Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook
In the last few years, electron microscopy has experienced a new methodological paradigm
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
The rise of nonnegative matrix factorization: algorithms and applications
YT Guo, QQ Li, CS Liang - Information Systems, 2024 - Elsevier
Although nonnegative matrix factorization (NMF) is widely used, some matrix factorization
methods result in misleading results and waste of computing resources due to lack of timely …
methods result in misleading results and waste of computing resources due to lack of timely …
Deep learning for automated materials characterisation in core-loss electron energy loss spectroscopy
Electron energy loss spectroscopy (EELS) is a well established technique in electron
microscopy that yields information on the elemental content of a sample in a very direct …
microscopy that yields information on the elemental content of a sample in a very direct …
[ΑΝΑΦΟΡΑ][C] Deep learning voor automatische materiaalidentificatie in elektronen energieverlies spectroscopie
A Annys, J Verbeeck, D Jannis