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International Workshop on Soft Computing Models in Industrial and Environmental Applications

Computational Intelligence in Security for Information Systems Conference

International Conference on EUropean Transnational Education

SOCO 2016, CISIS 2016, ICEUTE 2016: International Joint Conference SOCO’16-CISIS’16-ICEUTE’16 pp 279–289Cite as

Using GPUs to Speed up a Tomographic Reconstructor Based on Machine Learning

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 527))

Abstract

The next generation of adaptive optics (AO) systems require tomographic techniques in order to correct for atmospheric turbulence along lines of sight separated from the guide stars. Multi-object adaptive optics (MOAO) is one such technique. Here we present an improved version of CARMEN, a tomographic reconstructor based on machine learning, using a dedicated neural network framework as Torch. We can observe a significant improvement on the training an execution times of the neural network, thanks to the use of the GPU.

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    http://deeplearning.net/software_links/.

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Authors and Affiliations

  1. Department of Exploitation and Exploration of Mines, University of Oviedo, Oviedo, Spain

    Carlos González-Gutiérrez, Nieves Roqueñí Gutiérrez & Francisco Javier de Cos Juez

  2. Department of Physics, University of Oviedo, Oviedo, Spain

    Jesús Daniel Santos-Rodríguez

  3. Department of Architecture and Technology of Computers, University of León, León, Spain

    Ramón Ángel Fernández Díaz

  4. Department of Industrial Engineering, University of a Coruña, La Coruña, Spain

    Jose Luis Calvo Rolle

Authors
  1. Carlos González-Gutiérrez
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  2. Jesús Daniel Santos-Rodríguez
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  3. Ramón Ángel Fernández Díaz
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  4. Jose Luis Calvo Rolle
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  5. Nieves Roqueñí Gutiérrez
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  6. Francisco Javier de Cos Juez
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Corresponding author

Correspondence to Jesús Daniel Santos-Rodríguez .

Editor information

Editors and Affiliations

  1. Computational Intelligence Group, University of the Basque Country (UPV/EHU), San Sebastian, Spain

    Manuel Graña

  2. University of the Basque Country, Vitoria, Spain

    José Manuel López-Guede

  3. Computational Intelligence Group, University of the Basque Country (UPV/EHU), San Sebastian, Spain

    Oier Etxaniz

  4. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  5. University of Salamanca, Salamanca, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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González-Gutiérrez, C., Santos-Rodríguez, J.D., Díaz, R.Á.F., Rolle, J.L.C., Gutiérrez, N.R., de Cos Juez, F.J. (2017). Using GPUs to Speed up a Tomographic Reconstructor Based on Machine Learning. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. SOCO CISIS ICEUTE 2016 2016 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-47364-2_27

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  • DOI: https://doi.org/10.1007/978-3-319-47364-2_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47363-5

  • Online ISBN: 978-3-319-47364-2

  • eBook Packages: EngineeringEngineering (R0)

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