A Real-Time Multi-Class Human Activity Monitoring System Using mmWave Radar. [PDF]
Kim D, Lee S, Lee M.
europepmc +1 more source
Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia +1 more
wiley +1 more source
Multi-UAV-Borne Surveillance Radar Trajectory Planning Method Based on Imitation Learning. [PDF]
Gao X, Li M, Guan K, Ge J.
europepmc +1 more source
Radar Detection of Fluctuating Targets under Heavy-Tailed Clutter Using Track-Before-Detect. [PDF]
Gao J, Du J, Wang W.
europepmc +1 more source
AS‐pHopt: An Optimal pH Prediction Model Enhanced by Active Site of Enzymes
To address the low accuracy of enzyme optimal pH (pHopt) prediction, this study develops active site‐based pHopt (AS‐pHopt), a prediction model enhanced by active site information and pseudo‐label prediction. Integrating key structural and physicochemical features affecting enzyme pHopt, AS‐pHopt uses Evolutionary Scale Modeling (ESM)‐2 with active ...
Wenxiang Song +6 more
wiley +1 more source
Radar-Based Fall Detection Using Micro-Doppler Signatures: A Comparative Analysis of YOLO Architectures. [PDF]
Seflek I, Barstuğan M.
europepmc +1 more source
This paper presents a lidar‐based sensor node design and a rule‐based state observer for edge‐based traffic participant tracking. Unlike other state‐of‐the‐art methods, this state observer enables real‐time, CPU‐only edge processing without relying on machine learning approaches.
Simon Schäfer +2 more
wiley +1 more source
FPGA Implementation of a Radar-Based Fall Detection System Using Binarized Convolutional Neural Networks. [PDF]
Cho H, Kang S, Jung Y.
europepmc +1 more source
Concentric Rheostat Decoupled 3D Force‐Sensing Module for Smart Table Tennis Training
A 3D‐printed sensor array intrinsically decouples normal and shear forces through a unique concentric structural design. By integrating piezoresistive, sliding area‐varying capacitive, and concentric rheostat mechanisms, the 12‐sensor module achieves high‐resolution 3D force mapping without complex algorithms.
Zhe Liu +7 more
wiley +1 more source
Robust 3D Multi-Object Tracking via 4D mmWave Radar-Camera Fusion and Disparity-Domain Depth Recovery. [PDF]
Xie Y +6 more
europepmc +1 more source

