Reconstrução e reconhecimento de imagens binárias utilizando o algoritmo Máquina de Boltzmann

The objective of this work is to use algorithms known as Boltzmann Machine to rebuild and classify patterns as images. This algorithm has a similar structure to that of an Artificial Neural Network but network nodes have stochastic and probabilistic decisions. This work presents the theoretical framework of the main Artificial Neural Networks, General Boltzmann Machine algorithm and a variation of this algorithm known as Restricted Boltzmann Machine. Computer simulations are performed comparing algorithms Artificial Neural Network Backpropagation with these algorithms Boltzmann General Machine and Machine Restricted Boltzmann. Through computer simulations are analyzed executions times of the different described algorithms and bit hit percentage of trained patterns that are later reconstructed. Finally, they used binary images with and without noise in training Restricted Boltzmann Machine algorithm, these images are reconstructed and classified according to the bit hit percentage in the reconstruction of the images. The Boltzmann machine algorithms were able to classify patterns trained and showed excellent results in the reconstruction of the standards code faster runtime and thus can be used in applications such as image recognition.

Do reconhecimento à efetividade do direito fundamental à água: diálogos entre Nicarágua e Brasil

La humanidad está en un periodo de transición paradigmática, donde aún no existe acuerdo sobre en cual periodo nos encuadramos, sí modernidad o posmodernidad, pero el debate de ahí surgido se revela útil y transversal a los dilemas que enfrenta hoy la sociedad. Uno de esos dilemas tiene que ver con el reconocimiento del agua como derecho fundamental, ciertamente la era de la información ha permitido avanzar en armar el rompecabezas de la crisis hídrica mundial y sus riesgos, lo que ha develado una realidad importante, el acceso al agua tanto en naciones económicamente desarrolladas como en las menos desarrolladas constituye aun una aspiración, son varias las causas de esto, siendo una de las más comunes la atribuida al cambio climático, sin embargo, al profundizar en la materia surgen otras causas como la falta de voluntad política a nivel nacional, los intereses económicos que dan al agua tratamiento de mercancía y a nivel internacional la existencia de luces y sombras en el campo de los derechos humanos, estas constituyen las piezas del mencionado rompecabezas, aun por armar. Mientras tanto el Derecho tiene aquí un desafío de significativa importancia, adaptarse a fin de responder adecuadamente a las nuevas realidades: riesgos de diversas índoles. Esta investigación tiene como objetivo defender la existencia del derecho fundamental al agua en el ordenamiento jurídico brasileño y nicaragüense, comparando instrumentos que puedan asegurar su efectividad. Para la realización de este trabajo se utilizó un abordaje inductivo-comparativo, fuentes bibliográficas brasileñas, nicaragüenses y de países con experiencias relevantes para la comprensión del problema y que pudieran aportar propuestas de cara al reconocimiento y efectivación del derecho fundamental al agua. Este trabajo encontró que existen ambigüedades importantes en el campo del Derecho Humano al Agua motivadas por el trabajo de agencias que defienden el derecho pero a la vez establecen coordinaciones y normativas con quienes impulsan su privatización, fue posible presentar elementos que apoyan la fundamentalidad del derecho al agua desde un punto de vista material y de vinculación con otros derechos fundamentales, pero preocupa en el caso de Brasil y Nicaragua el poco avance jurisprudencial de cara al reconocimiento y finalmente la atribución de la crisis hídrica en muchos casos a la incertidumbre climática, cuando uno de los principales desafíos se encuentra en la explotación comercial del agua.

Classificação de atividade eletromiográfia facial de indivíduos saudáveis e com hanseníese por meio de máquina de vetores de suporte

A number of studies in the areas of Biomedical Engineering and Health Sciences have employed machine learning tools to develop methods capable of identifying patterns in different sets of data. Despite its extinction in many countries of the developed world, Hansen’s disease is still a disease that affects a huge part of the population in countries such as India and Brazil. In this context, this research proposes to develop a method that makes it possible to understand in the future how Hansen’s disease affects facial muscles. By using surface electromyography, a system was adapted so as to capture the signals from the largest possible number of facial muscles. We have first looked upon the literature to learn about the way researchers around the globe have been working with diseases that affect the peripheral neural system and how electromyography has acted to contribute to the understanding of these diseases. From these data, a protocol was proposed to collect facial surface electromyographic (sEMG) signals so that these signals presented a high signal to noise ratio. After collecting the signals, we looked for a method that would enable the visualization of this information in a way to make it possible to guarantee that the method used presented satisfactory results. After identifying the method's efficiency, we tried to understand which information could be extracted from the electromyographic signal representing the collected data. Once studies demonstrating which information could contribute to a better understanding of this pathology were not to be found in literature, parameters of amplitude, frequency and entropy were extracted from the signal and a feature selection was made in order to look for the features that better distinguish a healthy individual from a pathological one. After, we tried to identify the classifier that best discriminates distinct individuals from different groups, and also the set of parameters of this classifier that would bring the best outcome. It was identified that the protocol proposed in this study and the adaptation with disposable electrodes available in market proved their effectiveness and capability of being used in different studies whose intention is to collect data from facial electromyography. The feature selection algorithm also showed that not all of the features extracted from the signal are significant for data classification, with some more relevant than others. The classifier Support Vector Machine (SVM) proved itself efficient when the adequate Kernel function was used with the muscle from which information was to be extracted. Each investigated muscle presented different results when the classifier used linear, radial and polynomial kernel functions. Even though we have focused on Hansen’s disease, the method applied here can be used to study facial electromyography in other pathologies.

Classificação de atividade eletromiográfia facial de indivíduos saudáveis e com hanseníase por meio de máquina de vetores de suporte

A number of studies in the areas of Biomedical Engineering and Health Sciences have employed machine learning tools to develop methods capable of identifying patterns in different sets of data. Despite its extinction in many countries of the developed world, Hansen’s disease is still a disease that affects a huge part of the population in countries such as India and Brazil. In this context, this research proposes to develop a method that makes it possible to understand in the future how Hansen’s disease affects facial muscles. By using surface electromyography, a system was adapted so as to capture the signals from the largest possible number of facial muscles. We have first looked upon the literature to learn about the way researchers around the globe have been working with diseases that affect the peripheral neural system and how electromyography has acted to contribute to the understanding of these diseases. From these data, a protocol was proposed to collect facial surface electromyographic (sEMG) signals so that these signals presented a high signal to noise ratio. After collecting the signals, we looked for a method that would enable the visualization of this information in a way to make it possible to guarantee that the method used presented satisfactory results. After identifying the method's efficiency, we tried to understand which information could be extracted from the electromyographic signal representing the collected data. Once studies demonstrating which information could contribute to a better understanding of this pathology were not to be found in literature, parameters of amplitude, frequency and entropy were extracted from the signal and a feature selection was made in order to look for the features that better distinguish a healthy individual from a pathological one. After, we tried to identify the classifier that best discriminates distinct individuals from different groups, and also the set of parameters of this classifier that would bring the best outcome. It was identified that the protocol proposed in this study and the adaptation with disposable electrodes available in market proved their effectiveness and capability of being used in different studies whose intention is to collect data from facial electromyography. The feature selection algorithm also showed that not all of the features extracted from the signal are significant for data classification, with some more relevant than others. The classifier Support Vector Machine (SVM) proved itself efficient when the adequate Kernel function was used with the muscle from which information was to be extracted. Each investigated muscle presented different results when the classifier used linear, radial and polynomial kernel functions. Even though we have focused on Hansen’s disease, the method applied here can be used to study facial electromyography in other pathologies.