基于块稀疏贝叶斯学习的体域网心电压缩采样

为有效提高体域网的实时性和降低体域网的功耗,提出一种基于块稀疏贝叶斯学习的体域网心电压缩采样方法。该方法在体域网框架下,利用压缩采样理论,在体域网的传感节点利用二进制随机观测矩阵对心电信号进行压缩采样,远程监护中心获得采样值之后,利用块稀疏贝叶斯学习重构算法和离散余弦稀疏变换矩阵对心电信号进行重构。实验结果表明,当心电信号压缩率在70%~90%时,基于块稀疏贝叶斯学习的重构算法要比其他重构算法的重构信噪比高出3 dB~21 dB。该方法能有效减少数据采样,减轻后续的数据存储、数据传输压力,提高体域网的实时性。同时该方法具有功耗低,易于硬件实现的优点。     

基于社交信任的概率矩阵因子分解推荐算法

针对评分矩阵和信任矩阵的稀疏性以及推荐精度不高等问题，提出基于社交信任的概率矩阵因子分解推荐算法PMFTrustSVD。该文采用概率矩阵分解算法对信任矩阵进行分解，分别获得用户作为信任者和被信任者的潜在社交偏好；根据用户在作为信任者或被信任者时的偏好不同，将TrustSVD算法中的无向信任矩阵修正为有向矩阵；融合两种算法来预测用户的评分矩阵。在FilmTrust数据集上实验结果表明，该算法优于现有基准算法，能有效缓解用户信任矩阵稀疏的问题并提高推荐精度。     

基于GPU的稀疏矩阵Cholesky分解

稀疏矩阵Cholesky分解是求解大规模稀疏线性方程组的核心算法,也是求解过程中最耗时的部分.近年来,一系列并行算法通过图形处理器(GPU)获得了显著的加速比,然而,由于访存的不规则性以及任务间的大量数据依赖关系,稀疏矩阵Cholesky分解算法在GPU上的计算效率很低.文中实现了一种新的基于GPU的稀疏矩阵Cholesky分解算法.在数据组织方面,改进了稀疏矩阵超节点数据结构,通过超节点合并和分块控制计算粒度;在计算调度方面,将稀疏矩阵Cholesky分解过程映射为一系列的数据块任务,并设计了相应的任务生成与调度算法,在满足数据依赖性的前提下提高任务的并行性.实验结果表明,该算法能够显著提高稀疏矩阵Cholesky分解算法在GPU上的实现效率,在单个GPU上获得了相对4核CPU平台2.69~3.88倍的加速比.     

基于人脑部体数据的任意切面研究

本文结合表面绘制方法和体绘制方法,采用以三维矩阵数值运算与逻辑运算为基础的体数据面绘制算法,实现了医学断层图像序列的三维重建及任意切面显示.通过对子矩阵分解的分析,解决实时性要求与存储空间和运行时间的矛盾.实验结果表明,该算法既克服了表明绘制不能体现内部数据的缺点,又从一定程度上解决了体绘制的速度问题,可以从任意角度和位置来观察剖面的形状、大小、灰度分布等各种病理特征.     

稀疏限制的增量式鲁棒非负矩阵分解及其应用

针对鲁棒非负矩阵分解（RNMF）的运算规模随训练样本数量逐渐增多而不断增大的问题，提出一种稀疏限制的增量式鲁棒非负矩阵分解算法。首先，对初始数据进行鲁棒非负矩阵分解；然后，将其分解结果参与到后续迭代运算；最后，在对系数矩阵增加稀疏限制的情况下与增量式学习相结合，使目标函数值在迭代求解时下降地更快。该算法在节省运算时间的同时提高了分解后数据的稀疏度。在数值实验中，将所提算法与鲁棒非负矩阵分解算法、稀疏限制的鲁棒非负矩阵分解（RNMFSC）算法进行了比较。在ORL和YALE人脸数据库上的实验结果表明，所提算法在运算时间和分解后数据的稀疏度等方面均优于其他两个算法，并且还具有较好的聚类效果，尤其在YALE人脸数据库上当聚类类别数为3时该算法的聚类准确率达到了91.67%。     

海量数据GPU体绘制优化研究

高性能GPU使得体绘制在廉价的硬件上获得良好的性能,但海量数据体绘制的效率依旧低下.本文探讨了GPU体绘制中图形硬件的瓶颈,并提出新颖的算法解决这些问题:采用数据分块和八叉树划分体数据实现空单元跳过优化.该算法解决了海量数据超过可用纹理空间的难题,同时允许实时改变体绘制传递函数.     

一种基于相同评分矩阵的协同过滤补值算法*

针对协同过滤中面临的数据稀疏问题,提出了相同评分矩阵的概念和一种基于相同评分矩阵的协同过滤补值算法。通过补值过程中实时维护的相同评分矩阵中体现的相似性关系,高效地填补缺失数据。实验结果表明,该算法有效地解决了数据稀疏性问题,提高了协同过滤的推荐质量。     

基于SVD的K-means聚类协同过滤算法

针对基于内存的协同过滤算法在线计算量较大,数据稀疏且可扩展性较低的缺点,本文提出了一种基于SVD矩阵填充技术的K-means聚类协同过滤算法。本算法首先利用SVD降维方法对原始的高维稀疏矩阵进行预测填充,得到一个没有缺失值的评分矩阵,而后利用K-means聚类在填充完整的数据上对用户进行聚类,从而对完成对测试集上未知评分进行预测。该算法利用用户与项目之间的潜在关系克服了稀疏性问题,同时保留了聚类方法可离线建模、可扩展性好等优点。实验结果表明,该算法获得了更好的预测性能,同时具有良好的可扩展性。     

一种缓解协同过滤算法数据稀疏性的方法

协同过滤算法是推荐系统中最古老的算法之一,同时也是当今推荐系统中使用最广泛的一种算法。但是在简单,效率高的同时,协同过滤算法还存在数据稀疏性,冷启动等一些问题.本文针对其数据稀疏性的问题,提出了一种根据兴趣度预测用户未评分项目的方法。最后通过基于Netflix数据集的实验结果表明,该方法能够更好的处理稀疏矩阵,能缓解数据稀疏问题,从而提高了协同过滤算法的准确性。     

一种巴氏系数改进相似度的协同过滤算法

针对传统协同过滤算法中评分数据稀疏性及所造成推荐质量不高的问题,提出一种巴氏系数(Bhattacharyya Coefficient)改进相似度的协同过滤算法。在基于近邻协同过滤算法基础上,首先利用Jaccard相似性来计算用户间的全局相似性;其次使用巴氏系数获得评分分布的整体规律,并结合Pearson相关系数来计算其局部相似性;最后融合全局相似性和局部相似性得到最终的相似度矩阵。实验结果表明,该算法在稀疏数据集上获得更好的推荐结果,有效地缓解了评分数据稀疏性问题,提高了推荐的准确度。     

Interactive volume rendering of large sparse data sets using adaptive mesh refinement hierarchies

In this paper, we present an algorithm that accelerates 3D texture-based volume rendering of large, sparse data sets, i.e., data sets where only a traction of the voxels contain relevant information. In texture-based approaches, the rendering performance is affected by the fill-rate, the size of texture memory, and the texture I/O bandwidth. For sparse data, these limitations can be circumvented by restricting most of the rendering work to the relevant parts of the volume. In order to efficiently enclose the corresponding regions with axis-aligned boxes, we employ a hierarchical data structure, known as an AMR (adaptive mesh refinement) tree. The hierarchy is generated utilizing a clustering algorithm. A good balance is thereby achieved between the size of the enclosed volume, i.e., the amount to render in graphics hardware and the number of axis-aligned regions, i.e., the number of texture coordinates to compute in software. The waste of texture memory by the power-of-two restriction is minimized by a 3D packing algorithm which arranges texture bricks economically in memory. Compared to an octree approach, the rendering performance is significantly increased and less parameter tuning is necessary.     

Fast visualization of volume emissions using conservative subdivision

A hierarchical volume rendering algorithm is presented. Volume emissions in optically thin media are considered. A sparse hierarchical wavelet-based representation of the directional volume emission rate function is constructed top-down and then projected onto the image plane. This makes the algorithm fast, since the number of operations is proportional to the number of coefficients in the representation. Also, the sparsity minimizes the algorithm’s memory requirement. The representation is constructed using conservative subdivision, making the projection onto a lower-dimensional representation trivial. The error is controlled by a threshold parameter, allowing a direct trade-off between speed and accuracy. The algorithm is especially well suited when the directional volume emission rate is computationally expensive to evaluate, since the sparse representation minimizes the number of function evaluations for rendering a volume with a specified accuracy. Since a sparse oct-tree is constructed for a specific view point the method is best suited to situations where an image is to be generated from one view point.     

交互式动态体绘制及其加速算法

体绘制三维成象法是一门新兴的3D采样数据场可视化技术，在医学成象和科学可视化领域有着极为广泛的应用，但由于3D数据量大，其使用往往受到巨大计算开销的限制，因此很多研究人员致力于静态体绘制加速算法的研究，并解决医学图象三维可视化中三维体数据显示速度与成象质量问题，因而提出了一种交互式动态体绘制算法，即从任意的视点距离和视线方向进行动态编制，并在分析其算法复杂度的基础上，提出一种新的加速算法，同时使得动态体绘制过程几乎达到实时的效果，经验证，这种算法比标准算法快4～5倍。     

Decoupled Shading for Real‐time Heterogeneous Volume Illumination

Existing real‐time volume rendering techniques which support global illumination are limited in modeling distinct realistic appearances for classified volume data, which is a desired capability in many fields of study for illustration and education. Directly extending the emission‐absorption volume integral with heterogeneous material shading becomes unaffordable for real‐time applications because the high‐frequency view‐dependent global lighting needs to be evaluated per sample along the volume integral. In this paper, we present a decoupled shading algorithm for multi‐material volume rendering that separates global incident lighting evaluation from per‐sample material shading under multiple light sources. We show how the incident lighting calculation can be optimized through a sparse volume integration method. The quality, performance and usefulness of our new multi‐material volume rendering method is demonstrated through several examples.     

Incremental Volume Rendering Using Hierarchical Compression

We present a new algorithm here for efficient incremental rendering of volumetric datasets. The primary goal of this algorithm is to give average workstations the ability to efficiently render volume data received over relatively low bandwidth network links in such a way that rapid user feedback is maintained. Common limitations of workstation rendering of volume data include: large memory overheads, the requirement of expensive rendering hardware, and high speed processing ability. The rendering algorithm presented here overcomes these problems by making use of the efficient Shear-Warp Factorisation method which does not require specialised graphics hardware. However the original Shear-Warp algorithm suffers from a high memory overhead and does not provide for incremental rendering which is required should rapid user feedback be maintained. Our algorithm represents the volumetric data using a hierarchical data structure which provides for the incremental classification and rendering of volume data. This exploits the multiscale nature of the octree data structure. The algorithm reduces the memory footprint of the original Shear-Warp Factorisation algorithm by a factor of more than two, while maintaining good rendering performance. These factors make our octree algorithm more suitable for implementation on average desktop workstations for the purposes of interactive exploration of volume models over a network. Results from tests using typical volume datasets will be presented which demonstrate the ability of the algorithm to achieve high rendering rates for both incremental rendering and standard rendering while reducing the runtime memory requirements.     

非规则数据场并行体绘制算法

并行算法是实现体绘制加速的重要途径，然而现有的并行体制绘制算法大部分是针对规则数据场的。     

用于体绘制的可变模板法

作为投影成象的的一种重要方法，模板法在规则场的体绘制中取得了好的效果，然而，传统模板法要求样点的大小和形状一致，限制了其在曲线结构数据场和非规则数据场体绘制中的应用，因为这类场中样点的大小和形状变化很大。当前非规则场或曲线结构数据场中的体绘制计算复杂、成象速度很慢，严重影响了可视化的效率，本文提出了一种可变模板法，不受样点大小必须一致的限制，使得模板法能在曲线结构数据场和非规则场的体绘制中发挥充分     

针对全空子数据体的GPU体绘制

目的 体绘制是3维数据可视化的主要方法之一。用于体绘制的数据体中包含有大量的空体素,导致光线投射算法进行没有意义的重采样计算,必然降低绘制算法效率。针对全空子数据体体绘制低效问题,提出基于GPU体高效绘制方法。方法 利用八叉树数据结构组织数据,有效管理包含许多空体素的子数据体。通过绘制八叉树非全空叶子节点子数据体表面,使光线投射算法中起始和终止重采样位置更接近数据体中的可视部分,同时根据八叉树全空节点子数据体判定纹理查询结果,计算合适的跳跃步长,快速跳过八叉树中全空节点子数据体。结果 当数据体中空体素较多时,确定合适的八叉树深度,有效地跳过数据体中的空体素,减少体绘制运算量,实现对原基于体包围盒表面绘制的GPU光线投射算法的加速。结论 设计不透明度函数,凸显数据体中层位面,并将算法成功应用于地震数据可视化,取得很好应用效果。     

基于Web的时变体数据的体绘制方法

传统Web体绘制方法主要集中在利用服务器端进行预处理和绘制任务,浏览器端仅用于呈现绘制结果,这样会造成服务器负载过高,同时,当绘制参数发生更改时,必须向服务器请求新的绘制结果,这样也易受网络延迟的影响。为了解决以上问题,实现在浏览器本地进行体绘制和交互,本文提出一种基于WebGL的体绘制方法,以时变体数据为例,在浏览器端实现光线投射体绘制算法。同时,为了提升绘制效率和减少内存占用,本文基于维度压缩方法,优化时变体数据的预处理过程。最后,本文设计了Web体绘制系统,引入暴风时变数据集以验证方法的有效性,结果表明,本文方法能够在浏览器本地对时变体数据进行体绘制,绘制时间在50ms以下,帧速率可达到50 FPS以上,同时支持实时交互,并且当绘制参数发生更改时,系统能够直接在浏览器端进行重新绘制。     

Analysis of a parallel volume rendering system based on theshear-warp factorization

This paper presents a parallel volume rendering algorithm that can render a 256×256×225 voxel medical data set at over 15 Hz and a 512×512×334 voxel data set at over 7 Hz on a 32-processor Silicon Graphics Challenge. The algorithm achieves these results by minimizing each of the three components of execution time: computation time, synchronization time, and data communication time. Computation time is low because the parallel algorithm is based on the recently-reported shear-warp serial volume rendering algorithm which is over five times faster than previous serial algorithms. The algorithm uses run-length encoding to exploit coherence and an efficient volume traversal to reduce overhead. Synchronization time is minimized by using dynamic load balancing and a task partition that minimizes synchronization events. Data communication costs are low because the algorithm is implemented for shared-memory multiprocessors, a class of machines with hardware support for low-latency fine-grain communication and hardware caching to hide latency. We draw two conclusions from our implementation. First, we find that on shared-memory architectures data redistribution and communication costs do not dominate rendering time. Second, we find that cache locality requirements impose a limit on parallelism in volume rendering algorithms. Specifically, our results indicate that shared-memory machines with hundreds of processors would be useful only for rendering very large data sets