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A Scalable Approach to Internet of Things and Industrial Internet of Things Security: Evaluating Adaptive Self-Adjusting Memory K-Nearest Neighbor for Zero-Day Attack Detection.
Sensors (Basel)
January 2025
African Centre of Excellence for Internet of Things, University of Rwanda, Kigali P.O. Box 4285, Rwanda.
The Internet of Things (IoT) and Industrial Internet of Things (IIoT) have drastically transformed industries by enhancing efficiency and flexibility but have also introduced substantial cybersecurity risks. The rise of zero-day attacks, which exploit unknown vulnerabilities, poses significant threats to these interconnected systems. Traditional signature-based intrusion detection systems (IDSs) are insufficient for detecting such attacks due to their reliance on pre-defined attack signatures.
View Article and Find Full Text PDFEBR-YOLO: A Lightweight Detection Method for Non-Motorized Vehicles Based on Drone Aerial Images.
Sensors (Basel)
January 2025
College of Information Science and Technology, Donghua University, Shanghai 201620, China.
Modern city construction focuses on developing smart transportation, but the recognition of the large number of non-motorized vehicles in the city is still not sufficient. Compared to fixed recognition equipment, drones have advantages in image acquisition due to their flexibility and maneuverability. With the dataset collected from aerial images taken by drones, this study proposed a novel lightweight architecture for small objection detection based on YOLO framework, named EBR-YOLO.
View Article and Find Full Text PDFPerformance Improvement with Reduced Number of Channels in Motor Imagery BCI System.
Sensors (Basel)
December 2024
Department of Electronics and Communication Engineering, Istanbul Technical University, 34467 Istanbul, Istanbul, Turkey.
Classifying Motor Imaging (MI) Electroencephalogram (EEG) signals is of vital importance for Brain-Computer Interface (BCI) systems, but challenges remain. A key challenge is to reduce the number of channels to improve flexibility, portability, and computational efficiency, especially in multi-class scenarios where more channels are needed for accurate classification. This study demonstrates that combining Electrooculogram (EOG) channels with a reduced set of EEG channels is more effective than relying on a large number of EEG channels alone.
View Article and Find Full Text PDFBA-ATEMNet: Bayesian Learning and Multi-Head Self-Attention for Theoretical Denoising of Airborne Transient Electromagnetic Signals.
Sensors (Basel)
December 2024
Information Network Center, Chengdu University, Chengdu 610106, China.
Airborne transient electromagnetic (ATEM) surveys provide a fast, flexible approach for identifying conductive metal deposits across a variety of intricate terrains. Nonetheless, the secondary electromagnetic response signals captured by ATEM systems frequently suffer from numerous noise interferences, which impede effective data processing and interpretation. Traditional denoising methods often fall short in addressing these complex noise backgrounds, leading to less-than-optimal signal extraction.
View Article and Find Full Text PDFIntelligent Sensor Software for Robust and Energy-Sustainable Decision-Making in Welding of Steel Reinforcement for Concrete.
Sensors (Basel)
December 2024
PSYCOTRIP Group, Department of Mathematics and Computation, University of La Rioja, 26006 Logroño, Spain.
In today's industrial landscape, optimizing energy consumption, reducing production times, and maintaining quality standards are critical challenges, particularly in energy-intensive processes like resistance spot welding (RSW). This study introduces an intelligent decision support system designed to optimize the RSW process for steel reinforcement bars. By creating robust machine learning models trained on limited datasets, the system generates interactive heat maps that provide real-time guidance to production engineers or intelligent systems, enabling dynamic adaptation to changing conditions and external factors such as fluctuating energy costs.
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