|
MATHEMATICAL AND SOFTWARE OF COMPUTЕRS, COMPLEXES AND COMPUTER NETWORKS
Advanced electron microscopy image processing for analyzing amorphous alloys: Electron Microscopy Image Cluster Analyzer (EMICA). Tool and results
D. S. Dilla, E. V. Pustovalov, A. N. Fedorets Institute of Mathematics and Computer Technologies, Far Eastern Federal University
Abstract:
This article unveils EMICA, a Python-based software tool revolutionizing electron microscopy image processing for amorphous alloys. EMICA addresses the unique challenges posed by these materials, which lack long-range order, by providing specialized capabilities for cluster analysis and spatial pattern recognition. This research explored software tool development and application through illustrative examples, answering the key question of how they enhance amorphous alloy analysis. By integrating advanced image processing techniques and algorithms, EMICA uncovers hidden patterns, offering quantitative insights into cluster distributions. The key message emphasizes the application's transformative impact on material science research, providing a specialized solution for electron microscopy image analysis in the amorphous alloy domain. Our key findings, presented through real-world examples and case studies, attest to the efficacy of the software in revealing nuanced details of amorphous alloy structures. From identifying subtle variations in atomic configurations to quantifying cluster distributions, EMICA represents a significant leap forward in the field of advanced electron microscopy image processing, contributing significantly to the advancement of this domain.
Keywords:
amorphous alloys, electron microscopy, cluster analysis, clustering, software tools, algorithms.
Citation:
D. S. Dilla, E. V. Pustovalov, A. N. Fedorets, “Advanced electron microscopy image processing for analyzing amorphous alloys: Electron Microscopy Image Cluster Analyzer (EMICA). Tool and results”, Comp. nanotechnol., 11:1 (2024), 104–111
Linking options:
https://www.mathnet.ru/eng/cn464 https://www.mathnet.ru/eng/cn/v11/i1/p104
|
Statistics & downloads: |
Abstract page: | 14 | Full-text PDF : | 14 |
|