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Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems
Many of the next generation of adaptive optics systems on large and extremely large telescopes require tomographic techniques in order to correct for atmospheric turbulence over a large field of view. Multi-object adaptive optics is one such technique. In this paper, different implementations of a t...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5492298/ https://www.ncbi.nlm.nih.gov/pubmed/28574426 http://dx.doi.org/10.3390/s17061263 |
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author | González-Gutiérrez, Carlos Santos, Jesús Daniel Martínez-Zarzuela, Mario Basden, Alistair G. Osborn, James Díaz-Pernas, Francisco Javier De Cos Juez, Francisco Javier |
author_facet | González-Gutiérrez, Carlos Santos, Jesús Daniel Martínez-Zarzuela, Mario Basden, Alistair G. Osborn, James Díaz-Pernas, Francisco Javier De Cos Juez, Francisco Javier |
author_sort | González-Gutiérrez, Carlos |
collection | PubMed |
description | Many of the next generation of adaptive optics systems on large and extremely large telescopes require tomographic techniques in order to correct for atmospheric turbulence over a large field of view. Multi-object adaptive optics is one such technique. In this paper, different implementations of a tomographic reconstructor based on a machine learning architecture named “CARMEN” are presented. Basic concepts of adaptive optics are introduced first, with a short explanation of three different control systems used on real telescopes and the sensors utilised. The operation of the reconstructor, along with the three neural network frameworks used, and the developed CUDA code are detailed. Changes to the size of the reconstructor influence the training and execution time of the neural network. The native CUDA code turns out to be the best choice for all the systems, although some of the other frameworks offer good performance under certain circumstances. |
format | Online Article Text |
id | pubmed-5492298 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-54922982017-07-03 Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems González-Gutiérrez, Carlos Santos, Jesús Daniel Martínez-Zarzuela, Mario Basden, Alistair G. Osborn, James Díaz-Pernas, Francisco Javier De Cos Juez, Francisco Javier Sensors (Basel) Article Many of the next generation of adaptive optics systems on large and extremely large telescopes require tomographic techniques in order to correct for atmospheric turbulence over a large field of view. Multi-object adaptive optics is one such technique. In this paper, different implementations of a tomographic reconstructor based on a machine learning architecture named “CARMEN” are presented. Basic concepts of adaptive optics are introduced first, with a short explanation of three different control systems used on real telescopes and the sensors utilised. The operation of the reconstructor, along with the three neural network frameworks used, and the developed CUDA code are detailed. Changes to the size of the reconstructor influence the training and execution time of the neural network. The native CUDA code turns out to be the best choice for all the systems, although some of the other frameworks offer good performance under certain circumstances. MDPI 2017-06-02 /pmc/articles/PMC5492298/ /pubmed/28574426 http://dx.doi.org/10.3390/s17061263 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article González-Gutiérrez, Carlos Santos, Jesús Daniel Martínez-Zarzuela, Mario Basden, Alistair G. Osborn, James Díaz-Pernas, Francisco Javier De Cos Juez, Francisco Javier Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems |
title | Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems |
title_full | Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems |
title_fullStr | Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems |
title_full_unstemmed | Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems |
title_short | Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems |
title_sort | comparative study of neural network frameworks for the next generation of adaptive optics systems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5492298/ https://www.ncbi.nlm.nih.gov/pubmed/28574426 http://dx.doi.org/10.3390/s17061263 |
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