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2017云计算和大数据计算国际会议（2017 International Conference on Cloud and Big Data Computing）
2017 云计算和大数据计算 国际会议
2017/7/5
Cloud is a virtual computing resource, which realizes self-maintenance and management, usually in the form of some large server cluster, including computing servers, storage servers, bandwidth resourc...
FITTING A POINT CLOUD TO A 3D POLYHEDRAL SURFACE
Contactless measurement point clouds fitting Stretched Grid method Principal Component Analysis
2017/6/19
The ability to measure parameters of large-scale objects in a contactless fashion has a tremendous potential in a number of industrial applications. However, this problem is usually associated with an...
2017第二届IEEE云计算与大数据分析国际会议(ICCCBDA 2017) （2017 the 2nd IEEE International Conference on Cloud Computing and Big Data Analysis）
2017 第二届 IEEE云计算与大数据分析 国际会议
2017/1/11
计算和大数据是近年来非常热的话题，也是近年来非常重要的技术。随着大数据时代来临，在以云计算为代表的技术创新大幕的衬托下，它将在众多领域掀起变革的巨浪，大数据会逐步为人类创造更多的价值。与此同时，中央和国家今年也在力推“互联网+”以及大数据战略，以及在十三五规划中强调的创新驱动，均离不开当前信息技术中的云计算和大数据。
From Computing Machineries to Cloud Computing:The Minimal Levels of Abstraction of Inforgs through History
Computing Machineries Cloud Computing Minimal Levels
2015/7/22
Before the modern computing era, the word `computers' referred to human beings as living calculators---in fact, still Turing (1950) proposed his test for A.I. referring to `computing machinery', not `...
Elastic Resource Management in Cloud Computing Platforms
Autonomic Computing Capacity Planning Cloud Computing
2014/12/18
Large scale enterprise applications are known to observe dynamic workload; provisioning correct capacity for these applications remains an important and challenging problem. Predicting high variabilit...
The National Science Foundation (NSF) today announced two $10 million projects to create cloud computing testbeds--to be called "Chameleon" and "CloudLab"--that will enable the academic research commu...
基于非均匀细分的散乱点云数据精简算法（Algorithm of Scattered Point Cloud Data Reduction Based on Non-uniform Subdivision）
逆向工程 散乱点云 非均匀细分
2009/9/25
针对海量散乱点云数据精简问题，提出了基于非均匀细分的精简算法。采用八叉树结构对点云数据进行空间分割，由分割结果建立k邻域。对k邻域内的散乱点进行二次曲面拟合，以拟合曲面的平均曲率为判据决定是否对八叉树空间实行非均匀细分，细分过程中由数据点之间的最大间隔角决定细分程度。构造曲率差函数，识别出边界数据点，对其进行数据保护。该算法对具有曲率多样化特点的点云数据的精简具有实用性，通过实验验证了该算法的可靠...