Programa de Pós-Graduação em Engenharia Elétrica e de Computação
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Master Thesis Detecção de processos erosivos com aprendizado de máquina e a aplicação da Equação Universal de Perda de Solo (USLE)(Universidade Federal do Rio Grande do Norte, 2025-08-18) Marinho, Wesley José dos Santos; Alsina, Pablo Javier; http://lattes.cnpq.br/3653597363789712; http://lattes.cnpq.br/9223986328697943; Martins, Allan de Medeiros; https://orcid.org/0000-0002-9486-4509; http://lattes.cnpq.br/4402694969508077; Matos, Maria de Fátima Alves de; https://orcid.org/0000-0002-2864-2027; http://lattes.cnpq.br/7230788237148341; Laura, Tânia Luna; http://lattes.cnpq.br/8142774545918274The research in question investigates the detection of erosion processes through machine learning and the application of the Universal Soil Loss Equation (USLE). The primary issue addressed by the study is the identification and prevention of soil erosion, a significant environmental problem. Key objectives include implementing techniques to early detect erosion processes near power transmission lines, using the USLE as an additional validation factor. The motivation behind the study is to mitigate damage to power distribution networks caused by erosion and to seek effective methods for monitoring and preventing erosion. The methodology involves using machine learning algorithms, specifically Convolutional Neural Networks (CNNs), to analyze satellite images and identify areas prone to erosion. Additionally, the USLE is applied as a tool for calculating soil loss, aiding in the assessment of the likelihood of erosion in specific locations. The main contributions of the study include integrating machine learning technologies in the detection of erosion processes and validating these techniques with the USLE, demonstrating its effectiveness in this context. The results indicate the feasibility and accuracy of using machine learning for detecting erosion processes, as well as the importance of the USLE as an evaluation method. The study’s conclusions highlight the relevance of the practical application of these techniques for the preservation of power transmission lines and more efficient monitoring of their surroundings. Future research directions include expanding the study to other regions and improving the detection methodology.Doctoral Thesis Desenvolvimento de ferramenta computacional para biossensores baseados em ressonância de plasmon de superfície utilizando métodos híbridos e aprendizado de máquina(Universidade Federal do Rio Grande do Norte, 2025-06-26) Villarim, Mariana Rodrigues; Belfort, Diomadson Rodrigues; Ando Júnior, Oswaldo Hideo; https://orcid.org/0000-0002-6951-0063; http://lattes.cnpq.br/3515465412634126; https://orcid.org/0000-0001-5722-7394; http://lattes.cnpq.br/2241184480091242; http://lattes.cnpq.br/1234708675762453; Maciel, Joylan Nunes; https://orcid.org/0000-0003-0725-6917; http://lattes.cnpq.br/1177414528561833; Cavallari, Marco Roberto; https://orcid.org/0000-0002-1345-754X; http://lattes.cnpq.br/9041429608835546; Carmo, João Paulo Pereira do; https://orcid.org/0000-0001-7955-7503; http://lattes.cnpq.br/5589969124054528; Catunda, Sebastian Yuri Cavalcanti; http://lattes.cnpq.br/0873496251879638Doctoral Thesis Detecção e localização de crises epilépticas em sinais de EEG utilizando aprendizado de máquina e inteligência artificial explicável(Universidade Federal do Rio Grande do Norte, 2025-05-30) Vieira, Jusciaane Chacon; Oliveira, Luiz Affonso Henderson Guedes de; https://orcid.org/0000-0003-2690-1563; http://lattes.cnpq.br/7987212907837941; Teixeira, César; Gendriz, Ignacio Sanchez; http://lattes.cnpq.br/6338710569530857; Silva, Ivanovitch Medeiros Dantas da; https://orcid.org/0000-0002-0116-6489; http://lattes.cnpq.br/3608440944832201; Fernandes, Marcelo Augusto Costa; https://orcid.org/0000-0001-7536-2506; http://lattes.cnpq.br/3475337353676349Epilepsy is a neurological condition that affects millions of people worldwide and significantly impacts individuals’ quality of life. Epileptic seizures, which are transient events, vary in manifestations, including motor, sensory, and consciousness alterations, and represent a challenge both in diagnosis and management. This work proposes an innovative methodology for the detection and localization of epileptic seizures, using machine learning and explainable artificial intelligence approaches to optimize the identification process. The proposal is divided into two approaches: a generalist one, which uses simplified models with explainable feature and channel reduction, and a specific one, which personalizes detection for each patient based on a single electroencephalogram (EEG) channel. In the generalist approach, feature and channel reduction was explored, achieving performance above 0.95 in accuracy, precision, recall, and f1-score metrics using only six features and five channels. The use of the SHAP method allowed for the interpretation of each feature’s contribution by channel, reinforcing the explainability of the models and aligning the results with the visual knowledge of EEG specialists. The methodology proved effective, ensuring good generalization to the dataset with different patients. In the specific approach, a bipolar montage with adjacent electrodes was introduced, creating 58 channel combinations, and a centrality measure with topographic visualization was applied to identify the most relevant channels for each patient. Additionally, a 3-second temporal filter was developed to reduce the model’s false positives. A personalized supervised learning model, extreme gradient boosting, was trained for each patient using only one EEG channel. The results for the three investigated patients were remarkable, with accuracy scores of 1; 0.99; and 0.88, highlighting the feasibility of seizure detection using a single channel, considering the topographic location of seizures in each individual. This study highlights the potential to substantially reduce the number of features and channels required for epileptic seizure detection without compromising accuracy, and reinforces the importance of personalized models for each patient. Furthermore, the research contributes to the advancement of wearable devices for continuous epilepsy monitoring, facilitating the detection and localization of patients with epilepsy.Master Thesis Gerência de largura de banda do reúso fracionário de frequência em cenários dinâmicos com Hotspots utilizando aprendizado por reforço(Universidade Federal do Rio Grande do Norte, 2025-07-04) Silva, Eriberto de Souto; Sousa Júnior, Vicente Angelo de; https://orcid.org/0000-0003-2859-6136; http://lattes.cnpq.br/6358312955522220; http://lattes.cnpq.br/9594814897805733; Silva Neto, Valdemir Praxedes da; https://orcid.org/0000-0003-3867-3297; http://lattes.cnpq.br/4160231554601828; Melo, Yuri Victor Lima de; https://orcid.org/0000-0003-3026-7059; http://lattes.cnpq.br/3613517930531317The present work approaches the use of Inter-Cell Interference Coordination (ICIC) techniques, based on techniques Fractional Frequency Reuse (FFR) aimed at mitigating one of the main challenges faced when designing mobile networks: co-channel interference (CCI). ICIC is one of the primary factors contributing to the degradation of mobile communication system performance, especially in dynamic scenarios with randomly distributed user clusters, known as hotspots. Initially, the problem is characterized by the exploration of the chosen ICIC technique without automated parameterization, as well as the system’s performance in the aforementioned scenario. Then, considering the dynamic nature of the environment, the proposed solution involves the use of machine learning techniques—more specifically, the Multi-Armed Bandit (MAB)—customized for optimizing bandwidth management in ICIC techniques for 3GPP systems. The objective of the proposal is to adapt the spatial allocation of bandwidth, using system load variation and hotspot dynamics as decision-making parameters. Proof-of-concept experiments were conducted using the ns-3 network simulator. The results demonstrate that the MAB-based approach proposed in this dissertation outperforms static bandwidth distribution configurations in terms of throughput, even under varying system load conditions, showing significant gains.Master Thesis Otimização fim-a-fim de sistemas MIMO multiusuário usando autoencoders com estimação de canal bidirecional(Universidade Federal do Rio Grande do Norte, 2024-03-21) Velloso, Eduardo Nunes; Silveira, Luiz Felipe de Queiroz; https://orcid.org/0000-0002-7146-4916; http://lattes.cnpq.br/4139452169580807; https://orcid.org/0000-0002-9668-5547; http://lattes.cnpq.br/7673330596328097; Martins, Allan de Medeiros; https://orcid.org/0000-0002-9486-4509; http://lattes.cnpq.br/4402694969508077; Alencar, Marcelo Sampaio de; https://orcid.org/0000-0002-2849-1644; http://lattes.cnpq.br/0946722048975388; Lopes., Waslon Terllizzie Araújo; https://orcid.org/0000-0003-2486-9950; http://lattes.cnpq.br/5041048659000127The spectral efficiency gains introduced by multiuser MIMO systems render them relevant schemes for current and upcoming generations of mobile communication networks. Due to the intrinsic complexity of the mathematical models of these systems under realistic conditions and the interdependence between the processing steps of the transmitters and receivers, machine learning is an option that allows the complete system to be designed by training a noisy autoencoder. This paper proposes a neural network architecture for end-to-end optimization of a multiuser MIMO system. The performance of the system, measured in terms of symbol error rate, was compared to an M-PSK baseline with zero-forcing equalization and least-squares channel estimation. Simulations were performed using a Rayleigh fading channel model and the realistic 3GPP TR 38.901 model. A bidirectional channel estimator, based on the interpolation of sparse pilots, is proposed, reducing the control signaling to less than 3% in exchange for a fixed 10 ms delay. The results show that signicant gains can be achieved by applying the proposed model, but these vary with respect to the estimation errors during the pilot transmission times