一种面向海量遥感数据分类应用的并行解决方案

目前,遥感数据量呈海量增长趋势。如何在大数据环境下进行快速影像分类及信息挖掘,提升处理的业务化水平,是一个重要的研究方向。鉴于此,实现了一种高效的解决方案。首先,基于\"五层十五级\"数据结构,对以景为单位的影像进行离散化处理,建立以切片为单元的数据组织体系。其次,借助大数据云存储技术实现切片的集群分布式存储。其次采用了基于像元和对象的高效监督分类算法,并依据计算处理需求对集群环境下的并行架构和驱动机制进行适应性设计。最终,实现了该解决方案并以高分2号多光谱切片进行实验。结果表明:该方案在保证精度的前提下提高了分类处理的效率。     

像素级融合并行算法的模型研究

通过对多种遥感图像像素级融合算法的深入研究,实现了三种具有代表性的像素级融合并行算法,在分析了这些并行算法的数据划分方式、负载平衡及算法设计流程的基础上,总结性地提出了一种通用的像素级融合并行算法模型。该模型对遥感图像融合的并行算法研究提供了有益的指导和借鉴。     

一种格网补偿的遥感影像数据检索方法

随着遥感技术的快速发展,当前遥感影像数据的数据总量急剧增加。面对海量遥感影像数据的应用需求,如何快速、准确的获取全覆盖特定区域的无云遥感影像数据至关重要。因此,笔者提出了一种格网补偿的遥感数据全覆盖检索方法。首先,利用五层十五级格网切分模型对特定区域切分成较小的格网。其次,对海量遥感有云影像数据集进行预处理,采用基于快视图的云检测方法判断快视图局部是否无云,取出无云的部分对每个格网进行像素补偿。最后得出一张全覆盖特定区域的无云遥感图像,并筛选出所需的遥感影像数据。通过实验对比,该方法在海量遥感影像数据情况下,能快速、准确的获取全覆盖特定区域的无云遥感影像数据,具有很好的可行性和实用性。     

一种五层结构模型的三维远程教育系统研究

通过对现有远程教育系统和网络模型的研究,提出五层结构的三维远程教育系统设计方案.平台与模型组件分离的思想增强了整个远程教育系统的重用性.调度服务器以及消息传送机制的采用可以增强教育过程中的实时交互和相互协作的能力.     

基于GPU的遥感图像融合并行算法研究

基于通用GPU并行计算技术,结合遥感图像数据融合处理特点,利用NVIDIA公司的CUDA编程框架,在其GPU平台上对BROVEY变换和YIQ变换融合算法进行了并行研究与实现.实验结果表明,随着遥感图像融合算法的计算复杂度、融合处理的问题规模逐渐增加,GPU并行处理的加速性能优势也逐渐增大,GPU通用计算技术在遥感信息处理领域具有广阔的应用前景.     

基于看门狗的星载计算机五级保护系统设计

小卫星要求星载计算机可靠性高、重量轻、体积小、功耗低。针对探测小卫星（ Ｓ Ｅ Ｓ） 的特殊性,我们设计了用８０３８６ 芯片作处理器的星载计算机（ Ｓ Ｃ Ｓ） 系统。由于 Ｓ Ｃ Ｓ 的设计尽量少用硬件冗余,我们采用多级看门狗电路保证星载系统的可靠性,看门狗也用最少的硬件及软件实现。看门狗提供了五级保护措施,根据故障严重程度,保护级别逐步升级。在处理器或存储器出现软、硬件故障后,包括出现单粒子翻转（ Ｓ Ｅ Ｕ） 或死锁现象后,不同看门狗启动不同保护措施,使系统恢复正常。     

基于分布式计算的遥感图像水体识别研究

为了提高遥感数据的处理速度,解决遥感信息提取中的数据密集与计算密集问题,将并行计算的思想引入到遥感图像的处理与信息提取中,构建基于Landsat ETM+影像的分布式遥感图像水体提取模型。以渭干河流域为研究区,利用单波段阈值法、多波段谱间关系法、水体指数法等方法进行水体信息自动提取的实验。实验结果表明,该模型具有较高的识别精度,能够快速识别水体,并具有稳定的可扩展性和伸缩性。     

面向FPGA的RNA二级结构预测并行算法研究

动态规划是RNA二级结构预测最主要的算法,文中提出一种对动态规划矩阵采用分块技术的细粒度并行算法,通过对数据依赖关系的分析,引入了流水的策略,提高了算法的效率,在时钟模拟器上验证了算法的正确性,获得了一系列关于并行加速比、空泡率、存储访问带宽等问题的模拟结果,确定了FPGAPE阵列设计中的基本参数,为FPGA成功实现奠定了基础。     

基于LQR 和模糊插值的五级倒立摆控制

首先给出五级倒立摆的数学模型并验证了它的可控性;然后在分析与倒立摆系统稳定性有关因素的基础上,结合LQR理论构造出一种非对角型的权重矩阵,并给出了设定该矩阵中各元素的方法;最后设计出一个简单的基于二元分片插值函数的模糊控制器,成功地实现了具有定位功能的五级倒立摆的控制．仿真结果表明该方法是有效的．     

WOLF-Linux的五层结构设计与实现

构建基于Linux的嵌入式系统,对可靠性、安全性、功耗等具有较高的要求,因此需要一种模型来清晰完整的描述系统,指导系统的设计与实现.综合嵌入式系统各种要素,结合结构模型和层次思想,提出和分析了嵌入式系统五层结构模型,并将其应用于嵌入式Linux系统--WOLF-Linux的开发中.五层结构模型较好地指导了系统的完成,验证了该模型对于嵌入式系统设计与实现的有效性.     

A new parallel algorithm for constructing Voronoi tessellations from distributed input data

We present a new parallel algorithm for generating consistent Voronoi diagrams from distributed input data for the purposes of simulation and visualization. The algorithm functions by building upon any serial Voronoi tessellation algorithm. The output of such a serial tessellator is used to determine the connectivity of the distributed domains without any assumptions about how points are distributed across those domains, and then in turn to build the portion of the global tessellation local to each domain using information from that domains neighbors. The result is a generalized methodology for adding distributed capabilities to serial tessellation packages. Results from several two-dimensional tests are presented, including strong and weak scaling of its current implementation.     

Distributed data organization and parallel data retrieval methods for huge laser scanner point clouds

This paper proposes a novel method for distributed data organization and parallel data retrieval from huge volume point clouds generated by airborne Light Detection and Ranging (LiDAR) technology under a cluster computing environment, in order to allow fast analysis, processing, and visualization of the point clouds within a given area. The proposed method is suitable for both grid and quadtree data structures. As for distribution strategy, cross distribution of the dataset would be more efficient than serial distribution in terms of non-redundant datasets, since a dataset is more uniformly distributed in the former arrangement. However, redundant datasets are necessary in order to meet the frequent need of input and output operations in multi-client scenarios: the first copy would be distributed by a cross distribution strategy while the second (and later) would be distributed by an iterated exchanging distribution strategy. Such a distribution strategy would distribute datasets more uniformly to each data server. In data retrieval, a greedy algorithm is used to allocate the query task to a data server, where the computing load is lightest if the data block needing to be retrieved is stored among multiple data servers. Experiments show that the method proposed in this paper can satisfy the demands of frequent and fast data query.     

卫星遥感数据的山地散射辐射的计算与改正

地形条件决定的天空散射辐射光遮蔽角是影响卫片像元地面天空散射辐射的主要因素,本文利用与卫片匹配的数字地面模型建立了一个卫星遥感数据的山地散射辐射计算与改正模式。     

Dynamic optimal reactive power dispatch based on parallel particle swarm optimization algorithm

In this paper, Message Passing Interface (MPI) based parallel computation and particle swarm optimization (PSO) algorithm are combined to form the parallel particle swarm optimization (PPSO) method for solving the dynamic optimal reactive power dispatch (DORPD) problem in power systems. In the proposed algorithm, the DORPD problem is divided into smaller ones, which can be carried out concurrently by multi-processors. This method is evaluated on a group of IEEE power systems test cases with time-varying loads in which the control of the generator terminal voltages, tap position of transformers and reactive power sources are involved to minimize the transmission power loss and the costs of adjusting the control devices. The simulation results demonstrate the accuracy of the PPSO algorithm and its capability of greatly reducing the runtimes of the DORPD programs.     

一种可靠的多光谱CCD遥感图像配准方法

多源遥感图像数据的融合和后继应用必须先实现严格的配准。针对CBERS-02B遥感卫星多光谱CCD相机的成像尺寸大、像质差异性小及不同波段图像间的误差范围较固定等特点,提出一种基于小窗口多次匹配的图像配准方法。即在整幅大图像上均匀获取多个小窗口图像与基准图像进行灰度NCC匹配,将多个匹配结果通过剔除误匹配点后求平均的方法来获得具有子像素级精度的配准结果,从而保证配准的可靠性和精度。将该方法用于对多光谱CCD图像的配准实验中,结果表明本文提出的配准方法具有可行性和实用性。     

遥感数据的BN与ML分类对比研究

选择了北京奥运主场馆及其周围的地区作为实验区,购置陆地卫星ETM＋6个波段数据,从学习机制和技术流程上对贝叶斯网络分类和最大似然分类进行了对比,实验结果表明：贝叶斯网络分类方法在提高遥感数据的分类精度方面具有较大的研发潜力,贝叶斯网络为遥感数据分类处理提供了一种可选择途径。     

遥感图像海量存储系统的设计与分析

海量数据存储系统是遥感图像信息系统的关键组成部分之一。存储系统的研究包括硬件体系结构和关于存储调度的软件算法两个重要的方面，其中系统的设计和评价是其研究的核心内容。本在有关研究的基础上，主要从系统体系结构以及抽象的系统描述角度，研究和探讨了海量存储系统设计与分析中的基本技术问题。指出了视频数据存储系统和图像数据存储系统之间在要求上的差异，并提出了海量数据存储系统的抽象民相应的评价指标，紧后给出了一个适合海量图像数据存储的实际系统设计方案。     

A space-efficient parallel sequence comparison algorithm for a message-passing multiprocessor

We present a parallel algorithm for computing an optimal sequence alignment in efficient space. The algorithm is intended for a message-passing architecture with one-dimensional-array topology. The algorithm computes an optimal alignment of two sequences of lengthsM andN inO((M+N) ² /P) time andO((M+N)/P) space per processor, where the number of processors isP>=max(M, N). Thus, whenP=max(M, N) it achieves linear speedup and requires constant space per processor. Some experimental results on an Intel hypercube are provided.This research was supported by NIH Grant LM05110 from the National Library of Medicine.     

Cluster based parallel database management system for data intensive computing

This paper describes a computer-cluster based parallel database management system (DBMS), InfiniteDB, developed by the authors. InfiniteDB aims at efficiently support data intensive computing in response to the rapid growing in database size and the need of high performance analyzing of massive databases. It can be efficiently executed in the computing system composed by thousands of computers such as cloud computing system. It supports the parallelisms of intra-query, inter-query, intra-operation, inter-operation and pipelining. It provides effective strategies for managing massive databases including the multiple data declustering methods, the declustering-aware algorithms for relational operations and other database operations, and the adaptive query optimization method. It also provides the functions of parallel data warehousing and data mining, the coordinatorwrapper mechanism to support the integration of heterogeneous information resources on the Internet, and the fault tolerant and resilient infrastructures. It has been used in many applications and has proved quite effective for data intensive computing.     

ParaStream: A parallel streaming Delaunay triangulation algorithm for LiDAR points on multicore architectures

This paper presents a robust parallel Delaunay triangulation algorithm called ParaStream for processing billions of points from nonoverlapped block LiDAR files. The algorithm targets ubiquitous multicore architectures. ParaStream integrates streaming computation with a traditional divide-and-conquer scheme, in which additional erase steps are implemented to reduce the runtime memory footprint. Furthermore, a kd-tree-based dynamic schedule strategy is also proposed to distribute triangulation and merging work onto the processor cores for improved load balance. ParaStream exploits most of the computing power of multicore platforms through parallel computing, demonstrating qualities of high data throughput as well as a low memory footprint. Experiments on a 2-Way-Quad-Core Intel Xeon platform show that ParaStream can triangulate approximately one billion LiDAR points (16.4 GB) in about 16 min with only 600 MB physical memory. The total speedup (including I/O time) is about 6.62 with 8 concurrent threads.