Real-time Bragg peak analysis using AI@Edge near data source. This is a part towards full HEDM at Edge using AI/ML, in real-time.
This repo hosts code to localize (with sub-pixel accuracy) Bragg peaks from X-ray diffraction frames streamed from EPICS-enabled area detector. BraggNN trained using code in this repo or remote data center AI-system using this distributed workflow, is used to localize Bragg peaks faster than conventional psuedo-Voigt.
For debug and evaluation purpose, one can also use daq-simu-pva.py to simulate data (of given) streamed from the area detector.
If you use this code for your research, please cite our paper(s):
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Zhengchun Liu, Hemant Sharma, J-S. Park, Peter Kenesei, Antonino Miceli, Jonathan Almer, Rajkumar Kettimuthu, and Ian Foster. "BraggNN: fast X-ray Bragg peak analysis using deep learning." IUCrJ 9, no. 1 (2022).
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Zhengchun Liu, Ahsan Ali, Peter Kenesei, Antonino Miceli, Hemant Sharma, Nicholas Schwarz, Dennis Trujillo et al. "Bridging Data Center AI Systems with Edge Computing for Actionable Information Retrieval." In 2021 3rd Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing (XLOOP), pp. 15-23. IEEE, 2021.
Or via bibtex
@article{liu2022braggnn,
title={BraggNN: fast X-ray Bragg peak analysis using deep learning},
author={Liu, Zhengchun and Sharma, Hemant and Park, J-S and Kenesei, Peter and Miceli, Antonino and Almer, Jonathan and Kettimuthu, Rajkumar and Foster, Ian},
journal={IUCrJ},
volume={9},
number={1},
year={2022},
publisher={International Union of Crystallography}
}
@inproceedings{liu2021bridging,
title={Bridging Data Center AI Systems with Edge Computing for Actionable Information Retrieval},
author={Liu, Zhengchun and Ali, Ahsan and Kenesei, Peter and Miceli, Antonino and Sharma, Hemant and Schwarz, Nicholas and Trujillo, Dennis and Yoo, Hyunseung and Coffee, Ryan and Layad, Naoufal and others},
booktitle={2021 3rd Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing (XLOOP)},
pages={15--23},
year={2021},
organization={IEEE}
}