检索结果-内蒙古大学图书馆

5th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2019

作者： Qiao, Peng Sun, Weichu Dou, Yong Li, Rongchun Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology ChangshaHunan410073 China University of South China HengyangHunan421000 China

ISBN: (纸本)9789811501203

Many image denoising methods achieve state-of-the-art performance with little consideration of computation efficiency. With the popularity of high-definition imaging devices, these denoising methods do not scale well to the high-definition images. Therefore, a fast image denoising method is proposed to meet the demand of processing high-definition images. Based on the analysis of the distribution of the distance distrs between the similar patches and their reference patches from a semantic aspect, the large distrs s was found to occur while their contribution to the overall distribution was small. Therefore, the nonlocal filters was replaced in trainable non-local reaction diffusion (TNLRD) with local filters. For image with 4096 X 4096 resolution, the proposed method runs about 6 times faster than TNLRD via a single-thread CPU implementation. And the GPU implementation of the proposed method is about 10 times faster than the CPU implementation. Furthermore, the proposed model achieves competing denoising performance compared with TNLRD in terms of PSNR and SSIM. © Springer Nature Singapore Pte Ltd. 2019.

关键词： image denoising

来源：评论

学校读者我要写书评

暂无评论

Scalable methods for Genome Assembly

Scalable Methods for Genome Assembly

引用

作者： Ghosh, Priyanka Washington State University

学位级别：Ph.D.

De novo genome assembly is a fundamental problem in the field of computational biology. The goal is to reconstruct an unknown genome from short DNA fragments (called “reads”) obtained from it. Over the last decade, with the advent of numerous next-generation sequencing (NGS) platforms (e.g., Illumina, 454 Roche), billions of reads can be generated in a matter of hours, leading to vast amounts of data accumulation per day. This has necessitated efficient parallelization of the assembly process to meet the growing data demands. While multiple parallel solutions to the problem have been proposed in the past, there still exists a gap in terms of the processing power between massively parallel NGS technologies and the ability of current state-of-the-art assemblers to analyze and assemble large and complex genomes. Conducting genome assembly at scale remains a challenge owing to the intense computational and memory requirements of the problem, coupled with inherent complexities in existing parallel tools associated with data movement, use of complex data structures, unstructured memory accesses and repeated I/O operations. In this dissertation, we address the challenges of conducting genome assembly at scale and develop new methods for conducting extreme-scale genome assembly for microbial and complex eukaryotic genomes. Our approach to the problem is two-fold, wherein we make the following contributions: i) FastEtch- a new method targeting fast and space-efficient assemblies, using probabilistic data structures (Count-Min sketch) that executes efficiently on shared-memory platforms with a minimal computational footprint (both memory and time). ii) PaKman- a fully distributed method that tackles assembly of large genomes through the combination of a novel data-structure (PaK-Graph) and algorithmic strategies to simplify the communication and I/O footprint during the assembly process. We present an extensive performance and qualitative evaluation of both our algorithms inc

关键词： Computer science

来源：评论

学校读者我要写书评

暂无评论

Virtual-Real Fusion processing Based on parallel Distribution 14th

Virtual-Real Fusion Processing Based on Parallel Distributio...

引用

14th Conference on image and Graphics Technologies and Applications, IGTA 2019

作者： Zhai, Hongyu Zhang, Tongtong Xu, Richeng School of Computer Science and Technology Changchun University of Science and Technology ChangchunJilin130022 China

ISBN: (纸本)9789811399169

For the demand of domestic digital film industry to improve the quality and efficiency of special effects film and television works, This paper studies the transmission quality of virtual and real fusion and the fusion efficiency, and proposes a better video transmission method combined with virtual and real fusion technology, which made the digital video industry more complete. Firstly, the Hadoop platform is built as the basis of distributed transmission, and then the parallel distributed transmission method and the virtual and real fusion technology proposed in this paper are combined to complete the experiment. In this paper, the efficiency of this method is compared with other existing advanced transmission methods. The experimental results show that the efficiency and transmission quality are improved compared with other methods. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

Adaptable image segmentation via simple pixel classification

引用

COMPUTATIONAL INTELLIGENCE 2018年第2期34卷 734-762页

作者： Kharma, Nawwaf Mazhurin, Anton Saigol, Kamil Sabahi, Farzad Concordia Univ Dept Elect & Comp Engn 1455 Blvd Maisonneuve O Montreal PQ H3G 1M8 Canada Rubikloud Corp Toronto ON Canada Georgia Inst Technol Inst Robot & Intelligent Machines Atlanta GA 30332 USA

We propose an approach to image segmentation that views it as one of pixel classification using simple features defined over the local neighborhood. We use a support vector machine for pixel classification, making the approach automatically adaptable to a large number of image segmentation applications. Since our approach utilizes only local information for classification, both training and application of the image segmentor can be done on a distributed computing platform. This makes our approach scalable to larger images than the ones tested. This article describes the methodology in detail and tests it efficacy against 5 other comparable segmentation methods on 2 well-known image segmentation databases. Hence, we present the results together with the analysis that support the following conclusions: (i) the approach is as effective, and often better than its studied competitors;(ii) the approach suffers from very little overfitting and hence generalizes well to unseen images;(iii) the trained image segmentation program can be run on a distributed computing environment, resulting in linear scalability characteristics. The overall message of this paper is that using a strong classifier with simple pixel-centered features gives as good or better segmentation results than some sophisticated competitors and does so in a computationally scalable fashion.

关键词： image segmentation machine learning parallel processing pixel classification

来源：评论

学校读者我要写书评

暂无评论

OBELIX: A monolithic pixel sensor with triggered readout for the Belle ii upgrade

引用

Nuclear Instruments and methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 2025年 1080卷

作者： M. Babeluk D. Auguste M. Barbero P. Barrillon J. Baudot T. Bergauer F. Bernlochner G. Bertolone C. Bespin S. Bettarini A. Bevan M. Bona J. Bonis F. Bosi R. Boudagga P. Breugnon Y. Buch G. Casarosa L. Corona J. Dingfelder D. Xu Institute of High Energy Physics Austrian Academy of Sciences Nikolsdorfer Gasse 18 1050 Vienna Austria Laboratoire de Physique des 2 infinis Irène Joliot-Curie – IJCLab Université Paris-Saclay CNRS IN2P3 IJCLab 91405 Orsay France Aix Marseille Univ CNRS IN2P3 CPPM Marseille France Université de Strasbourg CNRS IPHC UMR 7178 F-67000 Strasbourg France Physikalisches Institut Rheinische Friedrich-Wilhelms-Universität Universität Bonn Nussallee 12 53115 Bonn Germany Dipartimento di Fisica “E. Fermi” Universita di Pisa L.go B. Pontecorvo 3 I-56127 Pisa Italy INFN Sezione di Pisa L.go B. Pontecorvo 3 I-56127 Pisa Italy School of Physical and Chemical Sciences Department of Physics and Astronomy Queen Mary University of London 327 Mile End Road London E1 4NS United Kingdom II. Physikalisches Institut Georg-August-Universität Göttingen Friedrich-Hund-Platz 1 37077 Göttingen Germany

The Upgrade of the Belle ii vertex detector (VTX) at the SuperKEKB accelerator in Japan is foreseen to improve tracking performance at the expected high beam backgrounds at target luminosity of 6 × 10 35 cm -2 s -1 . The OBELIX-1 chip is specifically developed for this purpose and used as sensor on all VTX layers. OBELIX-1 is a depleted monolithic active pixel sensor in 180 nm technology. The pixel matrix is inherited from TJ-Monopix2, but the periphery of the chip is entirely reworked. A newly designed 2-stage pixel memory matches Belle ii trigger requirements. OBELIX-1 includes LDO regulators and a precision timing module with less than 3 n s resolution. Furthermore, the chip can also contribute to the Belle ii trigger system with low latency, low granularity real-time streaming of pixel data in parallel to regular operation. Details of the inner working of the trigger memory are presented, as well as performance simulations to validate the requirements for the VTX Upgrade. The trigger memory has been studied in simulation using realistic scenarios, including Landau distributed charge and clustering to evaluate performance. In order to allow post-production testing, an additional module is presented. This allows the injection of pseudo-random data early in the processing chain, and replaces the need for industry standard scan-chains. It can be used stand-alone or during analog charge injection, in order to reach the full trigger memory during testing.

关键词： Belle ii Vertex detector VTX DMAPS Depleted monolithic active pixel sensor OBELIX

来源：评论

学校读者我要写书评

暂无评论

Fast Hardware Architecture for 2-D Separable Convolution Operations

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS ii-EXPRESS BRIEFS 2018年第12期65卷 2042-2046页

作者： Mukherjee, Debasish Mukhopadhyay, Susanta Indian Sch Mines Dhanbad Dept Comp Sci & Engn Indian Inst Technol Dhanbad 826004 Bihar India

2-D convolution is a widely used low-level image processing operation, especially in spatial filtering, sharpening, and edge detection. Real-time implementation of the convolution operation is a challenging task due to its increasing computational cost with increasing kernel and input image size. This brief focuses on the real-time implementation of a separable convolution operation based on distributed arithmetic with support for processing high-resolution images using large size kernel. The architecture processes pixels immediately whenever available from external memory, achieving low processing time compared to existing works. Two FPGA architectures, namely single window separable convolution (SWSC) architecture and multi-window separable convolution (MWSC) architecture, are proposed. These architectures are realized based on concurrent access of stored pixels and parallel processing of overlapped window in order to attain higher performance/area utilization. Implementation results show that SWSC architecture achieves better processing time and area utilization at the cost of lower throughput compared to MWSC architecture.

关键词： Separable convolution distributed arithmetic FPGA

来源：评论

学校读者我要写书评

暂无评论

An efficient and scalable SPARK preprocessing methodology for Genome Wide Association Studies

An efficient and scalable SPARK preprocessing methodology fo...

引用

Euromicro Conference on parallel, distributed and Network-Based processing

作者： Giuseppe Agapito Pietro Hiram Guzzi Mario Cannataro Data Analytics Research Center University Magna Græcia of Catanzaro Catanzaro Italy

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

The importance of the use of high-performance software frameworks to analyze omics data obtained by using High-Throughput (HT) essays is widely recognized. HT methodologies comprise microarrays, Genome-Wide Association Studies (GWAS), and Next Generation Sequencing (NGS), which provide a vast amount of data per a single experiment. Each HT vendor provides to the users only the software frameworks and the proprietary libraries for the annotation, and summarization of raw data. Consequently, the needs of algorithms for the preprocessing and analysis of omics data arise. GWAS aims to highlight the association between genetic variants and diseases by examining single nucleotide polymorphisms (SNPs), which differ in a statistically significant way between cases and controls. The effectiveness of GWAS analysis increases with the number of analyzed samples per single experiment. GWAS data analyzed through the use of statistical methods can detect associations among a single allelic variant and the clinical conditions of samples. To overcome these limitations, and to make it possible to discover multiple associations among allelic variants, it is possible to use Association Rules mining. Consequently, the need for the introduction of scalable Association Rule Mining (ARM) algorithms able to analyze GWAS data arises. Hence, the use of high-performance data analytics framework is needed. For this purpose, we propose a software framework called GARMS (GWAS Association Rule Mining in Spark) built on top of Apache Spark for the preprocessing, and mining of association rules from GWAS data sets. GARMS comprises a two steps analysis methodology: (i) in the first step, the GWAS data are preprocessed, along with the identification of the frequent itemsets; (ii) in the second step, frequent itemsets are employed to mine association rules without scanning the input data. We implemented our algorithm, and we tested it on some synthetic GWAS data sets. Preliminary results confirm that o

关键词： Sparks Task analysis Data mining Bioinformatics distributed databases Computational modeling Google

来源：评论

学校读者我要写书评

暂无评论

Spatial Attention Network for Few-Shot Learning 28th

Spatial Attention Network for Few-Shot Learning

引用

28th International Conference on Artificial Neural Networks (ICANN)

作者： He, Xianhao Qiao, Peng Dou, Yong Niu, Xin Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha Peoples R China

ISBN: (纸本)9783030304843;9783030304836

Metric learning is one of the feasible approaches to few-shot learning. However, most metric learning methods encode images through CNN directly, without considering image contents. The general CNN features may lead to hard discrimination among distinct classes. Based on observation that feature maps correspond to image regions, we assume that image regions relevant to target objects should be salient in image features. To this end, we propose an effective framework, called Spatial Attention Network (SAN), to exploit spatial context of images. SAN produces attention weights on clustered regional features indicating the contributions of different regions to classification, and takes weighted sum of regional features as discriminative features. Thus, SAN highlights important contents by giving them large weights. Once trained, SAN compares unlabeled data with class prototypes of few labeled data in nearest-neighbor manner and identifies classes of unlabeled data. We evaluate our approach on three disparate datasets: miniimageNet, Caltech-UCSD Birds and miniDogsNet. Experimental results show that when compared with state-of-the-art models, SAN achieves competitive accuracy in miniimageNet and Caltech-UCSD Birds, and it improves 5-shot accuracy in miniDogsNet by a large margin.

关键词： Few-Shot Learning Attention module Discriminative features

来源：评论

学校读者我要写书评

暂无评论

Error Resilient GPU Accelerated image processing for Space Applications

引用

IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2018年第9期29卷 1990-2003页

作者： Davidson, R. L. Bridges, C. P. Univ Surrey Surrey Space Ctr Guildford GU2 7XH Surrey England

Significant advances in spaceborne imaging payloads have resulted in new big data problems in the Earth Observation (EO) field. These challenges are compounded onboard satellites due to a lack of equivalent advancement in onboard data processing and downlink technologies. We have previously proposed a new GPU accelerated onboard data processing architecture and developed parallelised image processing software to demonstrate the achievable data processing throughput and compression performance. However, the environmental characteristics are distinctly different to those on Earth, such as available power and the probability of adverse single event radiation effects. In this paper, we analyse new performance results for a low power embedded GPU platform, investigate the error resilience of our GPU image processing application and offer two new error resilient versions of the application. We utilise software based error injection testing to evaluate data corruption and functional interrupts. These results inform the new error resilient methods that also leverages GPU characteristics to minimise time and memory overheads. The key results show that our targeted redundancy techniques reduce the data corruption from a probability of up to 46 percent to now less than 2 percent for all test cases, with a typical execution time overhead of 130 percent.

关键词： GPU parallel processing error resilience EO image processing image compression onboard satellites

来源：评论

学校读者我要写书评

暂无评论

Spatial Bayesian Modeling and Computation with Application To Neuroimaging Data

Spatial Bayesian Modeling and Computation with Application T...

引用

作者： Guo, Cui University of Michigan

学位级别：博士

As both clinical and cognitive neuroscience matures, the need for sophisticated neuroimaging analyses becomes more important. The use of imaging markers to predict clinical outcomes, or even imaging outcomes, can have great impact on public health. However, such analyses are still under development since it is challenging for several reasons: 1) the images are of high dimension, and 2) the images may exhibit complex spatial correlation structure. Bayesian methods play an important role in solving these problems by dealing with spatial data flexibly and applying efficient sampling algorithms. This dissertation aims to develop spatial Bayesian models to predict either scalar or imaging outcomes by using imaging predictors and seeks computationally efficient approaches. In Chapter I, we propose a Bayesian scalar-on-image regression model with application to Multiple Sclerosis (MS) Magnetic Resonance Imaging (MRI) data. Specifically, we build up a multinomial logistic regression model to predict the clinical subtypes of MS patients by using their 3D MRI lesion data. Parameters corresponding to MRI predictors are spatially varying in the image space and are assumed to have a Gaussian Process (GP) prior distribution. Since the covariates are highly correlated, we use the Hamiltonian Monte Carlo algorithm, which is more statistically efficient than other Markov Chain Monte Carlo methods when the parameters are highly correlated. Finally, to reduce computational burden, we code the problem to run in parallel on a graphical processing unit. Results from simulation studies and a real MS data set show that our method has high prediction accuracy as evaluated by leave-one-out cross validation using an importance-sampling scheme. In Chapter ii, we propose a novel image-on-image regression model, by extending a spatial Bayesian latent factor model to neuroimaging data, where low dimensional latent factors are adopted to make connections between high-dimensional image outcomes a

关键词： Bayesian methods Nueroimaging Data Analysis Thesis

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：