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检索条件"任意字段=7th International Conference on Real Time Computing Systems and Applications"

共 13062 条记录，以下是71-80 订阅

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WFQ-Based SLA-Aware Edge applications Provisioning

WFQ-Based SLA-Aware Edge Applications Provisioning

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15th international conference on Cloud computing and Services Science, CLOSER 2025

作者： Henrique Sachete Garcia, Pedro Lorenzon, Arthur Francisco Luizelli, Marcelo Caggiani Silas Severo de Souza, Paulo Rossi, Fábio Diniz Federal University of Pampa Brazil Federal Institute Farroupilha Brazil Federal University of Rio Grande do Sul Brazil

ISBN: (纸本)9789897587474

Provisioning delays can severely degrade the performance of real-time applications, especially in critical sectors such as healthcare, smart cities, and autonomous vehicles, where fast and reliable responses are essential. While managing data traffic effectively, existing flow scheduling techniques often fail to account for high-level metrics like Service-Level Agreements, leading to suboptimal prioritization of critical applications. this paper introduces a novel algorithm designed to optimize network bandwidth allocation for edge applications by incorporating SLA-based metrics. Evaluations demonstrate that our proposal outperforms conventional scheduling techniques. the results show that under varying infrastructure usage levels, our proposal consistently reduces provisioning times and minimizes delay violations for edge applications. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Resource allocation

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Resource-Aware Learning Automata for Low-Latency Task Scheduling in Fog computing 5th

Resource-Aware Learning Automata for Low-Latency Task Schedu...

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5th international conference on computing and Network Communications, CoCoNet 2023

作者： Sheela, S. Vishnupriya, G.M. Kumar, S. M. Dilip Bangalore University Bengaluru India

ISBN: (纸本)9789819745395

Cloud computing capabilities are extended to the edge of the network with fog computing in the modern computing landscape. this proximity to data sources and end-users offers several advantages, including reduced latency, enhanced real-time processing, and improved privacy. In this context, task scheduling becomes crucial to orchestrate computational tasks efficiently across fog nodes. Effective task scheduling ensures optimal resource utilization, minimizes latency, and enhances the overall performance of fog computing systems. this proposed work introduces a resource-aware task scheduling approach in a fog computing environment to improve latency and responsiveness for latency-sensitive applications. the model considers the residual energy along with memory constraint of the fog node as the basis for assigning the fog node for task execution. Tournament Selection algorithm is used by the task scheduler for action selection. the results show that the proposed approach performs better in reducing the task response time compared to existing approaches. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fog computing

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Synthetic Data for Enhancing Perception and Cooperative Localization in Autonomous Vehicles 7

Synthetic Data for Enhancing Perception and Cooperative Loca...

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7th IEEE international conference on Artificial Intelligence and eXtended and Virtual reality, AIxVR 2025

作者： Anagnostopoulos, Christos Piperigkos, Nikos Gkillas, Alexandros Lalos, Aris S. University of Ioannina Dpt. of Informatics & Telecom. Arta Greece Industrial Systems Institute Athena Research Center Patras Greece

ISBN: (纸本)9798331521578

the design and development of complex cyber-physical systems, as well as systems of systems, demand a broad array of methods and tools, with simulations playing a crucial role in developing and evaluating multiple system aspects. While real-world testing remains essential, simulation offers a valuable supplementary approach, facilitating faster development by providing scalable, reproducible results at minimal cost. In this context, we extend an open-source simulation framework by integrating object detection and Simultaneous Localization and Mapping (SLAM) algorithms, enabling the implementation and evaluation of approaches in cooperative localization and the enhancement of low-quality sensor data. the incorporation of synthetic data in our evaluation pipeline yielded promising results, demonstrating improvements in localization accuracy and sensor performance for autonomous vehicle applications. the dataset is uploaded on the following repository https://***/mips-group1/***. © 2025 IEEE.

关键词： autonomous vehicles cooperative localization simulation super resolution

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24th international conference on Embedded Computer systems: Architectures, Modeling, and Simulation, SAMOS 2024

24th International Conference on Embedded Computer Systems: ...

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24th international conference on Embedded Computer systems: Architectures, Modeling, and Simulation, SAMOS 2024

ISBN: (纸本)9783031783760

the proceedings contain 38 papers. the special focus in this conference is on Embedded Computer systems: Architectures, Modeling, and Simulation. the topics include: QCEDA: Using Quantum Computers for EDA;real-time Linux on RISC-V: Long-Term Performance Analysis of PREEMPT_RT Patches;RV-VP2: Unlocking the Potential of RISC-V Packed-SIMD for Embedded Processing;A Novel System Simulation Framework for HBM2 FPGA Platforms;ONNX-To-Hardware Design Flow for Adaptive Neural-Network Inference on FPGAs;efficient Post-training Augmentation for Adaptive Inference in Heterogeneous and Distributed IoT Environments;pooling On-the-Go for NoC-Based Convolutional Neural Network Accelerator;Vitamin-V: Serverless Cloud computing Porting on RISC-V;Design and Implementation of an Open Source OpenGL SC 2.0.1 Installable Client Driver and Offline Compiler;Plan Your Defense: A Comparative Analysis of Leakage Detection Methods on RISC-V Cores;iVault: Architectural Code Concealing Techniques to Protect Cryptographic Keys;I2DS: FPGA-Based Deep Learning Industrial Intrusion Detection System;ACRA: A Cutting-Edge Analytics Platform for Advanced real-time Corruption Risk Assessment and Investigation Prioritization;post Quantum Cryptography Research Lines in the Italian Center for Security and Rights in Cyberspace;advancing Future 5G/B5G systems: the Int5Gent Approach;RISC-V Accelerators, Enablement and applications for Automotive and Smart Home in the ISOLDE Project;PMDI: An AI-Enabled Ecosystem for Cooperative Urban Mobility;Open Source Software Randomisation Framework for Probabilistic WCET Prediction on Multicore CPUs, GPUs and Accelerators;a Hypervisor Based Platform for the Development and Verification of Reliable Software applications.

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24th international conference on Embedded Computer systems: Architectures, Modeling, and Simulation, SAMOS 2024

24th International Conference on Embedded Computer Systems: ...

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24th international conference on Embedded Computer systems: Architectures, Modeling, and Simulation, SAMOS 2024

ISBN: (纸本)9783031783791

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Model Migration and Joint Communication and computing Resource Allocation in Native AI Wireless Networks 3rd

Model Migration and Joint Communication and Computing Resour...

引用

3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： He, Zhimin Deng, Juan Li, Yaru Wang, Xiaoyun China Mobile Research Institute Beijing China ZGC Institute of Ubiquitous-X Innovation and Applications Beijing China China Mobile Communications Group Corporation Beijing China

ISBN: (纸本)9789819616930

As artificial intelligence (AI) continues to drive industrial upgrading, the demands for efficient and real-time AI services have escalated. However, meeting these demands poses challenges, particularly within the framework of 5G networks, where the infrastructure for providing AI services lacks real-time collaborative control over the required resources, such as the limited communication, computing and model resources. Anticipating the future of 6G networks, this paper advocates for the design with native AI integration from inception, leveraging wireless network infrastructure for ubiquitous real-time AI services. In this case, we address the issue of limited computing, model resources by proposing a distributed optimal resource utilization model migration algorithm. Additionally, a joint communication and computing resources allocation algorithm, based on a greedy strategy, is introduced to enhance resource utilization and support a higher number of AI inference tasks. Simulation results demonstrate that the performance of the proposed algorithm can approach the exhaustive search scheme at lower computational complexity. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 5G mobile communication systems

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ONNX-To-Hardware Design Flow for Adaptive Neural-Network Inference on FPGAs 1

引用

24th international conference on Embedded Computer systems: Architectures, Modeling, and Simulation

作者： Manca, Federico Ratto, Francesco Palumbo, Francesca Univ Sassari Piazza Univ 21 I-07100 Sassari Italy Univ Cagliari Via Marengo 2 I-09123 Cagliari Italy

ISBN: (数字)9783031783807

ISBN: (纸本)9783031783791;9783031783807

the challenges involved in executin Neural Networks (NNs) at the edge include providing diversity, flexibility, and sustainability. that implies, for instance, supporting evolving applications and algorithms energy-efficiently. Using hardware (hw) or software accelerators can deliver fast and efficient computation of the NNs, while flexibility can be exploited to support long-term adaptivity. Nonetheless, handcrafting a NN for a specific device, despite the possibility of leading to an optimal solution, takes time and experience, and that's why frameworks for hw accelerators are being developed. this work, starting from a preliminary semi-integrated ONNX-to-hardware toolchain [23], focuses on enabling Approximate computing (AC) leveraging the distinctive ability of the original toolchain to favor adaptivity. the goal is to allow lightweight adaptable NN inference on FPGAs at the edge.

关键词： Convolutional Neural Networks Approximate computing FPGAs Cyber-Physical systems

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Integrating Blockchain-Based Security and Privacy with QML in Edge computing for 6G Networks 37th

Integrating Blockchain-Based Security and Privacy with QML i...

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37th international conference on Computer applications in Industry and Engineering

作者： Seol, Jongho Kim, Jongyeop Kancharla, Abhilash Middle Georgia State Univ Macon GA 31206 USA Georgia Southern Univ Statesboro GA 30460 USA Univ Tampa Tampa FL 33606 USA

ISBN: (纸本)9783031762727;9783031762734

this paper presents a robust theoretical framework to integrate blockchain-based security and privacy mechanisms with quantum machine learning (QML) in the edge computing domain within 6G networks. the burgeoning deployment of edge devices necessitates secure, privacy-preserving, and trustworthy infrastructures to support collaborative QML tasks while upholding data confidentiality at the network periphery. Leveraging blockchain technology's decentralized and immutable ledger capabilities, this framework manages access control, ensures data provenance, and verifies integrity in edge computing environments. Furthermore, integrating quantum-resistant cryptographic primitives is explored to fortify defenses against potential threats from quantum adversaries. In addition to these considerations, the paper incorporates the theory of quantum probability within the framework, particularly in the context of the central limit theorem, to account for the probabilistic nature of quantum systems and its implications on statistical inference in QML tasks. Detailed latency analysis reveals that blockchain processing time increases with transaction complexity, quantum processing time grows more slowly, and 6G transmission time remains constant due to high bandwidth capabilities. Incorporating machine learning components such as data preprocessing and model inference times provides a comprehensive understanding of edge computing performance. Combining blockchain-based security and privacy measures with QML techniques like federated learning and differential privacy, the envisioned framework strives to establish a secure and trusted ecosystem for collaborative QML tasks at the network edge. this theoretical endeavor, enriched by quantum probability theory and detailed latency analysis, lays a solid groundwork for further research and development in this burgeoning interdisciplinary domain, promising advancements in edge computing applications' efficiency, reliability, and security w

关键词： Blockchain Quantum Machine Learning (QML) Edge computing 6G Networks Security

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Micro-PMU Data-Driven Anomalous Voltage Event Detection for the Power Distribution System 12th

Micro-PMU Data-Driven Anomalous Voltage Event Detection for...

引用

12th international conference on Frontiers of Intelligent computing: theory and applications, FICTA 2024

作者： Parameswaran, Saththiyan Dey, Maitreyee Patel, Preeti Department of Computer Science and Applied Computing London Metropolitan University 166–220 Holloway Rd. LondonN7 8DB United Kingdom

ISBN: (纸本)9789819601424

As Distributed Energy Resources become more integrated into the power grid, the complexity and scale of challenges faced by grid operators have increased. the integration of micro-synchrophasors (micro-PMUs) into the grid infrastructure has provided access to high-resolution data, yet analyzing such vast amounts of information presents its own set of obstacles. this study examines thirty days of micro-PMU data from April 2023, employing statistical analysis and unsupervised learning techniques to identify various voltage events over time. real-world data from grid-connected solar farms in Norfolk, England, are utilized to detect and analyze these voltage events, as well as to explore their distinct patterns. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Power grids

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Classifiers for Human Pose Recognition: Analysis for real-time applications 12th

Classifiers for Human Pose Recognition: Analysis for Real-Ti...

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12th international conference on Recent Trends in computing, ICRTC 2024

作者： Panchal, Piyush Tirkey, Nikhil Singh, Kaustubh Ranjan Bawana Road Delhi110042 India

ISBN: (纸本)9789819789450

Action detection is a rapidly growing field that involves identifying and tracking particular body key points in photos/videos to ascertain an individual’s action. Its difficulties arise from the natural fluctuations in lighting, a wide range of clothes, and the multiplicity of body actions that are seen in videos. Robust classification algorithms that can precisely anticipate pose angles and distances are necessary to tackle these problems. Action detection has many uses in several fields. the accurate analysis of body positions in fitness tracking helps create customized workout plans. In the medical field, awareness and detection of actions is essential for patient mobility monitoring and rehabilitation. Accurate action identification is beneficial to motion analysis, enabling advances in surveillance systems, sports analytics, and animation. the difficulty of detecting actions in videos is becoming increasingly popular due to its many uses in fields such as motion analysis, fitness tracking, and healthcare. this study first, compares the performance of several classifiers, including Support Vector Machine (SVM), k-nearest neighbors, logistic regression, decision trees, naive Bayes classification, and convolutional Neural Network (CNN) in the complex problems of posture detection, and secondly estimates various actions in real-time where the model is trained on video datasets after selecting the most suited machine learning model, the main motivation behind this research is to find the most accurate and effective classifier possible and using this information to classify actions while taking input from machine’s camera in real-time. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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