Carlos Granero Belinchon
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Documents
Researcher identifiers
carlos-granero-belinchon
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0000-0002-4095-0807
- Google Scholar : https://scholar.google.fr/citations?user=vKwDG3kAAAAJ&hl=fr
Presentation
Carlos Granero Belinchón is graduated in physics from the Universidad Autónoma de Madrid, Spain, 2014. Next to a Master diploma in plasma physics from Paris VI and École Polytechnique, he received the PH.D. degree in physics and signal processing from the ENS de Lyon, France, 2018. From 2018 to 2020, he was a post-doctoral fellow at ONERA, Toulouse. Currently, he is an associate professor in the Mathematical and Electrical Engineering department of IMT Atlantique, and member of the Ocean Signal and Environment group of Lab-STICC.
His research lives in the interface between non-linear physics and signal and image processing. Thus, it mainly deals with the development of new statistical signal processing methodologies for the study of complex processes and systems. He is particularly interested by multi-scale modelling, scale-invariance phenomena and multi-resolution analysis as well as by using this kind of approaches for the characterization of turbulent phenomena and ocean dynamics.
Carlos Granero Belinchón est licencié en physique par l'Universidad Autónoma de Madrid, Espagne, 2014. Après avoir obtenu un diplôme de Master en physique de plasmas à l'université Paris VI et l'École Polytechnique, il a obtenu un doctorat en physique et traitement de signal à l'ENS de Lyon, France, 2018. Entre 2018 et 2020, il a occupé une position post-doctorale à l'ONERA de Toulouse. Actuellement, il est enseignant chercheur dans le département Mathematical and Electrical Engineering de l'IMT Atlantique, et membre de l'équipe Ocean Signal and Environment du Lab-STICC.
Sa recherche se trouve à l'interface entre la physique non-lineaire et le traitement de signal et image. Ainsi, elle concerne principalement le développement de nouvelles méthodologies d'analyse statistique du signal pour l'étude de processus et systèmes complexes. Il est principalement intéressé par la modélisation multi-échelles, le phenomène d'invariance d'échelle et l'analyse multirésolution ainsi que par l'utilisation de ce type d'approches pour la caractérisation du phenomène de turbulence et les dynamiques océaniques.
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Deep learning for Lagrangian drift simulation at the sea surfaceIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023), Jun 2023, Rhodes, Greece
Conference papers
hal-03852489v1
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Deep Learning approaches to simulate Lagrangian particle dynamics at sea surfaceSea Tech Week 2022, Session "Marine Litter Solutions for Monitoring Mitigation and Prevention", Sep 2022, BREST, France
Conference papers
hal-03795063v1
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Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution2022 30th European Signal Processing Conference (EUSIPCO), Aug 2022, Belgrade, Serbia. ⟨10.23919/EUSIPCO55093.2022.9909569⟩
Conference papers
hal-03580148v2
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Land Surface Temperature Retrieval over Urban areas from simulated TRISHNA data2019 Joint Urban Remote Sensing Event (JURSE), May 2019, Vannes, France. pp.1-4, ⟨10.1109/jurse.2019.8808979⟩
Conference papers
hal-03195869v1
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Mutual Information for Intrapartum fetal Heart Rate AnalysisEMBC 2017, Jul 2017, Jeju Island, South Korea
Conference papers
ensl-01637094v1
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Assessing the usefulness of Land Surface Temperature spatial disaggregation for water stress mapping in the frame of the preparation of the Trishna missionRecent Advances In Quantitative Remote Sensing, Sep 2022, Torrent (Valencia), Spain
Conference poster
hal-03923959v1
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A multiscale and multicriteria Generative Adversarial Network to synthesize 1-dimensional turbulent fields2023
Preprints, Working Papers, ...
hal-04173074v1
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Multiscale and Anisotropic Characterization of Images Based on Complexity: an Application to Turbulence2023
Preprints, Working Papers, ...
hal-04190553v1
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Multiscale Information Transfer in TurbulenceSignal and Image Processing. Université de Lyon, 2018. English. ⟨NNT : 2018LYSEN040⟩
Theses
tel-01968074v1
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