Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection
This paper introduces Grounding DINO 1.5, a suite of advanced open-set object detection models developed by IDEA Research, which aims to advance the "Edge" of open-set object detection. The suite encompasses two models: …
Edge-computingFew-Shot Object Detectionobject-detectionObject Detection+1Networking Systems for Video Anomaly Detection: A Tutorial and Survey
The increasing utilization of surveillance cameras in smart cities, coupled with the surge of online video applications, has heightened concerns regarding public security and privacy protection, which propelled automated…
Anomaly DetectionEdge-computingSurveyVideo Anomaly DetectionDistributed Threat Intelligence at the Edge Devices: A Large Language Model-Driven Approach
With the proliferation of edge devices, there is a significant increase in attack surface on these devices. The decentralized deployment of threat intelligence on edge devices, coupled with adaptive machine learning tech…
Edge-computingIn-Context LearningLanguage ModelingLanguage Modelling+1A Decentralized and Self-Adaptive Approach for Monitoring Volatile Edge Environments
Edge computing provides resources for IoT workloads at the network edge. Monitoring systems are vital for efficiently managing resources and application workloads by collecting, storing, and providing relevant informatio…
Edge-computingEdge Computing for IoT: Novel Insights from a Comparative Analysis of Access Control Models
IoT edge computing positions computing resources closer to the data sources to reduce the latency, relieve the bandwidth pressure on the cloud, and enhance data security. Nevertheless, data security in IoT edge computing…
Autonomous VehiclesCloud ComputingEdge-computingeCAR: edge-assisted Collaborative Augmented Reality Framework
We propose a novel edge-assisted multi-user collaborative augmented reality framework in a large indoor environment. In Collaborative Augmented Reality, data communication that synchronizes virtual objects has large netw…
Edge-computingObjectControlling Chaos Using Edge Computing Hardware
Machine learning provides a data-driven approach for creating a digital twin of a system - a digital model used to predict the system behavior. Having an accurate digital twin can drive many applications, such as control…
Cloud ComputingEdge-computingLatency and Energy Minimization in NOMA-Assisted MEC Network: A Federated Deep Reinforcement Learning Approach
Multi-access edge computing (MEC) is seen as a vital component of forthcoming 6G wireless networks, aiming to support emerging applications that demand high service reliability and low latency. However, ensuring the ultr…
Deep Reinforcement LearningEdge-computingReCycle: Fast and Efficient Long Time Series Forecasting with Residual Cyclic Transformers
Transformers have recently gained prominence in long time series forecasting by elevating accuracies in a variety of use cases. Regrettably, in the race for better predictive performance the overhead of model architectur…
Edge-computingTime SeriesTime Series ForecastingSparrowSNN: A Hardware/software Co-design for Energy Efficient ECG Classification
Heart disease is one of the leading causes of death worldwide. Given its high risk and often asymptomatic nature, real-time continuous monitoring is essential. Unlike traditional artificial neural networks (ANNs), spikin…
ECG ClassificationEdge-computingHeartbeat ClassificationMillimeter Wave Radar-based Human Activity Recognition for Healthcare Monitoring Robot
Healthcare monitoring is crucial, especially for the daily care of elderly individuals living alone. It can detect dangerous occurrences, such as falls, and provide timely alerts to save lives. Non-invasive millimeter wa…
Activity RecognitionEdge-computingHuman Activity RecognitionGradient-Congruity Guided Federated Sparse Training
Edge computing allows artificial intelligence and machine learning models to be deployed on edge devices, where they can learn from local data and collaborate to form a global model. Federated learning (FL) is a distribu…
Edge-computingFederated LearningRobust Decentralized Learning with Local Updates and Gradient Tracking
As distributed learning applications such as Federated Learning, the Internet of Things (IoT), and Edge Computing grow, it is critical to address the shortcomings of such technologies from a theoretical perspective. As a…
Adversarial RobustnessEdge-computingFederated LearningC2FDrone: Coarse-to-Fine Drone-to-Drone Detection using Vision Transformer Networks
A vision-based drone-to-drone detection system is crucial for various applications like collision avoidance, countering hostile drones, and search-and-rescue operations. However, detecting drones presents unique challeng…
Collision AvoidanceEdge-computingEnhancing IoT Security: A Novel Feature Engineering Approach for ML-Based Intrusion Detection Systems
The integration of Internet of Things (IoT) applications in our daily lives has led to a surge in data traffic, posing significant security challenges. IoT applications using cloud and edge computing are at higher risk o…
Edge-computingFeature EngineeringIntrusion DetectionHLSTransform: Energy-Efficient Llama 2 Inference on FPGAs Via High Level Synthesis
Graphics Processing Units (GPUs) have become the leading hardware accelerator for deep learning applications and are used widely in training and inference of transformers; transformers have achieved state-of-the-art perf…
CPUEdge-computingGPUHigh-Level SynthesisJoint ADS-B in 5G for Hierarchical Aerial Networks: Performance Analysis and Optimization
Unmanned aerial vehicles (UAVs) are widely applied in multiple fields, which emphasizes the challenge of obtaining UAV flight information to ensure the airspace safety. UAVs equipped with automatic dependent surveillance…
Edge-computingImplementation of Big AI Models for Wireless Networks with Collaborative Edge Computing
Big Artificial Intelligence (AI) models have emerged as a crucial element in various intelligent applications at the edge, such as voice assistants in smart homes and autonomous robotics in smart factories. Training big …
Edge-computingSchedulingBeyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories
Mobile Edge Computing (MEC) broadens the scope of computation and storage beyond the central network, incorporating edge nodes close to end devices. This expansion facilitates the implementation of large-scale "connected…
Edge-computingReinforcement Learning (RL)SchedulingEdge-Assisted ML-Aided Uncertainty-Aware Vehicle Collision Avoidance at Urban Intersections
Intersection crossing represents one of the most dangerous sections of the road infrastructure and Connected Vehicles (CVs) can serve as a revolutionary solution to the problem. In this work, we present a novel framework…
Collision AvoidanceDecoderEdge-computing