# Signal Processing Challenges and Examples for it in-situ Transmission Electron Microscopy

arXiv: https://arxiv.org/abs/2104.08688 · HTML: https://mlsanity.com/paper/2104.08688/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 62 references that are in the map.
- First posted on arXiv 18 Apr 2021.

## Builds on (most cited first, 4 of 4)

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [A survey of sparse representation: algorithms and applications](https://mlsanity.com/paper/1602.07017/) (2016)
- [Robust Sequential Change-Point Detection by Convex Optimization](https://mlsanity.com/paper/1701.06952/) (2017)

## Abstract (arXiv)

Transmission Electron Microscopy (TEM) is a powerful tool for imaging material structure and characterizing material chemistry. Recent advances in data collection technology for TEM have enabled high-volume and high-resolution data collection at a microsecond frame rate. Taking advantage of these advances in data collection rates requires the development and application of data processing tools, including image analysis, feature extraction, and streaming data processing techniques. In this paper, we highlight a few areas in materials science that have benefited from combining signal processing and statistical analysis with data collection capabilities in TEM and present a future outlook on opportunities of integrating signal processing with automated TEM data analysis.