Results 211 to 220 of about 70,734 (340)

Monitoring of ultra-high performance concrete manufacturing for reproducible quality and waste reduction. [PDF]

Sci Rep
Rezazadeh F, Abrishambaf A, Zimmermann G, Kroll A.   +3 more
europepmc   +1 more source

Compressed Sensing for Efficient Encoding of Dense 3D Meshes Using Model-Based Bayesian Learning

, 2016
Aris S. Lalos, Iason Nikolas, Evangelos Vlachos, Κωνσταντίνος Μουστάκας   +3 more
openalex   +2 more sources

Processing GPR data with 2D Bayesian compressive sensing inverse scattering approaches [PDF]

, 2018
Paolo Rocca, Giorgio Gottardi, Michael Bertolli, F. Robol, Toshifumi Moriyama, Takashi Takenaka   +5 more
openalex   +1 more source

Digital-Twin Enabled "Living Fresco". [PDF]

ACS Omega
Wu S, Chen Q, Hu C, An Y, Han F.
europepmc   +1 more source

Laplace Prior-Based Bayesian Compressive Sensing Using K-SVD for Vibration Signal Transmission and Fault Detection [PDF]

, 2019
Yunfei Ma, Xisheng Jia, Qiwei Hu, Daoming Xu, Chiming Guo, Qiang Wang, Shuangchuan Wang   +6 more
openalex   +1 more source

CARE‐ing for concussions: Development of the Calgary Adapted aRm Ergometer (CARE) exertion test: A physiological alternative to the Calgary Concussion Cycle Test

Experimental Physiology, EarlyView.
Abstract Aerobic exercise testing helps facilitate recovery post‐concussion. Current protocols (Calgary Concussion Cycling Test: CCCT) are inaccessible for athletes with lower‐body impairments (i.e. Para athletes). This study compared physiological parameters for a novel arm crank test, the Calgary Adapted aRm Ergometer (CARE) test, with the CCCT ...
Jonathan D. Smirl, Joshua J. Burkart, Matthew G. Neill, Jean‐Michel Galarneau, Elizabeth K. S. Fletcher, Joel S. Burma, Nathan E. Johnson, John J. Leddy, Mohammad N. Haider, William M. Adams, Cheri Blauwet, Chantel T. Debert, Carolyn A. Emery   +12 more
wiley   +1 more source

Spectral-Based Fault Detection Method in Marine Diesel Engine Operation. [PDF]

Sensors (Basel)
Radić J, Šarić M, Rubić A.
europepmc   +1 more source

SPARSE SIGNAL RECONSTRUCTION FROM LIMITED DATA USING BAYESIAN COMPRESSIVE SENSING VIA BELIEF PROPAGATION

, 2019
Prateek Paliwal, Manish Sharma
openalex   +1 more source

Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection.
Xu Wang, Daeun Choi, Damian Adams, Jaehyun Ahn, Kelly Balmant, Raquel Dias, Carlos D. Messina, Rafael Muñoz‐Carpena, Vance M. Whitaker, Haipeng Yu, Ziwen Yu, Chang Zhao, Changying Li   +12 more
wiley   +1 more source

AI-Powered Structural Health Monitoring Using Multi-Type and Multi-Position PZT Networks. [PDF]

Sensors (Basel)
Gharavi H, Taban F, Korivand S, Jalili N.   +3 more
europepmc   +1 more source