On stochastic methods for surface reconstruction

In this article, we present and discuss three statistical methods for surface reconstruction. A typical input to a surface reconstruction technique consists of a large set of points that has been sampled from a smooth surface and contains uncertain data in the form of noise and outliers. We first present a method that filters out uncertain and redundant information yielding a more accurate and economical surface representation. Then we present two methods, each of which converts the input point data to a standard shape representation; the first produces an implicit representation while the second yields a triangle mesh.     

Manifold surface reconstruction of an environment from sparse Structure-from-Motion data

The majority of methods for the automatic surface reconstruction of an environment from an image sequence have two steps: Structure-from-Motion and dense stereo. From the computational standpoint, it would be interesting to avoid dense stereo and to generate a surface directly from the sparse cloud of 3D points and their visibility information provided by Structure-from-Motion. The previous attempts to solve this problem are currently very limited: the surface is non-manifold or has zero genus, the experiments are done on small scenes or objects using a few dozens of images. Our solution does not have these limitations. Furthermore, we experiment with hand-held or helmet-held catadioptric cameras moving in a city and generate 3D models such that the camera trajectory can be longer than one kilometer.     

Task exposures in an office environment: a comparison of methods

Task-related factors such as frequency and duration are associated with musculoskeletal disorders in office settings. The primary objective was to compare various task recording methods as measures of exposure in an office workplace. A total of 41 workers from different jobs were recruited from a large urban newspaper (71% female, mean age 41 years SD 9.6). Questionnaire, task diaries, direct observation and video methods were used to record tasks. A common set of task codes was used across methods. Different estimates of task duration, number of tasks and task transitions arose from the different methods. Self-report methods did not consistently result in longer task duration estimates. Methodological issues could explain some of the differences in estimates seen between methods observed. It was concluded that different task recording methods result in different estimates of exposure likely due to different exposure constructs. This work addresses issues of exposure measurement in office environments. It is of relevance to ergonomists/researchers interested in how to best assess the risk of injury among office workers. The paper discusses the trade-offs between precision, accuracy and burden in the collection of computer task-based exposure measures and different underlying constructs captures in each method.     

主动目标几何建模研究方法综述

目的 目标建模是机器视觉领域的主要研究方向之一,主动目标建模是在保证建模完整度的情况下,通过有计划地调节相机的位姿参数,以更少的视点和更短的运动路径实现目标建模的智能感知方法。为了反映主动目标建模的研究现状和最新进展,梳理分析了2004年以来的相关文献,对国内外研究方法做出概括性总结。方法 以重构模型类型和规划视点所用信息作为划分依据,将无模型的主动目标建模方法分为基于表面的主动目标建模方法、基于搜索的目标建模方法和两者相结合的方法3大类,重点对前两类方法进行综述,首先解释了每类方法的基本思想,总结每类方法涉及的问题,然后对相关问题的主要研究方法进行归纳和分析,最后将各个问题的解决方法进行合理的搭配组合,形成不同的主动目标建模方法,并对各类方法的优势和局限性进行了总结。结果 各类主动目标建模算法在适用场景范围、计算复杂度等方面存在差异,但相对于传统的被动目标建模方法,当前的主动目标建模算法已经能够极大程度地提高建模任务的质量和降低建模所需代价。结论 基于表面的主动目标建模方法思想相对简单,但仅适用于表面简单的目标建模。基于搜索的目标建模方法能够量化地评价每一个候选视点,适用广泛且涉及的问题相对于基于表面的方法有更大的解决空间,有更多的研究成果产生。将二者涉及问题的不同研究方法相搭配,可以构成不同的主动目标建模方法子类。     

Asynchronous Schwarz methods applied to constrained mechanical structures in grid environment

This paper deals with the parallel solution of the stationary obstacle problem with convection–diffusion operator. The obstacle problem can be formulated by various ways and in the present study it is formulated like a multivalued problem. Another formulation by complementary problem is also considered. Appropriate discretization schemes are considered for the numerical solution on decentralised memory machines by using parallel synchronous and asynchronous Schwarz alternating algorithms. The considered discretization schemes ensure the convergence of the parallel synchronous or asynchronous Schwarz alternating methods on one hand for the solution of the multivalued problem and on the other hand for the solution of the complementary problem. Finally the implementation of the algorithms is described and the results of parallel simulations are presented.     

Negotiation strategy for continuous long-term tasks in a grid environment

Nowadays, much research is concerned with execution of long-term continuous tasks, which produce data in real time, e.g. monitoring applications. These tasks can be run for months or years and they are usually resource intensive in terms of the large amounts of data which is processed per time unit. A Grid can potentially provide the amount of resources necessary to execute these tasks, but it might prove to be impossible or non-beneficial for a Grid to allocate resources for such long durations as these resources can be also requested by other clients or might join a Grid only for some periods of time. To resolve these differences, a client and a Grid Resource Allocator negotiate, and a client has to agree for a shorter execution period at the end of which it needs to negotiate again. In this paper, we discuss in detail a decision-making mechanism for a client as part of its negotiation strategy, which aims to increase the duration of execution periods and to decrease the duration of interruptions. This new strategy, ConTask, has been tested on a realistic Grid resource simulator, and it demonstrates better utilities than our strategy which has not been specifically designed for continuous tasks under various conditions.     

Independent checkpointing in a heterogeneous grid environment

The EU-funded XtreemOS project implements an open-source grid operating system based on Linux. In order to provide fault tolerance and migration for grid applications, it integrates a distributed grid-checkpointing service called XtreemGCP. This service is designed to support various checkpointing protocols and different checkpointer packages (e.g. BLCR, LinuxSSI, OpenVZ, etc.) in a transparent manner through a uniform checkpointer interface. In this paper, we present the integration of a backward error recovery protocol based on independent checkpointing into the XtreemGCP service. The solution we propose is not checkpointer bound and thus can be transparently used on top of any checkpointer package.To evaluate the prototype we run it within a heterogeneous environment composed of single-PC nodes and a Single System Image (SSI) cluster. The experimental results demonstrate the capability of the XtreemGCP service to integrate different checkpointing protocols and independently checkpoint a distributed application within a heterogeneous grid environment. Moreover, the performance evaluation also shows that our solution outperforms the existing coordinated checkpointing protocol in terms of scalability.     

GPU Local Triangulation: an interpolating surface reconstruction algorithm

A GPU capable method for surface reconstruction from unorganized point clouds without additional information, called GLT (GPU Local Triangulation), is presented. The main objective of this research is the generation of a GPU interpolating reconstruction based on local Delaunay triangulations, inspired by a pre‐existing reconstruction algorithm. Current graphics hardware accelerated algorithms are approximating approaches, where the final triangulation is usually performed through either marching cubes or marching tetrahedras. GPU‐compatible methods and data structures to perform normal estimation and the local triangulation have been developed, plus a variation of the Bitonic Merge Sort algorithm to work with multi‐lists. Our method shows an average gain of one order of magnitude over previous research.     

一种应用于网格计算环境的任务调度模式*

提出了一种新的网格任务调度模式,针对网格计算资源有组织、松耦合、自治等特性,建立基于多层次虚拟组织形式的计算资源模型;根据网格环境中应用任务粗粒度、特定资源依赖等特点,建立了网格任务的描述模型;提出并实现了相应的子任务生成算法、任务初始调度算法及自动调整算法。设计实现了能够支持仿真及实际网格计算环境可扩展网格任务调度器,通过理论分析和仿真实验对算法的正确性、效果和效率进行了评价。     

OpenGR: A directive-based grid programming environment

A new grid programming environment for remote procedure call (RPC) based master–worker type task parallelization is presented. The environment is realized through the use of a set of compiler directives, called OpenGR, and is implemented in the present study based on the Omni OpenMP compiler system and Ninf-G grid-enabled RPC system as a parallel execution mechanism. Using OpenGR directives, existing sequential applications can be readily adapted to the grid environment as master–worker parallel programs using the RPC architecture. The combination of OpenGR and OpenMP directives also allows for the hybrid parallelization of sequential programs, supporting both synchronous and asynchronous parallelism.     

Parameter optimization in 3D reconstruction on a large scale grid

Reaching as high structural resolution as possible in 3D electron microscopy of biological specimens is crucial to understanding their function and interactions. Technical and biological limitations make electron microscopy projections of such specimens quite noisy. Under those circumstances, the broadly used Weighted Back-Projection algorithm presents some limitations for 3D reconstruction. Iterative tomographic reconstruction algorithms are well suited to provide high resolution 3D structures under such noisy conditions. Nevertheless, these iterative algorithms present two major challenges: computational expensiveness and some free parameters which need to be correctly tuned to obtain the best possible resolution. This work applies global optimization techniques to search for the optimal set of parameters and makes use of the high-throughput capabilities of grid computing to perform the required computations. Fault tolerance techniques have been included in our application to deal with the dynamic nature and complexity of large scale computational grids. The approach for parameter optimization presented here has been successfully evaluated in the European EGEE grid, obtaining good levels of speedup, throughput and transfer rates.     

Cooperative decision making in a knowledge grid environment

A knowledge grid is an intelligent interconnection environment, built on top of a computational grid, to facilitate the creation of virtual organizations. An important feature of a virtual environment is its support for collaborative decision-making. A major difficulty with current approaches is that they cannot easily handle environments where decision makers are added or removed dynamically. In this article, a new approach to alter the number of decision makers dynamically is suggested. The amount of decision accuracy made by each decision maker, for a given subject, is determined subjectively considering the other decision makers’ opinions. The effect of decisions made by each decision maker varies gradually considering its past decisions. Assuming each decision maker provides a fuzzy answer set in response to each decision problem, an operator for fusing of the decision makers’ decision sets is suggested. The aim of the fusion is improvement of the decision quality. The fusing operator provides a fuzzy answer set that is a function of the accuracy possibility of each decision maker and its fuzzy answer set.     

Monte Carlo methods for matrix computations on the grid

Many scientific and engineering applications involve inverting large matrices or solving systems of linear algebraic equations. Solving these problems with proven algorithms for direct methods can take very long to compute, as they depend on the size of the matrix. The computational complexity of the stochastic Monte Carlo methods depends only on the number of chains and the length of those chains. The computing power needed by inherently parallel Monte Carlo methods can be satisfied very efficiently by distributed computing technologies such as Grid computing. In this paper we show how a load balanced Monte Carlo method for computing the inverse of a dense matrix can be constructed, show how the method can be implemented on the Grid, and demonstrate how efficiently the method scales on multiple processors.     

网格环境下负载平衡研究

随着Internet上网络应用和网格技术的飞速发展,负载平衡的研究和发展面临着新的机遇和挑战。这些新的技术、概念和方法提供了更新的环境来研究和发展负载平衡系统;结合网格和负载平衡两种技术,依据开放网格服务体系、网格数据服务接口模型和网格服务资源框架,研究了在网格环境下的负载平衡实施,提出了一个负载平衡系统的体系结构模型。针对负载平衡中的两个关键问题——信息分发收集过程和有效的负载估计算法做了详细的论述。     

Data discovery algorithm for scientific data grid environment

In modern scientific computing communities, scientists are involved in managing massive amounts of very large data collections in a geographically distributed environment. Research in the area of grid computing has given us various ideas and solutions to address these requirements. Data grid mostly deals with large computational problems and provides geographically distributed resources for large-scale data-intensive applications that generate large data sets. Peer-to-peer (P2P) networks have also become a major research topic over the last few years. In a distributed P2P system, a discovery algorithm is required to locate specific information, applications, or users within the system. In this research work, we present our scientific data grid as a large P2P-based distributed system model. By using this model, we study various discovery algorithms for locating data sets in a data grid system. The algorithms we studied are based on the P2P architecture. We investigate these algorithms using our Grid Simulator developed using PARSEC. In this paper, we illustrate our scientific data grid model and our Grid Simulator. We then analyze the performance of the discovery algorithms relative to their average number of hop, success rates and bandwidth consumption.     

Numerical fixed grid methods for differential inclusions

Summary Numerical methods for initial value problems for differential inclusions usually require a discretization of time as well as of the set valued right hand side. In this paper, two numerical fixed grid methods for the approximation of the full solution set are proposed and analyzed. Convergence results are proved which show the combined influence of time and (phase) space discretization. Supported by CRC 701 “Spectral Analysis and Topological Methods in Mathematics”.     

无人机多机协作探索煤矿灾变环境算法

针对目前煤矿灾变环境下救援机器人探索效率低的问题,提出了一种使用无人机多机协同探索煤矿灾变环境的改进型边界探索算法。该算法在效用值边界探索算法的基础上增加了对无人机导航角度因素的考虑,同时引入分散度函数作为评判机制来构建目标函数,并使用蚁群算法对该目标函数进行求解。最后利用Matlab软件在栅格化地图上进行了仿真实验。实验结果表明,和效用值边界探索算法相比,改进型边界探索算法减少了探测过程中的重复覆盖和拥挤现象,缩短了探测时间,降低了约30%的能量消耗,提高了无人机多机系统的整体探索效率。     

网格环境中动态信任模型研究

为了提高网格交互的安全性,客观地反映信任的主观性、模糊性及主体问信任关系的动态性,提出了基于模糊理论的动态信任管理方法.通过特征向量和隶属度等概念,实现了信任的动态描述.针对不同的上下文环境,结合推荐节点的直接交互经验,建立了信任的模糊综合评判模型并通过反模糊化,得出量化评估结果.基于时间衰减和模糊贴近度两因素,给出了信任更新模型.仿真实验分析表明,该方法对网格环境中的信任评估是可靠的、有效的.     

基于多Agent的网格信任模型

在对相关信任模型研究的基础上,提出了一个基于多Agent的信任模型,该模型采用在每个管理域设置多个Agent,每个实体和多个Agent关联的方式来管理网格环境中实体间的信任关系.实验和分析结果表明,与其它基于声誉的信任模型相比,这种模型不但缩短了交易响应时间,而且提高了作业成功率,从而能更加有效地解决网格环境中存在的安全问题.