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26th International Conference on parallel and distributed Computing (Euro-Par)

作者： Fiorillo, Luca Conte, Mattia Esposito, Andrea Musella, Francesco Flora, Francesco Chiariello, Andrea M. Bianco, Simona Univ Napoli Federico II Dipartimento Fis I-80126 Naples Italy Complesso Univ Monte St Angelo INFN Napoli I-80126 Naples Italy

ISBN: (纸本)9783030715939;9783030715922

Understanding the mechanisms driving the folding of chromosomes in nuclei is a major goal of modern Molecular Biology. Recent technological advances in microscopy (FISH, STORM) and sequencing approaches (Hi-C, GAM, SPRITE) enabled to collect quantitative data about chromatin 3D architecture, revealing a non-random and highly specific organization. To transform such tremendous amount of data into valuable insights on genome folding, heavy computational analyses are required. Here, we study the performances of PRISMR, a computational tool based on Machine Learning strategies and Polymer Physics principles, to explore genome 3D structure from Genome Architecture Mapping (GAM) data. Using such data, we show that PRISMR can successfully reconstruct the 3D structure of real genomic regions at various length scales, from mega-base sized loci to whole chromosomes. Importantly, the inferred structures are validated against independent Hi-C data. Finally, we show how PRISMR can be effectively employed to explore differences between experimental methods.

关键词： Chromatin organization Machine learning GAM

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基于云计算的船舶图像网络数据低时延传输方法

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舰船科学技术 2024年第16期46卷 178-181页

作者：李小丽王磊河南开放大学信息工程与人工智能学院河南郑州450046 河南理工大学计算机科学与技术学院河南焦作454003

为实时传输海量船舶图像,为有效监控船舶实时航行状况提供保障,研究基于云计算的船舶图像网络数据低时延传输方法。结合云计算技术,构建船舶图像网络数据传输平台,通过平台数据接收单元将大量船舶网络数据存入云端服务器内;数据处理单... 详细信息

为实时传输海量船舶图像,为有效监控船舶实时航行状况提供保障,研究基于云计算的船舶图像网络数据低时延传输方法。结合云计算技术,构建船舶图像网络数据传输平台,通过平台数据接收单元将大量船舶网络数据存入云端服务器内;数据处理单元基于云计算的分布式处理特点,结合并行运行框架Map Reduce和并行化K-means聚类算法,并行挖掘云端服务器内的船舶图像网络数据;信道均衡单元结合所构建的船舶通信网络节点能量消耗模型,均衡船舶通信网络信道,运用均衡信道实现所挖掘船舶图像网络数据的传输。结果显示,该方法的数据挖掘速率高,数据传输过程中各信道能量开销均衡,且可低至3.5 k J,传输船舶图像网络数据的时延可低至10.23 ms,实现了数据的低时延传输。

关键词：云计算船舶图像网络数据低时延传输聚类算法信道均衡

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Design optimisation of multiplier-free parallel pipelined FFT on field programmable gate array

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IET CIRCUITS DEVICES & SYSTEMS 2020年第7期14卷 995-1000页

作者： Godi, Prasanna Kumar Krishna, Battula Tirumala Kotipalli, Pushpa Jawaharlal Nehru Technol Univ Elect & Commun Engn Kakinada India Shri Vishnu Engn Coll Women Autonomous Elect & Commun Engn Bhimavaram India

Fast Fourier transform (FFT) is utilised to minimise the complexity of discrete Fourier transform by converting signals from frequency domain to time domain and conversely. Digital signal processing systems like image processing, general filtering, sonar, spread-spectrum communications and convolutions use this FFT operations. Radix-2 decimation in frequency (R2DIF) method is designed to execute an efficient FFT architecture in this study. Each and every state of the FFT stores the input and output the data using the R2DIF method. Also, the complex twiddle factors in FFT are replaced by the proposed uniform Montgomery algorithm. This technique simply performs the shift-add method instead of the multiplication process which also enhances the convergence of the calculation. So, the FFT implementation is done with the help of the proposed method which reduces the usage of chips in the process. Based on this approach, it performs the operation of FFT from 16 points to 1024 points and the performance of this proposed method is compared with existing approaches. Moreover, it does not require expensive dedicated functional blocks and uses only distributed logic resources. The simulation is carried out by the Xilinx platform using Verilog coding. The proposed design outperforms conventional methods in terms of less usage power and high speed.

关键词： discrete Fourier transforms hardware description languages pipeline processing digital signal processing chips fast Fourier transforms field programmable gate arrays mathematics computing design optimisation multiplier-free parallel field programmable gate array frequency domain time domain digital signal image processing general filtering spread-spectrum communications FFT operations radix-2 decimation frequency method efficient FFT architecture FFT stores R2DIF method complex twiddle factors uniform Montgomery algorithm shift-add method multiplication process FFT implementation

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Towards a SOLAP Based Decision Support System for Forest Fire Management

Towards a SOLAP Based Decision Support System for Forest Fir...

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Earth Observation and Geo-Spatial Information (ICEOGI), International Conference on

作者： Asma Belaroussi Khalissa Derbal Rachid Benabdellah Mostefa Belhadj-Aissa Image Processing and Radiation Laboratory LTIR USTHB Algiers Algeria LSI Laboratory USTHB Algiers Algeria General Directorate of Forestry DGF Algiers Algeria

Risk, suppression, and damage assessment are essential parts of forest fire management. The involved data is distributed and heterogeneous, concerning meteorology, topomorphology, and socio-economy. In addition, it has a multiscale spatiotemporal analysis aspect. These reinforce the need to use the appropriate methods and technologies to enhance decision-making. This research investigates the potential of adopting spatial data warehousing techniques to build a SOLAP-based DSS that supports the whole decisional chain. (i) source collection and integration; (ii) data repository design and population; (iii) SOLAP analytics; and finally, (iv) data visualization in numerous formats, including tabular, graphic, and dynamic maps.

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A New Statistical Iterative Reconstruction Algorithm for a CT Scanner with Flying Focal Spot 20th

A New Statistical Iterative Reconstruction Algorithm for a C...

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20th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Cierniak, Robert Pluta, Piotr Czestochowa Tech Univ Dept Intelligent Comp Syst Armii Krajowej 36 PL-42200 Czestochowa Poland

ISBN: (纸本)9783030878979;9783030878962

This work is related to the originally formulated 3D statistical model-based iterative reconstruction algorithm adopted to computed tomography with flying focal spot. This new reconstruction method is based on a continuous-to-continuous data model, where the forward model is formulated as a shift invariant system. The proposed approach resembles the well-known Feldkamp (FDK) algorithm, which cannot be used with a flying focal spot scanner as the paths of the X-rays used are not equi-angularly distributed. In this situation, a so-called "nutating" reconstruction algorithm is usually used, which is based on a rebinning methodology, thus transforming the reconstruction problem to that of a parallel beam system. Our approach has some significant advantages compared with the FBP methods. Moreover, although our method belongs to the category of iterative reconstruction approaches, thanks to the fact that our proposed model is derived as a shift invariant system, it is possible to use an FFT algorithm to accelerate the calculations that have to be performed. Because of this, we can obtain diagnostic images in a time comparable to that of FBP methods. Computer simulations have shown that the reconstruction method presented here outperforms referential FBP methods with regard to the image quality obtained and can be competitive in terms of time of calculation.

关键词： image reconstruction from projections X-ray computed tomography Statistical reconstruction algorithm Flying focal spot

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SUPERVISED MULTI-VIEW distributed HASHING

SUPERVISED MULTI-VIEW DISTRIBUTED HASHING

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IEEE International Conference on image processing (ICIP)

作者： Tang, Yunpeng Shen, Xiaobo Ji, Zexuan Wang, Tao Fu, Peng Sun, Quan-Sen Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781728163956

Multi-view hashing efficiently integrates multi-view data for learning compact hash codes, and achieves impressive large-scale retrieval performance. In real-world applications, multi-view data are often stored or collected in different locations, where hash code learning is more challenging yet less studied. To fulfill this gap, this paper proposes a novel supervised multi-view distributed hashing (SMvDisH) for hash code learning from multi-view data in a distributed manner. SMvDisH yields the discriminative latent hash codes by joint learning of latent factor model and classifier. With local consistency assumption among neighbor nodes, the distributed learning problem is divided into a set of decentralized subproblems. The subproblems can be solved in parallel, and the computational and communication costs are low. Experimental results on three large-scale image datasets demonstrate that SMvDisH achieves competitive retrieval performance and trains faster than state-of-the-art multi-view hashing methods.

关键词： Hashing distributed learning multi-view data

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DSPG: Decentralized Simultaneous Perturbations Gradient Descent Scheme 28

DSPG: Decentralized Simultaneous Perturbations Gradient Desc...

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28th Euromicro International Conference on parallel, distributed and Network-Based processing (PDP)

作者： Ramaswamy, Arunselvan Paderborn Univ Dept Elect & Comp Sci Engn D-33098 Paderborn Germany

ISBN: (纸本)9781728165820

distributed descent-based methods are an essential toolset to solving optimization problems in multi-agent system scenarios. Here the agents seek to optimize a global objective function through mutual cooperation. Oftentimes, cooperation is achieved over a wireless communication network that is prone to delays and errors. There are many scenarios wherein the objective function is either non-differentiable or merely observable. In this paper, we present a cross-entropy based distributed stochastic approximation algorithm (SA) that finds a minimum of the objective, using only samples. We call this algorithm Decentralized Simultaneous Perturbation Stochastic Gradient, with Constant Sensitivity Parameters (DSPG). This algorithm is a two fold improvement over the classic Simultaneous Perturbation Stochastic Approximations (SPSA) algorithm. Specifically, DSPG allows for (i) the use of old information from other agents and (ii) easy implementation through the use simple hyper-parameter choices. We analyze the biases and variances that arise due to these two allowances. We show that the biases due to communication delays can be countered by a careful choice of algorithm hyper-parameters. The variance of the gradient estimator and its effect on the rate of convergence is studied. We present numerical results supporting our theory. Finally, we discuss an application to the stochastic consensus problem.

关键词： distributed optimization distributed approximate gradient method cross-entropy based gradient approximation optimization over networks with unbounded delays

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基于分布式执行框架的低频射电干涉阵列成像管线优化

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天文学报 2023年第6期64卷 99-112页

作者：韦耀杰符杰林劳保强桂林电子科技大学信息与通信学院认知无线电与信息处理教育部重点实验室桂林541004 中国科学院上海天文台上海200030 云南大学物理与天文学院昆明650500

平方公里阵列(Square Kilometre Array,SKA)项目是建设全球最大射电望远镜的国际合作项目,其灵敏度和测量速度将比当前所有的射电望远镜都要高出一个数量级.连续谱巡天是SKA的主要观测模式之一,基于连续谱成像建立巡天区域的标准星图,... 详细信息

平方公里阵列(Square Kilometre Array,SKA)项目是建设全球最大射电望远镜的国际合作项目,其灵敏度和测量速度将比当前所有的射电望远镜都要高出一个数量级.连续谱巡天是SKA的主要观测模式之一,基于连续谱成像建立巡天区域的标准星图,将能为后续天文科学研究奠定重要基础.银河系与河外星系全天默奇森宽场阵列拓展巡天(GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended,GLEAM-X)是2018--2020年利用SKA先导望远镜默奇森宽场阵列(Murchison Wide-field Array,MWA)二期拓展阵列开展的新的射电连续谱巡天项目,观测期间积累了大量的低频巡天观测数据.海量观测数据的自动化、大批量处理是SKA望远镜项目所面临的的最大挑战和难题之一,基于分布式执行框架的成像管线优化经验将有助于解决海量数据处理问题.详细介绍了GLEAM-X成像管线并对其进行整合和改进,在中国SKA区域中心原型机(China SKA Regional Centre Prototype,CSRC-P)上实现了多条管线并行处理,使用GLEAM-X观测数据验证成像管线系统的部署和测试其正确性.随后为了优化管线提高处理效率,使用数据激活流图形引擎(Data Activated Liu Graph Engine,DALiuGE),将成像管线集成入DALiuGE执行框架中实现了管线的自动化分布式并行处理.通过性能测试与结果分析表明,基于DALiuGE执行框架进行优化的成像管线相较于传统的并行方式具有更好的性能优势、更灵活的适配性和可扩展性,可支持未来SKA第一阶段试运行期间的大规模连续谱成像实验.

关键词：仪器:干涉仪方法:观测技术:干涉测量技术:图像处理射电连续谱:普通

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OpenMedIA: Open-Source Medical image Analysis Toolbox and Benchmark under Heterogeneous AI Computing Platforms

arXiv

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arXiv 2022年

作者： Zhuang, Jia-Xin Huang, Xiansong Yang, Yang Chen, Jiancong Yu, Yue Gao, Wei Li, Ge Chen, Jie Zhang, Tong Peng Cheng Laboratory Shenzhen China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Electronic and Computer Engineering Peking University China National Laboratory for Parallel and Distributed Processing National University of Defense Technology China

In this paper, we present OpenMedIA, an open-source toolbox library containing a rich set of deep learning methods for medical image analysis under heterogeneous Artificial Intelligence (AI) computing platforms. Various medical image analysis methods, including 2D/3D medical image classification, segmentation, localisation, and detection, have been included in the toolbox with PyTorch and/or MindSpore implementations under heterogeneous NVIDIA and Huawei Ascend computing systems. To our best knowledge, OpenMedIA is the first open-source algorithm library providing compared PyTorch and MindSpore implementations and results on several benchmark datasets. The source codes and models are available at https://***/ OpenMedIA. © 2022, CC BY-NC-SA.

关键词： Open systems

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Classification Of Human Facial Expressions For Emotion Recognition Using A distributed Computer System 5

Classification Of Human Facial Expressions For Emotion Recog...

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5th International Conference on Computer Science and Engineering (UBMK)

作者： Korkmaz, Timucin Erol, Hamza Mersin Univ Grad Sch Nat & Appl Sci Dept Comp Engn Mersin Turkey Mersin Univ Engn Fac Dept Comp Engn Mersin Turkey

ISBN: (纸本)9781728175652

A computer system was developed to classify human facial expressions for emotion recognition using a distributed computer architecture within the scope of big data. Computers with normal standards and features were used in the distributed computer architecture. Necessary system software for the distributed computer architecture was established and mutual communication protocols were provided between the computers and databases in the computer network. Visual C# parallel programming was used as the software language on the distributed computer architecture. In the software prepared, face image files were processed and threads were created. The threads created later were processed in the processors of the computers. The threads were run on the processors in the distributed computer system, and facial expressions were classified for emotion recognition. In the distributed computer architecture: the number of image files, the volume of big data and the load of the threads to be processed arc taken into account;and databases and parallel programming were used and the classification of human face images was performed. Emotion analysis methods and facial expression recognition techniques were used in the classification process. The distributed computer system is in low cost and high processing speed. With the distributed computer system architecture, big data analytics has become convenient and feasible.

关键词： Big data distributed System Emotion analysis image classification parallel Programing Face expression recognition

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