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Edge-computing

Deep Learning on EDGE devices

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Showing 576–600 of 1279 papers

TitleStatusHype
Consensus Based Multi-Layer Perceptrons for Edge Computing—0
Fair Computation Offloading for RSMA-Assisted Mobile Edge Computing Networks—0
Fail-Safe Human Detection for Drones Using a Multi-Modal Curriculum Learning Approach—0
Conformer-Based Speech Recognition On Extreme Edge-Computing Devices—0
A Primer on Techtile: An R&D Testbed for Distributed Communication, Sensing and Positioning—0
Exploring Scaling Laws for Local SGD in Large Language Model Training—0
Exploring Neural Network Pruning with Screening Methods—0
Computation Rate Maximum for Mobile Terminals in UAV-assisted Wireless Powered MEC Networks with Fairness Constraint—0
DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey—0
FallDeF5: A Fall Detection Framework Using 5G-based Deep Gated Recurrent Unit Networks—0
False Data Injection Attack Detection in Edge-based Smart Metering Networks with Federated Learning—0
Exploring Lip Segmentation Techniques in Computer Vision: A Comparative Analysis—0
Computation Rate Maximization in UAV-Enabled Wireless Powered Mobile-Edge Computing Systems—0
Fast Cerebral Blood Flow Analysis via Extreme Learning Machine—0
Fast-Convergent Dynamics for Distributed Allocation of Resources Over Switching Sparse Networks with Quantized Communication Links—0
Fast Deep Autoencoder for Federated learning—0
Fast fluorescence lifetime imaging analysis via extreme learning machine—0
FASTNav: Fine-tuned Adaptive Small-language-models Trained for Multi-point Robot Navigation—0
Fast Wireless Sensor Anomaly Detection based on Data Stream in Edge Computing Enabled Smart Greenhouse—0
A Portable, Self-Contained Neuroprosthetic Hand with Deep Learning-Based Finger Control—0
F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Clouds—0
Exploration of TPUs for AI Applications—0
Exploiting Heterogeneity in Timescales for Sparse Recurrent Spiking Neural Networks for Energy-Efficient Edge Computing—0
Explainability and Continual Learning meet Federated Learning at the Network Edge—0
Computation Offloading in Multi-Access Edge Computing Networks: A Multi-Task Learning Approach—0
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