Publications

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Journal Article
Monge-Álvarez, J., C. Hoyos-Barceló, K. Dahal, and P. Casaseca-de-la-Higuera, "Audio-cough event detection based on moment theory", Applied Acoustics, vol. 135, pp. 124–135, 2018.
Rabanillo-Viloria, I., A. Zhu, S. Aja-Fernández, C. Alberola-López, and D. Hernando, "Computation of exact g-factor maps in 3D GRAPPA reconstructions", Magnetic resonance in medicine, vol. 81, pp. 1353–1367, 2019.
Pourdarbani, R., S. Sabzi, D. Kalantari, J. Luis Hernández-Hernández, and J. I. Arribas, "A Computer Vision System Based on Majority-Voting Ensemble Neural Network for the Automatic Classification of Three Chickpea Varieties", Foods, vol. 9, pp. 113, 2020.
Roig-Herrero, A., Á. Planchuelo-Gómez, M. Hernández-García, R. de Luis-García, I. Fernández-Linsenbarth, R. M. Beño-Ruiz-de-la-Sierra, and V. Molina, "Default mode network components and its relationship with anomalous self-experiences in schizophrenia: A rs-fMRI exploratory study", Psychiatry Research: Neuroimaging, vol. 324, pp. 111495, 2022.
Hoyos-Barceló, C., J. Monge-Álvarez, Z. Pervez, L. M. San-José-Revuelta, and P. Casaseca-de-la-Higuera, "Efficient computation of image moments for robust cough detection using smartphones", Computers in biology and medicine, vol. 100, pp. 176–185, 2018.
Hoyos-Barceló, C., J. Monge-Álvarez, M. Zeeshan Shakir, J-M. Alcaraz-Calero, and P. Casaseca-de-la-Higuera, "Efficient k-NN Implementation for Real-Time Detection of Cough Events in Smartphones", IEEE Journal of Biomedical and Health Informatics, vol. 23, pp. 1662-1671, 2018.
Rabanillo-Viloria, I., S. Aja-Fernández, C. Alberola-Lopez, and D. Hernando, "Exact Calculation of Noise Maps and g-Factor in GRAPPA using a k-space Analysis", IEEE Transactions on Medical Imaging, vol. 37, issue 2, pp. 480-490, 2018.
Trigo, J., D. García-Azorín, Á. Planchuelo-Gómez, E. Martínez-Pías, B. Talavera, I. Hernández-Pérez, G. Valle-Peñacoba, P. Simón-Campo, M. de Lera, A. Chavarría-Miranda, et al., "Factors associated with the presence of headache in hospitalized COVID-19 patients and impact on prognosis: a retrospective cohort study", The Journal of Headache and Pain, vol. 21, pp. 94, Jul, 2020.
Curiale, A. H., A. Haak, G. Vegas-Sánchez-Ferrero, B. Ren, S. Aja-Fernández, and J. G. Bosch, "Fully Automatic Detection of Salient Features in 3-D Transesophageal Images", Ultrasound in medicine & biology, vol. 40, issue 12: Elsevier, pp. 2868-2884, 07/2014.
Monge-Alvarez, J., C. Hoyos-Barcelo, L. M. San Jose-Revuelta, and P. Casaseca-de-la-Higuera, "A Machine Hearing System for Robust Cough Detection Based on a High-Level Representation of Band-Specific Audio Features", IEEE Transactions on Biomedical Engineering, vol. 66, pp. 2319-2330, 2019.
Planchuelo-Gómez, Á., M. Descoteaux, H. Larochelle, J. Hutter, D. K. Jones, and C. M. W. Tax, "Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning", Medical Image Analysis, vol. 94, pp. 103134, 2024.
Peña-Nogales, Ó., Y. Zhang, X. Wang, R. de Luis-García, S. Aja-Fernández, J. H. Holmes, and D. Hernando, "Optimized Diffusion-Weighting Gradient Waveform Design (ODGD) formulation for motion compensation and concomitant gradient nulling", Magnetic resonance in medicine, vol. 81, pp. 989–1003, 2019.
Peña-Nogales, Ó., Y. Zhang, X. Wang, R. de Luis-García, S. Aja-Fernández, J. H. Holmes, and D. Hernando, "Optimized Diffusion-Weighting Gradient Waveform Design (ODGD) formulation for motion compensation and concomitant gradient nulling", Magnetic resonance in medicine, 2018.
Peña-Nogales, Ó., Y. Zhang, X. Wang, R. de Luis-García, S. Aja-Fernández, J. H. Holmes, and D. Hernando, "Optimized Diffusion-Weighting Gradient Waveform Design (ODGD) formulation for motion compensation and concomitant gradient nulling", Magnetic resonance in medicine, 2018.
Peña-Nogales, Ó., Y. Zhang, X. Wang, R. de Luis-García, S. Aja-Fernández, J. H. Holmes, and D. Hernando, "Optimized Diffusion-Weighting Gradient Waveform Design (ODGD) formulation for motion compensation and concomitant gradient nulling", Magnetic resonance in medicine, vol. 81, pp. 989–1003, 2019.
Monge-Álvarez, J., C. Hoyos-Barceló, P. Lesso, and P. Casaseca-de-la-Higuera, "Robust detection of audio-cough events using local Hu moments", IEEE journal of biomedical and health informatics, vol. 23, pp. 184–196, 2019.
Fernández-Linsenbarth, I., Á. Planchuelo-Gómez, R. M. Beño-Ruiz-de-la-Sierra, A. Díez, A. Arjona, A. Pérez, A. Rodríguez-Lorenzana, P. del Valle, R. de Luis-García, G. Mascialino, et al., "Search for schizophrenia and bipolar biotypes using functional network properties", Brain and Behavior, vol. 11, pp. e2415, 2021.
Aja-Fernández, S., A. Tristán-Vega, and W. Scott Hoge, "Statistical noise analysis in GRAPPA using a parametrized noncentral Chi approximation model", Magnetic resonance in medicine, vol. 65, no. 4: Wiley Subscription Services, Inc., A Wiley Company, pp. 1195–1206, 2011.
Molina, V., A. Bachiller, R. de Luis-García, A. Lubeiro, J. Poza, R. Hornero, J. Francesc Alonso, M. Angel Mañanas, P. Marqués, and S. Romero, "Topography of activation deficits in schizophrenia during P300 task related to cognition and structural connectivity", European Archives of Psychiatry and Clinical Neuroscience, Feb, 2018.
Aja-Fernández, S., C. Martín-Martín, Á. Planchuelo-Gómez, A. Faiyaz, M. Nasir Uddin, G. Schifitto, A. Tiwari, S. J. Shigwan, R. Kumar Singh, T. Zheng, et al., "Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies", NeuroImage: Clinical, vol. 39, pp. 103483, 2023.
Dadashzadeh, M., Y. Abbaspour-Gilandeh, T. Mesri-Gundoshmian, S. Sabzi, J. Luis Hernández-Hernández, M. Hernández-Hernández, and J. I. Arribas, "Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields", Plants, vol. 9, pp. 559, 2020.
Dadashzadeh, M., Y. Abbaspour-Gilandeh, T. Mesri-Gundoshmian, S. Sabzi, J. Luis Hernández-Hernández, M. Hernández-Hernández, and J. I. Arribas, "Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields", Plants, vol. 9, pp. 559, 2020.

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