Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
System Integration of Xilinx DPU and HDMI for Real-Time inference in PYNQ Environment with Image Enhancement
Use of edge computing in application of Computer Vision (CV) is an active field of research. Today, most CV applications make use of Convolutional Neural Networks (CNNs) to inference on and interpret video data. These ed…
Edge-computingImage EnhancementADA-YOLO: Dynamic Fusion of YOLOv8 and Adaptive Heads for Precise Image Detection and Diagnosis
Object detection and localization are crucial tasks for biomedical image analysis, particularly in the field of hematology where the detection and recognition of blood cells are essential for diagnosis and treatment deci…
Blood Cell CountEdge-computingMedical Object DetectionObject+2Multi-IRS Aided Mobile Edge Computing for High Reliability and Low Latency Services
Although multi-access edge computing (MEC) has allowed for computation offloading at the network edge, weak wireless signals in the radio access network caused by obstacles and high network load are still preventing effi…
Edge-computingSecure Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless MEC Networks
This paper proposes a blockchain-secured deep reinforcement learning (BC-DRL) optimization framework for {data management and} resource allocation in decentralized {wireless mobile edge computing (MEC)} networks. In our …
Deep Reinforcement LearningEdge-computingManagementMobile Edge Computing and AI Enabled Web3 Metaverse over 6G Wireless Communications: A Deep Reinforcement Learning Approach
The Metaverse is gaining attention among academics as maturing technologies empower the promises and envisagements of a multi-purpose, integrated virtual environment. An interactive and immersive socialization experience…
Deep Reinforcement LearningEdge-computingOffloading and Quality Control for AI Generated Content Services in 6G Mobile Edge Computing Networks
AI-Generated Content (AIGC), as a novel manner of providing Metaverse services in the forthcoming Internet paradigm, can resolve the obstacles of immersion requirements. Concurrently, edge computing, as an evolutionary p…
Edge-computingCoordination-free Decentralised Federated Learning on Complex Networks: Overcoming Heterogeneity
Federated Learning (FL) is a well-known framework for successfully performing a learning task in an edge computing scenario where the devices involved have limited resources and incomplete data representation. The basic …
Edge-computingFederated LearningEdge computing service deployment and task offloading based on multi-task high-dimensional multi-objective optimization
The Mobile Edge Computing (MEC) system located close to the client allows mobile smart devices to offload their computations onto edge servers, enabling them to benefit from low-latency computing services. Both cloud ser…
DiversityEdge-computingEvaluation of Infrastructure-based Warning System on Driving Behaviors-A Roundabout Study
Smart intersections have the potential to improve road safety with sensing, communication, and edge computing technologies. Perception sensors installed at a smart intersection can monitor the traffic environment in real…
Edge-computingNavigateA Self-Commissioning Edge Computing Method for Data-Driven Anomaly Detection in Power Electronic Systems
Ensuring the reliability of power electronic converters is a matter of great importance, and data-driven condition monitoring techniques are cementing themselves as an important tool for this purpose. However, translatin…
Anomaly DetectionDiversityEdge-computingTowards Decentralized Task Offloading and Resource Allocation in User-Centric Mobile Edge Computing
In the traditional cellular-based mobile edge computing (MEC), users at the edge of the cell are prone to suffer severe inter-cell interference and signal attenuation, leading to low throughput even transmission interrup…
Deep Reinforcement LearningEdge-computingAge-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach
With the rapid development of Mobile Edge Computing (MEC), various real-time applications have been deployed to benefit people's daily lives. The performance of these applications relies heavily on the freshness of colle…
Deep Reinforcement LearningEdge-computingReinforcement Learning (RL)SchedulingVREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning
Assisted and autonomous driving are rapidly gaining momentum and will soon become a reality. Artificial intelligence and machine learning are regarded as key enablers thanks to the massive amount of data that smart vehic…
Autonomous DrivingEdge-computingFederated LearningImage Segmentation+2Learning for Semantic Knowledge Base-Guided Online Feature Transmission in Dynamic Channels
With the proliferation of edge computing, efficient AI inference on edge devices has become essential for intelligent applications such as autonomous vehicles and VR/AR. In this context, we address the problem of efficie…
Autonomous VehiclesDecision MakingDeep Reinforcement LearningEdge-computing+1URLLC-Awared Resource Allocation for Heterogeneous Vehicular Edge Computing
Vehicular edge computing (VEC) is a promising technology to support real-time vehicular applications, where vehicles offload intensive computation tasks to the nearby VEC server for processing. However, the traditional V…
Deep Reinforcement LearningEdge-computingLiDAR-based Outdoor Crowd Management for Smart Campus on the Edge
Crowd management is crucial for a smart campus. Popular methods are camera-based. However, conventional camera-based approaches may leak users' personally identifiable features, jeopardizing user's privacy, which limits …
Edge-computingFeature EngineeringManagementMergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation
Recently, federated learning (FL) has emerged as a popular technique for edge AI to mine valuable knowledge in edge computing (EC) systems. To mitigate the computing/communication burden on resource-constrained workers a…
Edge-computingFederated LearningBitformer: An efficient Transformer with bitwise operation-based attention for Big Data Analytics at low-cost low-precision devices
In the current landscape of large models, the Transformer stands as a cornerstone, playing a pivotal role in shaping the trajectory of modern models. However, its application encounters challenges attributed to the subst…
Edge-computingUncertainty Estimation in Multi-Agent Distributed Learning
Traditionally, IoT edge devices have been perceived primarily as low-power components with limited capabilities for autonomous operations. Yet, with emerging advancements in embedded AI hardware design, a foundational sh…
Edge-computingQuantizationA Survey of Blockchain, Artificial Intelligence, and Edge Computing for Web 3.0
Web 3.0, as the third generation of the World Wide Web, aims to solve contemporary problems of trust, centralization, and data ownership. Driven by the latest advances in cutting-edge technologies, Web 3.0 is moving towa…
Edge-computing