Portal de Programas de Pós-Graduação (UnB)

SIGAA - Sistema Integrado de Gestão de Atividades Acadêmicas

PGEA PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE SISTEMAS ELETRÔNICOS E AUTOMAÇÃO DEPTO ENGENHARIA ELETRICA Teléfono/Ramal: No informado E-mail: No informado https://www.unb.br/pos-graduacao

Disertación/Tesis

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2023

	Tesis
1	Tony Alexandre Medeiros da Silva Radiomics and machine learning to identify 1p/19q chromosome codelection in low-grade gliomas Líder : JOAO LUIZ AZEVEDO DE CARVALHO MIEMBROS DE LA BANCA : TÚLIO AUGUSTO ALVES MACEDO CRISTIANO JACQUES MIOSSO RODRIGUES MENDES FLAVIA MARIA GUERRA DE SOUSA ARANHA OLIVEIRA JOAO LUIZ AZEVEDO DE CARVALHO JOAO SOUZA NETO Data: 13-feb-2023 Resumen Espectáculo Gliomas represent about 40% of brain tumors. Of these, 50% are low-grade, almost entirely represented by oligodendrogliomas and astrocytomas, both grade II or III. Since 1p/19q chromosome codelection is an important tumor marker of positive prognostic factor in these gliomas, it is proposed to use radiomics in image analysis as a comprehensive quantifier of noninvasive tumor phenotypes to identify the status of 1p/19q chromosome codeletion in gliomas low grade. This is an approach potentially used in oncology, helping in the detection, diagnosis and prognosis of cancer, prediction of response to treatment and monitoring of the state of the disease. Using the PyRadiomics platform, which extracts radiomic features that quantify the intensity, shape and texture of the tumor image in magnetic resonance imaging, a dataset with 159 patients diagnosed with low-grade gliomas was used and 120 features were extracted . Then, 4 experiments were implemented where data pre-processing techniques were applied, such as selection, scaling, resizing and data augumentation, and supervised machine learning. After evaluating a set of test data in each experiment, promising results were obtained for accuracy, sensitivity, specificity, precision, area under the curve (AUC) and F1 score. They are competitive when compared to the state of the art. The results underscore that radiomics in conjunction with machine learning constitutes a promising approach in identifying the status of 1p/19q chromosome codelection in low-grade gliomas.

2022

	Tesis
1	Sana Alamgeer Deep Learning Based Objective Quality Assessment of Multidimensional Visual Content Líder : MYLENE CHRISTINE QUEIROZ DE FARIAS MIEMBROS DE LA BANCA : MYLENE CHRISTINE QUEIROZ DE FARIAS JOAO LUIZ AZEVEDO DE CARVALHO LI WEIGANG CARLOS ALEXANDRE DE BARROS MELLO CARLA LIBERAL PAGLIARI Data: 01-jul-2022 Resumen Espectáculo In the last decade, there has been a tremendous increase in the popularity of multimedia applications, hence increasing multimedia content. When these contents are generated, transmitted, reconstructed and shared, their original pixel values are transformed. In this scenario, it becomes more crucial and demanding to assess visual quality of the affected visual content so that the requirements of end-users are satisfied. In this work, we investigate effective spatial, temporal, and angular features by developing no-reference algorithms that assess the visual quality of distorted multi-dimensional visual content. We use machine learning and deep learning algorithms to obtain prediction accuracy. For two-dimensional (2D) image quality assessment, we use multiscale local binary patterns and saliency information, and train / test these features using Random Forest Regressor. For 2D video quality assessment, we introduce a novel concept of spatial and temporal saliency and custom objective quality scores. We use a Convolutional Neural Network (CNN) based light-weight model for training and testing on selected patches of video frames. For objective quality assessment of four-dimensional (4D) light field images (LFI), we propose seven LFI quality assessment (LF-IQA) methods in total. Considering that LFI is composed of dense multi-views, Inspired by Human Visual System (HVS), we propose our first LF-IQA method that is based on a two-streams CNN architecture. The second and third LF-IQA methods are also based on a two-stream architecture, which incorporates CNN, Long Short-Term Memory (LSTM), and diverse bottleneck features. The fourth LF-IQA is based on CNN and Atrous Convolution layers (ACL), while the fifth method uses CNN, ACL, and LSTM layers. The sixth LF-IQA method is also based on a two-stream architecture, in which, horizontal and vertical EPIs are processed in the frequency domain. Last, but not least, the seventh LF-IQA method is based on a Graph Convolutional Neural Network. For all of the methods mentioned above, we performed intensive experiments, and the results show that these methods outperformed state-of-the-art methods on popular quality datasets.
2	Sylvia de Sousa Faria Development of Computational Models for the Prevention of Atrioesophageal Fistula During the Cardiac Radioablation Procedure Líder : ADSON FERREIRA DA ROCHA MIEMBROS DE LA BANCA : ADSON FERREIRA DA ROCHA FLAVIA MARIA GUERRA DE SOUSA ARANHA OLIVEIRA JOAO LUIZ AZEVEDO DE CARVALHO FÁTIMA MRUÉ TALLES MARCELO GONÇALVES DE ANDRADE BARBOSA Data: 04-nov-2022 Resumen Espectáculo Atrial Fibrillation (AF) is a disease that affects about 2.5% of the world population. And it consists of a disorder of the atrial electrical rhythm in the heart. Currently, the most used technique for treating AF, when permanent, is Radiofrequency Cardiac Ablation (RFCA). RFCA is a minimally invasive procedure that uses Radiofrequency (RF) and the Joule effect through the ablation electrode, heats the tissue and causes cellular destruction at the ablated point. Despite being efficient, RFCA can generate serious complications, as the heat generated can reach organs close to the left atrium (LA), such as the esophagus. Among the thermal injuries that can occur is the Atrial-Esophageal Fistula (AEF). The AEF is a communicating tube between the LA and the esophagus, which can arise due to the overheating of the organs, leading the patient to death. Thus, this thesis proposes to develop computer simulations of RFCA, with 2D and 3D models, with studies that analyze situations that can prevent and avoid AEF with and without esophageal wall (EW) cooling. The first study developed, an analysis of the depth of thermal injury with different tissue thicknesses during RFCA, with and without esophageal cooling, showed: (i) cooling did not change the RFCA procedure – it was possible to do an effective ablation with cooling; (ii) it reduced the size of the lesion and, in the scenario with the thinnest tissue, made RFCA possible; and (iii) associated lesion size - ablation electrode temperature and time. The second study, an analysis of heat propagation during EW cooling, the model presented blood flow, showed that: (i) during cooling, the temperature of internal organs decreases, but does not change the RFCA; and (ii) cooling retards the propagation of heat. An analysis of the effect of ablation electrode angulation showed that the lesion depth is more significant at angulations closer to the horizontal during the RFCA. This third study confirmed the findings of the other studies. Obtaining data on a safer RFCA, analyzing the internal temperature of the organs adjacent to the left atrium, the size of the thermal lesions, and the best positioning of the ablation electrode show a possibility to inform and prevent the formation of AEF. Further studies will be carried out to consolidate these results.
3	Muhammad Irshad Avaliação de Qualidade de Imagens Subaquaticas aprimoradas com redes neurais convolucionais. Líder : MYLENE CHRISTINE QUEIROZ DE FARIAS MIEMBROS DE LA BANCA : MYLENE CHRISTINE QUEIROZ DE FARIAS JOAO LUIZ AZEVEDO DE CARVALHO CRISTIANO JACQUES MIOSSO RODRIGUES MENDES WAMBERTO JOSÉ LIRA DE QUEIROZ JOSE GABRIEL RODRIGUEZ CARNEIRO GOMES Data: 25-nov-2022 Resumen Espectáculo Image enhancement algorithms have the goal of improving the image quality and, therefore, the usefulness of an image for a given task. Although there are several image enhancement algorithms, there is no consensus on how to estimate the performance of these enhancement algorithms. Since the final consumers of the resulting enhanced visual content are human viewers, the performance of these algorithms should take into account the perceived visual quality of the resulting enhanced images. Unfortunately, although in the last decades a lot of progress has been made in the area of image quality assessment, designing metrics to estimate the quality of enhanced and restored images remains a challenge. This is particularly true for underwater image application, where images frequently need to be restored because of the severity of the degradations introduced by the underwater environment. Therefore, there is a great need for quality metrics that can estimate the quality of enhanced and restored images. In this thesis, our goal is to design metrics for this scenario. First, we have designed a quality metric based on texture operators and saliency. Second, we also designed a quality metric based on a deep learning architecture convolutional Neural Network (CNN). Experimental results on the underwater image database demonstrate that our approaches outperform the state-of-art methods compared. Third, we have developed a new dataset for Underwater image quality assessment. Additionally, we also present a psychophysical study based on crowd-sourcing interface, in which we analyze the perceptual quality of images enhanced with several types of enhancement algorithms. In this experiment, we have developed a database that can be used to train image quality metrics, and also can detect both increments and decrements in the perceived quality.
4	Tássio Melo Linhares Dynamic output feedback for Takagi-Sugeno fuzzy systems subjected to inexact premise variables matching Líder : EDUARDO STOCKLER TOGNETTI MIEMBROS DE LA BANCA : EDUARDO STOCKLER TOGNETTI EDVALDO ASSUNÇÃO FLÁVIO ANDRADE FARIA JOAO YOSHIYUKI ISHIHARA RENATO ALVES BORGES Data: 15-dic-2022 Resumen Espectáculo This work presents new design conditions of full-order dynamic output feedback controllers for continuous and discrete-time Takagi-Sugeno (T-S) fuzzy systems allowing the selection of premise variables to be used in the control law. The fuzzy output controller is allowed to have a different number of fuzzy rules and a different set of membership functions from the T-S model. This includes the cases of complete or partial immeasurable premise variables. The main aspect of the proposed methodology is to present conditions where the control gains are independent of the premise variables that cannot be measured allowing flexibility for the designer in a realistic output feedback context. Moreover, the design conditions are expressed as linear matrix inequality relaxations combined with scalar parameters that provide extra degrees of freedom. The proposed control methodology also deals with model uncertainties for continuous and discrete-time systems and the use of fuzzy Lyapunov functions. The effectiveness and applicability of the methodology are shown through numerical examples