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地球信息科学学报 2017年第10期19卷 1346-1354页

作者：景维鹏霍帅起东北林业大学信息与计算机工程学院哈尔滨150040

图像镶嵌是遥感图像处理中的重要内容,在跨区域遥感图像分析中发挥重要作用。为了解决传统遥感图像并行算法中存在的计算节点利用率低、频繁数据I/O等问题,本文根据Spark分布式内存计算框架,充分利用Spark利于迭代数据处理的优势,提出... 详细信息

图像镶嵌是遥感图像处理中的重要内容,在跨区域遥感图像分析中发挥重要作用。为了解决传统遥感图像并行算法中存在的计算节点利用率低、频繁数据I/O等问题,本文根据Spark分布式内存计算框架,充分利用Spark利于迭代数据处理的优势,提出了一种基于Spark自定义RDD(弹性分布式数据集)的并行镶嵌方法。该方法首先在集群的多个节点上通过相位相关法执行图像重叠区域估计操作,从而提高了图像重叠区域估计的多节点并行计算;然后,通过重写Spark中RDD的compute和get Partitions方法,自定义针对遥感图像处理的RDD,并将图像镶嵌中的重叠区域估计、图像配准和图像融合3个关键步骤作为自定义RDD的Transformation类型的操作算子;最后,通过隐式转换创建自定义RDD,并调用自定义RDD的操作算子实现图像镶嵌的并行处理。实验结果表明,与传统基于MPI的并行镶嵌算法相比,该方法在保证图像镶嵌效果的基础上,能够有效提高大数据量的图像镶嵌效率。

关键词：遥感图像并行镶嵌 Spark 相位相关法自定义RDD

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distributed texture-based land cover classification algorithm using hidden Markov model for multispectral data

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SURVEY REVIEW 2016年第351期48卷 430-437页

作者： Jenicka, S. Suruliandi, A. Einstein Coll Engn Comp Sci & Engn Tirunelveli Tamil Nadu India Manonmaniam Sundaranar Univ Dept Informat Technol Tirunelveli 627012 Tamil Nadu India

Land cover classification is a vital application area in the satellite image processing domain. Texture is a useful feature in land cover classification. In this paper, we propose a distributed texture-based land cover classification algorithm using Hidden Markov Model (HMM). Here, HMM is used for texture-based classification of remotely sensed images. Furthermore, to enhance the performance, data-intensive remotely sensed image is segmented and distributed into parallel sessions. Experiments were conducted on IRS P6 LISS-iv data, and the results were evaluated based on the confusion matrix, classification accuracy, and Kappa statistics. These results indicate that the proposed algorithm achieves a classification accuracy of 88.75%.

关键词： Cartography Classification algorithm Classification accuracy distributed algorithms Hidden Markov models image segmentation image texture analysis

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A New Approach for Large-Scale Scene image Retrieval Based on Improved parallel k-Means Algorithm in MapReduce Environment

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MATHEMATICAL PROBLEMS IN ENGINEERING 2016年第1期2016卷 1-17页

作者： Cao, Jianfang Wang, Min Shi, Hao Hu, Guohua Tian, Yun Xinzhou Teachers Univ Dept Comp Sci & Technol Xinzhou 034000 Peoples R China Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan 030024 Peoples R China

The rapid growth of digital images has caused the traditional image retrieval technology to be faced with new challenge. In this paper we introduce a new approach for large-scale scene image retrieval to solve the problems of massive image processing using traditional image retrieval methods. First, we improved traditional k-Means clustering algorithm, which optimized the selection of the initial cluster centers and iteration procedure. Second, we presented a parallel design and realization method for improved k-Means algorithm applied it to feature clustering of scene images. Finally, a storage and retrieval scheme for large-scale scene images was put forward using the large storage capacity and powerful parallel computing ability of the Hadoop distributed platform. The experimental results demonstrated that the proposed method achieved good performance. Compared with the traditional algorithms with single node architecture and parallel k-Means algorithm, the proposed method has obvious advantages for use in large-scale scene image data retrieval in terms of retrieval accuracy, retrieval time overhead, and computational performance (speedup and efficiency, sizeup, and scaleup), which is a significant improvement from applying parallel processing to intelligent algorithms with large-scale datasets.

关键词： image retrieval K-means clustering DIGITAL images image processing PROBLEM solving

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SCALEDEEP: A Scalable Compute Architecture for Learning and Evaluating Deep Networks 17

SCALEDEEP: A Scalable Compute Architecture for Learning and ...

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44th Annual International Symposium on Computer Architecture (ISCA)

作者： Venkataramani, Swagath Ranjan, Ashish Banerjee, Subarno Das, Dipankar Avancha, Sasikanth Jagannathan, Ashok Durg, Ajaya Nagaraj, Dheemanth Kaul, Bharat Dubey, Pradeep Raghunathan, Anand Purdue Univ Sch ECE W Lafayette IN 47907 USA Intel Corp Parallel Comp Lab Santa Clara CA 95051 USA IBM Corp TJ Watson Res Ctr Yorktown Hts NY 10598 USA Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781450348928

Deep Neural Networks (DNNs) have demonstrated state-of-the-art performance on a broad range of tasks involving natural language, speech, image, and video processing, and are deployed in many real world applications. However, DNNs impose significant computational challenges owing to the complexity of the networks and the amount of data they process, both of which are projected to grow in the future. To improve the efficiency of DNNs, we propose SCALEDEEP, a dense, scalable server architecture, whose processing, memory and interconnect subsystems are specialized to leverage the compute and communication characteristics of DNNs. While several DNN accelerator designs have been proposed in recent years, the key difference is that SCALEDEEP primarily targets DNN training, as opposed to only inference or evaluation. The key architectural features from which SCALEDEEP derives its efficiency are: (i) heterogeneous processing tiles and chips to match the wide diversity in computational characteristics (FLOPs and Bytes/FLOP ratio) that manifest at different levels of granularity in DNNs, (ii) a memory hierarchy and 3-tiered interconnect topology that is suited to the memory access and communication patterns in DNNs, (iii) a low-overhead synchronization mechanism based on hardware data-flow trackers, and (iv) methods to map DNNs to the proposed architecture that minimize data movement and improve core utilization through nested pipelining. We have developed a compiler to allow any DNN topology to be programmed onto SCALEDEEP, and a detailed architectural simulator to estimate performance and energy. The simulator incorporates timing and power models of SCALEDEEP's components based on synthesis to Intel's 14nm technology. We evaluate an embodiment of SCALEDEEP with 7032 processing tiles that operates at 600 MHz and has a peak performance of 680 TFLOPs (single precision) and 1.35 PFLOPs (half-precision) at 1.4KW. Across 11 state-of-the-art DNNs containing 0.65M-14.9M neurons and

关键词： Deep Neural Networks Hardware Accelerators System Architecture

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A Hierarchical distributed processing Framework for Big image Data

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IEEE Transactions on Big Data 2016年第4期2卷 297-309页

作者： Dong, Le Lin, Zhiyu Liang, Yan He, Ling Zhang, Ning Chen, Qi Cao, Xiaochun Izquierdo, Ebroul University of Electronic Science and Technology of China Chengdu611731 China China Academy of Engineering Physics Mianyang Sichuan621900 China Cyberspace Administration of the People's Republic of China Beijing100010 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100864 China Queen Mary University of London LondonE1 4NS United Kingdom

This paper introduces an effective processing framework nominated image Cloud processing (ICP) to powerfully cope with the data explosion in image processing field. While most previous researches focus on optimizing the image processing algorithms to gain higher efficiency, our work dedicates to providing a general framework for those image processing algorithms, which can be implemented in parallel so as to achieve a boost in time efficiency without compromising the results performance along with the increasing image scale. The proposed ICP framework consists of two mechanisms, i.e., Static ICP (SICP) and Dynamic ICP (DICP). Specifically, SICP is aimed at processing the big image data pre-stored in the distributed system, while DICP is proposed for dynamic input. To accomplish SICP, two novel data representations named P-image and Big-image are designed to cooperate with MapReduce to achieve more optimized configuration and higher efficiency. DICP is implemented through a parallel processing procedure working with the traditional processing mechanism of the distributed system. Representative results of comprehensive experiments on the challenging imageNet dataset are selected to validate the capacity of our proposed ICP framework over the traditional state-of-The-Art methods, both in time efficiency and quality of results. © 2015 IEEE.

关键词： MapReduce

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On-Demand processing for Remote Sensing Big Data Analysis

On-Demand Processing for Remote Sensing Big Data Analysis

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Zhenchun Huang Anrun Zhong Guoqing Li Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9781538637913

In the recent decades, remote sensing data are rapidly growing in size and variety, and considered as “big geo data” because of their huge data volume, significant heterogeneity and challenge of fast analysis. In the traditional remote sensing analysis workflows, the data transfer for downloading raw image files to local workstations often costs a lot of time and slows down the data analysis workflows. Because results of remote sensing data analysis models are usually much smaller than raw data to be processed, “on-demand processing”, which tries to upload data analysis models and execute them “near” where data stores, can significantly accelerate the execution of remote sensing analysis workflows. In this paper, a framework for on-demand remote sensing data analysis is proposed based on three-layered architecture; XML/JSON based runtime environment description; and on-demand model deployment methods. The evaluation on a prototype system shows that on-demand processing framework accelerates the execution of analysis models in 2.8 ~ 12.7 times by reducing data transfers, especially for those analysis workflows which transfer data through low bandwidth Internet. By on-demand processing, classical remote sensing data service systems can evolve into remote sensing data processing infrastructures, which provide IaaS (Infrastructure-as-a-Service) and PaaS (Platform-as-a-Service) services, and make it possible to exchange knowledge among scientists by sharing models. Furthermore, a remote sensing data analysis platform for carbon satellites is designed based on the on-demand processing proposed by this paper and will soon be implemented under the support of SunWay-TaihuLight, the world's most powerful super computer.

关键词： Tools XML distributed processing Conferences Ubiquitous computing Sensors Data analysis

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Direct Numerical Computation of the Stationary Radiative Transport Equation on Modern parallel Architectures

Direct Numerical Computation of the Stationary Radiative Tra...

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International Symposium on distributed Computing and Applications to Business, Engineering and Science (DCABES)

作者： Hiroshi Fujiwara Naoya Oishi Graduate School of Informatics Kyoto University Kyoto Japan Graduate School of Medicine Kyoto University Kyoto Japan

ISBN: (纸本)9781538621639

We study direct numerical computations of the stationary radiative transport equation for light propagation in tissue with a three dimensional MR image and bio-optical parameters. We employ an upwind finite difference scheme which has mathematical reliability. The computational times in modern three parallel architectures are compared. Although its discretization is a large scale problem with several billions unknowns, our result shows practicality of direct numerical computation from viewpoints of computational time and modern computer resources.

关键词： Mathematical model Scattering Iterative methods Graphics processing units Biomedical imaging Reliability Jacobian matrices

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Simulation of parallel processing for distributed Multi-Target Tracking in Smart Camera Networks 16

Simulation of Parallel Processing for Distributed Multi-Targ...

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10th International Conference on distributed Smart Cameras (ICDSC)

作者： Jafarizadeh, Mehdi YadollahPour, Naeeme Shahid Beheshti Univ Comp Engn Dept Tehran Iran Univ Tehran Dept Management & Accounting Tehran Iran

ISBN: (纸本)9781450347860

In order to reduce the real-time processing gap of distributed algorithms on smart camera networks, we have evaluated a parallel processing method which exploits the processing capabilities of free cameras as auxiliary for busy ones. The communication infrastructure or camera processing capabilities are not included in the traditional evaluation methods like datasets or even virtual reality tools, so they are not suitable to analyze the complexity of distributed algorithms. For this purpose, we have developed a modular framework based on OMNET++ simulation environment named CAM-DIST, in which the velocity model of soccer players are emulated. Simulation results show that parallel processing improves overall efficiency, but could have side effects on individual camera estimations;so choosing optimal sharing value and destinations will enhance the performance.

关键词： Smart camera networks OMNET plus complexity analysis of distributed algorithms

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distributed travel-time seismic tomography in large-scale sensor networks

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JOURNAL OF parallel AND distributed COMPUTING 2016年 89卷 50-64页

作者： Kamath, Goutham Shi, Lei Song, Wen-Zhan Lees, Jonathan Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Univ N Carolina Dept Geol Sci Chapel Hill NC USA

Current geophysical techniques for visualizing seismic activity employ image reconstruction methods that rely on a centralized approach for processing the raw data captured by seismic sensors. The data is either gathered manually, or relayed by expensive broadband stations, and then processed at a base station. This approach is time-consuming (weeks to months) and hazardous as the task involves manual data gathering in extreme conditions. Also, raw seismic samples are typically in the range of 16-24 bit, sampled at 50-200 Hz and transferring this high fidelity sample from large number of sensors to a centralized station results in a bottleneck due to bandwidth limitations. To avoid these issues, a new distributed method is required which processes raw seismic samples inside each node and obtains a high resolution seismic tomography in real time. In this paper, we present a component-averaged distributed multi-resolution evolving tomography algorithm for processing data and inverting volcano tomography in the network while avoiding centralized computation and costly data collection. The algorithm is first evaluated for the correctness using a synthetic model in a CORE emulator. Later, our proposed algorithm runs using the real data obtained from Mt. St. Helens, WA, USA. The results validate that our distributed algorithm is able to obtain a satisfactory image similar to centralized computation under constraints of network resources, while distributing the computational burden to sensor nodes. (C) 2015 Elsevier Inc. All rights reserved.

关键词： distributed computing Seismic tomography Sensor network Mt. St. Helens

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Location-Based parallel Tag Completion for Geo-Tagged Social image Retrieval

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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2017年第3期8卷 38-38页

作者： Zhang, Jiaming Wang, Shuhui Huang, Qingming Chinese Acad Sci Key Lab Intellectual Informat Proc Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

Having benefited from tremendous growth of user-generated content, social annotated tags get higher importance in the organization and retrieval of large-scale image databases on Online Sharing Websites (OSW). To obtain high-quality tags from existing community contributed tags with missing information and noise, tag-based annotation or recommendation methods have been proposed for performance promotion of tag prediction. While images from OSW contain rich social attributes, they have not taken full advantage of rich social attributes and auxiliary information associated with social images to construct global information completion models. In this article, beyond the image-tag relation, we take full advantage of the ubiquitous GPS locations and image-user relationship to enhance the accuracy of tag prediction and improve the computational efficiency. For GPS locations, we define the popular geo-locations where people tend to take more images as Points of Interests (POI), which are discovered by mean shift approach. For image-user relationship, we integrate a localized prior constraint, expecting the completed tag sub-matrix in each POI to maintain consistency with users' tagging behaviors. Based on these two key issues, we propose a unified tag matrix completion framework, which learns the image-tag relation within each POI. To solve the optimization problem, an efficient proximal sub-gradient descent algorithm is designed. The model optimization can be easily parallelized and distributed to learn the tag sub-matrix for each POI. Extensive experimental results reveal that the learned tag sub-matrix of each POI reflects the major trend of users' tagging results with respect to different POIs and users, and the parallel learning process provides strong support for processing large-scale online image databases. To fit the response time requirement and storage limitations of Tag-based image Retrieval (TBIR) on mobile devices, we introduce Asymmetric Locality Sensitive H

关键词： Tag matrix completion geo-location information social image retrieval asymmetric locality sensitive hashing

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