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The modern structurator: increased performance for calculating the structure function

The autocorrelation function is a statistical tool that is often combined with dynamic light scattering (DLS) techniques to investigate the dynamical behavior of the scattered light fluctuations in order to measure, for example, the diffusive behavior of transparent particles dispersed in a fluid. A...

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Autores principales: Norouzisadeh, Mojtaba, Chraga, Mohammed, Cerchiari, Giovanni, Croccolo, Fabrizio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8639561/
https://www.ncbi.nlm.nih.gov/pubmed/34855019
http://dx.doi.org/10.1140/epje/s10189-021-00146-2
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author Norouzisadeh, Mojtaba
Chraga, Mohammed
Cerchiari, Giovanni
Croccolo, Fabrizio
author_facet Norouzisadeh, Mojtaba
Chraga, Mohammed
Cerchiari, Giovanni
Croccolo, Fabrizio
author_sort Norouzisadeh, Mojtaba
collection PubMed
description The autocorrelation function is a statistical tool that is often combined with dynamic light scattering (DLS) techniques to investigate the dynamical behavior of the scattered light fluctuations in order to measure, for example, the diffusive behavior of transparent particles dispersed in a fluid. An alternative approach to the autocorrelation function for the analysis of DLS data has been proposed decades ago and consists of calculating the autocorrelation function starting from difference of the signal at different times by using the so-called structure function. The structure function approach has been proven to be more robust than the autocorrelation function method in terms of noise and drift rejection. Therefore, the structure function analysis has gained visibility, in particular in combination with imaging techniques such as dynamic shadowgraphy and differential dynamic microscopy. Here, we show how the calculation of the structure function over thousands of images, typical of such techniques, can be accelerated, with the aim of achieving real-time analysis. The acceleration is realized by taking advantage of the Wiener–Khinchin theorem, i.e., by calculating the difference of images through Fourier transform in time. The new algorithm was tested both on CPU and GPU hardware, showing that the acceleration is particularly large in the case of CPU.
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spelling pubmed-86395612021-12-03 The modern structurator: increased performance for calculating the structure function Norouzisadeh, Mojtaba Chraga, Mohammed Cerchiari, Giovanni Croccolo, Fabrizio Eur Phys J E Soft Matter Tips and Tricks - Flowing Matter The autocorrelation function is a statistical tool that is often combined with dynamic light scattering (DLS) techniques to investigate the dynamical behavior of the scattered light fluctuations in order to measure, for example, the diffusive behavior of transparent particles dispersed in a fluid. An alternative approach to the autocorrelation function for the analysis of DLS data has been proposed decades ago and consists of calculating the autocorrelation function starting from difference of the signal at different times by using the so-called structure function. The structure function approach has been proven to be more robust than the autocorrelation function method in terms of noise and drift rejection. Therefore, the structure function analysis has gained visibility, in particular in combination with imaging techniques such as dynamic shadowgraphy and differential dynamic microscopy. Here, we show how the calculation of the structure function over thousands of images, typical of such techniques, can be accelerated, with the aim of achieving real-time analysis. The acceleration is realized by taking advantage of the Wiener–Khinchin theorem, i.e., by calculating the difference of images through Fourier transform in time. The new algorithm was tested both on CPU and GPU hardware, showing that the acceleration is particularly large in the case of CPU. Springer Berlin Heidelberg 2021-12-02 2021 /pmc/articles/PMC8639561/ /pubmed/34855019 http://dx.doi.org/10.1140/epje/s10189-021-00146-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Tips and Tricks - Flowing Matter
Norouzisadeh, Mojtaba
Chraga, Mohammed
Cerchiari, Giovanni
Croccolo, Fabrizio
The modern structurator: increased performance for calculating the structure function
title The modern structurator: increased performance for calculating the structure function
title_full The modern structurator: increased performance for calculating the structure function
title_fullStr The modern structurator: increased performance for calculating the structure function
title_full_unstemmed The modern structurator: increased performance for calculating the structure function
title_short The modern structurator: increased performance for calculating the structure function
title_sort modern structurator: increased performance for calculating the structure function
topic Tips and Tricks - Flowing Matter
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8639561/
https://www.ncbi.nlm.nih.gov/pubmed/34855019
http://dx.doi.org/10.1140/epje/s10189-021-00146-2
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