Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE)
<jats:title>Abstract</jats:title><jats:p>Extracting quantitative information about highly scattering surfaces from an imaging system is challenging because the phase of the scattered light undergoes multiple folds upon propagation, resulting in complex speckle patterns. One specifi...
| Main Authors: | , , , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Science and Business Media LLC
2023
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| Online Access: | https://hdl.handle.net/1721.1/150777 |
| _version_ | 1826204977769480192 |
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| author | Zhang, Qihang Gamekkanda, Janaka C Pandit, Ajinkya Tang, Wenlong Papageorgiou, Charles Mitchell, Chris Yang, Yihui Schwaerzler, Michael Oyetunde, Tolutola Braatz, Richard D Myerson, Allan S Barbastathis, George |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Zhang, Qihang Gamekkanda, Janaka C Pandit, Ajinkya Tang, Wenlong Papageorgiou, Charles Mitchell, Chris Yang, Yihui Schwaerzler, Michael Oyetunde, Tolutola Braatz, Richard D Myerson, Allan S Barbastathis, George |
| author_sort | Zhang, Qihang |
| collection | MIT |
| description | <jats:title>Abstract</jats:title><jats:p>Extracting quantitative information about highly scattering surfaces from an imaging system is challenging because the phase of the scattered light undergoes multiple folds upon propagation, resulting in complex speckle patterns. One specific application is the drying of wet powders in the pharmaceutical industry, where quantifying the particle size distribution (PSD) is of particular interest. A non-invasive and real-time monitoring probe in the drying process is required, but there is no suitable candidate for this purpose. In this report, we develop a theoretical relationship from the PSD to the speckle image and describe a physics-enhanced autocorrelation-based estimator (PEACE) machine learning algorithm for speckle analysis to measure the PSD of a powder surface. This method solves both the forward and inverse problems together and enjoys increased interpretability, since the machine learning approximator is regularized by the physical law.</jats:p> |
| first_indexed | 2024-09-23T13:05:09Z |
| format | Article |
| id | mit-1721.1/150777 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:05:09Z |
| publishDate | 2023 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1507772023-05-20T03:24:22Z Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE) Zhang, Qihang Gamekkanda, Janaka C Pandit, Ajinkya Tang, Wenlong Papageorgiou, Charles Mitchell, Chris Yang, Yihui Schwaerzler, Michael Oyetunde, Tolutola Braatz, Richard D Myerson, Allan S Barbastathis, George Massachusetts Institute of Technology. Department of Mechanical Engineering <jats:title>Abstract</jats:title><jats:p>Extracting quantitative information about highly scattering surfaces from an imaging system is challenging because the phase of the scattered light undergoes multiple folds upon propagation, resulting in complex speckle patterns. One specific application is the drying of wet powders in the pharmaceutical industry, where quantifying the particle size distribution (PSD) is of particular interest. A non-invasive and real-time monitoring probe in the drying process is required, but there is no suitable candidate for this purpose. In this report, we develop a theoretical relationship from the PSD to the speckle image and describe a physics-enhanced autocorrelation-based estimator (PEACE) machine learning algorithm for speckle analysis to measure the PSD of a powder surface. This method solves both the forward and inverse problems together and enjoys increased interpretability, since the machine learning approximator is regularized by the physical law.</jats:p> 2023-05-19T13:34:24Z 2023-05-19T13:34:24Z 2023-03-01 2023-05-19T13:31:48Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/150777 Zhang, Qihang, Gamekkanda, Janaka C, Pandit, Ajinkya, Tang, Wenlong, Papageorgiou, Charles et al. 2023. "Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE)." Nature Communications, 14 (1). en 10.1038/s41467-023-36816-2 Nature Communications Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media LLC Nature |
| spellingShingle | Zhang, Qihang Gamekkanda, Janaka C Pandit, Ajinkya Tang, Wenlong Papageorgiou, Charles Mitchell, Chris Yang, Yihui Schwaerzler, Michael Oyetunde, Tolutola Braatz, Richard D Myerson, Allan S Barbastathis, George Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE) |
| title | Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE) |
| title_full | Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE) |
| title_fullStr | Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE) |
| title_full_unstemmed | Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE) |
| title_short | Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE) |
| title_sort | extracting particle size distribution from laser speckle with a physics enhanced autocorrelation based estimator peace |
| url | https://hdl.handle.net/1721.1/150777 |
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