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UAV Trajectory Control and Power Optimization for Low-Latency C-V2X Communications in a Federated Learning Environment.
Sensors (Basel)
December 2024
Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B2K3, Canada.
Unmanned aerial vehicle (UAV)-enabled vehicular communications in the sixth generation (6G) are characterized by line-of-sight (LoS) and dynamically varying channel conditions. However, the presence of obstacles in the LoS path leads to shadowed fading environments. In UAV-assisted cellular vehicle-to-everything (C-V2X) communication, vehicle and UAV mobility and shadowing adversely impact latency and throughput.
View Article and Find Full Text PDFHigh-Performance CP Magneto-Electric Dipole Antenna Fed by Printed Ridge Gap Waveguide at Millimeter-Wave.
Sensors (Basel)
December 2024
Centre-Energie Materiaux et Telecommunications, Institut National de la Recherche Scientifique, Montreal, QC H5A 1K6, Canada.
This paper presents a high-performance circularly polarized (CP) magneto-electric (ME) dipole antenna optimized for wideband millimeter-wave (mm-wave) frequencies, specifically targeting advancements in 5G and 6G technologies. The CP antenna is excited through a transverse slot in a printed ridge gap waveguide (PRGW), which operates in a quasi-transverse electromagnetic (Q-TEM) mode. Fabricated on Rogers RT 3003 substrate, selected for its low-loss and cost-effective properties at high frequencies, the design significantly enhances both impedance and axial ratio (AR) bandwidths.
View Article and Find Full Text PDFEnhancing Deep-Learning Classification for Remote Motor Imagery Rehabilitation Using Multi-Subject Transfer Learning in IoT Environment.
Sensors (Basel)
December 2024
College of Computer and Information Sciences (CCIS), King Saud University, Riyadh 11543, Saudi Arabia.
One of the most promising applications for electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is motor rehabilitation through motor imagery (MI) tasks. However, current MI training requires physical attendance, while remote MI training can be applied anywhere, facilitating flexible rehabilitation. Providing remote MI training raises challenges to ensuring an accurate recognition of MI tasks by healthcare providers, in addition to managing computation and communication costs.
View Article and Find Full Text PDFDetermination of the Bentonite Content in Molding Sands Using AI-Enhanced Electrical Impedance Spectroscopy.
Sensors (Basel)
December 2024
Faculty of Engineering Science, University of Bayreuth, 95440 Bayreuth, Germany.
Molding sand mixtures in the foundry industry are typically composed of fresh and reclaimed sands, water, and additives such as bentonite. Optimizing the control of these mixtures and the recycling of used sand after casting requires an efficient in-line monitoring method, which is currently unavailable. This study explores the potential of an AI-enhanced electrical impedance spectroscopy (EIS) system as a solution.
View Article and Find Full Text PDFEnhancing Greenhouse Efficiency: Integrating IoT and Reinforcement Learning for Optimized Climate Control.
Sensors (Basel)
December 2024
Informática Industrial y Redes de Computadores (I2RC), University of Alicante, 03690 Alicante, Spain.
Automated systems, regulated by algorithmic protocols and predefined set-points for feedback control, require the oversight and fine tuning of skilled technicians. This necessity is particularly pronounced in automated greenhouses, where optimal environmental conditions depend on the specialized knowledge of dedicated technicians, emphasizing the need for expert involvement during installation and maintenance. To address these challenges, this study proposes the integration of data acquisition technologies using Internet of Things (IoT) protocols and optimization services via reinforcement learning (RL) methodologies.
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