Publicaciones en colaboración con investigadores/as de University of Regensburg (63)

2024

  1. ReSurveyEurope: A database of resurveyed vegetation plots in Europe

    Journal of Vegetation Science, Vol. 35, Núm. 2

2022

  1. Quantum gravity phenomenology at the dawn of the multi-messenger era—A review

    Progress in Particle and Nuclear Physics, Vol. 125

2021

  1. The Large Hadron–Electron Collider at the HL-LHC

    Journal of Physics G: Nuclear and Particle Physics, Vol. 48, Núm. 11

2017

  1. The Global Ant Genomics Alliance (GAGA)

    Myrmecological News, Vol. 25, pp. 61-66

2016

  1. An optimal approach for selecting discriminant regions for the diagnosis of alzheimer's disease

    Current Alzheimer Research, Vol. 13, Núm. 7, pp. 838-844

  2. Functional biomedical images of Alzheimer’s disease. A green’s function-based empirical mode decomposition study

    Current Alzheimer Research, Vol. 13, Núm. 6, pp. 695-707

  3. MRI brain segmentation using hidden Markov random fields with alpha-stable distributions

    2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop, NSS/MIC/RTSD 2016

2015

  1. A posterization strategy for the registration of [123I]FP-CIT SPECT brain images

    VISAPP 2015 - 10th International Conference on Computer Vision Theory and Applications; VISIGRAPP, Proceedings

  2. Building a FP-CIT SPECT Brain Template Using a Posterization Approach

    Neuroinformatics, Vol. 13, Núm. 4, pp. 391-402

  3. Study of the histogram of the hippocampus in MRI using the α-stable distribution

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

2014

  1. Affine registration of [123I]FP-CIT SPECT brain images

    Studies in Health Technology and Informatics

  2. An Invitation to Lorentzian Geometry

    Jahresbericht der Deutschen Mathematiker-Vereinigung, Vol. 115, Núm. 3-4, pp. 153-183

  3. BIDIMENSIONAL ENSEMBLE EMPIRICAL MODE DECOMPOSITION OF FUNCTIONAL BIOMEDICAL IMAGES

    ADVANCES IN DATA SCIENCE AND ADAPTIVE ANALYSIS, Vol. 6, Núm. 1

  4. Why Using the Alpha-stable Distribution in Neuroimage?

    2014 INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND MULTIMEDIA APPLICATIONS (SIGMAP)

  5. Why using the alpha-stable distribution in neuroimage?

    SIGMAP 2014 - Proceedings of the 11th International Conference on Signal Processing and Multimedia Applications, Part of ICETE 2014 - 11th International Joint Conference on e-Business and Telecommunications