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231224s2017 xx |||||o 00| ||eng c |
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|a 10.1107/S1600577517000157
|2 doi
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|a pubmed24n0897.xml
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Wright, Christopher J
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Computer-assisted area detector masking
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1 |
|c 2017
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 11.07.2017
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|a Date Revised 13.07.2017
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Area detectors have become the predominant type of detector for the rapid acquisition of X-ray diffraction, small-angle scattering and total scattering. These detectors record the scattering for a large area, giving each shot good statistical significance to the resulting scattered intensity I(Q) pattern. However, many of these detectors have pixel level defects, which cause error in the resulting one-dimensional patterns. In this work, new software to automatically find and mask these dead pixels and other defects is presented. This algorithm is benchmarked with both ideal simulated and experimental datasets
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|a Journal Article
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|a Research Support, U.S. Gov't, Non-P.H.S.
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|a Python
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|a area detector
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| 650 |
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|a masking
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| 700 |
1 |
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|a Zhou, Xiao Dong
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Journal of synchrotron radiation
|d 1994
|g 24(2017), Pt 2 vom: 01. März, Seite 506-508
|w (DE-627)NLM09824129X
|x 1600-5775
|7 nnns
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| 773 |
1 |
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|g volume:24
|g year:2017
|g number:Pt 2
|g day:01
|g month:03
|g pages:506-508
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| 856 |
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|u http://dx.doi.org/10.1107/S1600577517000157
|3 Volltext
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|a AR
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|d 24
|j 2017
|e Pt 2
|b 01
|c 03
|h 506-508
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