检索结果-内蒙古大学图书馆

Accelerating Hyperspectral Anomaly Detection With Enhanced Multivariate Gaussianization Based on FPGA

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Yu, Kun Wu, Zebin Sun, Jin Zhang, Yi Xu, Yang Wei, Zhihui Zheng, Shangdong Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210023 Peoples R China

Hyperspectral anomaly detection (AD), as a frontier research topic in the field of remotely sensed data processing, aims to identify targets of interest from complex and vast images. Existing AD methods typically involve complex models and many parameters, posing challenges in meeting the requirements of computational efficiency in hyperspectral AD. To address this issue, this article presents an AD acceleration algorithm based on the multivariate Gaussian model as well as its field programmable gate array (FPGA) implementation. By exploiting the parallel processing capabilities of FPGA, we introduce an innovative spectral dimensionality reduction method in which the data processing flow can be accomplished in a distributed manner. Then, we employ an improved linear rotation strategy based on correlation coefficients to accelerate the convergence rate of the proposed AD algorithm. The rotation of Gaussianization in the improved strategy is independent of eigenvalue decomposition, thereby substantially reducing the computational complexity involved during the rotation procedure. Furthermore, we apply a pipeline parallel mechanism to facilitate the FPGA implementation of the AD algorithm and to significantly enhance the computational efficiency. Experimental results on an embedded FPGA platform demonstrate that the FPGA implementation of the hyperspectral AD algorithm proposed in this article achieves a significant acceleration rate with guaranteed high detection accuracy.

关键词： Field programmable gate arrays Hyperspectral imaging Computational modeling Detectors Convergence Computational efficiency Computational complexity Classification algorithms Accuracy Reliability Anomaly detection (AD) field programmable gate array (FPGA) hyperspectral image (HSI) linear rotation

来源：评论

学校读者我要写书评

暂无评论

Efficient and parallel Separable Dictionary Learning 27

Efficient and Parallel Separable Dictionary Learning

引用

27th IEEE International conference on parallel and distributed Systems, ICPADS 2021

作者： Rusu, Cristian Irofti, Paul University Politehnica Bucharest Faculty of Automatic Control and Computers Department of Automatic Control and Computers Bucharest Romania University of Bucharest Faculty of Mathematics and Computer Science Department of Computer Science Bucharest Romania

ISBN: (纸本)9781665408783

Separable, or Kronecker product, dictionaries provide natural decompositions for 2D signals, such as images. In this paper, we describe a highly parallelizable algorithm that learns such dictionaries which reaches sparse representations competitive with the previous state of the art dictionary learning algorithms from the literature but at a lower computational cost. We highlight the performance of the proposed method to sparsely represent image and hyperspectral data, and for image denoising. © 2021 IEEE.

关键词： image denoising

来源：评论

学校读者我要写书评

暂无评论

Towards an Optimized Heterogeneous distributed Task Scheduler in OpenMP Cluster

Towards an Optimized Heterogeneous Distributed Task Schedule...

引用

High Performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the International conference for

作者： Rémy Neveu Rodrigo Ceccato Gustavo Leite Guido Araujo Jose M. Monsalve Diaz Hervé Yviquel Instituto de Computação Universidade Estadual de Campinas (UNICAMP) Campinas Brazil

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

This paper addresses the challenges of optimizing task scheduling for a distributed, task-based execution model in OpenMP for cluster computing environments. Traditional OpenMP implementations are primarily designed for shared-memory parallelism and offer limited control over task scheduling. However, improved scheduling mechanisms are critical to achieving performance and portability in distributed and heterogeneous environments. OpenMP Cluster (OMPC) was introduced to overcome these limitations, extending OpenMP with the Heterogeneous Earliest Finish Time (HEFT) task scheduling algorithm tailored for large-scale systems. To improve scheduling and enable better system utilization, the runtime system must resolve challenges such as changes in the application balance, amount of parallelism, and varying communication *** work presents three key contributions: first, the refactoring of the OMPC runtime to unify task scheduling across devices and hosts; second, the optimization of the HEFT-based scheduling algorithm to ensure efficient task execution in distributed environments; and third, an extensive evaluation of Work Stealing and HEFT scheduling mechanisms in real-world clusters. While the HEFT implementation in OMPC is not fully optimized, this work provides a significant step toward improving distributed task scheduling in cluster computing, offering insights and incremental advancements that support the development of scalable and high-performance applications. Results show improvements of up to 24% in scheduling time while opening up to more extensions in the scheduling methods.

关键词： Runtime Scheduling algorithms parallel programming Optimal scheduling Cluster computing parallel processing Dynamic scheduling Large-scale systems Resource management Iterative methods

来源：评论

学校读者我要写书评

暂无评论

distributed and Heterogeneous SAR Backprojection with Halide

Distributed and Heterogeneous SAR Backprojection with Halide

引用

IEEE High Performance Extreme Computing conference (HPEC)

作者： Imes, Connor Li, Tzu-Mao Glines, Mark Khan, Rishi Walters, John Paul Univ Southern Calif Informat Sci Inst Los Angeles CA 90007 USA Univ Calif San Diego La Jolla CA 92093 USA Extreme Scale Solut New Castle DE USA MIT CSAIL Cambridge MA USA

ISBN: (纸本)9781665423694

Writing efficient, scalable, and portable HPC synthetic aperture radar (SAR) applications is increasingly challenging due to the growing diversity and heterogeneity in distributed systems. Considerable developer and computational resources are often spent to port applications to new HPC platforms and architectures, which is both time consuming and expensive. Domain-specific languages have been shown to be highly productive for development effort, but additionally achieving both scalable computational efficiency and platform portability remains challenging. The Halide programming language is both productive and efficient for dense data processing, supports common CPU architectures and heterogeneous resources like GPUs, and has previously been extended for distributed processing. We propose to use a distributed Halide implementation for scalable and heterogeneous HPC SAR processing. We implement a backprojection algorithm for SAR image reconstruction and demonstrate scalability on the OLCF Summit supercomputer up to 1,024 compute nodes (43,008 cores, each with 4 hardware threads) with a large 32,768x32,768 dataset, and up to 8 distributed GPUs with a 8,192x 8,192 dataset. Our results show excellent scaling and portability to heterogeneous resources, and motivate additional improvements in Halide to better support distributed high-performance signal processing.

关键词： parallel programming high performance computing synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Simulation of distributed Big Data Intelligent Fusion Algorithm Based on Machine Learning

Simulation of Distributed Big Data Intelligent Fusion Algori...

引用

2022 International conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2022

作者： Tian, Di Henan Finance University Zhengzhou450000 China

ISBN: (数字)9781665454575

ISBN: (纸本)9781665454575

Different from intelligent analysis applications in traditional small-scale data scenarios, intelligent analysis applications in big data scenarios are no longer a single AI algorithm model problem, but a fusion of big data, big models, and big computing. The existing data fusion algorithm has problems such as high network energy consumption and short lifetime of network nodes after data fusion. It is necessary to consider algorithm model design, big data processing and efficient distributed parallel computing at the same time, which brings a series of new challenges and problems to the research on the basic theoretical methods and key technologies of big data intelligent analysis. The method used in this paper is machine learning, setting an algorithm to have the machine analysis the data by itself using deep mining. It can largely finish the work that cannot be done manually. We need complex analysis based on machine learning and data mining, strong computing power and storage capacity to achieve massive data analysis. Through the research in this paper, it is concluded that the algorithm in this paper can achieve better time-to-time performance than the traditional algorithm. © 2022 IEEE.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Medical image Analysis using distributed Deep Learning Models

Medical Image Analysis using Distributed Deep Learning Model...

引用

Global conference for Advancement in Technology (GCAT)

作者： Raji Ramachandran Abhiram Prasad Ritika R Prasad Vysakh S Nair Lakshmi G Pillai Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Medical image analysis plays a crucial role in modern medicine, serving as an indispensable tool for clinicians to enhance diagnostic precision and establish personalized treatment programs. Our team has devised an innovative method for medical image analysis, employing distributed deep learning techniques for both COVID-19 and brain tumor datasets. Traditional approaches to medical image analysis often demand significant computational resources and extensive data, rendering their implementation challenging in real-world scenarios. In order to overcome these obstacles, we propose a distributed deep learning framework that enables parallel processing of medical images across multiple nodes. Our framework leverages convolutional neural networks and transfer learning to achieve exceptional accuracy in the detection of COVID-19 and brain tumors from inputted images, respectively. Through the evaluation of an extensive dataset of medical images, we have successfully demonstrated the efficiency of our approach in accurately detecting these conditions

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Spatial Alignment Method for Medical Ear Canal Based on Simplified Transformer Module and MultiScale Iterative Structure

A Spatial Alignment Method for Medical Ear Canal Based on Si...

引用

Robotics, Control and Automation (ICRCA), International conference on

作者： Chaokun Luan Qinglei Zhang Ying Zhou Weiping Liu Biao Shu Zhijian Li Xiaoyao Liu Haopeng Yang Logistics Engineering College Shanghai Maritime University Shanghai China Shanghai Smart Surgery Center Shanghai MicroPort MedBot (Group) Co. Ltd Shanghai China Key Accounts Department Shanghai MicroPort MedBot (Group) Co. Ltd Shanghai China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

Alignment of deformed medical images is a challenging task in which the images are subject to large deformations due to some constraints in the image acquisition process. In the field of deformation image alignment, convolutional neural network-based methods have a local acceptance domain and cannot handle deformed images. In this regard, we propose a new multi-scale parallel architecture model of Transformer-CNN, which takes advantage of Transformer's goodness at modeling remote dependencies, while simplifying the module and reducing the number of model parameters. The multiscale iterative structure of CNN can acquire local information and connect feature information at different scales. Experimental results on the medical ear canal dataset show that the method has excellent performance in terms of alignment accuracy, outperforming existing deep learning and some traditional alignment methods, while reducing the number of model parameters to some extent, making it lightweight.

关键词： Irrigation Accuracy Deformation Ear parallel processing Transformers Iterative methods Convolutional neural networks Robots Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Segment-Based, User-Generated image Styling with Neural Style Transfer

Segment-Based, User-Generated Image Styling with Neural Styl...

引用

distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International conference on

作者： C Vamshi Krishna Karthik Venkatesh Sooraj Nibin Vishakh H Manjula G Nair Department of Electrical and Electronics Engineering Amrita Vishwa Vidhyapeetam Amritapuri India

With the recent improvement in machine learning and deep learning technologies due to an increase in computation power, its uses for image processing and computer vision have also increased. The style of the image, through orthodox methods, has been changed with the help of filters that are not capable of applying the style from only a few sets of options, with no consideration for semantic or contextual data. The transfer of style is also limited to the entire image, with no provisions for applying style to only a selected portion of it. Through advanced deep learning technologies, style transfer can be achieved with semantic and contextual accuracy while also providing the ability to apply to a selected portion of the image. The style can also be generated through the use of images generated for the text using deep learning.

关键词：

来源：评论

学校读者我要写书评

暂无评论

REVIEW ON MECHANIC MODELING methods AND APPLICATIONS OF MUSCLES IN SOFT ACTUATIONS 1

REVIEW ON MECHANIC MODELING METHODS AND APPLICATIONS OF MUSC...

引用

1st International conference on Mechanical System Dynamics, ICMSD 2022

作者： Xu, Jiayi Fu, Kangjia Huang, Yiyong Jiang, Zhijie National Innovation Institute of Defense Technology Academy of Military Sciences Beijing China

ISBN: (数字)9781839537837

ISBN: (纸本)9781839537837

The main function of muscles is to contract when receiving electrical signals from nerves, resulting in the movement or deformation of related bones and tissues. Muscles possess the characteristics of flexibility and high efficiency, which are suitable for distributed actuation and human-computer interaction. Recently, bionic actuators, including pneumatic actuators, cable-driven mechanisms, and shape memory alloys/polymers, have been widely used in soft robots. Fundamentally, studying the actuation mechanism of muscles and extracting a simplified model is of great significance for achieving bionic actuation. The core problem of muscle movement is to establish the relationship between length and tension of muscles. Various modeling methods have been proposed for describing mechanics of muscles including fitting methods based on experimental data and simplified theoretical models. Recently, the simulation of muscle contour comes into being with the development of image processing and animation. This paper introduces the development of modeling methods of muscle mechanics including experimental modeling methods and theoretical/simulation modeling methods. Besides, the advantages and disadvantages of these modeling methods and their applications are analysed. Therefore, it has guiding significance for the design of soft robots including pneumatic actuators, cable-driven actuators, etc. © 2022 IET conference Proceedings. All rights reserved.

关键词： Muscle

来源：评论

学校读者我要写书评

暂无评论

Adaptable Machine Learning methods for a Twitter Followee Recommendation System

Adaptable Machine Learning Methods for a Twitter Followee Re...

引用

IoT, Communication and Automation Technology (ICICAT), International conference on

作者： Sandeep Yadav Shashank Srivastav Computer Science and Engineering Department Buddha Institute of Technology Gorakhpur India

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

Recommender systems for social networking have recently used machine learning (ML) methods. This paper describes a multi-agent system that simulates a Twitter recommender system to give users a list of helpful suggestions, i.e., a list of users who are followed, or they want to follow. When processing data in parallel on multi-node distributed systems, a machine learning technique (such as a neural network or multilayer perceptron) is evaluated for scalability using a simulator. A multi-agent modelling simulates the dispersed environment. The quantity of nodes, the method used within the model recommender framework, and the real followers and followees data are the first parameters that need to be configured on the simulator. The experimental findings were acquired on three different datasets to evaluate the speed and precision of a simulated recommender system when evaluating the machine learning method under various conditions.

关键词： Social networking (online) Scalability Blogs Neural networks distributed databases Machine learning Multilayer perceptrons Data models Recommender systems Multi-agent systems

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：