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| 001 | 1040542 | ||
| 005 | 20250428202211.0 | ||
| 037 | _ | _ | |a FZJ-2025-01916 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Baumeister, Paul F. |0 P:(DE-Juel1)156619 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a Computational and Data Science Seminar |g CaDS |c Jülich |d 2024-09-10 - 2024-09-10 |w Germany |
| 245 | _ | _ | |a Data Compression for Live Transmission Electron Microscopy |f 2024-09-10 - |
| 260 | _ | _ | |c 2024 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a LECTURE_SPEECH |2 ORCID |
| 336 | 7 | _ | |a Talk (non-conference) |b talk |m talk |0 PUB:(DE-HGF)31 |s 1745844343_9744 |2 PUB:(DE-HGF) |x Other |
| 336 | 7 | _ | |a Other |2 DINI |
| 520 | _ | _ | |a Scanning Transmission Electron Microscopy (STEM) has become a powerful imaging technique with resolutions enabling to spot single atoms. STEM devices produce vast data volumes while scanning the probe, so there a need for both, fast processing pipelines and data compression. We present an innovative technique to compress and post-process STEM images on-the-fly providing visual feedback to the scientist operating the microscope. An essential ingredient to this are harmonic function sets allowing to avoid several Fourier transforms in the post-processing pipeline completely. Furthermore, transformation of images into the representation in harmonic functions can be as efficient as matrix-matrix-multiplications on the GPU. |
| 536 | _ | _ | |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5111 |c POF4-511 |f POF IV |x 0 |
| 700 | 1 | _ | |a Bangun, Arya |0 P:(DE-Juel1)184644 |b 1 |u fzj |
| 700 | 1 | _ | |a Clausen, Alexander |0 P:(DE-Juel1)174151 |b 2 |u fzj |
| 700 | 1 | _ | |a Weber, Dieter |0 P:(DE-Juel1)171370 |b 3 |u fzj |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1040542/files/20240910_CaDS_SDLen_slides.pdf |y Restricted |
| 909 | C | O | |o oai:juser.fz-juelich.de:1040542 |p VDB |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)156619 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)184644 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)174151 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)171370 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5111 |x 0 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-8-20210421 |k IAS-8 |l Datenanalyse und Maschinenlernen |x 1 |
| 920 | 1 | _ | |0 I:(DE-Juel1)ER-C-1-20170209 |k ER-C-1 |l Physik Nanoskaliger Systeme |x 2 |
| 920 | 1 | _ | |0 I:(DE-Juel1)ER-C-20211020 |k ER-C |l ER-C 2.0 |x 3 |
| 980 | _ | _ | |a talk |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-8-20210421 |
| 980 | _ | _ | |a I:(DE-Juel1)ER-C-1-20170209 |
| 980 | _ | _ | |a I:(DE-Juel1)ER-C-20211020 |
| 980 | _ | _ | |a UNRESTRICTED |
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