Structure of МеSi Silicide Films (Me: Li, Rb, K and Cs) According to Electron Microscopy Data and the Diffraction of Slow Electrons

2020 ◽  
Vol 14 (5) ◽  
pp. 1066-1071
Author(s):  
M. T. Normuradov ◽  
A. S. Risbaev ◽  
J. B. Khujaniyozov ◽  
D. A. Normuradov
Keyword(s):  
Electron Microscopy ◽  
Electron Microscopy Data ◽  
Microscopy Data ◽  
Slow Electrons

scholarly journals Label-free fluorescence predictions from large-scale correlative light and electron microscopy data

2021 ◽  
Vol 27 (S1) ◽  
pp. 94-95
Author(s):  
Ryan Lane ◽  
Luuk Balkenende ◽  
Simon van Staalduine ◽  
Anouk Wolters ◽  
Ben Giepmans ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Large Scale ◽  
Light And Electron Microscopy ◽  
Label Free ◽  
Electron Microscopy Data ◽  
Microscopy Data

scholarly journals Automatic instance segmentation of mitochondria in electron microscopy data

2021 ◽  
Author(s):  
Luke Nightingale ◽  
Joost de Folter ◽  
Helen Spiers ◽  
Amy Strange ◽  
Lucy M Collinson ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Large Scale ◽  
Machine Learning Algorithms ◽  
Public Repository ◽  
Post Processing ◽  
Human Cortex ◽  
Electron Microscopy Data ◽  
Processing Procedure ◽  
Microscopy Data ◽  
Instance Segmentation

We present a new method for rapid, automated, large-scale 3D mitochondria instance segmentation, developed in response to the ISBI 2021 MitoEM Challenge. In brief, we trained separate machine learning algorithms to predict (1) mitochondria areas and (2) mitochondria boundaries in image volumes acquired from both rat and human cortex with multi-beam scanning electron microscopy. The predictions from these algorithms were combined in a multi-step post-processing procedure, that resulted in high semantic and instance segmentation performance. All code is provided via a public repository.

scholarly journals Влияние температуры деформации в камере Бриджмена на особенности формирования дефектной структуры технически чистого титана

2018 ◽  
Vol 44 (20) ◽  
pp. 70
Author(s):  
Н.А. Шурыгина ◽  
А.М. Глезер ◽  
Д.Л. Дьяконов ◽  
А.А. Томчук ◽  
А.Г. Кадомцев ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Hydrostatic Pressure ◽  
High Hydrostatic Pressure ◽  
Defect Structures ◽  
Cryogenic Temperatures ◽  
Two Phase ◽  
Α Phase ◽  
Electron Microscopy Data ◽  
Phase Mixture ◽  
Microscopy Data

AbstractTransmission electron microscopy data showed evidence of the formation of structural regions corresponding to deformation (dislocated) fragments and dynamically recrystallized grains in α-phase titanium upon torsion at high hydrostatic pressure at room and cryogenic temperatures. It is shown that the previously proposed “two-phase mixture” model is applicable to description of these defect structures.

PM-02 Analysis of Lithium Ion Battery Materials Using Statistical Machine Learning and Image Processing of Electron Microscopy Data

Microscopy ◽  
2019 ◽  
Vol 68 (Supplement_1) ◽  
pp. i35-i35
Author(s):  
Hiromochi Tanaka ◽  
Tetsushi Watari ◽  
Takahiro Tsubouchi ◽  
Hisao Yamashige ◽  
Takashi Kato ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Machine Learning ◽  
Image Processing ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Statistical Machine Learning ◽  
Battery Materials ◽  
Electron Microscopy Data ◽  
Microscopy Data
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