Prof. Dr. Óscar Barquero-Pérez - Biomedical Engineering - Best Researcher Award
University Rey Juan Carlos - Spain
Author Profile
🎓 Early Academic Pursuits
Prof. Dr. Óscar Barquero-Pérez embarked on his academic journey with a strong foundation in mathematics and engineering. He first earned a Licenciatura em Matemática e Aplicações from Universidade Aberta de Portugal, demonstrating an early inclination toward complex problem-solving and analytical thinking. His passion for interdisciplinary sciences led him to pursue a Mestrado em Engenharia Biomédica from Universidade do Porto, where he gained expertise in biomedical engineering, a field that integrates engineering principles with medical applications. Further cementing his technical acumen, he obtained a degree in Telecommunications Engineering from Universidad Carlos III de Madrid, providing him with a strong background in signal processing and communication systems.
💼 Professional Endeavors
Prof. Barquero-Pérez has dedicated his career to advancing biomedical engineering through both research and academia. He has been actively involved in innovative teaching methodologies, completing multiple seminars focused on improving education. His teaching expertise spans several disciplines, including Discrete Time Systems, Optimización, Sistemas Lineales y Aplicación a Circuitos, and Procesamiento Digital de la Información, among others. His courses, taught at Universidad Rey Juan Carlos, highlight his commitment to integrating advanced concepts into biomedical and telecommunications engineering education.
🔬 Contributions and Research Focus
His research primarily revolves around biomedical engineering, signal processing, and telecommunications systems, making significant contributions to medical technology and computational applications. His work in Procesamiento Digital de la Información and Introducción a la Bioingeniería has played a crucial role in developing cutting-edge signal processing techniques that are essential in modern medical diagnostics. He has also integrated machine learning applications into biomedical research, contributing to advancements in biomedical engineering technologies.
🌍 Impact and Influence
Prof. Barquero-Pérez has had a profound impact on the academic and scientific communities. His expertise has influenced numerous students and researchers through his involvement in teaching and curriculum development. His efforts in applying innovative didactic methodologies have significantly improved educational strategies in biomedical engineering and related fields. His role as an educator, combined with his extensive research, positions him as a key figure in the intersection of telecommunications and medical technology.
🏆Academic Cites
His research contributions have been recognized and cited in various academic circles. His work in biomedical engineering, particularly in signal processing and digital information management, has been widely referenced in studies related to telecommunications, medical imaging, and computational engineering. The impact of his research is reflected in the significant number of citations in internationally recognized academic journals.
🌟 Legacy and Future Contributions
Looking ahead, Prof. Barquero-Pérez aims to continue his work in biomedical engineering, focusing on integrating advanced communication systems and computational models to enhance medical diagnostics and treatment methods. His ongoing research and commitment to education ensure a lasting legacy in the fields of biomedical technology, machine learning applications, and digital signal processing. His innovative contributions will continue to shape the future of biomedical and telecommunications engineering, influencing both academia and industry.
📝Biomedical Engineering
Prof. Dr. Óscar Barquero-Pérez has significantly contributed to the advancement of biomedical engineering, particularly in the areas of digital signal processing, optimization, and AI applications in medical diagnostics. His expertise in biomedical engineering is evident through his impactful research and teaching in biomedical engineering, telecommunications, and applied mathematics.
Notable Publication
📝Near infrared spectroscopy (NIRS) and machine learning as a promising tandem for fast viral detection in serum microsamples: A preclinical proof of concept
Authors: J. Gómez, José; Ó. Barquero-Pérez, Óscar; J. Gonzalo, Jennifer; C.M. Fernández-Rodríguez, Conrado Manuel; M. Catalá, Myriam
Journal: Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy
Year: 2024
Citations: 0
📝Main causes of producing honey bee colony losses in southwestern Spain: a novel machine learning-based approach
Authors: E.J. García-Vicente, Eduardo José; M. Benito-Murcia, María; M. Martín Domínguez, María; Ó. Barquero-Pérez, Óscar; D. Risco Pérez, David
Journal: Apidologie
Year: 2024
Citations: 1
📝Non-invasive estimation of atrial fibrillation driver position using long-short term memory neural networks and body surface potentials
Authors: M. Gutiérrez-Fernández-Calvillo, Miriam; M.A. Camara-Vazquez, Miguel Angel; I. Hernandez-Romero, Ismael; C. Fambuena-Santos, Carlos; Ó. Barquero-Pérez, Óscar
Journal: Computer Methods and Programs in Biomedicine
Year: 2024
Citations: 0
📝EGM Reconstruction from BSPs in Atrial Fibrillation Using Deep Learning
Authors: M. Gutierrez-Fernandez, Miriam; M.A. Camara-Vazqueza, Miguel Angel; I. Hernandez-Romero, Ismael; A.M. Climent, Andreu M.; Ó. Barquero-Pérez, Óscar
Source: [No source information available]
Citations: 0
📝Modeling Gender Differences in Heart Rate During the Diving Reflex: Insights into Physiological Adaptability
Authors: M. Rey-Paredes, Marta; Ó. Barquero-Pérez, Óscar; R. Goya-Esteban, Rebeca; D. Grassi, D.; F. Suarez, F.
Source: [No source information available]
Citations: 0
📝Deep Neural Network: An Alternative to Traditional Channel Estimators in Massive MIMO Systems
Authors: A. Melgar, Antonio; A. de la Fuente, Alejandro; L. Carro-Calvo, Leopoldo; Ó. Barquero-Pérez, Óscar; E. Morgado, E.
Journal: IEEE Transactions on Cognitive Communications and Networking
Year: 2022
Citations: 22