Autoencoder-aided measurement of concentration from a single line of speckle
| Autor: | Bernd Dammann, Mirza Suljagic, Kenan Šehić, Emir Karamehmedovic, Mirza Karamehmedovic |
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| Rok vydání: | 2019 |
| Předmět: |
Computer Science::Machine Learning
Artificial neural network business.industry Pattern recognition Image processing 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences Autoencoder Atomic and Molecular Physics and Optics 010309 optics Support vector machine Statistics::Machine Learning Speckle pattern Optics 0103 physical sciences Principal component analysis Softmax function Artificial intelligence 0210 nano-technology business Classifier (UML) Mathematics |
| Zdroj: | Karamehmedović, M, Sehic, K, Dammann, B, Suljagić, M & Karamehmedović, E 2019, ' Autoencoder-aided measurement of concentration from a single line of speckle ', Optics Express, vol. 27, no. 20, pp. 29098-29123 . https://doi.org/10.1364/OE.27.029098 |
| ISSN: | 1094-4087 |
| Popis: | We demonstrate that a single 6mm line sample of simulated near-field speckleintensity suffices for accurate estimation of the concentration of dielectric micro-particles over a range from 104 to 6 · 106 particles per ml. For this estimation, we analyze the speckle using both standard methods (linear principal component analysis, support vector machine (SVM)) and a neural network, in the form of a sparse stacked autoencoder (SSAE) with a softmax classifier or with an SVM. Using an SSAE with SVM, we classify line speckle samples according to particle concentration with an average accuracy of over 78%, with other methods close behind. |
| Databáze: | OpenAIRE |
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