O Programa de Pós-Graduação em Engenharia de Eletricidade - PPGEE/UFMA foi criado através da Resolução Nº 35/94 do CONSEPE de 27 de outubro de 1994. O PPGEE teve início ofical em 1995, tendo sido reconhecido pela CAPES no nivel de mestrado nas áreas de concentração: Automação e Controle, Ciências da Computação e Sistemas de Energia, e homologado pelo CNE através da portaria Nº 2.878 de 24/08/1995. https://tedebc.ufma.br/jspui/handle/tede/1313 2026-06-05T04:12:15Z 2026-06-05T04:12:15Z OLIVEIRA, Marcus Vinicius Silva Lima de https://tedebc.ufma.br/jspui/handle/tede/7027 2026-06-01T12:29:58Z 2026-04-23T00:00:00Z

Título: Atenção Multiescala em Redes U-Net: Uma Abordagem para Segmentação de Rins, Tumores e Cistos em Tomografia Computadorizada Autor: OLIVEIRA, Marcus Vinicius Silva Lima de Primeiro orientador: BRAZ JUNIOR, Geraldo Abstract: Early detection and diagnosis of renal cancer play a crucial role in patient prognosis and treatment, significantly increasing the chances of survival and cure. In this context, advances in radiological imaging have enabled medical specialists to analyze and identify suspicious lesions more effectively. Computed Tomography (CT) stands out as a widely used tool for renal cancer diagnosis due to its ability to generate high-resolution images of internal body structures, including the kidneys, cysts, and tumors. However, manual analysis of these exams is time-consuming and error-prone, often affected by fatigue and distraction, highlighting the need for computational methods to support the automatic segmentation of these structures. In recent years, Convolutional Neural Networks (CNNs) have shown significant potential in this task, providing valuable support to medical imaging specialists. In this study, we propose a convolutional model named Dual-Scale SE U-Net, which leverages multi-scale feature extraction through parallel convolutions (3×3 and 7×7), combined with channel attention mechanisms based on the Squeeze-and-Excitation (SE) module, integrated into the U-Net architecture for the segmentation of kidneys, cysts, and tumors in CT images. As a preprocessing step, the images were resized from 512×512 to 256×256 pixels to improve computational efficiency. The proposed methodology achieved promising results on the KiTS23 dataset, evaluated using five-fold cross-validation, yielding Dice similarity coefficients of 90.94% for Kidneys and Masses, 89.52% for Renal Masses, and 86.27% for Renal Tumors. In the analysis of individual structures, the model achieved 93.80% for kidneys, 92.76% for cysts, and 86.27% for tumors, demonstrating the effectiveness of the proposed approach as a supportive tool for medical diagnosis. Instituição: Universidade Federal do Maranhão Tipo do documento: Dissertação

2026-04-23T00:00:00Z MARCOS, Paulo Roberto Silva https://tedebc.ufma.br/jspui/handle/tede/7024 2026-05-29T12:37:51Z 2026-04-15T00:00:00Z

Título: Um simulador de trajetória de VANT de asa fixa aplicado ao monitoramento espectral de radiofrequência Autor: MARCOS, Paulo Roberto Silva Primeiro orientador: OLIVEIRA, Alexandre César Muniz de Abstract: The use of unmanned aerial vehicles (VANTs) in radio frequency spectrum monitoring activities is one of the many possible applications of this type of vehicle, whether operating autonomously, pre-programmed to follow a planned route, or remotely controlled, and several studies have proposed algorithms for this purpose in different application contexts. This research proposes a trajectory simulator to be executed by a fixed-wing VANT, suggesting routes that include horizontal and vertical deviations during spectral monitoring of radio frequency signals emitted by known transmission stations, while also assisting in the identification of signals from unknown stations along the path. As part of the planning stage, the approach is based on refining sequences generated by basic routing algorithms used in the traveling salesman problem (TSP), as well as through modeling in the Gekko optimization suite and APOPT solver, combined with the definition of the search space derived from radio frequency characteristics of real stations and signal-to-noise ratio thresholds calculated using a propagation model appropriate to the monitored band and the VANT’s hypothetical flight altitude. The results demonstrated that the minimization achieved by the hill-climbing algorithm led to a significant reduction in trajectory length, which was adequately simulated by the proposed model. The simulation considered the presence of large obstacles requiring altitude variation, as well as horizontal deviations to bypass smaller obstacles, aided by the energy consumption function, allowing prediction of return moments for refueling and continuation of the initially proposed sequence. Instituição: Universidade Federal do Maranhão Tipo do documento: Dissertação

2026-04-15T00:00:00Z FERNANDES, Saulo Enock Rodrigues https://tedebc.ufma.br/jspui/handle/tede/7009 2026-05-26T13:12:49Z 2026-05-04T00:00:00Z

Título: Diagnóstico automático da paralisia do sexto nervo em vídeos de motilidade ocular por classificação de séries temporais e aprendizado profundo Autor: FERNANDES, Saulo Enock Rodrigues Primeiro orientador: ALMEIDA, João Dallyson Sousa de Abstract: The sixth cranial nerve innervates the lateral rectus muscle, which is responsible for left-to-right eye movements. Paralysis of this nerve impairs the lateral rectus muscle’s function, potentially causing headaches, migraines, blurred vision, dizziness, and diplopia (double vision) when attempting to move the eye toward the outer corner. Therefore, early diagnosis of this condition is essential to prevent long-term effects. Since available diagnostic techniques are invasive or expensive, this study proposes an automated method for diagnosing sixth cranial nerve palsy based on classifying time series extracted from eye-tracking data of ophthalmological patients using deep learning models, aiming to assist specialists in the diagnostic process. The proposed method uses the YOLO network to track eye movement accurately and quickly in ophthalmological videos, along with the MediaPipe facial detection model, in a strategy to compensate for eye trajectories, removing occasional head movements and isolating the actual eye movement. In a cross validation experiment, the proposed method achieved a mean sensitivity of 70% across the folds and 75% in the best-trained fold, indicating that our methodology has potential for clinical application. Instituição: Universidade Federal do Maranhão Tipo do documento: Dissertação

2026-05-04T00:00:00Z RIBEIRO JUNIOR, Osmar Aguiar https://tedebc.ufma.br/jspui/handle/tede/7001 2026-05-22T12:32:40Z 2026-04-17T00:00:00Z

Título: Otimização Evolutiva Híbrida para Docking Proteína–Peptídeo Autor: RIBEIRO JUNIOR, Osmar Aguiar Primeiro orientador: CARMONA CORTÉS, Omar Andrés Abstract: Protein–peptide interactions play an important role in biological processes associated with molecular recognition, cellular signaling, and the development of therapeutic strategies. However, the computational prediction of these complexes remains challenging, especially in blind docking scenarios, in which the binding site is not previously known and the conformational space to be explored is broad. The central problem investigated in this work is to reduce the computational cost of the conformational search in blind protein–peptide docking without compromising the energetic quality of the final poses. In this context, this dissertation presents the development, implementation, and validation of a hybrid evolutionary optimization approach named ABC–GA–VGOS, applied to protein– peptide molecular docking. The proposed approach combines population-based exploration mechanisms, genetic operators, adaptive local refinement, and a single-objective geometric function based on KD-tree queries, used as a computationally efficient evaluation strategy during the search process. At the end of the optimization, the best pose obtained in each execution is submitted to an energy rescoring step inspired by AutoDock Vina, allowing the separation between fast geometric screening and final energetic evaluation. The experiments were performed with ten protein–peptide complexes obtained from the Protein Data Bank (PDB), considering binding energy, execution time, and comparative statistical analysis among the evaluated algorithms. The results indicated that the hybrid method obtained the lowest mean energies in five evaluated systems and achieved the shortest mean execution time in all cases, maintaining competitive behavior across different complexes. Thus, the main contribution of this work lies in the integration of complementary evolutionary strategies with efficient geometric evaluation, offering an alternative to reduce the computational cost of protein–peptide docking without relying exclusively on more expensive energy functions throughout the entire search. Instituição: Universidade Federal do Maranhão Tipo do documento: Dissertação

2026-04-17T00:00:00Z