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Comparative study of neural network frameworks for the next generation of adaptive optics systems.

González-Gutiérrez, Carlos and Santos, Jesús and Martínez-Zarzuela, Mario and Basden, Alistair and Osborn, James and Díaz-Pernas, Francisco and De Cos Juez, Francisco (2017) 'Comparative study of neural network frameworks for the next generation of adaptive optics systems.', Sensors., 17 (6). p. 1263.

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.

Item Type:Article
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Available under License - Creative Commons Attribution.
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.3390/s17061263
Publisher 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).
Date accepted:30 May 2017
Date deposited:06 July 2017
Date of first online publication:02 June 2017
Date first made open access:06 July 2017

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