An Improved Image Segmentation System: A Cooperative Multi-agent Strategy for 2D/3D Medical Images

In this paper, we present a solution-based cooperation approach for strengthening the image segmentation.This paper proposes a cooperative method relying on Multi-Agent System. The main contribution of this work is to highlight the importance of cooperation between the contour and region growing based on Multi-Agent System (MAS). Consequently, agents’ interactions form the main part of the whole process for image segmentation. Similar works were proposed to evaluate the effectiveness of the proposed solution. The main difference is that our Multi-Agent System can perform the segmentation process ensuring efficiency. Our results show that the performance indices in the system were higher. Furthermore, the integration of thecooperation paradigm allows to speed up the segmentation process. Besides, the tests reveal the robustness of our method by proving competitive results. Our proposal achieved an accuracy of 93,51%± 0,8, a sensitivity of 93,53%± 5,08 and a specificity rate of 92,64%± 4,01

Movement Identification from the intention of grasping, based on deep learning, with signals EMGs for use as HMI in robotic devices

Orientadores: João Mauricio Rosario, Oscar Fernando AvilésTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: Esta tese contribui com a parametrização e caracterização dos sinais de eletromiografia de superfície usando a aprendizagem em profundidade (Deep Learning) como técnica avançada no reconhecimento de padrões para reproduzir os movimentos de preensão de uma mão robótica em ambientes industriais através da interação homem-máquina. A análise para reproduzir os movimentos da mão é realizada a partir da interação dos sinais de eletromiografia das ações, que geram uma resposta cognitiva com o objetivo de replica-lo para que o dispositivo robótico possa realizar o movimento de preensão de acordo com o movimento realizado pelo usuário. Nesta tese, parte-se da bancada experimental MUC-1 previamente desenvolvida no Laboratório de Automação Integrada e Robótica (LAIR) da Universidade Estadual de Campinas e acrescentam-se funções que aumentam o escopo e melhoram a exequibilidade dos testes. A técnica de obtenção dos valores experimentais dos dados é baseada na adaptação do sensor MYO armband® por meio dos oito bio-sensores de eletromiografia relacionando a cinemática e dinâmica da mão pela identificação dos músculos do braço correspondente aos métodos de preensão, os quais são aprimorados por médio do método baseado em redes neuronais convolucionais da aprendizagem em profundidade previamente investigado na literatura para o reconhecimento de padrões. Para validação do sistema proposto, foi construído três arquiteturas de redes convolucionais, viabilizando a execução do teste virtual por meio da mão implementada no Simmechanics de Matlab® e no modelo real MUC-1. Por fim, o procedimento experimental resultante é documentado e as etapas prévias de modelagem e filtragem são descritas de acordo com as condições de preensão de objetos de figuras geométricas preestabelecidas que são executadas no dispositivo robótico de forma naturalAbstract: This thesis contributes to the parametrization and characterization of surface electromyography signals using deep learning as an advanced technique in pattern recognition to reproduce the grip movements of a robotic hand in industrial environments through the man-machine interaction. The analysis to reproduce the movements of the hand is made from the interaction of the electromyography signals of the actions, which generate a cognitive response to replicate it so that the robotic device can perform the grip movement, in accordance with the movement made by the user. Some part of this thesis is experimental bench MUC-1 previously developed in the Laboratory of Automation and Robotics (LAIR) at the State University of Campinas and added functions that increase the scope and improve the feasibility of testing. The technique of obtaining the experimental values of the data is based on the adaptation of the MYO armband® sensor through the eight bio-sensors of electromyography relating the kinematics and dynamics of the hand by the identification of the muscles of the arm corresponding to the grasping methods, which are improved by the method based on convolutional neuronal networks of in-depth learning previously research in the literature for the recognition of patterns. For the validation of the proposed system, three convolutional network architectures were built, enabling the virtual test execution through the hand implemented in the Matlab® Simmechanics and in the real MUC-1 model. Finally, the resulting experimental process is documented and the previous stages of modeling and filtering are described according to the prehension conditions of objects of geometric figures that are executed in the robotic device in a natural wayDoutoradoMecatrônicaDoutor em Engenharia Mecânic

Robust object detection under partial occlusion

This thesis focuses on the problem of object detection under partial occlusion in complex scenes through exploring new bottom-up and top-down detection models to cope with object discontinuities and ambiguity caused by partial occlusion and allow for a more robust and adaptive detection of varied objects from different scenes

Solución rápida y automática de parámetros hipocentrales para eventos sísmicos, mediante el empleo de técnicas de aprendizaje de máquina

La generación de alertas tempranas para sismos es de gran utilidad, en particular para la ciudad de Bogotá-Colombia, dada su importancia social y económica para el país. Con base en la información de la estación sismológica de El Rosal, la cual es una estación de banda ancha y tres componentes, localizada muy cerca de la ciudad, perteneciente al Servicio Geológico Colombiano (SGC) se desarrolló un modelo de regresión basado en máquinas de vectores de soporte (SVM), con un kernel polinomial normalizado, usando como datos de entrada algunas características de la porción inicial de la onda P empleadas en trabajos anteriores tales como la amplitud máxima, los coeficientes de regresión lineal de los mismos, los parámetros de ajuste logarítmico de la envolvente y los valores propios de la relación de las tres componentes del sismograma. El modelo fue entrenado y evaluado aplicando correlación cruzada, permitiendo llevar a cabo el cálculo de la magnitud y la localización de un evento sísmico con una longitud de señal de tan solo cinco segundos. Con el modelo propuesto se logró la determinación de la magnitud local con una precisión de 0.19 unidades de magnitud, la distancia epicentral con una precisión de alrededor de 11 kilómetros, la profundidad hipocentral con una precisión de aproximadamente 40 kilómetros y el azimut de llegada con una precisión de 45°. Las precisiones obtenidas en magnitud y distancia epicentral son mejores que las encontradas en trabajos anteriores, donde se emplean gran número de eventos para la determinación del modelo y en los demás parámetros hipocentrales son del mismo orden. Este trabajo de investigación realiza un aporte considerable en la generación de alertas tempranas para sismos, no solamente para el país sino para cualquier otro lugar donde se deseen implementar los modelos aquí propuestos y es un excelente punto de partida para investigaciones futuras.Abstract. Earthquake early warning alerts generation is very useful, especially for the city of Bogotá-Colombia, given the social and economic importance of this city for the country. Based on the information from the seismological station “El Rosal”, which is a broadband and three components station, located very near the city that belongs to the Servicio Geológico Colombiano (SGC) a Support Vector Machine Regression (SVMR) model was developed, using a Normalized Polynomial Kernel, using as input some characteristics of the initial portion of the P wave used in earlier works such as the maximum amplitude, the linear regression coefficients of such amplitudes, the logarithmic adjustment parameters of the envelope of the waveform and the eigenvalues of the relationship between the three seismogram components of each band. The model was trained and evaluated by applying a cross-correlation strategy, allowing to calculate the magnitude and location of a seismic event with only five seconds of signal. With the proposed model it was possible to estimate local magnitude with an accuracy of 0.19 units of magnitude, epicentral distance with an accuracy of about 11 km, the hipocentral depth with a precision of approximately 40 km and the arrival back-azimut with a precision of 45°. Accuracies obtained in magnitude and epicentral distance are better that those found in earlier works, where a large number of events were used for model determination, and the other hipocentral parameters precisions obtained here are of the same order. This research work makes a considerable contribution in the generation of seismic early warning alerts, not only for the country but for any other place where proposed models here can be applied and is a very good starting point for future research.Doctorad