Publicaciones en las que colabora con JESUS DANIEL SANTOS RODRIGUEZ (15)

2022

  1. Comparative study of Shack-Hartmann configurations for atmospheric turbulence reconstructions in solar adaptive optics

    Optics and Lasers in Engineering, Vol. 158

  2. The K2-OjOS Project*New and revisited planets and candidates in K2 campaigns 5, 16, & 18

    Monthly Notices of the Royal Astronomical Society, Vol. 509, Núm. 1, pp. 1075-1095

2021

  1. Compensating atmospheric turbulence with CNNs for defocused pupil image wavefront sensors

    Logic Journal of the IGPL, Vol. 29, Núm. 2, pp. 180-192

  2. Defocused image deep learning designed for wavefront reconstruction in tomographic pupil image sensors

    Mathematics, Vol. 9, Núm. 1, pp. 1-15

  3. Fully convolutional approaches for numerical approximation of turbulent phases in solar adaptive optics

    Mathematics, Vol. 9, Núm. 14

  4. Point source detection with fully convolutional networks: Performance in realistic microwave sky simulations

    Astronomy and Astrophysics, Vol. 648

2020

  1. Early Fully-Convolutional Approach to Wavefront Imaging on Solar Adaptive Optics Simulations

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2019

  1. Convolutional neural networks approach for solar reconstruction in SCAO configurations

    Sensors (Switzerland), Vol. 19, Núm. 10

  2. Experience with artificial neural networks applied in multi-object adaptive optics

    Publications of the Astronomical Society of the Pacific, Vol. 131, Núm. 1004

2018

  1. Compensating atmospheric turbulence with convolutional neural networks for defocused pupil image wave-front sensors

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  2. Improving adaptive optics reconstructions with a deep learning approach

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2017

  1. Analysing the performance of a tomographic reconstructor with different neural networks frameworks

    Advances in Intelligent Systems and Computing

  2. Comparative study of neural network frameworks for the next generation of adaptive optics systems

    Sensors (Switzerland), Vol. 17, Núm. 6

  3. Using GPUS to speed up a tomographic reconstructor based on machine learning

    Advances in Intelligent Systems and Computing

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

    International Joint Conference SOCO’16-CISIS’16-ICEUTE’16: San Sebastián, Spain, October 19th-21st, 2016 Proceedings