دورية أكاديمية

Deep learning at the edge enables real-time streaming ptychographic imaging

التفاصيل البيبلوغرافية
العنوان: Deep learning at the edge enables real-time streaming ptychographic imaging
المؤلفون: Anakha V. Babu, Tao Zhou, Saugat Kandel, Tekin Bicer, Zhengchun Liu, William Judge, Daniel J. Ching, Yi Jiang, Sinisa Veseli, Steven Henke, Ryan Chard, Yudong Yao, Ekaterina Sirazitdinova, Geetika Gupta, Martin V. Holt, Ian T. Foster, Antonino Miceli, Mathew J. Cherukara
المصدر: Nature Communications, Vol 14, Iss 1, Pp 1-9 (2023)
بيانات النشر: Nature Portfolio, 2023.
سنة النشر: 2023
المجموعة: LCC:Science
مصطلحات موضوعية: Science
الوصف: Abstract Coherent imaging techniques provide an unparalleled multi-scale view of materials across scientific and technological fields, from structural materials to quantum devices, from integrated circuits to biological cells. Driven by the construction of brighter sources and high-rate detectors, coherent imaging methods like ptychography are poised to revolutionize nanoscale materials characterization. However, these advancements are accompanied by significant increase in data and compute needs, which precludes real-time imaging, feedback and decision-making capabilities with conventional approaches. Here, we demonstrate a workflow that leverages artificial intelligence at the edge and high-performance computing to enable real-time inversion on X-ray ptychography data streamed directly from a detector at up to 2 kHz. The proposed AI-enabled workflow eliminates the oversampling constraints, allowing low-dose imaging using orders of magnitude less data than required by traditional methods.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2041-1723
Relation: https://doaj.org/toc/2041-1723
DOI: 10.1038/s41467-023-41496-z
URL الوصول: https://doaj.org/article/0be7c60ee09a488f891cd431678364da
رقم الأكسشن: edsdoj.0be7c60ee09a488f891cd431678364da
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20411723
DOI:10.1038/s41467-023-41496-z