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检索条件"任意字段=21st International Symposium on Computer Architecture and High Performance Computing"

共 1769 条记录，以下是411-420 订阅

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Infrastructure-centric, NetworkServer-agnostic LoRaWAN Roaming

Infrastructure-centric, NetworkServer-agnostic LoRaWAN Roami...

引用

IEEE international symposium on Network computing and Applications

作者： Giovanni Merlino Rafael Asorey-Cacheda Luca D’Agati Francesco Longo Antonio-Javier Garcia-Sanchez Joan Garcia-Haro Antonio Puliafito Department of Engineering University of Messina Italy CINI: National Interuniversity Consortium for Informatics Rome Italy Department of Information and Communication Technologies Universidad Politécnica de Cartagena Spain

ISBN: (纸本)9798350397314

The development of Low-Power Wide-Area Networks (LPWAN) has significantly boosted the uptake of Internet of Things technologies, fostering their adoption in such domains as Smart Cities and Industry 4.0. In the context of LPWANs, LoRa is one of the most promising developments, experiencing massive growth in recent years. This situation has led to the steadily growing trend of redundant deployments across locations, or scenarios, where it would be more natural to be able to leverage (e.g., rent) LoRa infrastructure belonging to a third party. Key to reversing this unsustainable trend is roaming approaches. Although the LoRa specifications define mechanisms for roaming, gaps and challenges make roaming difficult to deploy and operate in real-world scenarios. This paper analyzes these concerns and proposes a novel architecture compliant with the LoRa specification to design and accommodate roaming services in a consistent, efficient, and scalable way.

关键词： Smart cities Collaboration computer architecture Logic gates Market research Fourth Industrial Revolution Internet of Things

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MAPA: Multi -Accelerator Pattern Allocation Policy for Multi -Tenant GPU Servers 21

MAPA: Multi -Accelerator Pattern Allocation Policy for Multi...

引用

international Conference for high performance computing, Networking, storage and Analysis (SC21)

作者： Ranganath, Kiran Suetterlein, Joshua D. Manzano, Joseph B. Song, Shuaiwen Leon Wong, Daniel Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA Pacific Northwest Natl Lab High Performance Comp Grp Richland WA 99352 USA Univ Sydney Future Syst Architecture Lab Sch Comp Sci Sydney NSW Australia

ISBN: (纸本)9781450384421

Mtdti-accelerator servers are increasingly being deployed in shared multi -tenant environments (such as in cloud data centers) in order to meet the demands of large-scale compute -intensive workloads. In addition, these accelerators are increasingly being inter-connected in complex topologies and workloads are exhibiting a wider variety of inter-accelerator communication patterns. However, existing allocation policies are ill-suited for these emerging use-cases. Specifically, this work identifies that multi-accelerator workloads are commonly fragmented leading to reduced bandwidth and increased latency for inter-accelerator communication. We propose Multi-Accelerator Pattern Allocation (MAPA), a graph pattern mining approach towards providing generalized allocation support for allocating multi-accelerator workloads on multi-accelerator servers. We demonstrate that MAPA is able to improve the execution time of multi-accelerator workloads and that MAPA is able to provide generalized benefits across various accelerator topologies. Finally, we demonstrate a speedup of 12.4% for 75th percentile of jobs with the worst case execution time reduced by up to 35% against baseline policy using MAPA.

关键词： Topology

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Finer-LRU: A Scalable Page Management Scheme for HPC Manycore architectures 35

Finer-LRU: A Scalable Page Management Scheme for HPC Manycor...

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35th IEEE international Parallel and Distributed Processing symposium (IPDPS)

作者： Bang, Jiwoo Kim, Chungyong Kim, Sunggon Chen, Qichen Lee, Cheongjun Byun, Eun-Kyu Lee, Jaehwan Eom, Hyeonsang Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea Korea Aerosp Univ Dept Elect & Informat Engn Goyang South Korea Korea Inst Sci & Technol Informat Div Natl Supercomp Daejeon South Korea

ISBN: (纸本)9781665440660

In HPC systems, the increasing need for a higher level of concurrency has led to packing inure cores within a single chip. However, since multiple processes share memory space, the frequent access to resources in critical sections where only atomic operation has to be executed can result in poor performance. In this paper, we focus on reducing lock contention on the memory management system of an HPC manycore architecture. One of the critical sections causing severe lock contention in the I/O path is in the page management system, which uses multiple Least Recently Used (LRU) lists with a single lock instance. To solve this problem, we propose a Finer-LRU scheme, which optimizes the page reclamation process by splitting LRU lists into multiple sub-lists, each having its own lock instance. Our evaluation result shows that the Finer-LRU scheme can improve sequential write throughput by 57.93% and reduce latency by 98.94% compared to the baseline Linux kernel version 5.2.8 in the Intel Knights Landing (KNL) architecture.

关键词： high performance computing Manycore architecture Page reclamation process Fine-grained lock

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Multi-Scale Multi-stage Single Image Super-Resolution Reconstruction Algorithm Based on Transformer

Multi-Scale Multi-Stage Single Image Super-Resolution Recons...

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international symposium on Distributed computing and Applications to Business, Engineering and Science (DCABES)

作者： Wei Wang Yinfang Zhu Dewu Ding Jing Li Yu Luo School of Mathematics and Computer Science Yichun University Yichun School of Computer Science and Technology Guangdong University of Technology Guangzhou

ISBN: (纸本)9781665454636

In this paper, creatively combining Transformer with image super-resolution reconstruction, we proposes a multi-scale multi-stage single image super-resolution reconstruction algorithm based on Transformer (MstN). The algorithm uses Transformer as a feature sharing module, thus it realizes network parameter sharing, dynamically focuses on the correlation between feature information of adjacent stages, and then extracts the high-frequency texture information embedded in the current stage features from the feature information learned in the previous stage, which achieves a coarse-to-fine enhancement of image reconstruction. Experiments show that our method can not only per-form better image super-resolution reconstruction compared with other advanced methods, but also reduce the network parameters to a great extent.

关键词： Correlation Heuristic algorithms Superresolution Reconstruction algorithms Transformers Feature extraction Data mining

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Research on Unmanned Vehicle Path Planning based on Improved Bat Algorithm

Research on Unmanned Vehicle Path Planning based on Improved...

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international symposium on Distributed computing and Applications to Business, Engineering and Science (DCABES)

作者： Yizhu Jiang Yefu Wu Miao Wang Hubei Key Laboratory of Transportation Internet of Things School of Computer and Artificial Intelligence Wuhan University of Technology Wuhan Hubei P.R China Chongqing Guoyuan Port Co. Ltd Chongqing China

ISBN: (纸本)9781665454636

Vehicle driving technology has developed in the direction of informatization and intelligence, and the research hotspot is unmanned driving and path planning [1] . This paper proposes an improved bat algorithm (Improved Bat Algorithm, IBA). First, a weighted speed update mechanism is introduced in BA, so that with the increase of the number of iterations, the speed changes adaptively to avoid the algorithm falling into local extreme values prematurely. Then, in the subsequent iterations, the quadratic differential change mechanism is used to maintain the diversity of the bat population; and under the static road network, the optimal route is planned through the MAT LAB simulation experiment, which verifies that the IBA algorithm has better performance.

关键词： Roads Computational modeling Sociology Vehicle driving Path planning statistics Distributed computing

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performance Evolution of Yolo Models in Remote Sensing Images

Performance Evolution of Yolo Models in Remote Sensing Image...

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international Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Irfan Hassan Zhang Xinyou School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu PR China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Object detection (OD) in Remote Sensing (RS) satellite imagery presents unique challenges due to high variability and complex backgrounds. These complexities in RS data often lead to increased omission and false detection rates in object recognition. This paper presents a relative study of the performance of YOLO models (v5, v8, v9, v10, and 11) on two datasets: Airbus Aircraft and SkyFusion. Through a detailed ablation study, we analyze the impact of hyper-parameter tuning and dataset-specific, fine-tuning techniques for the performance of YOLO models and their variants. Experiments are conducted on datasets of varying sizes, including a smaller dataset of 103 images and a larger dataset of over 2995 images. The results highlight the improvements across YOLO versions in overcoming RS challenges, such as object sizes, high variability, and diverse contextual dependencies. This study emphasizes YOLO's adaptability towards RS tasks, which is highlighted in terms of fine-tuning strategies and performance optimization according to different scales of datasets.

关键词： YOLO Adaptation models Atmospheric modeling Real-time systems Sensors Satellite images Object recognition Remote sensing Tuning Optimization

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Classification of Animal Species Using a Deep Neural Network-Based Feature Extraction Method

Classification of Animal Species Using a Deep Neural Network...

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international symposium on high Capacity Optical Networks and Enabling Technologies, HONET

作者： Mohammed Ibrahim Khamis Al-Kubaise Ali Alkapti Abdullah AlMusa Osama Abdelaziz Somaya Al-maadeed Kishor Kumar Sadasivuni Computer Science and Engineering Department Qatar University Doha Qatar Center for Advanced Materials Qatar University Doha Qatar

ISBN: (数字)9798350378078

ISBN: (纸本)9798350378085

This study presents an innovative approach to animal classification and recognition utilizing machine learning and deep learning methodologies. Leveraging advanced algorithms, the proposed system achieves remarkable accuracy in identifying diverse animal species. By integrating sophisticated image processing techniques, the system enhances image quality, improving overall performance. The research demonstrated that the SVM model combined with deep neural network-based feature extraction achieved the highest accuracy of 95.65%. This paper represents a significant stride toward improving the precision and efficiency of animal classification, offering promising applications in biodiversity conservation and ecological monitoring by using advanced feature extraction approach with deep learning.

关键词： Deep learning Support vector machines Accuracy Machine learning algorithms Animals Smart cities System performance Wildlife Feature extraction Classification algorithms

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Design and Implementation of a Probe for 3D Scanning with FPGA-Based architecture

Design and Implementation of a Probe for 3D Scanning with FP...

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international Conference on Electrical Engineering, computing Science and Automatic Control (CCE)

作者： Juan F. Pintor-Michimani M.A. Diozcora Vargas-Treviño Sergio Vergara-Limon Jesús López-Gómez Margarita C. García-López Luis A. Alcantar-Vergara Facultad Ciencias de la electrónica Benem. Univers. Autónoma de Puebla Puebla City Mexico Ingeniería Eléctrica y Electrónica. Div. Acad. de Ingeniería y Arquitectura Univers. Juárez Autónoma de Tabasco Cunduacán City Mexico

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

This work presents the design of a probe, which is a tool that is installed in a Cartesian CNC robot of 3 degrees of freedom (GDL). The probe was designed to perform high-resolution scanning of parts. In order to study the behavior of the mechanism, the dynamic model of the robot is presented, by means of the methodology of lumped parameters using the Euler-Lagrange equation. On the basis of this study, a control system for the robot is developed using an embedded system in a Cyclone V FPGA (Field Programmable Gate Array) from Altera, which allows the position of each motor to be controlled.

关键词： Solid modeling Three-dimensional displays Service robots Mathematical models Software computer numerical control Probes Field programmable gate arrays System analysis and design standards

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DisasterViT: A Lightweight Vision Transformer for Disaster Classification

DisasterViT: A Lightweight Vision Transformer for Disaster C...

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international Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Shamas Tabraiz Huang Haiyu Irfan Hassan Majid Manzoor Muhammad Haider Abbas School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu PR China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Natural disasters can cause catastrophic damage, and often strike unexpectedly, leaving communities vulnerable and disrupting critical infrastructure. Response authorities must quickly classify the disaster to mitigate its effects, which can be challenging because of transportation and communication breakdowns in affected areas. Unmanned Aerial Vehicles (UAVs) can be used for real-time surveillance in remote regions, capturing critical visuals that enable computer Vision (CV) techniques to analyze and classify the disaster type, supporting timely response efforts. For the classification task, we proposed DisasterViT a lightweight Vision Transformer (ViT) with an External Attention (EA) mechanism, optimized for high performance. DisasterViT was trained on a custom dataset of 3,720 images from four distinct disaster categories: cyclone, earthquake, flood, and wildfire, achieved an impressive overall accuracy of 91% in classifying diverse disaster scenarios. The code is publicly available at: https://***/shamastabraiz/disaster_vit.

关键词： Wavelet transforms computer vision Wildfires Accuracy Disasters Earthquakes Transformers Real-time systems Cyclones Floods

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A password-authentication scheme based on ciphertext random sampling transmission

A password-authentication scheme based on ciphertext random ...

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IEEE international symposium on Parallel and Distributed Processing with Applications and IEEE international Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Lizhu Yang Yong Qin Xuwen Qin Dongcheng Zhang School of Computer Science and Technology Dongguan University of Technology Dongguan China School of Cyberspace Security Dongguan University of Technology Dongguan China G2 Grade Shenzhen College of International Education Shenzhen China

The traditional identity authentication approach of username and password has the security risk of password leakage in the transmission process. For the security of password transmission in the blockchain education architecture, an asymmetric encryption algorithm is used to encrypt the transmitted password. Then use an improved random sampling algorithm to randomly extract part of the ciphertext and transmit those selected ciphertexts. The authentication credentials are sampled ciphertext and the location index. At the same time, the authentication factor is generated through the Time-stamp technology, and mutual authentication is realized based on the Response-Challenge protocol. Through security analysis and experimental analysis, the proposed scheme is proved to be secure.

关键词： Protocols Education Authentication Focusing Passwords Ubiquitous computing Blockchains

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