J
Javier Macias-Guarasa
Researcher at University of Alcalá
Publications - 67
Citations - 1450
Javier Macias-Guarasa is an academic researcher from University of Alcalá. The author has contributed to research in topics: Deep learning & Acoustic source localization. The author has an hindex of 18, co-authored 63 publications receiving 1140 citations. Previous affiliations of Javier Macias-Guarasa include Technical University of Madrid.
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A project-based learning approach to design electronic systems curricula
Javier Macias-Guarasa,Juan Manuel Montero,Rubén San-Segundo,Alvaro Araujo,Octavio Nieto-Taladriz +4 more
TL;DR: An important result is that all students have developed more complex and sophisticated electronic systems, while considering that the results are worth the effort invested.
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Toward Prevention of Pipeline Integrity Threats Using a Smart Fiber-Optic Surveillance System
Javier Tejedor,Hugo F. Martins,Daniel Piote,Javier Macias-Guarasa,Juan Pastor-Graells,Sonia Martin-Lopez,Pedro Corredera Guillen,Filip De Smet,Willy Postvoll,Miguel Gonzalez-Herraez +9 more
TL;DR: The results obtained are promising given the complexity of the task and open the path to future improvements toward fully functional pipeline threat detection systems operating in real conditions.
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Speech to sign language translation system for Spanish
Rubén San-Segundo,R. Barra,Ricardo de Córdoba,Luis Fernando D'Haro,F. Fernández,Javier Ferreiros,J. M. Lucas,Javier Macias-Guarasa,Juan Manuel Montero,José Manuel Pardo +9 more
TL;DR: The development of and the first experiments in a Spanish to sign language translation system in a real domain focusing on the sentences spoken by an official when assisting people applying for, or renewing their Identity Card are described.
Journal ArticleDOI
A Novel Fiber Optic Based Surveillance System for Prevention of Pipeline Integrity Threats
Javier Tejedor,Javier Macias-Guarasa,Hugo F. Martins,Daniel Piote,Juan Pastor-Graells,Sonia Martin-Lopez,Pedro Corredera,Miguel Gonzalez-Herraez +7 more
TL;DR: The results show that the system combination from the contextual feature information improves the results for each individual class in both operational modes, as well as the overall classification accuracy, with statistically-significant improvements.
Journal ArticleDOI
Machine Learning Methods for Pipeline Surveillance Systems Based on Distributed Acoustic Sensing: A Review
Javier Tejedor,Javier Macias-Guarasa,Hugo F. Martins,Juan Pastor-Graells,Pedro Corredera,Sonia Martin-Lopez +5 more
TL;DR: The fundamentals of the machine learning approaches when applied to DAS systems are described, and the most common issues related to real field deployment and evaluation of DAS+PRS for pipeline threat monitoring are addressed.