Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
Enabling AI Quality Control via Feature Hierarchical Edge Inference
With the rise of edge computing, various AI services are expected to be available at a mobile side through the inference based on deep neural network (DNN) operated at the network edge, called edge inference (EI). On the…
Edge-computingOptimal Privacy Preserving for Federated Learning in Mobile Edge Computing
Federated Learning (FL) with quantization and deliberately added noise over wireless networks is a promising approach to preserve user differential privacy (DP) while reducing wireless resources. Specifically, an FL proc…
Edge-computingFederated LearningPrivacy PreservingQuantizationBlue Communications for Edge Computing: the Reconfigurable Intelligent Surfaces Opportunity
Wireless traffic is exploding, due to the myriad of new connections and the exchange of capillary data at the edge of the networks to operate real-time processing and decision making. The latter especially affects the up…
Decision MakingEdge-computingFramework Construction of an Adversarial Federated Transfer Learning Classifier
As the Internet grows in popularity, more and more classification jobs, such as IoT, finance industry and healthcare field, rely on mobile edge computing to advance machine learning. In the medical industry, however, goo…
ClassificationDiagnosticEdge-computingFederated Learning+56G Mobile-Edge Empowered Metaverse: Requirements, Technologies, Challenges and Research Directions
The Metaverse has emerged as the successor of the conventional mobile internet to change people's lifestyles. It has strict visual and physical requirements to ensure an immersive experience (i.e., high visual quality, l…
Edge-computingSemantic CommunicationDeep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review
Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. While performance seems to be improving on b…
Autonomous DrivingEdge-computingEfficient Compressed Ratio Estimation Using Online Sequential Learning for Edge Computing
Owing to the widespread adoption of the Internet of Things, a vast amount of sensor information is being acquired in real time. Accordingly, the communication cost of data from edge devices is increasing. Compressed sens…
compressed sensingData CompressionEdge-computingreinforcement-learning+13D Harmonic Loss: Towards Task-consistent and Time-friendly 3D Object Detection on Edge for V2X Orchestration
Edge computing-based 3D perception has received attention in intelligent transportation systems (ITS) because real-time monitoring of traffic candidates potentially strengthens Vehicle-to-Everything (V2X) orchestration. …
3D Object DetectionEdge-computingobject-detectionObject DetectionA review of TinyML
In this current technological world, the application of machine learning is becoming ubiquitous. Incorporating machine learning algorithms on extremely low-power and inexpensive embedded devices at the edge level is now …
Decision MakingDiversityEdge-computingA Ferroelectric Tunnel Junction-based Integrate-and-Fire Neuron
Event-based neuromorphic systems provide a low-power solution by using artificial neurons and synapses to process data asynchronously in the form of spikes. Ferroelectric Tunnel Junctions (FTJs) are ultra low-power memor…
Edge-computingA Convergence Theory for Federated Average: Beyond Smoothness
Federated learning enables a large amount of edge computing devices to learn a model without data sharing jointly. As a leading algorithm in this setting, Federated Average FedAvg, which runs Stochastic Gradient Descent …
Edge-computingFederated LearningA 5G Enabled Adaptive Computing Workflow for Greener Power Grid
5G wireless technology can deliver higher data speeds, ultra low latency, more reliability, massive network capacity, increased availability, and a more uniform user experience to users. It brings additional power to hel…
Cloud ComputingEdge-computingGPUOptimal Task Offloading Policy in Edge Computing Systems with Firm Deadlines
The recent drastic increase in mobile data traffic has pushed the mobile edge computing systems to the limit of their capacity. A promising solution to this problem is the task migration provided by unmanned aerial vehic…
Edge-computingHybrid HMM Decoder For Convolutional Codes By Joint Trellis-Like Structure and Channel Prior
The anti-interference capability of wireless links is a physical layer problem for edge computing. Although convolutional codes have inherent error correction potential due to the redundancy introduced in the data, the p…
DecoderEdge-computingDeep Edge Intelligence: Architecture, Key Features, Enabling Technologies and Challenges
With the breakthroughs in Deep Learning, recent years have witnessed a massive surge in Artificial Intelligence applications and services. Meanwhile, the rapid advances in Mobile Computing and Internet of Things has also…
Edge-computingGraph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing
This paper studies the computational offloading of CNN inference in dynamic multi-access edge computing (MEC) networks. To address the uncertainties in communication time and Edge servers' available capacity, we use earl…
Decision MakingDeep Reinforcement LearningEdge-computingreinforcement-learning+2Drastically Reducing the Number of Trainable Parameters in Deep CNNs by Inter-layer Kernel-sharing
Deep convolutional neural networks (DCNNs) have become the state-of-the-art (SOTA) approach for many computer vision tasks: image classification, object detection, semantic segmentation, etc. However, most SOTA networks …
Edge-computingimage-classificationImage Classificationobject-detection+2Frequency of Interest-based Noise Attenuation Method to Improve Anomaly Detection Performance
Accurately extracting driving events is the way to maximize computational efficiency and anomaly detection performance in the tire frictional nose-based anomaly detection task. This study proposes a concise and highly us…
Anomaly DetectionComputational EfficiencyEdge-computingEvent Extraction+1TransVisDrone: Spatio-Temporal Transformer for Vision-based Drone-to-Drone Detection in Aerial Videos
Drone-to-drone detection using visual feed has crucial applications, such as detecting drone collisions, detecting drone attacks, or coordinating flight with other drones. However, existing methods are computationally co…
Computational EfficiencyEdge-computingLoad-aware Dynamic Partition of Deep Neutral Networks for Multi-User Offloading
The emerging edge computing technique provides support for the computation tasks that are delay-sensitive and compute-intensive, such as deep neural network inference, by offloading them from a user-end device to an edge…
Edge-computing