Adaptive client participation mechanism for federated learning in heterogeneous vehicular networks. [PDF]
Sci RepLin W, Zhou Y.
europepmc +1 more source
A Multi-Agent Deep Reinforcement Learning-Based Popular Content Distribution Scheme in Vehicular Networks. [PDF]
Entropy (Basel), 2023 Chen W, Huang X, Guan Q, Zhao S.
europepmc +1 more source
Advanced Robotics Research, EarlyView.This work presents a robotic control method for human–robot collaborative assembly based on a biomechanics‐constrained digital human model. Reinforcement learning is used to generate physiologically plausible human motion trajectories, which are integrated into a virtual environment for robot control learning.
Bitao Yao +4 more
wiley +1 more source
Deep Learning-Based Dynamic Time Division ISAC Beamforming for Vehicular Networks. [PDF]
Sensors (Basel)Lim J, So J.
europepmc +1 more source
Deep Reinforcement Learning-Based Coordinated Beamforming for mmWave Massive MIMO Vehicular Networks. [PDF]
Sensors (Basel), 2023 Tarafder P, Choi W.
europepmc +1 more source
Multimodal Human–Robot Interaction Using Human Pose Estimation and Local Large Language Models
Advanced Robotics Research, EarlyView.A multimodal human–robot interaction framework integrates human pose estimation (HPE) and a large language model (LLM) for gesture‐ and voice‐based robot control. Speech‐to‐text (STT) enables voice command interpretation, while a safety‐aware arbitration mechanism prioritizes gesture input for rapid intervention.
Nasiru Aboki +2 more
wiley +1 more source
Authentication Mechanisms in the Internet of Things: A Comparative Analysis Across RFID, Smart Grids, Vehicular Networks, and Smart Homes. [PDF]
Sensors (Basel)Ayyamgari S +7 more
europepmc +1 more source
Intelligent Terrestrial and Non-Terrestrial Vehicular Networks with Green AI and Red AI Perspectives. [PDF]
Sensors (Basel), 2023 Kim H, Ben-Othman J, Mokdad L.
europepmc +1 more source
Data‐Driven Bulldozer Blade Control for Autonomous Terrain Leveling
Advanced Robotics Research, EarlyView.A simulation‐driven framework for autonomous bulldozer leveling is presented, combining high‐fidelity terramechanics simulation with a neural‐network‐based reduced‐order model. Gradient‐based optimization enables efficient, low‐level blade control that balances leveling quality and operation time.
Harry Zhang +5 more
wiley +1 more source
Deep Reinforcement Learning for Secure and Low-Latency Communications in UAV-Mounted STAR-RIS Assisted Urban Vehicular Networks. [PDF]
Sensors (Basel)Tang J, Yuan J, Zhao H, Chen M, Peng Y.
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