تقرير
Nanoparticle Classification in Wide-field Interferometric Microscopy by Supervised Learning from Model
| العنوان: | Nanoparticle Classification in Wide-field Interferometric Microscopy by Supervised Learning from Model |
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| المؤلفون: | Avci, Oguzhan, Yurdakul, Celalettin, Unlu, M. Selim |
| سنة النشر: | 2017 |
| المجموعة: | Physics (Other) |
| مصطلحات موضوعية: | Physics - Computational Physics |
| الوصف: | Interference enhanced wide-field nanoparticle imaging is a highly sensitive technique that has found numerous applications in labeled and label-free sub-diffraction-limited pathogen detection. It also provides unique opportunities for nanoparticle classification upon detection. More specif- ically, the nanoparticle defocus images result in a particle-specific response that can be of great utility for nanoparticle classification, particularly based on type and size. In this work, we com- bine a model based supervised learning algorithm with a wide-field common-path interferometric microscopy method to achieve accurate nanoparticle classification. We verify our classification schemes experimentally by using gold and polystyrene nanospheres. Comment: 5 pages, 2 figures |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1364/AO.56.004238 |
| URL الوصول: | http://arxiv.org/abs/1703.02997 |
| رقم الأكسشن: | edsarx.1703.02997 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1364/AO.56.004238 |
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