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15th IEEE International Symposium on parallel and distributed processing with Applications (ISPA) / 16th IEEE International Conference on Ubiquitous Computing and Communications (IUCC)

作者： Jin, Xiaotian Zheng, Wenbo Mo, Shaocong Qu, Yili Jin, Xin Zhou, Jiangwei Duan, Pengfei Zheng, Tao Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430070 Hubei Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Shaanxi Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Guangdong Peoples R China Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Hubei Peoples R China Wuhan Univ Technol Hubei Key Lab Transportat Internet Things Wuhan 430070 Hubei Peoples R China Wuhan Univ Technol Sch Foreign Languages Wuhan 430070 Hubei Peoples R China

ISBN: (纸本)9781538637906

Ant colony optimization (ACO) can be used to solve complex optimization problems in engineering, economic management and military strategy. Most of these are NP hard problems, which are difficult to solve with traditional methods. An improved parallel ACO algorithm based on pattern learning is proposed in this paper. It extracts parameters automatically to reduce solution space and enhance calculation efficiency. Various parameters in the algorithm are analyzed, and a refining strategy is formed according to ACOs characteristics. The parallel ACO algorithm is carried out under the MIC/CPU architecture, and it can significantly enhance performance.

关键词： Pattern learning Ant Colony Optimization MIC

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Marine bathymetry processing through GPGPU virtualization in high performance cloud computing

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2018年第24期30卷

作者： Montella, Raffaele Marcellino, Livia Galletti, Ardelio Di Luccio, Diana Kosta, Sokol Laccetti, Giuliano Giunta, Giulio Univ Naples Parthenope Dept Sci & Technol Ctr Direz Isola C4 I-80143 Naples Italy Univ Chicago Computat Inst Ctr Robust Decis Making Climate & Energy Policy Chicago IL 60637 USA Aalborg Univ Ctr Commun Media & Informat Technol Copenhagen Denmark Univ Naples Federico II Dept Math & Applicat Naples Italy

Fast technology development has influenced the widespread use of low-power devices in different scientific, environmental, and everyday life areas, giving birth to the Internet of Things. In this paper, we focus on the context of marine studies, addressing the problem of marine bathymetry data processing and analysis via pervasive and Internet-connected sensors and low-power distributed devices. Pervasive and Internet-connected low-power devices (as the components involved in the sensing and processing actions) made diverse and different "things" as a worldwide-distributed system. Given the high complexity of the algorithms involved in these studies, which usually involve general-purpose graphic processing unit (GPGPU) computation, it is impossible for the limited devices to perform the required calculations. To overcome these limitations, in this paper, we propose and implement a vertical application of GVirtuS, the open-source GPGPU virtualization and remoting service, for achieving high performance geographical data interpolation in a high performance cloud computing scenario. We present an innovative implementation by comparing, in terms of performance and accuracy, the inverse distance weighting and kriging interpolation methods in their parallel implementations leveraging on CUDA-enabled GPGPUs. We present a real-world use case related to high-resolution bathymetry interpolation in a crowdsource data context in the Bay of Pozzuoli, Italy.

关键词： geographic data interpolation GPGPU virtualization high performance computing IDW kriging

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On Extending a Fixed Size, Persistent and Lock-Free Hash Map Design to Store Sorted Keys

On Extending a Fixed Size, Persistent and Lock-Free Hash Map...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Miguel Areias Ricardo Rocha CRACS & INESC TEC University of Porto Porto Portugal

ISBN: (纸本)9781728111421;9781728111414

Searching is a crucial time-consuming part of many programs, and using a good search method instead of a bad one often leads to a substantial increase in performance. Hash tries are a trie-based data structure with nearly ideal characteristics for the implementation of hash maps. In this paper, we present a novel, simple and concurrent hash map design that fully supports the concurrent search, insert and remove operations on hash tries designed to store sorted keys. To the best of our knowledge, our design is the first concurrent hash map design that puts together the following characteristics: (i) use fixed size data structures; (ii) use persistent memory references; (iii) be lock-free; and (iv) store sorted keys. Experimental results show that our design is quite competitive when compared against other state-of-the-art designs implemented in Java.

关键词： Sorting Indexes Arrays Search methods Java Synchronization

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FD-MOBILENET: IMPROVED MOBILENET WITH A FAST DOWNSAMPLING STRATEGY

FD-MOBILENET: IMPROVED MOBILENET WITH A FAST DOWNSAMPLING ST...

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IEEE International Conference on image processing

作者： Zheng Qin Zhaoning Zhang Xiaotao Chen Changjian Wang Yuxing Peng Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China

We present Fast-Downsampling MobileNet (FD-MobileNet), an efficient and accurate network for very limited computational budgets (e.g., 10-140 MFLOPs). Our key idea is applying a fast down-sampling strategy to MobileNet framework. In FD-MobileNet, we perform 32× downsampling within 12 layers, only half the layers in the original MobileNet. This design brings three advantages: (i) It remarkably reduces the computational cost. (ii) It increases the information capacity and achieves significant performance improvements. (iii) It is engineering-friendly and provides fast actual inference speed. Experiments on ILSVRC 2012 and PASCAL VOC datasets demonstrate that FD-MobileNet consistently outperforms MobileNet and achieves comparable results with ShuffleNet under different computational budgets, for instance, surpassing Mobile-Net by 5.5% on the ILSVRC 2012 top-1 accuracy and 8.3% on the VOC 2007 mAP under a complexity of 12 MFLOPs. On an ARM-based device, FD-MobileNet achieves 1.11× inference speedup over MobileNet and 1.82× over ShuffleNet under the same complexity.

关键词： Computer architecture Complexity theory Neural networks Training Hardware Computational modeling Standards

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Storage-Aware Task Scheduling for Performance Optimization of Big Data Workflows

Storage-Aware Task Scheduling for Performance Optimization o...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Qianwen Ye Chase Q. Wu Huiyan Cao Nageswara S.V. Rao Aiqin Hou Department of Computer Science New Jersey Institute of Technology Newark NJ USA Computer Science & Mathematics Division Oak Ridge National Laboratory Oak Ridge TN School of Info Science and Tech Northwest University Xi'an Shaanxi China

ISBN: (纸本)9781728111421;9781728111414

Many large-scale applications in various domains are generating big data, which are increasingly processed and analyzed by MapReduce-based workflows deployed in Hadoop systems. In addition to computing time, the makespan of such data-intensive workflows is also largely affected by communication cost. Particularly, there are two levels of data movement during the execution of distributed workflows in Hadoop: i) from map tasks to reduce tasks within each individual MapReduce module and ii) between each pair of adjacent modules in the workflow. Traditionally, these two aspects of network traffic have been treated separately as data locality at the task and module or job level, respectively. However, the interactions between these two levels of data movement may create complicated dynamics and their compound effects remain largely unexplored. In this paper, we formulate a task scheduling problem that considers data movement at both levels to minimize the end-to-end delay of a MapReduce-based workflow. We show this problem to be NP-complete, and design a storage-aware big data workflow scheduling algorithm, referred to as SA-BWS, to optimize workflow performance in Hadoop environments. The performance superiority of SA-BWS is illustrated by extensive simulations in comparison with the default workflow engine in Hadoop and existing scheduling methods.

关键词： Task analysis Big Data Scheduling Data models Processor scheduling distributed databases Delays

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A Hough Voting Strategy for Registering Historical Aerial images to Present-Day Satellite imagery 19th

A Hough Voting Strategy for Registering Historical Aerial Im...

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19th International Conference on image Analysis and processing (ICIAP)

作者： Zambanini, Sebastian Sablatnig, Robert TU Wien Comp Vis Lab Vienna Austria

ISBN: (纸本)9783319685601;9783319685595

In this paper we present an approach for the georeferencing of historical World War ii images by registering the images to present-day satellite imagery, with the aim of supporting the risk assessment of unexploded ordnances. We propose to exploit the local geometry of corresponding interest points in a Hough voting scheme to identify the most likely transformation parameters between the images. Our method combines the evidences from local as well as global correspondences and uses a spatial zoning rule to establish solutions with preferably uniformly distributed correspondences. An experimental evaluation is conducted on a set of 42 pairs of historical and present-day images and reveals the outstanding performance of our method compared to state-of-the-art image matching and registration algorithms, including commonly used hypothesize-and-verify and graph matching methods.

关键词： Historical image registration Georeferencing Hough voting

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Study about the Edge Detection Algorithm and its Applications 18

Study about the Edge Detection Algorithm and its Application...

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18th International Carpathian Control Conference (ICCC)

作者： Popa, Bogdan Univ Craiova Dept Automat & Appl Informat Craiova Romania

ISBN: (纸本)9781509048625

Nowadays one of the most used algorithm for the image processing systems is the edge detection algorithm, starting from the identification systems in different domains to the complex systems direct from industry. With more different approaches this algorithm is today necessary in priority areas and his performance of time processing, quality differ in each case. In the backside of the algorithm there are a lot of mathematical calculus that influence the speed and the accuracy of the results. This article highlights some methods that compose this algorithm and propose some ideas and some tests over the possibility of distributed calculus that influences his performance. In this study it is proposed a set of analysis over the results over more tests and case scenarios that show them main problems in the calculus part. Also a parallel implementation that can offer a better perspective of this algorithm is proposed. Starting with this point over the edge detection of the algorithm, in this paper it is revealed also a good utilization in another different domain for this algorithm.

关键词： image processing edge detection algorithm performance case study

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parallel Regional Growth Marching Cubes: Efficient Surface Reconstruction Based on MPI 9th

Parallel Regional Growth Marching Cubes: Efficient Surface R...

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9th International Conference on image and Graphics (ICIG)

作者： Wei, Xiaohui Bao, Xinyan Pang, Xiaoli Cui, Haolong Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Clin Hosp 1 Changchun 130012 Peoples R China

ISBN: (纸本)9783319715896;9783319715889

Three-dimensional surface reconstruction from medical images is an important task in clinical medical practice. Of all the methods for extracting surfaces from scalar fields, marching cubes algorithm is most popular due to its simplicity. In this contribution, we developed a robust and efficient algorithm for reconstructing surfaces from tomography images such as CT and MRI, in which we combine the regional growth marching cubes algorithm with message passing interface to highly parallelize the extracting process. The scalar field is first partitioned into parts and for each part, a process is created to run the regional growth marching cubes on the part. The parallel processing is implemented using MPI. Experiments show that the method has a higher efficiency in time than both marching cubes and regional growth marching cubes algorithms.

关键词： Marching cubes Regional growth marching cubes 3D reconstruction parallel computing MPI

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Cloud masking and removal in remote sensing image time series

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JOURNAL OF APPLIED REMOTE SENSING 2017年第1期11卷

作者： Gomez-Chova, Luis Amoros-Lopez, Julia Mateo-Garcia, Gonzalo Munoz-Mari, Jordi Camps-Valls, Gustau Univ Valencia Image Proc Lab Catedratico Jose Beltran 2 E-46980 Valencia Spain

Automatic cloud masking of Earth observation images is one of the first required steps in optical remote sensing data processing since the operational use and product generation from satellite image time series might be hampered by undetected clouds. The high temporal revisit of current and forthcoming missions and the scarcity of labeled data force us to cast cloud screening as an unsupervised change detection problem in the temporal domain. We introduce a cloud screening method based on detecting abrupt changes along the time dimension. The main assumption is that image time series follow smooth variations over land (background) and abrupt changes will be mainly due to the presence of clouds. The method estimates the background surface changes using the information in the time series. In particular, we propose linear and nonlinear least squares regression algorithms that minimize both the prediction and the estimation error simultaneously. Then, significant differences in the image of interest with respect to the estimated background are identified as clouds. The use of kernel methods allows the generalization of the algorithm to account for higher-order (nonlinear) feature relations. After the proposed cloud masking and cloud removal, cloud-free time series at high spatial resolution can be used to obtain a better monitoring of land cover dynamics and to generate more elaborated products. The method is tested in a dataset with 5-day revisit time series from SPOT-4 at high resolution and with Landsat-8 time series. Experimental results show that the proposed method yields more accurate cloud masks when confronted with state-of-the-art approaches typically used in operational settings. In addition, the algorithm has been implemented in the Google Earth Engine platform, which allows us to access the full Landsat-8 catalog and work in a parallel distributed platform to extend its applicability to a global planetary scale. (C) 2017 Society of Photo-Optical Instrumenta

关键词： image time series multitemporal analysis change detection cloud masking kernel ridge regression Google Earth Engine

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image Annotation by Deep Neural Networks with Attention Shaping 9

Image Annotation by Deep Neural Networks with Attention Shap...

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9th International Conference on Digital image processing (ICDIP)

作者： Zheng, Kexin Lv, Shaohe Ma, Fang Chen, Fei Jin, Chi Dou, Yong Natl Univ Def Technol Natl Lab Parallel & Distributed Proc 109 Deya Rd Changsha 410073 Hunan Peoples R China Univ South China Sch Comp Sci & Technol Changsha 410012 Hunan Peoples R China

ISBN: (纸本)9781510613058;9781510613041

image annotation is a task of assigning semantic labels to an image. Recently, deep neural networks with visual attention have been utilized successfully in many computer vision tasks. In this paper, we show that conventional attention mechanism is easily misled by the salient class, i.e., the attended region always contains part of the image area describing the content of salient class at different attention iterations. To this end, we propose a novel attention shaping mechanism, which aims to maximize the non-overlapping area between consecutive attention processes by taking into account the history of previous attention vectors. Several weighting polices are studied to utilize the history information in different manners. In two benchmark datasets, i.e., PASCAL VOC2012 and MIRFlickr-25k, the average precision is improved by up to 10% in comparison with the state-of-the-art annotation methods.

关键词： computer vision image annotation DNN attention mechanism

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