2026-04-26T21:58:33Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/567042022-11-03T20:03:51Zcom_10324_1191com_10324_931com_10324_894col_10324_1379

González Gutiérrez, Carlos Santos, Jesús Daniel Martínez Zarzuela, Mario Basden, Alistair Osborn, James Díaz Pernas, Francisco Javier Cos Juez, Francisco Javier de 2022-11-03T12:19:40Z 2022-11-03T12:19:40Z 2017 Sensors, 2017, vol. 17, n. 6, p. 1263 https://uvadoc.uva.es/handle/10324/56704 10.3390/s17061263 1263 6 Sensors 17 1424-8220 Producción Científica 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. Ministerio de Economía y Competitividad through grant AYA2014-57648-P Gobierno del Principado de Asturias (Consejería de Economía y Empleo), through grant FC-15-GRUPIN14-017 This work is also funded by the UK Science and Technology Facilities Council, grant ST/K003569/1, and a consolidated grant ST/L00075X/1 application/pdf eng MDPI info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ © 2017 The Author(s) Atribución 4.0 Internacional Adaptive optics Neural networks Tomographic reconstructor Parallel processing 33 Ciencias Tecnológicas Comparative study of neural network frameworks for the next generation of adaptive optics systems info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://www.mdpi.com/1424-8220/17/6/1263 SI