Papers Edge-computing
“Edge-computing” 태그가 달린 논문 1,279편 · 필터 해제
Mobile-Seed: Joint Semantic Segmentation and Boundary Detection for Mobile Robots
Precise and rapid delineation of sharp boundaries and robust semantics is essential for numerous downstream robotic tasks, such as robot grasping and manipulation, real-time semantic mapping, and online sensor calibratio…
Boundary DetectionEdge-computingGPUReal-Time Semantic Segmentation+1Exploring Lip Segmentation Techniques in Computer Vision: A Comparative Analysis
Lip segmentation is crucial in computer vision, especially for lip reading. Despite extensive face segmentation research, lip segmentation has received limited attention. The aim of this study is to compare state-of-the-…
Edge-computingLip ReadingSegmentationDigital Twin-Based User-Centric Edge Continual Learning in Integrated Sensing and Communication
In this paper, we propose a digital twin (DT)-based user-centric approach for processing sensing data in an integrated sensing and communication (ISAC) system with high accuracy and efficient resource utilization. The co…
Continual LearningEdge-computingIntegrated sensing and communicationISACA Novel Neural Network-Based Federated Learning System for Imbalanced and Non-IID Data
With the growth of machine learning techniques, privacy of data of users has become a major concern. Most of the machine learning algorithms rely heavily on large amount of data which may be collected from various source…
Edge-computingFederated LearningFedFusion: Manifold Driven Federated Learning for Multi-satellite and Multi-modality Fusion
Multi-satellite, multi-modality in-orbit fusion is a challenging task as it explores the fusion representation of complex high-dimensional data under limited computational resources. Deep neural networks can reveal the u…
Edge-computingFederated LearningNeuroscience inspired scientific machine learning (Part-2): Variable spiking wavelet neural operator
We propose, in this paper, a Variable Spiking Wavelet Neural Operator (VS-WNO), which aims to bridge the gap between theoretical and practical implementation of Artificial Intelligence (AI) algorithms for mechanics appli…
Edge-computingA Quality-of-Service Compliance System using Federated Learning and Optimistic Rollups
Edge computing brings a new paradigm in which the sharing of computing, storage, and bandwidth resources as close as possible to the mobile devices or sensors generating a large amount of data. A parallel trend is the ri…
Edge-computingFederated LearningJoint Computation and Communication Resource Optimization for Beyond Diagonal UAV-IRS Empowered MEC Networks
Recent advancements in 6G systems signal a leap towards universal connectivity and ultra-reliable, low-latency communications for real-time data devices. Yet, these advancements encounter obstacles such as limited device…
Edge-computingNeural Network Methods for Radiation Detectors and Imaging
Recent advances in image data processing through machine learning and especially deep neural networks (DNNs) allow for new optimization and performance-enhancement schemes for radiation detectors and imaging hardware thr…
Deep LearningEdge-computingEfficient and Low-Footprint Object Classification using Spatial Contrast
Event-based vision sensors traditionally compute temporal contrast that offers potential for low-power and low-latency sensing and computing. In this research, an alternative paradigm for event-based sensors using locali…
Edge-computingEvent-based visionObjectQECO: A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning for Mobile Edge Computing
In the realm of mobile edge computing (MEC), efficient computation task offloading plays a pivotal role in ensuring a seamless quality of experience (QoE) for users. Maintaining a high QoE is paramount in today's interco…
Deep Reinforcement LearningEdge-computingUltra-Efficient On-Device Object Detection on AI-Integrated Smart Glasses with TinyissimoYOLO
Smart glasses are rapidly gaining advanced functionality thanks to cutting-edge computing technologies, accelerated hardware architectures, and tiny AI algorithms. Integrating AI into smart glasses featuring a small form…
BenchmarkingEdge-computingimage-classificationImage Classification+3A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainability
Edge Intelligence (EI) integrates Edge Computing (EC) and Artificial Intelligence (AI) to push the capabilities of AI to the network edge for real-time, efficient and secure intelligent decision-making and computation. H…
Decision MakingEdge-computingOrchestration of Emulator Assisted Mobile Edge Tuning for AI Foundation Models: A Multi-Agent Deep Reinforcement Learning Approach
The efficient deployment and fine-tuning of foundation models are pivotal in contemporary artificial intelligence. In this study, we present a groundbreaking paradigm integrating Mobile Edge Computing (MEC) with foundati…
Computational EfficiencyDeep Reinforcement LearningEdge-computingFedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing
The emergence of foundation models, including language and vision models, has reshaped AI's landscape, offering capabilities across various applications. Deploying and fine-tuning these large models, like GPT-3 and BERT,…
Deep Reinforcement LearningEdge-computingFederated Learningparameter-efficient fine-tuningAdaptive Resource Management for Edge Network Slicing using Incremental Multi-Agent Deep Reinforcement Learning
Multi-access edge computing provides local resources in mobile networks as the essential means for meeting the demands of emerging ultra-reliable low-latency communications. At the edge, dynamic computing requests requir…
Deep Reinforcement LearningEdge-computingIncremental LearningManagement+1Adaptive Digital Twin for UAV-Assisted Integrated Sensing, Communication, and Computation Networks
In this paper, we study a digital twin (DT)-empowered integrated sensing, communication, and computation network. Specifically, the users perform radar sensing and computation offloading on the same spectrum, while unman…
Edge-computingAn Efficient Imbalance-Aware Federated Learning Approach for Wearable Healthcare with Autoregressive Ratio Observation
Widely available healthcare services are now getting popular because of advancements in wearable sensing techniques and mobile edge computing. People's health information is collected by edge devices such as smartphones …
Edge-computingFederated LearningPoster: Real-Time Object Substitution for Mobile Diminished Reality with Edge Computing
Diminished Reality (DR) is considered as the conceptual counterpart to Augmented Reality (AR), and has recently gained increasing attention from both industry and academia. Unlike AR which adds virtual objects to the rea…
Edge-computingObjectLC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding
The biological neurons use precise spike times, in addition to the spike firing rate, to communicate with each other. The time-to-first-spike (TTFS) coding is inspired by such biological observation. However, there is a …
Edge-computingimage-classificationImage ClassificationImage Reconstruction+1