EAGER: Exploring Compressive Sampling for Extreme-Scale Data Visualization

EAGER：探索超大规模数据可视化的压缩采样

基本信息

批准号：
1048508
负责人：
Alireza Entezari
金额：
$ 8.5万
依托单位：
University of Florida
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2010
资助国家：
美国
起止时间：
2010-09-01 至 2012-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1048508&HistoricalAwards=false
关键词：
EAGER Exploring Compressive Sampling Extreme

项目摘要

AbstractThis EAGER aims to provide practical evidence of feasibility for a larger project called instance-optimal sampling. The instance-optimal sampling is a foundational framework for optimal representation of extreme-scale (scattered, unstructured, and structured) datasets. Using the dense polytope packing algorithms, the instance-optimal sampling framework develops strategies for sampling a given dataset at the optimally minimal sampling rate. The instance-optimal representation is derived based on the multidimensional notion of Nyquist frequencies; therefore, this approach is best complemented with the compressive sampling (CS) methods that exploit the sparsity of a dataset to reduce the sampling rate significantly below the Nyquist rate with no loss of information.The main motivation in this research is that the synergy of compressive sampling and instance-optimal sampling would potentially allow the reduction of an extreme-scale dataset to sizes that are logarithmically proportional to number of samples in that dataset and linearly proportional to its sparsity. The research addresses the computational efficiency issue of sparse reconstruction for volumetric and time-varying datasets, which can lay the basis for applying CS to computer graphics problems. The main challenge is the computational cost of the reconstruction algorithm for 3-D or time-varying data. This research examines the feasibility of adopting a tensor-product approach to compressive sampling.

摘要这一渴望旨在为一个称为实例 - 最佳抽样的较大项目提供可行性的实际证据。实例最佳采样是一种基础框架，用于最佳表示极端尺度（分散，非结构化和结构化）数据集的基础框架。使用密集的多层填料算法，实例最佳采样框架制定了以最佳最小的采样率来对给定数据集进行采样的策略。实例 - 最佳表示是基于Nyquist频率的多维概念得出的。因此，这种方法最好与充分利用数据集的稀疏性的压缩抽样（CS）方法相辅相成，以降低采样率，显着降低了无信息的主要动机，而没有信息的主要动机。该数据集中的示例，并与其稀疏性成正比。该研究涉及体积和时变数据集的稀疏重建的计算效率问题，这可以为将CS应用于计算机图形问题提供基础。主要挑战是3-D或时变数据的重建算法的计算成本。这项研究研究了采用张量产品方法进行压缩采样的可行性。