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5th IEEE International Conference on parallel, distributed and Grid Computing (PDGC)

作者： Bhardwaj, Charu Jain, Shruti Sood, Meenakshi Jaypee Univ Informat Technol Dept ECE Solan HP India

ISBN: (纸本)9781728106465

Retinopathy is a retinal vascular disease caused by abnormal blood flow that damages the retina and it accounts for 5% of blindness worldwide. There are different causes of Retinopathy as retinopathy of prematurity, diabetic retinopathy and hypertensive retinopathy;this paper mainly focuses on preprocessing techniques employed for the detection of diabetic retinopathy. In this work, literatures reporting different methods for automated diabetic retinopathy detection in color images of retina are analyzed for their strength and shortcoming. Extensive techniques for retinal image screening have been published so far thus this paper presents the evaluation of these methods on a common database of image screening. Different image pre-processing techniques are implemented on fundus images and their performance is compared using performance metrics like mean square error, peak signal to noise ratio and root mean square error. Pre-processing steps used in this work can also be applied to other images of interest to identify and recognize the objects in an image.

关键词： Computer aided screening diabetic retinopathy hypertensive retinopathy retinopathy of prematurity

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Real-Time Massively distributed Multi-Object Adaptive Optics Simulations for the European Extremely Large Telescope 32

Real-Time Massively Distributed Multi-Object Adaptive Optics...

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32nd IEEE International parallel and distributed processing Symposium (IPDPS)

作者： Ltaief, Hatem Charara, Ali Gratadour, Damien Doucet, Nicolas Hadri, Bilel Gendron, Eric Feki, Saber Keyes, David King Abdullah Univ Sci & Technol Extreme Comp Res Ctr Jeddah 23955 Saudi Arabia King Abdullah Univ Sci & Technol Supercomp Lab Jeddah 23955 Saudi Arabia LESIA Paris Observ Paris France

ISBN: (纸本)9781538643686

The European Extremely Large Telescope (E-ELT) is one of today's most challenging projects in ground based astronomy. Addressing one of the key science cases for the E-ELT, the study of the early Universe, requires the implementation of multi-object adaptive optics (MOAO), a dedicated concept relying on turbulence tomography. We use a novel pseudo-analytical approach to simulate the performance of tomographic reconstruction of the atmospheric turbulence in a MOAO system on real datasets. We simulate simultaneously 4K galaxies in a common field of view on massively parallel supercomputers during a single night of observations. We are able to generate a first-ever high resolution galaxy map at almost a real-time throughput. This simulation scale opens new research horizons in numerical methods for experimental astronomy, some core components of the pipeline standing as pathfinders toward actual operations and future astronomic discoveries on the E-ELT.

关键词： Tomography Telescopes image reconstruction Adaptive optics Computational modeling Adaptation models"

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A Modeling Language for MapReduce Programing in a Storage System Perspective

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JOURNAL OF SIGNAL processing SYSTEMS FOR SIGNAL image AND VIDEO TECHNOLOGY 2018年第8-9期90卷 1133-1150页

作者： Jing, Yuxin Wang, Hanpin Huang, Yu Zhang, Lei Cao, Yongzhi Peking Univ Sch Elect Engn & Comp Sci Inst Software Beijing 100871 Peoples R China Peking Univ Natl Engn Res Ctr Software Engn Beijing 100871 Peoples R China

MapReduce is a powerful distributed data analysis programming model. It runs on big data storage systems and processes data in a parallel way. An appropriate way to ensure the correctness of MapReduce programs is formal method analysis, which requires firstly a formal model of MapReduce. In this paper we propose a modeling language to establish the formal model of the MapReduce framework. Unlike other approaches, our language describes the processing of data in the MapReduce programs from a perspective of underlying files and blocks, so that the details of data processing can be clearly demonstrated. The language is based on our previous work, a language describing the management of massive data storage systems, with extensions from two aspects: block content data refinement and concurrency support. Based on our language, the features of the MapReduce programming model can be discussed.

关键词： Big data processing Modeling langugage MapReduce Concurrency Formal methods

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LogoPhish: A New Two-dimensional Code Phishing Attack Detection Method 16

LogoPhish: A New Two-dimensional Code Phishing Attack Detect...

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Yao, Wenbin Ding, Yuanhao Li, Xiaoyong Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Beijing Peoples R China Beijing Univ Posts & Telecommun Minist Educ Key Lab Trustworthy Distributed Comp & Serv BUPT Beijing Peoples R China

ISBN: (纸本)9781728111414

Traditional phishing detection methods are mostly based on computer platforms and cannot be directly applied to mobile devices. This paper proposes a new two-dimensional code phishing detection method called LogoPhish. We use the logo to determine the identity between a two-dimensional code's actual identity and the described identity. The method includes two processes: logo extraction and identity detection. The first process uses a mobile phone to scan the two-dimensional code to extract the logo and perform image processing. The second process uses the Google image search engine to determine the identity of the logo. Since the relationship between the logo and the domain name is exclusive, it is reasonable to use the domain name as an identifier. The experimental results show that LogoPhish has a good effect and is superior to the traditional detection method in the detection of two-dimensional code phishing attacks.

关键词： Phishing attack Two-dimensional code Logo recognition

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A MULTICORE CONVEX OPTIMIZATION ALGORITHM WITH APPLICATIONS TO VIDEO RESTORATION 25

A MULTICORE CONVEX OPTIMIZATION ALGORITHM WITH APPLICATIONS ...

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25th IEEE International Conference on image processing (ICIP)

作者： Abboud, Feriel Chouzenoux, Emilie Pesquet, Jean-Christophe Talbot, Hugues WITBE France Puteaux La Defense France Univ Paris Saclay Cent Supelec INRIA Saclay CVN Paris France Univ Paris Est Marne La Vallee ESIEE CNRS LIGMUMR 8049 Champs Sur Marne France

ISBN: (纸本)9781479970612

In this paper, we present a new distributed algorithm for minimizing a sum of non-necessarily differentiable convex functions composed with arbitrary linear operators. The overall cost function is assumed strongly convex. Each involved function is associated with a node of a hypergraph having the ability to communicate with neighboring nodes sharing the same hyperedge. Our algorithm relies on a primal-dual splitting strategy with established convergence guarantees. We show how it can be efficiently implemented to take full advantage of a multicore architecture. The good numerical performance of the proposed approach is illustrated in a problem of video sequence denoising, where a significant speedup is achieved.

关键词： convex optimization distributed algorithms proximal methods video processing parallel programming

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Timely Identification of Disease by parallel Real-Time Automated processing of Huge Medical Databases of images distributed Geographically, Through Knowledge Sharing

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CURRENT BIOINFORMATICS 2018年第2期13卷 170-175页

作者： Bardou, Dalal Zhang, Kun Ahmad, Sayed Mohammad Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210000 Jiangsu Peoples R China Lareb Technol Delhi India

Background: The diagnosis of diseases correctly became a challenge, and any error can cost patients life, especially when there is a lack of knowledge or expertise related to a disease, it often results in patient's death or takes the form of an epidemic, as we have seen in the case of Ebola. Objective: The automation and the development of reliable diagnostic systems became a necessity. Through the use of technology, we can automatically share the knowledge without formal interaction as well as we can identify areas where the disease is spreading while it is not known by the doctors there. methods: We have presented a complete system that utilizes a combination of one of the best techniques in the field of parallelism, classification, and knowledge sharing. We have used two data sets (DDSM and Belarus Tuberculosis data) to test the applicability of the idea. After retrieving the data, the images are preprocessed, and then Gray level co-occurrence matrix features have been extracted and finally passed to training using three versions of support vector machines. Results: GPU-Accelerated SVM outperformed both parallelized SVM and sequential SVM using breast cancer data, but with lung CT images, GPU-accelerated LIBSVM have not given a remarkable speed-up because the data is small and the gain is lost due to the gpu-cpu memory and cpu-gpu transfer time. The accuracy performances given by three SVMs were identical. Conclusion: Automation through knowledge sharing and parallel computing can help to deal across the world with diseases and it will be easy for doctors to draw the inference.

关键词： Diseases identification image classification image processing knowledge sharing parallel support vector machines parallelism

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Red Blood Cells Segmentation: A Fully Convolutional Network Approach 16

Red Blood Cells Segmentation: A Fully Convolutional Network ...

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Sadafi, Ario Radolko, Martin Serafeimidis, Iosif Hadlak, Steffen Univ Rostock Rostock Germany Arivis AG Munich Germany

ISBN: (纸本)9781728111414

Red blood cell segmentation in microscopic images is the first step for various clinical studies carried out on blood samples such as cell counting, cell shape identification, etc. Conventional methods while often showing a high accuracy are heavily depending on the acquisition modality. Deep learning approaches have shown to be more robust regarding such modalities and still showing a comparable accuracy. In this paper, we first investigate necessary steps to apply a specific type of deep learning methods, namely fully convolutional networks, to red blood cell segmentation. Based on data given and constraints imposed by our partners mainly regarding a high throughput of their data we then describe an exemplary application. First results show, that even with a focus on high performance a good accuracy above 90% can be reached.

关键词： Microscopic image analysis Red blood cells Cell Segmentation Fully convolutional networks

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基于Hadoop技术的大规模数据分布式存储技术研究

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自动化与仪器仪表 2024年第10期 182-186页

作者：魏迎咸阳职业技术学院陕西咸阳712000

随着数据信息的规模不断增加,数据存储技术引来的全新的调整,为了提升大规模数据的信息存储能力,研究设计了Hadoop生态系统的核心组件,对大规模数据分布式存储的处理方法。Hadoop分布式文件系统通过其独特的存储机制和高容错性,有效地... 详细信息

随着数据信息的规模不断增加,数据存储技术引来的全新的调整,为了提升大规模数据的信息存储能力,研究设计了Hadoop生态系统的核心组件,对大规模数据分布式存储的处理方法。Hadoop分布式文件系统通过其独特的存储机制和高容错性,有效地管理了大型文件的存储问题。而MapReduce模型则简化了并行数据处理的复杂性,使得对大规模数据集的处理变得更加高效和可靠。结果显示,分布式处理方式的图片处理速度总是小于单机处理方式,二者处理速度最大相差1502230 ms。研究提出的方法,对系统的CPU占用率更低,可以使系统处理更多的数据信息,该方法对系统CPU的占用率最高仅63%左右。研究提出的数据存储技术可以减小系统面对大规模数据时的压力,为大规模数据存储提供了一种新的技术方案。

关键词： Hadoop技术分布式存储数据存储大规模数据

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基于Hadoop的景区人流密度预测模型设计与实现

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电子设计工程 2021年第18期29卷 39-43页

作者：马骞西安航空职业技术学院陕西西安710089

针对景区人流监控精细化、高效化的需求,文中对视频图像的背景建模与轮廓提取方法进行研究,构建了面向海量计算需求的人流密度预测模型。该模型需要对输入模型的图像质量进行检测,校核图像的分辨率、像素是否满足算法的输入要求。对图... 详细信息

针对景区人流监控精细化、高效化的需求,文中对视频图像的背景建模与轮廓提取方法进行研究,构建了面向海量计算需求的人流密度预测模型。该模型需要对输入模型的图像质量进行检测,校核图像的分辨率、像素是否满足算法的输入要求。对图像的背景引入混合高斯模型进行建模,并借助图像的前景边缘检测描绘人体轮廓,基于Canny算子对人体轮廓进行跟踪、提取特征,以这些轮廓为基础实现人流密度的计算。将最终的提取结果输入到神经网络中,根据时间分布完成人流的预测。在算法的实现上考虑到计算需求,文中搭建了7个计算集群的Hadoop分布式计算平台,实现了算法的分布式存储和并行化计算。计算结果表明,相较于传统算法,该算法提取的人体轮廓更精细、轮廓长度更长,漏检率由13.21%降低到5.24%,人流的预测精度达到了92.43%,提升了7.12%。

关键词：边缘监测背景建模图像处理 Hadoop 人流预测

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Indigo: A Domain-Specific Language for Fast, Portable image Reconstruction 32

Indigo: A Domain-Specific Language for Fast, Portable Image ...

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27th International Heterogeneity in Computing Workshop in conjunction with 32nd IEEE International parallel and distributed processing Symposium (IPDPS)

作者： Driscoll, Michael Brock, Benjamin Ong, Frank Tamir, Jonathan Liu, Hsiou-Yuan Lustig, Michael Fox, Armando Yelick, Katherine Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA 94720 USA

ISBN: (纸本)9781538643686

Linear operators used in iterative methods like conjugate gradient have typically been implemented either as "matrix-driven" subroutines backed by explicit sparse or dense matrices, or as "matrix-free" subroutines that implement specific linear operations directly (e.g. FFTs). The matrix-driven approach is generally more portable because it can target widely-available BLAS libraries, but it can be inefficient in terms of time and space complexity. In contrast, the matrix-free approach is more performant because it leverages structure in operations, but it requires each operator be re-implemented on each new platform. To increase performance and portability, we propose a hybrid approach that represents linear operators as expression trees. Leaf nodes in the tree are either matrix-free or matrix-driven operators, and interior nodes represent mathematical compositions (sums, products, transposes) or structural compositions (stacks, block diagonals, etc.) of the leaf operators. This representation enables expert-guided reordering and fusion transformations that can improve performance or reduce memory pressure. We implement our approach in a domain-specific language called Indigo. We assess Indigo on image reconstruction problems arising in four application areas: magnetic resonance imaging, ptychography, magnetic particle imaging, and fluorescent microscopy. We give performance results from vendor BLAS libraries, and we introduce specializations to Sparse BLAS routines that achieve near-Roofline performance on multi-core, many-core, and GPU systems.

关键词： Sparse matrices image reconstruction Algorithms Vegetation Production facilities Imaging Integrated circuits"

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