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4th International Conference on Software Engineering and Computer Systems (ICSECS)

作者： Khor, Hui Liang Liew, Siau-Chuin Zain, Jasni Mohd. Univ Malaysia Pahang Fac Comp Syst & Software Engn Lebuhraya Tun Razak Kuantan 26300 Pahang Darul Ma Malaysia

ISBN: (纸本)9781467367226

An efficient implementation are necessary, as most medical imaging methods are computational expensive, and the amount of medical imaging data is growing. Graphic processing units (GPUs) can solve large data parallel problems at a higher speed than the traditional CPU, while being more affordable and energy efficient than distributed systems. This review investigates the use of GPUs to accelerate medical imaging methods. A set of criteria for efficient use of GPUs are defined. The review concludes that most medical image processing methods may benefit from GPU processing due to the methods' data parallel structure and high thread count. However, factors such as synchronization, branch divergence and memory usage can limit the speedup.

关键词： parallel computing Medical image processing GPU multicores

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Local feature point extraction for quantum images

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QUANTUM INFORMATION processing 2015年第5期14卷 1573-1588页

作者： Zhang, Yi Lu, Kai Xu, Kai Gao, Yinghui Wilson, Richard Natl Univ Def Technol Coll Comp Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Coll Elect Sci & Engn Changsha 410073 Hunan Peoples R China Univ York Dept Comp Sci York YO10 5GH N Yorkshire England

Quantum image processing has been a hot issue in the last decade. However, the lack of the quantum feature extraction method leads to the limitation of quantum image understanding. In this paper, a quantum feature extraction framework is proposed based on the novel enhanced quantum representation of digital images. Based on the design of quantum image addition and subtraction operations and some quantum image transformations, the feature points could be extracted by comparing and thresholding the gradients of the pixels. Different methods of computing the pixel gradient and different thresholds can be realized under this quantum framework. The feature points extracted from quantum image can be used to construct quantum graph. Our work bridges the gap between quantum image processing and graph analysis based on quantum mechanics.

关键词： Quantum image Feature extraction Quantum adder Quantum image transformation

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A Mobile Agent Team Works Model for HPC Big Data Analysis: Fuzzy Logic Application 5

A Mobile Agent Team Works Model for HPC Big Data Analysis: F...

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5th International Conference on Information and Communication Technology and Accessbility (ICTA)

作者： Benchara, Fatema Zahra Youssfi, Mohamed Bouattane, Omar Ouajji, Hassan Hassan II Univ Casablanca ENSET Mohammedia Lab SSDIA Mohammadia Morocco

ISBN: (纸本)9781467387491

The aim of this paper is to present a distributed implementation of the Type-2 Fuzzy Logic (T2FL) algorithm in a mobile agent based distributed computing platform. The proposed algorithm is assigned to be implemented on an SPMD (Single Program Multiple Data) architecture which is based on a cooperative mobile agent model. It is constituted by a set of mobile agents as AVPEs (Agent Virtual processing Elements) in order to improve the processing resources needed for performing the big data image segmentation. In this work we focused on the application of this algorithm to process the big data MRI (Magnetic Resonance images) image. The input image is splitted into elementary images by the Mobile Team leader Agent and encapsulated one per AVPE. Each AVPE perform and exchange the segmentation results and maintain asynchronous communication with their Team leader agent until the convergence of this algorithm. The obtained experimental results in terms of accuracy and efficiency analysis of the proposed distributed implementation are achieved thanks to the mobile agents several interesting skills introduced in this distributed computational model.

关键词： distributed Type-2 Fuzzy algorithm image processing Mobile Agents parallel and distributed Computing

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A novel three-dimension fast radar imaging method based on far-field-approximation

A novel three-dimension fast radar imaging method based on f...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Long Gang Wang Lianlin Li Tie Jun Cui Peking University Beijing Beijing CN Southeast University Nanjing Jiangsu CN

Three-dimension imaging and identification with radar has recently gained a lot of attention. Resulting from the over-simple imaging model and over-large computational complexity, the existing three-dimension radar imaging methods are limited in few scenarios. To break above bottlenecks, we introduce a novel physics-driven three-dimension fast radar imaging method based on far-field-approximation assumption, in which the whole imaging region would be decomposed with a series of overlapping-patches to accelerate the imaging speed in parallel. The proposed method has four key steps: e.g., first, the whole imaging region would be divided into a series of overlapping-patches, which indicates that the large-scale imaging problem is efficiently decomposed into a set of small-scale imaging sub-problems and can be solved in the parallel or distributed manner. Second, the dyadic Green's function based on far-field-approximation will be applied to construct system response function of radar imaging problem. Third, a dual transform is further introduced to turn the imaging problem into the problem of physical-driven image processing problem. In this way, the image processing system response functions for all overlapping-patches will be identical for fixed transmitters and receivers. In other words, the system response functions of overlapping-patches are independent of specific imaging regions. So few system response functions matrices need be memorized. At last, the first-order method is introduced to implement the imaging process of all overlapping-patches in parallel. The far-field-approximation imaging method drastically decreases the memory requirement while maintaining fast imaging speed and high imaging quality. Therefore, the proposed fast imaging method based on far-field-approximation is applicable to more large-scale imaging scenarios. Some selected simulation results are presented to demonstrate the state-of-the-art performance of the far-field-approximation method

关键词： Imaging Radar imaging image processing Electromagnetics Electromagnetic scattering Computer science Millimeter wave technology

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HireSome-ii: Towards Privacy-Aware Cross-Cloud Service Composition for Big Data Applications

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IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2015年第2期26卷 455-466页

作者： Dou, Wanchun Zhang, Xuyun Liu, Jianxun Chen, Jinjun Nanjing Univ Dept Comp Sci & Technol State Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China Univ Technol Fac Engn & Informat Technol Broadway NSW 2007 Australia Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411201 Peoples R China

Cloud computing promises a scalable infrastructure for processing big data applications such as medical data analysis. Cross-cloud service composition provides a concrete approach capable for large-scale big data processing. However, the complexity of potential compositions of cloud services calls for new composition and aggregation methods, especially when some private clouds refuse to disclose all details of their service transaction records due to business privacy concerns in cross-cloud scenarios. Moreover, the credibility of cross-clouds and on-line service compositions will become suspicional, if a cloud fails to deliver its services according to its "promised'' quality. In view of these challenges, we propose a privacy-aware cross-cloud service composition method, named HireSome-ii (History record-based Service optimization method) based on its previous basic version HireSome-I. In our method, to enhance the credibility of a composition plan, the evaluation of a service is promoted by some of its QoS history records, rather than its advertised QoS values. Besides, the k-means algorithm is introduced into our method as a data filtering tool to select representative history records. As a result, HireSome-ii can protect cloud privacy, as a cloud is not required to unveil all its transaction records. Furthermore, it significantly reduces the time complexity of developing a cross-cloud service composition plan as only representative ones are recruited, which is demanded for big data processing. Simulation and analytical results demonstrate the validity of our method compared to a benchmark.

关键词： Cloud service composition QoS big data transaction history records

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A HADOOP-BASED distributed FRAMEWORK FOR EFFICIENT MANAGING AND processing BIG REMOTE SENSING imageS 1

A HADOOP-BASED DISTRIBUTED FRAMEWORK FOR EFFICIENT MANAGING ...

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ISPRS International Workshop on Spatiotemporal Computing

作者： Wang, C. Hu, F. Hu, X. Zhao, S. Wen, W. Yang, C. Natl Adm Surveying Mapping & Geoinformat China Hainan Geomat Ctr Haikou 570203 Hainan Peoples R China George Mason Univ Dept Geog & GeoInformat Sci Fairfax VA 22030 USA George Mason Univ Ctr Intelligent Spatial Comp Fairfax VA 22030 USA Natl Adm Surveying Mapping & Geoinformat China Inst Photogrammetry 4 Haikou 570203 Hainan Peoples R China

Various sensors from airborne and satellite platforms are producing large volumes of remote sensing images for mapping, environmental monitoring, disaster management, military intelligence, and others. However, it is challenging to efficiently storage, query and process such big data due to the data- and computing-intensive issues. In this paper, a Hadoop-based framework is proposed to manage and process the big remote sensing data in a distributed and parallel manner. Especially, remote sensing data can be directly fetched from other data platforms into the Hadoop distributed File System ( HDFS). The Orfeo toolbox, a ready-to-use tool for large image processing, is integrated into MapReduce to provide affluent image processing operations. With the integration of HDFS, Orfeo toolbox and MapReduce, these remote sensing images can be directly processed in parallel in a scalable computing environment. The experiment results show that the proposed framework can efficiently manage and process such big remote sensing data.

关键词： Remote Sensing image processing HDFS MapReduce GIS parallel Computing

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An algebraic parallel treecode in arbitrary dimensions 29

An algebraic parallel treecode in arbitrary dimensions

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29th IEEE International parallel and distributed processing Symposium (IPDPS)

作者： March, William B. Xiao, Bo Yu, Chenhan D. Biros, George Univ Texas Austin Inst Computat Engn & Sci Austin TX 78712 USA

ISBN: (纸本)9781479986484

We present a parallel treecode for fast kernel summation in high dimensions-a common problem in data analysis and computational statistics. Fast kernel summations can be viewed as approximation schemes for dense kernel matrices. Treecode algorithms (or simply treecodes) construct low-rank approximations of certain off-diagonal blocks of the kernel matrix. These blocks are identified with the help of spatial data structures, typically trees. There is extensive work on treecodes and their parallelization for kernel summations in three dimensions, but there is little work on high-dimensional problems. Recently, we introduced a novel treecode, ASKIT, which resolves most of the shortcomings of existing methods. We introduce novel parallel algorithms for ASKIT, derive complexity estimates, and demonstrate scalability on synthetic, scientific, and image datasets. In particular, we introduce a local essential tree construction that extends to arbitrary dimensions in a scalable manner. We introduce data transformations for memory locality and use GPU acceleration. We report results on the "Maverick" and "Stampede" systems at the Texas Advanced Computing Center. Our largest computations involve two billion points in 64 dimensions on 32,768 x86 cores and 8 million points in 784 dimensions on 16,384 x86 cores.

关键词： GPUs Kernel summation randomized linear algebra treecodes

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An Efficient 256-Tap parallel FIR Digital Filter Implementation Using distributed Arithmetic Architecture 11

An Efficient 256-Tap Parallel FIR Digital Filter Implementat...

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11th International Conference on image and Signal processing (ICISP)

作者： Nandal, Amita Vigneswarn, T. Rana, Ashwani K. Dhaka, Arvind NIT Hamirpur Dept ECE Hamirpur Himachal Prades India VIT Univ Dept ECE Madras Tamil Nadu India NIT Hamripur Deparment CSE Hamirpur Himachal Prades India

This paper discusses FPGA implementation of Finite Impulse Response (FIR) filters using distributed Arithmetic (DA) which substitute multiply and accumulate operations with a series of Look-Up-Table (LUT) accesses. parallel FIR digital filter can be used either for high speed or low-power applications. The distributed arithmetic provides a multiplication-free method for calculating inner products of fixed-point data, based on table lookups of pre calculated partial products. The implementation results are provided to demonstrate a high-speed and low power proposed architecture. The proposed filter is implemented in very high speed integrated circuit hardware description language (VHDL) and verified via simulation. The proposed method offers average reductions of 60% in the number of LUT, 40% reduction in occupied slices and 50% reduction in the number gates for parallel FIR filter implementation. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： distributed arithmetic DSP Finite impulse response LUT MAC and parallel filters

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基于远心照相的新型照相探头及用于多相搅拌槽测量

基于远心照相的新型照相探头及用于多相搅拌槽测量

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作者：王冠琦中国科学院大学(中国科学院过程工程研究所)

学位级别：硕士

多相反应器中分散相颗粒局部性质分布是反应器性能的精细反映,是多相反应器优化设计和工程放大的重要依据,也能够为多相反应器数学模型关键参数确定和数值模拟验证提供基础数据。目前多相反应器中分散相颗粒局部性质的实验测量方法都有... 详细信息

多相反应器中分散相颗粒局部性质分布是反应器性能的精细反映,是多相反应器优化设计和工程放大的重要依据,也能够为多相反应器数学模型关键参数确定和数值模拟验证提供基础数据。目前多相反应器中分散相颗粒局部性质的实验测量方法都有一定的局限性,尤其是对于气液固三相这种含有多种分散相颗粒的体系,还没有成熟的测量方法。本文开发了一种基于远心照相的新型照相探头,并发展了相应的测量方法,可用于两相及三相反应器中局部特性参数(如粒度和浓度等)的测量。测量时将照相探头安装在待测多相反应器中,按照预设的频闪光和CMOS(Complementary metal oxide semiconductor)同步控制模式即可拍照获得测量点处多相流动图像,采用image-Pro Plus图像处理软件结合相应的测量方法即可获得该点处分散相颗粒的粒度和浓度等性质信息。远心照相探头有四个优势:(1)远心镜头特有的平行光线使得拍摄的图像几乎不产生形变,与其他照相探头相比测量结果更准确;(2)探头尺寸小、远心镜头工作距离足够长,可用于测量大反应器内各个位置的局部特性参数;(3)可同时测量的局部特性参数包括颗粒(气泡、固体颗粒或液滴)的尺寸、相含率和界面接触面积;(4)克服了非侵入性照相法要求器壁透明等应用局限性问题。通过与游标卡尺测量的100个尺寸约1 mm的颗粒粒径结果对比表明,该实验照相探头的平均测量误差约是0.92%。在一种模拟的均匀膨胀床中测量颗粒浓度的结果表明,在不同膨胀高度条件下,该照相探头的测量误差最大是13.1%,平均测量误差为7.1%。这表明本文研制的远心照相探头在颗粒粒度和浓度测量中具有较高的准确性。利用远心照相探头拍摄了不同反光性的颗粒、具有不规则形貌的颗粒和含多个分散相的体系,表明在频闪光和CMOS同步控制模式下,或在特殊条件下辅以反光板(低亮度颗粒情况下),探头均能够清晰地捕捉到多相流动图像。在液-固搅拌槽内测量了局部相含率分布,测量结果与现有的认识定性相符;在气-液搅拌槽内测量了气泡尺寸分布,表明该探头能够测得100 μm以下的气泡,相对探针类方法具有明显优势;在气-液-固搅拌槽内同时测量了气泡和固体颗粒尺寸分布,表明该方法能应用于含有多个分散相体系的测量。

关键词：照相探头图像处理颗粒粒径局部相含率多相反应器

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Energy Efficient Techniques using FFT for Deep Convolutional Neural Networks

Energy Efficient Techniques using FFT for Deep Convolutional...

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International Conference on Advanced Technologies for Communications (ATC)

作者： Nhan Nguyen-Thanh Han Le-Duc Duc-Tuyen Ta Van-Tam Nguyen Univ Paris Saclay CNRS LTCI Telecom ParisTech F-75013 Paris France Stanford Univ Dept Elect Engn Stanford CA 94305 USA

ISBN: (纸本)9781509027118

Deep convolutional neural networks (CNNs) has been developed for a wide range of applications such as image recognition, nature language processing, etc. However, the deployment of deep CNNs in home and mobile devices remains challenging due to substantial requirements for computing resources and energy needed for the computation of high-dimensional convolutions. In this paper, we propose a novel approach designed to minimize energy consumption in the computation of convolutions in deep CNNs. The proposed solution includes (i) an optimal selection method for Fast Fourier Transform (FFT) configuration associated with splitting input feature maps, (ii) a reconfigurable hardware architecture for computing high-dimensional convolutions based on 2D-FFT, and (iii) an optimal pipeline data movement scheduling. The FFT size selecting method enables us to determine the optimal length of the split input for the lowest energy consumption. The hardware architecture contains a processing engine (PE) array, whose PEs are connected to form parallel flexible-length Radix-2 single-delay feedback lines, enabling the computation of variable-size 2D-FFT. The pipeline data movement scheduling optimizes the transition between row-wise FFT and column-wise FFT in a 2D-FFT process and minimizes the required data access for the element-wise accumulation across input channels. Using simulations, we demonstrated that the proposed framework improves the energy consumption by 89.7% in the inference case.

关键词： Computer architecture Hardware Radio frequency Pipelines Clocks Delays Neural networks Fast Fourier transform pipeline Computer Architecture Neural network Timing devices radiofrequency Computer hardware energy consumption delay piping selection methods

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