State‐of‐the‐Art in GPU‐Based Large‐Scale Volume Visualization

This survey gives an overview of the current state of the art in GPU techniques for interactive large‐scale volume visualization. Modern techniques in this field have brought about a sea change in how interactive visualization and analysis of giga‐, tera‐ and petabytes of volume data can be enabled on GPUs. In addition to combining the parallel processing power of GPUs with out‐of‐core methods and data streaming, a major enabler for interactivity is making both the computational and the visualization effort proportional to the amount and resolution of data that is actually visible on screen, i.e. ‘output‐sensitive’ algorithms and system designs. This leads to recent output‐sensitive approaches that are ‘ray‐guided’, ‘visualization‐driven’ or ‘display‐aware’. In this survey, we focus on these characteristics and propose a new categorization of GPU‐based large‐scale volume visualization techniques based on the notions of actual output‐resolution visibility and the current working set of volume bricks—the current subset of data that is minimally required to produce an output image of the desired display resolution. Furthermore, we discuss the differences and similarities of different rendering and data traversal strategies in volume rendering by putting them into a common context—the notion of address translation. For our purposes here, we view parallel (distributed) visualization using clusters as an orthogonal set of techniques that we do not discuss in detail but that can be used in conjunction with what we present in this survey.     

一种无线传感器网络的盲源分离算法

无线网络协议的校验纠错机制繁重复杂,加重了通信带宽的负担.盲源分离无需任何先验知识就可重构出源信号,成为无线传感器领域的研究热点.但网络部署中,传感器信号的量化位数和Modem个数难以确定.针对上述问题,提出一种基于平方根容积卡尔曼滤波(SRCKF)的盲源分离算法.算法根据空间状态方程和测量方程,用平方根容积卡尔曼估计分离向量.实验结果表明:SRCKF算法在保持极高分离精度的情况下,能有效计算出量化位数和Modem个数的最优值,SRCKF算法效率是同类无先导卡尔曼滤波(UKF)算法效率的1.3578倍.     

基于体绘制的动态云场景模拟

为了实现动态云场景的创建与绘制,文中提出一种基于体绘制的三维云模拟算法。算法用Perlin噪声干扰椭球模型,得到云场景区域内体素颜色的基本数据,然后对体素颜色进行软化模糊和添加阴影处理,建立绘制区域的云数据模型;同时通过ray casting对绘制区域进行体数据采样渲染,模拟出三维云的效果。仿真实验结果表明：该算法模拟出的云场景可让云形状颜色位置动态改变,也可让视点穿过云层,具有动态和交互效果,是一种可行的动态云场景模拟算法。     

基于RFID标签阵列的睡眠期间呼吸量连续监测系统

睡眠期间连续且准确的呼吸量监测有助于推断用户的睡眠阶段以及提供一些慢性疾病的线索。现有工作主要针对呼吸频率进行感知和监测,缺乏对呼吸量进行连续监测的手段。针对上述问题提出了一种基于商用无线射频识别(RFID)标签的无线感知用户睡眠期间呼吸量的系统——RF-SLEEP。RF-SLEEP通过阅读器连续收集附着在胸部表面的标签阵列返回的相位值及时间戳数据,计算出呼吸引起的胸部不同点的位移量,基于广义回归神经网络(GRNN)构建胸部不同点的位移量与呼吸量之间的关系模型,从而实现对用户睡眠期间呼吸量的评估。RF-SLEEP通过在用户肩膀处附着双参考标签,消除用户睡眠期间翻转身体对胸部位移计算造成的误差。实验结果表明,RFSLEEP对不同用户睡眠期间的呼吸量连续监测的平均精确度为92.49%。     

基于高斯混合模型的阴影消除算法

关于阴影消除是智能监控系统的重要组成部分,且对后续的识别和跟踪有很大的影响。根据改进的高斯混合模型检测出目标,用高斯混合阴影模型来检测阴影,并用高斯平滑滤波处理检测的结果。然后动态选择合适的阈值对图像做二值化处理,进一步对结果进行形态学滤波。经过整个过程的处理,得到了较为完整的轮廓,很好的消除了阴影,提高后续的识别和跟踪的准确性和可靠性。实验表明,算法能较好地消除阴影,且具有较好的实时性和鲁棒性。     

Predicting the density of molten alloys using computational thermodynamics

The density of a molten alloy can be calculated from the quotient of its molar mass divided by its molar volume. The molar volume of a molten alloy, however, often deviates from the average of the molar volumes of its constituents. The deviation is caused mainly by the affinity (or lack of it) between dissimilar atoms, which can be quantified by the enthalpy of mixing. Up to now, the link between the enthalpy of mixing and the volume change has been determined empirically through the regression of experimental measurements of alloy densities. In the present study, the derivative of molar volume with respect to enthalpy was deduced and the molar volumes of molten alloys were computed entirely based on the properties of pure elements and the enthalpy of mixing of the alloys. The very slight increase in the packing density due to the size difference of different atoms was also considered. The effect of cluster formation due to short range ordering was also addressed. Over six hundred data points were used in validations. Excellent agreements were achieved between the calculated values and the experimental measurements.     

Graph-Based Transfer Function for Volume Rendering

A good transfer function in volume rendering requires careful consideration of the materials present in a volume. A manual creation is tedious and prone to errors. Furthermore, the user interaction to design a higher dimensional transfer function gets complicated. In this work, we present a graph-based approach to design a transfer function that takes volumetric structures into account. Our novel contribution is in proposing an algorithm for robust deduction of a material graph from a set of disconnected edges. We incorporate stable graph creation under varying noise levels in the volume. We show that the deduced material graph can be used to automatically create a transfer function using the occlusion spectrum of the input volume. Since we compute material topology of the objects, an enhanced rendering is possible with our method. This also allows us to selectively render objects and depict adjacent materials in a volume. Our method considerably reduces manual effort required in designing a transfer function and provides an easy interface for interaction with the volume.     

基于GPU Raycasting算法的矢量/栅格混合绘制研究*

Raycasting体绘制算法由于成像质量高而被广泛应用于体数据的可视化,但当线、面表达的矢量数据和三维栅格表达的体数据同时绘制到同一场景时,由于绘制方法的差异会造成矢量和栅格数据空间遮挡关系不一致。在GPU实现Raycasting算法的基础上,通过矢量和栅格数据先后绘制,采用FBO离屏绘制等技术将矢量数据绘制到深度缓存纹理并在体绘制采样和融合中统一考虑矢栅颜色融合。实验结果表明,该算法在矢量数据非透明模式下能正确处理矢量栅格数据的混合绘制。     

三维地震数据场的并行体绘制算法

光线投射算法是一种应用广泛的体绘制基本算法,其存在的主要问题是绘制速度较慢.为了提高光线投射算法的绘制速度,利用光线间和光线内的并行性,结合三维地震数据场的特点和文件存储特性,提出了一种基于工作站机群的三维地震数据场的并行体绘制算法.地震油气解释实际应用表明本算法能满足地震解释的要求.     

Co-volume method for Riemannian mean curvature flow in subjective surfaces multiscale segmentation

We introduce semi-implicit complementary volume numerical scheme for solving the level setformulation of Riemannian mean curvature flow problem arising in image segmentation, edge detection, missing boundary completion and subjective contour extraction. The scheme is robust and efficient since it is linear, and it is stable in L_∞ and weighted W ^1,1 sense without any restriction on a time step. The computational results related to medical image segmentation with partly missing boundaries and subjective contours extraction are presented.