Browsing by Author "Amadio, Ariel"

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    2do congreso sobre medios de transporte y sus tecnologías asociadas
    (edUTecNe, 2019-12-20) Perez Arrieu, Juan; Dulcich, Federico; Dri, Juan Pablo; Canzian, Adrian; Otero, Dino; Amadio, Ariel; Mirassou, Horacio M.; ett all, .
    Continuando con la excelente experiencia del 1er Congreso de Medios de Transporte y sus Tecnologías Asociadas realizado en la Facultad Regional Haedo, se realizó este Segundo Congreso. Se presentaron trabajos de gran calidad, ponencias, conferencias y un panel. Los trabajos fueron evaluados por profesionales del Comité Científico. La labor de los evaluadores fue impecable. Las ponencias, conferencias y el panel fueron invitados y cumplieron en exceso las expectativas puestas en ellas. Participaron del Congreso más de setenta profesionales en los dos días y medio que duró. A continuación se presenta el material expuesto durante su realización.
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    An alternative computation of the entropy of 1D signals based on geometric properties
    (2022-09-01) Bonini, Cristian; Rey, Andrea; Otero, Dino; Amadio, Ariel; García Blesa, Manuel; Legnani, Walter
    The objective of this work is to present a novel methodology based on the computation of a couple of geometric characteristics of the position of the data points in 1D signal to propose an alternative estimation of signal entropy. The conditions to be fulfilled by the signal are minimal; only those necessary to meet the sampling theorem requirement are enough. This work shows some examples in which the proposed methodology can distinguish among signals that cannot be differentiated by other in-use alternatives. Additionally an original example where the usual ordinal pattern algorithm to compute entropy is not applicable, is presented and analyzed. The proposal developed through this work carries some advantages over other alternatives and constitutes a true advancement in the pathway to compute the distribution function of the sequential points of 1D signals later used to compute the entropy of the signal.
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    Inteligencia artificial y redes neuronales. Fundamentos, ejercicios y aplicaciones con Python y Matlab
    (edUTecNe, 2022-03-28) Vorobioff, Juan; Cerrotta, Santiago; Eneas Morel, Nicolas; Amadio, Ariel; ; ; ;
    Este libro se basa en la experiencia profesional de los autores en el área de Inteligencia Artificial (IA) aplicado al análisis y procesamiento de datos, señales e imágenes. La IA contiene distintas disciplinas como aprendizaje automático, aprendizaje profundo, minería de datos, entre otras. Analizamos principalmente las redes neuronales. Sin embargo, también mostramos algunos métodos de reconocimiento estadístico de patrones y métodos de visión artificial, ya que presentan algunas ventajas (y desventajas) respecto de las redes neuronales. Dentro de las redes neuronales presentamos una introducción a las redes básicas, a las redes convolucionales de aprendizaje profundo y a las redes dinámicas. Para cada tema se presenta un marco teórico, desarrollos matemáticos, ejercicios analíticos, ejemplos de aplicaciones y ejercicios en Matlab®, Python y otros entornos. El libro contiene los códigos QR para descargar los programas. Se espera que el contenido del libro le sirva al lector para comprender diferentes técnicas de IA, así también para desarrollar y comparar distintos algoritmos con aplicaciones científico tecnológicas y/o comerciales.
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    Mathematical and informational tools for classifying blood glucose signals - a pilot study
    (2023-07-20) Amadio, Ariel; Rey, Andrea; Legnani, Walter; García Blesa, Manuel; Bonini, Cristian; Otero, Dino
    A survey campaign was carried out on the dynamics of blood glucose measured through interstitial sensors of relative recent diffusion in the market. These sensors generated time series that were labeled according to medical diagnosis in diabetics and non-diabetics, and that constituted the data core of the classification models. Based on the calculation of the distribution of ordinal patterns of the time series, the corresponding points in the entropycomplexity causal plane were located. Moreover, the transition matrices of these ordinal patterns (OPTMs) were calculated in order to find the proximity using the Manhattan distance of every OPTM with respect to the mean of each group, associating the corresponding signal to each class. On the other hand, the Frobenius norm of every OPTM and the norm of its stationary vector were computed given different values for the considered classes. The effect of repeated values in a signal was also analyzed. Notable differences were obtained in the properties of the OPTMs of each class. In another sense, it is shown that diabetes is a disease that reduces the entropy of the temporal evolution of blood glucose in well-defined time periods, and presents values of complexity significantly higher than those obtained in subjects without diabetes. The selected alternatives coincide in detecting patients positively diagnosed with type II diabetes mellitus. The calculations on the OPTMs show the correlation among patterns of the signals. At the same time, in the entropy-complexity plane, the considered groups were located in well-defined regions showing the differentiating power of these information measures, and indicating variations in the dynamics of the biological system when diabetes is present. With the four mathematical tools selected and the dynamical characterization given by the causal plane, it was possible to define an index that clearly differentiates the classes under study.