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Sensors 2017, 17(6), 1263; doi:10.3390/s17061263

Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems

1
Mining Exploitation and Prospecting Department, University of Oviedo, 33004 Oviedo, Spain
2
Department of Physics, University of Oviedo, 33004 Oviedo, Spain
3
Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, Spain
4
Department of Physics, Centre for Advanced Instrumentation, University of Durham, South Road, Durham DH1 3LE, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 10 May 2017 / Revised: 30 May 2017 / Accepted: 30 May 2017 / Published: 2 June 2017
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [2362 KB, uploaded 2 June 2017]   |  

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. View Full-Text
Keywords: adaptive optics; neural networks; tomographic reconstructor; parallel processing adaptive optics; neural networks; tomographic reconstructor; parallel processing
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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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González-Gutiérrez, C.; Santos, J.D.; Martínez-Zarzuela, M.; Basden, A.G.; Osborn, J.; Díaz-Pernas, F.J.; De Cos Juez, F.J. Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems. Sensors 2017, 17, 1263.

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