A step towards automated TEM and SEM characterisation using deep learning : nano-tetrahedrons
Convolutional neural networks (CNNs) have attracted huge amount of attentions since the emergence of deep learning, because of their ability to learn and adapt features directly from the input data, and obtain accurate classification. Although CNNs have resulted in a variety of advances in fields pa...
Saved in:
Main Author: | Tang, Si An |
---|---|
Other Authors: | Li Shuzhou |
Format: | Final Year Project |
Language: | English |
Published: |
2019
|
Subjects: | |
Online Access: | http://hdl.handle.net/10356/78727 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
The overview of the impacts of electron radiation on semiconductor failure analysis by SEM, FIB and TEM
by: Liu, Binghai, et al.
Published: (2020) -
Detailed characterisation of TiO2 nano-aggregate morphology using TEM image analysis
by: Manuputty, Manoel Y., et al.
Published: (2020) -
Analysis of TEM data using machine learning methods
by: Muhammed Imran Khairul Alam
Published: (2021) -
Microscopic characterization of FO/PRO membranes : a comparative study of CLSM, TEM and SEM
by: Wang, Yi-Ning, et al.
Published: (2013) -
CORRELATING THERMOELECTRIC PROPERTIES TO NANO STRUCTURES BY ADVANCED STEM/TEM TECHNIQUES
by: YU YONG
Published: (2022)