Abstract
Correction of atmospheric turbulences with the use of guide stars as reference, is one of the most relevant issues of adaptive optics (AO). This is addressed with tomographic techniques such as Multi-object adaptive optics (MOAO). Next generations of extremely large telescopes, will require improvements in computational capabilities of real time control systems. An improved version of CARMEN, a tomographic reconstructor based on machine learning, is presented here. The performing time of two dedicated neural network frameworks, Torch and Theano, is compared, with significant improvements on the training and execution times of the neural networks due to calculations on GPU. Also, the differences between both frameworks are discussed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
VRAM: GPU Video RAM
RAM: CPU RAM.
References
Guzmán, D., Juez, F.J.D.C., Myers, R., Guesalaga, A., Lasheras, F.S.: Modeling a MEMS deformable mirror using non-parametric estimation techniques. Opt. Express 18(20), 21356–21369 (2010)
Guzmán, D., de Cos Juez, F.J., Lasheras, F.S., Myers, R., Young, L.: Deformable mirror model for open-loop adaptive optics using multivariate adaptive regression splines. Opt. Express 18(7), 6492–6505 (2010)
de Cos Juez, F.J., Sánchez Lasheras, F., Roqueñí, N., Osborn, J.: An ANN-based smart tomographic reconstructor in a dynamic environment. Sensors (Basel) 12(7), 8895–8911 (2012)
Ellerbroek, B.L.: First-order performance evaluation of adaptive-optics systems for atmospheric-turbulence compensation in extended-field-of-view astronomical telescopes. J. Opt. Soc. Am. A 11(2), 783 (1994)
Vidal, F., Gendron, E., Rousset, G.: Tomography approach for multi-object adaptive optics. J. Opt. Soc. Am. A. Opt. Image Sci. Vis. 27(11), 253–264 (2010)
Osborn, J., et al.: Using artificial neural networks for open-loop tomography. Opt. Express 20(3), 2420–2434 (2012)
Nieto García, P.J., Alonso Fernández, J.R., Sánchez Lasheras, F., de Cos Juez, F.J., Díaz Muñiz, C.: A new improved study of cyanotoxins presence from experimental cyanobacteria concentrations in the Trasona reservoir (Northern Spain) using the MARS technique. Sci. Total Environ. 430, 88–92 (2012)
de Andrés, J., Sánchez-lasheras, F., Lorca, P., Javier, F., de Cos Juez, F.J.: A hybrid device of Self Organizing Maps (SOM) and Multivariate Adaptive Regression Splines (MARS) for the forecasting of firms’ bankruptcy. Account. Manag. Inf. Syst. 10(3), 351–374 (2011)
Suárez Sánchez, A., Riesgo Fernández, P., Sánchez Lasheras, F., de Cos Juez, F.J., García Nieto, P.J.: Prediction of work-related accidents according to working conditions using support vector machines. Appl. Math. Comput. 218(7), 3539–3552 (2011)
VilánVilán, J.A., Alonso Fernández, J.R., García Nieto, P.J., Sánchez Lasheras, F., de Cos Juez, F.J., Díaz Muñiz, C.: Support vector machines and multilayer perceptron networks used to evaluate the cyanotoxins presence from experimental cyanobacteria concentrations in the Trasona reservoir (Northern Spain). Water Resour. Manag. 27(9), 3457–3476 (2013)
Álvarez Menéndez, L., de Cos Juez, F.J., Sánchez Lasheras, F., Álvarez Riesgo, J.A.: Artificial neural networks applied to cancer detection in a breast screening programme. Math. Comput. Model. 52(7), 983–991 (2010)
Lasheras, F.S., De Cos Juez, F.J., Suárez Sánchez, A., Krzemień, A., Fernández, P.R.: Forecasting the COMEX copper spot price by means of neural networks and ARIMA models. Resour. Policy 45, 37–43 (2015)
Osborn, J., et al.: Open-loop tomography with artificial neural networks on CANARY: on-sky results. Mon. Not. R. Astron. Soc. 441(3), 2508–2514 (2014)
Ramsay, S.K., Casali, M.M., González, J.C., Hubin, N.: The E-ELT instrument roadmap: a status report, p. 91471Z (2014)
Basden, A.G., et al.: Experience with wavefront sensor and deformable mirror interfaces for wide-field adaptive optics systems. Mon. Not. R. Astron. Soc. 459(2), 1350–1359 (2016)
González-Gutiérrez, C., Santos-Rodríguez, J.D., Díaz, R.Á.F., Rolle, J.L.C., Gutiérrez, N.R., Cos Juez, F.J.: 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.) ICEUTE/SOCO/CISIS-2016. AISC, vol. 527, pp. 279–289. Springer, Heidelberg (2017). doi:10.1007/978-3-319-47364-2_27
Osborn, J., et al.: Open-loop tomography using artificial nueral networks. Adapt. Opt. Extrem. Large Telesc. II (2011)
Al-Rfou, R., et al.: Theano: a Python framework for fast computation of mathematical expressions, p. 19 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Gómez, S.L.S., Gutiérrez, C.G., Rodríguez, J.D.S., Rodríguez, M.L.S., Lasheras, F.S., de Cos Juez, F.J. (2017). Analysing the Performance of a Tomographic Reconstructor with Different Neural Networks Frameworks. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_103
Download citation
DOI: https://doi.org/10.1007/978-3-319-53480-0_103
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-53479-4
Online ISBN: 978-3-319-53480-0
eBook Packages: EngineeringEngineering (R0)