider (IPTV), advertisers and a semi-trusted server. To target potential customers, the advertiser can utilize not only demographic information of subscribers, but also their watching habits. The latter includes watching history, preferences for IPTV content and watching rate, which are periodically (e.g., weekly) published on a semi-trusted server (e.g., cloud server) along with anonymized demographics. Since the published data may leak sensitive information about subscribers, it is safeguarded using cryptographic techniques in addition to the anonymization of demographics. The techniques used by the advertiser, which can be manifested in its queries to the server, are considered (trade) secrets and therefore are protected as well. The server is oblivious to the published data and the queries of the advertiser as well as its own responses to these queries. Only a legitimate advertiser, endorsed with so-called trapdoors by the IPTV, can query the cloud server and access the query results. Even when some background information about users is available, query responses do not leak sensitive information about the IPTV users. The performance of the proposed scheme is evaluated with experiments, which show that the scheme is practical. The algorithms demonstrate both weak and strong scaling property and take advantage of high level of parallelism. The scheme can also be applied as a recommendation system.     

3DIVE: An Immersive Environment for Interactive Volume Data Exploration

This paper describes an immersive system,called 3DIVE,for interactive volume data visualization and exploration inside the CAVE virtual environment.Combining interactive volume rendering and virtual reality provides a netural immersive environment for volumetric data visualization.More advanced data exploration operations,such as object level data manipulation,simulation and analysis ,are supported in 3DIVE by several new techniques,In particular,volume primitives and texture regions ae used for the rendering,manipulation,and collision detection of volumetric objects;and the region-based rendering pipeline is integrated with 3D image filters to provide an image-based mechanism for interactive transfer function design.The system has been recently released as public domain software for CAVE/ImmersaDesk users,and is currently being actively used by various scientific and biomedical visualization projects.     

Parallel implementation of molecular dynamics simulation for short-ranged interaction

A parallel molecular dynamics simulation method, designed for large-scale problems, employing dynamic spatial domain decomposition for short-ranged molecular interactions is proposed. In this parallel cellular molecular dynamics (PCMD) simulation method, the link-cell data structure is used to reduce the searching time required for forming the cut-off neighbor list as well as for domain decomposition, which utilizes the multi-level graph-partitioning technique. A simple threshold scheme (STS), in which workload imbalance is monitored and compared with some threshold value during the runtime, is proposed to decide the proper time for repartitioning the domain. The simulation code is implemented and tested on the memory-distributed parallel machine, e.g., PC-cluster system. Parallel performance is studied using approximately one million L-J atoms in the condensed, vaporized and supercritical states. Results show that fairly good parallel efficiency at 49 processors can be obtained for the condensed and supercritical states (∼60%), while it is comparably lower for the vaporized state (∼40%).     

Clustering revealed in high‐resolution simulations and visualization of multi‐resolution features in fluid–particle models

Simulating natural phenomena at greater accuracy results in an explosive growth of data. Large‐scale simulations with particles currently involve ensembles consisting of between 10⁶ and 10⁹ particles, which cover 10⁵–10⁶ time steps. Thus, the data files produced in a single run can reach from tens of gigabytes to hundreds of terabytes. This data bank allows one to reconstruct the spatio‐temporal evolution of both the particle system as a whole and each particle separately. Realistically, for one to look at a large data set at full resolution at all times is not possible and, in fact, not necessary. We have developed an agglomerative clustering technique, based on the concept of a mutual nearest neighbor (MNN). This procedure can be easily adapted for efficient visualization of extremely large data sets from simulations with particles at various resolution levels. We present the parallel algorithm for MNN clustering and its timings on the IBM SP and SGI/Origin 3800 multiprocessor systems for up to 16 million fluid particles. The high efficiency obtained is mainly due to the similarity in the algorithmic structure of MNN clustering and particle methods. We show various examples drawn from MNN applications in visualization and analysis of the order of a few hundred gigabytes of data from discrete particle simulations, using dissipative particle dynamics and fluid particle models. Because data clustering is the first step in this concept extraction procedure, we may employ this clustering procedure to many other fields such as data mining, earthquake events and stellar populations in nebula clusters. Copyright © 2003 John Wiley & Sons, Ltd.     

Recent Advances in Volume Visualization

In the past few years, there have been key advances in the three main approaches to the visualization of volumetric data: isosurfacing, slicing and volume rendering, which together make up the field of volume visualization.
In this survey paper we set the scene by describing the fundamental techniques for each of these approaches, using this to motivate the range of advances which have evolved over the past few years.
In isosurfacing, we see how the original marching cubes algorithm has matured, with improvements in robustness, topological consistency, accuracy and performance. In the performance area, we look in detail at pre-processing steps which help identify data which contributes to the particular isosurface required. In slicing too, there are performance gains from identifying active cells quickly.
In volume rendering, we describe the two main approaches of ray casting and projection. Both approaches have evolved technically over the past decade, and the holy grail of real-time volume rendering has arguably been reached.
The aim of this review paper is to pull these developments together in a coherent review of recent advances in volume visualization.     

An implementation of granular dynamics for simulating frictional elastic particles based on the DL_POLY code

We have modified Daresbury Laboratory's replicated data strategy (RDS) parallel molecular dynamics (MD) package DL_POLY (version 2.13) to study the granular dynamics of frictional elastic particles. DL_POLY [Smith and Forester, The DL_POLY_2 User Manual v2.13, 2001; Forester and Smith, The DL_POLY_2 Reference Manual v2.13, 2001] is a MD package originally developed to study liquid state and macromolecular systems by accounting for various molecular interaction forces. The particles of interest in this study are macroscopic grains in pharmaceutical powders, with sizes ranging from tens to hundreds of microns. We have therefore substituted the molecular interaction forces with contact forces (including linear-dashpot, HKK interaction forces and Coulombic friction) while taking advantage of the RDS scheme. In effect, we have created a parallel Discrete Element Simulation (DES) code. In this paper, we describe the modifications made to the original DL_POLY code and the results from the validation tests of the granular dynamics simulations for systems of monodisperse spherical particles settling under gravity. The code can also be utilized to study particle packings generated via uniaxial compaction and, in some cases, simultaneous application of shear, at constant strain.     

医学体数据三维可视化技术

医学体数据的可视化是科学计算可视化的重要研究领域,其处理过程包括体数据的获取、模型的建立、数据的映射、绘制等操作。论文对医学体数据可视化的相关技术进行了综述,讨论了医学体数据的结构模型和表示方法,全面地分析了医学体数据可视化中各种算法和技术的特点,及相关的加速技术,探讨了目前医学体数据可视化存在的问题及发展趋势。