Carlos González-Gutiérrez
Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems
González-Gutiérrez, Carlos; Santos, Jesús; Martínez-Zarzuela, Mario; Basden, Alistair; Osborn, James; Díaz-Pernas, Francisco; De Cos Juez, Francisco
Authors
Jesús Santos
Mario Martínez-Zarzuela
Dr Alastair Basden a.g.basden@durham.ac.uk
Hpc Technical Manager
Professor James Osborn james.osborn@durham.ac.uk
Professor
Francisco Díaz-Pernas
Francisco De Cos Juez
Abstract
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.
Citation
González-Gutiérrez, C., Santos, J., Martínez-Zarzuela, M., Basden, A., Osborn, J., Díaz-Pernas, F., & De Cos Juez, F. (2017). Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems. Sensors, 17(6), Article 1263. https://doi.org/10.3390/s17061263
Journal Article Type | Article |
---|---|
Acceptance Date | May 30, 2017 |
Online Publication Date | Jun 2, 2017 |
Publication Date | Jun 2, 2017 |
Deposit Date | Jul 6, 2017 |
Publicly Available Date | Jul 6, 2017 |
Journal | Sensors |
Electronic ISSN | 1424-8220 |
Publisher | MDPI |
Peer Reviewed | Peer Reviewed |
Volume | 17 |
Issue | 6 |
Article Number | 1263 |
DOI | https://doi.org/10.3390/s17061263 |
Public URL | https://durham-repository.worktribe.com/output/1353779 |
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Copyright Statement
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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