Results 221 to 230 of about 89,157 (306)
Acoustic monitoring with miniature drones shows reduced Myotis bat occurrence with altitude and drone movement. [PDF]
Dobie L, Bird DM, Elliott KH.
europepmc +1 more source
Flexible tactile sensors have considerable potential for broad application in healthcare monitoring, human–machine interfaces, and bioinspired robotics. This review explores recent progress in device design, performance optimization, and intelligent applications. It highlights how AI algorithms enhance environmental adaptability and perception accuracy
Siyuan Wang +3 more
wiley +1 more source
Winter is (not) coming: Acoustic monitoring and temperature variation across important bat hibernacula. [PDF]
Toshkova N +4 more
europepmc +1 more source
Machine Learning Driven Inverse Design of Broadband Acoustic Superscattering
Multilayer acoustic superscatterers are designed using machine learning to achieve broadband superscattering and strong sound insulation. By incorporating a weighted mean absolute error into the loss function, the forward and inverse neural networks accurately map structural parameters to spectral responses.
Lijuan Fan, Xiangliang Zhang, Ying Wu
wiley +1 more source
Passive acoustic monitoring of baleen whale seasonal presence across the New York Bight. [PDF]
Estabrook BJ +10 more
europepmc +1 more source
This article implements a unified human digital twin framework that integrates cutting edge actuation, sensing, simulation, and bidirectional feedback capability. The approach includes integrating multimodal sensing, AI, and biomechanical simulation into one compact system.
Tajbeed Ahmed Chowdhury +4 more
wiley +1 more source
Soundscape and fish passive acoustic monitoring around a North Sea gas-production platform in the Dogger Bank. [PDF]
Bolgan M, Bhalla SJ, Todd IB, Todd VLG.
europepmc +1 more source
This study proposes a deep learning approach to evaluate the fatigue crack behavior in metals under overload conditions. Using digital image correlation to capture the strain near crack tips, convolutional neural networks classify crack states as normal, overload, or recovery, and accurately predict fatigue parameters.
Seon Du Choi +5 more
wiley +1 more source

