دورية أكاديمية
Machine-learning approach to holographic particle characterization.
| العنوان: | Machine-learning approach to holographic particle characterization. |
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| المؤلفون: | Yevick A, Hannel M, Grier DG |
| المصدر: | Optics express [Opt Express] 2014 Nov 03; Vol. 22 (22), pp. 26884-90. |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't |
| اللغة: | English |
| بيانات الدورية: | Publisher: Optica Publishing Group Country of Publication: United States NLM ID: 101137103 Publication Model: Print Cited Medium: Internet ISSN: 1094-4087 (Electronic) Linking ISSN: 10944087 NLM ISO Abbreviation: Opt Express Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Publication: Washington, DC : Optica Publishing Group Original Publication: Washington, DC : Optical Society of America, 1997- |
| مستخلص: | Holograms of colloidal dispersions encode comprehensive information about individual particles' three-dimensional positions, sizes and optical properties. Extracting that information typically is computationally intensive, and thus slow. Here, we demonstrate that machine-learning techniques based on support vector machines (SVMs) can analyze holographic video microscopy data in real time on low-power computers. The resulting stream of precise particle-resolved tracking and characterization data provides unparalleled insights into the composition and dynamics of colloidal dispersions and enables applications ranging from basic research to process control and quality assurance. |
| تواريخ الأحداث: | Date Created: 20141118 Date Completed: 20150406 Latest Revision: 20181023 |
| رمز التحديث: | 20231215 |
| DOI: | 10.1364/OE.22.026884 |
| PMID: | 25401836 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1094-4087 |
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| DOI: | 10.1364/OE.22.026884 |