Listar Maestría en Ingeniería - Automatización Industrial por autor "Grupo de Control y Procesamiento Digital de Señales"
Mostrando documentos 1-6 de 6
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Brain Music : Sistema compositivo, gráfico y sonoro creado a partir del comportamiento frecuencial de las señales cerebrales
Pérez Nastar, Hernán DaríoEsta tesis de maestría presenta una metodología de aprendizaje profundo multimodal innovadora que fusiona un modelo de clasificación de emociones con un generador musical, con el propósito de crear música a partir de señales ... -
Characterization of faults in rotating machines using multivariate component analysis
Ruales-Torres, Anderson AlbertoThis thesis aims to develop a set of methodologies that allow the feature extraction and blind source separation, to diagnose the different types of faults in gearboxes and bearings. First, it is proposed the implementation ... -
A deep learning approach for image-based semantic segmentation with preserved interpretability
Aguirre Arango, Juan CarlosSemantic segmentation is pivotal in various industries, showcasing its significant impact across numerous applications. Semantic segmentation offers invaluable insights that drive advancements in fields such as autonomous ... -
Estrategia de procesamiento de señales EEG en sistemas BCI utilizando aprendizaje profundo y medidas de conectividad
Gomez Rivera, Yessica AlejandraLas Interfaces Cerebro Computadora (BCI) basadas en Electroencefalografía (EEG) crean una conexión directa entre el cerebro humano y una computadora. Los paradigmas de Imaginación Motora (MI) permiten que los usuarios ... -
Feature representation frameworks for decoding brain motor imagery patterns
Zapata Castaño, Frank YesidThe EEG recording records the electrical activity of the brain and measures activity with electrodes on the scalp; This record is the most widely used in the clinical and research fields due to its low cost and high ... -
A machine learning framework to support multi-channel time series classification in BCI systems with preserved interpretability
Tobón Henao, MateoBrain-Computer Interfaces (BCIs) based on Electroencephalography (EEG) have gained significant attention as a practical approach for human-technology interaction. Motor imagery (MI) paradigms, wherein users mentally simulate ...