Rapid tomographic reconstruction through GPU-based adaptive optics

[1]  Francisco Javier de Cos Juez,et al.  Successful sulfur recovery in low sulfurate compounds obtained from the zinc industry: Evaporation-condensation method. , 2017, Journal of hazardous materials.

[2]  Francisco Javier de Cos Juez,et al.  Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems , 2017, Sensors.

[3]  Francisco Javier de Cos Juez,et al.  Analysis of the Temporal Structure Evolution of Physical Systems with the Self-Organising Tree Algorithm (SOTA): Application for Validating Neural Network Systems on Adaptive Optics Data before On-Sky Implementation , 2017, Entropy.

[4]  José Luís Calvo-Rolle,et al.  Using GPUs to Speed up a Tomographic Reconstructor Based on Machine Learning , 2016, SOCO-CISIS-ICEUTE.

[5]  Qiang Wang,et al.  Benchmarking State-of-the-Art Deep Learning Software Tools , 2016, 2016 7th International Conference on Cloud Computing and Big Data (CCBD).

[6]  T. Fusco,et al.  Experience with wavefront sensor and deformable mirror interfaces for wide-field adaptive optics systems , 2016, 1603.07527.

[7]  Mohak Shah,et al.  Comparative Study of Deep Learning Software Frameworks , 2015, 1511.06435.

[8]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[9]  N. Hubin,et al.  The E-ELT instrument roadmap: a status report , 2014, Astronomical Telescopes and Instrumentation.

[10]  G. Rousset,et al.  Open-loop tomography with artificial neural networks on CANARY: on-sky results , 2014, 1405.6862.

[11]  Patrick M. Pilarski,et al.  First steps towards an intelligent laser welding architecture using deep neural networks and reinforcement learning , 2014 .

[12]  Geoffrey E. Hinton,et al.  Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[13]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[14]  Francisco Javier de Cos Juez,et al.  An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment , 2012, Sensors.

[15]  Dani Guzman,et al.  Using artificial neural networks for open-loop tomography. , 2011, Optics express.

[16]  Francisco Javier de Cos Juez,et al.  A Hybrid Device of Self Organizing Maps (SOM) and Multivariate Adaptive Regression Splines (MARS) for the Forecasting of Firms’ Bankruptcy , 2011 .

[17]  G. Rousset,et al.  Tomography approach for multi-object adaptive optics. , 2010, Journal of the Optical Society of America. A, Optics, image science, and vision.

[18]  Richard H. Myers,et al.  Modeling a MEMS deformable mirror using non-parametric estimation techniques. , 2010, Optics express.

[19]  Francisco Javier de Cos Juez,et al.  Deformable mirror model for open-loop adaptive optics using multivariate adaptive regression splines. , 2010, Optics express.

[20]  P. J. García Nieto,et al.  Non-linear numerical analysis of a double-threaded titanium alloy dental implant by FEM , 2008, Appl. Math. Comput..

[21]  Eric Gendron,et al.  CANARY: the on-sky NGS/LGS MOAO demonstrator for EAGLE , 2008, Astronomical Telescopes + Instrumentation.

[22]  Fernando Rosa,et al.  Atmospheric wavefront phase recovery by use of specialized hardware: graphical processing units and field-programmable gate arrays. , 2005, Applied optics.

[23]  R. Shack,et al.  History and principles of Shack-Hartmann wavefront sensing. , 2001, Journal of refractive surgery.

[24]  B. Ellerbroek.  First-order performance evaluation of adaptive optics systems for atmospheric turbulence compensatio , 1994 .

[25]  R. Tyson.  Principles of Adaptive Optics , 1992 .

[26]  W. Southwell.  Wave-front estimation from wave-front slope measurements , 1980 .