Programa de Pós-Graduação em Engenharia Elétrica e de Computação
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Doctoral Thesis Transmissão vertical da sífilis no Brasil: análises baseadas em ciência de dados na saúde pública(Universidade Federal do Rio Grande do Norte, 2024-10-30) Silva, Rodrigo Dantas da; Valentim, Ricardo Alexsandro de Medeiros; https://orcid.org/0000-0002-9216-8593; http://lattes.cnpq.br/3181772060208133; https://orcid.org/0000-0002-2549-2414; http://lattes.cnpq.br/1947688093671056; Campos, Antonio Luiz Pereira de Siqueira; http://lattes.cnpq.br/1982228057731254; Coutinho, Karilany Dantas; Santos, Marquiony Marques dos; Morais, Antonio Higor Freire de; Lima, Thaisa Gois Farias de Moura Santos; Valentim, Janaína Luana Rodrigues da Silva; Santos, João Paulo Queiroz Dos; 01065547439This study examines the shortcomings of the Clinical Protocol and Therapeutic Guidelines (PCDT) for the management of gestational and congenital syphilis in Brazil, demonstrating that, in addition to the limitations of the protocol itself, there are also serious structural failures in the healthcare system. The research aims to prove that the inefficacy of the PCDT, combined with a lack of integration between levels of care and health surveillance, contributes to the increase in vertical transmission rates of syphilis. Using Stochastic Petri Nets (SPN) modeling and data from SINAN, treatment and diagnostic scenarios were simulated, revealing critical bottlenecks such as inadequate treatment for pregnant women and insufficient case monitoring. The study highlights that the failure to apply the guidelines, alongside poor prenatal care and a lack of proper training for healthcare professionals, is one of the greatest challenges in controlling congenital syphilis in the country.Doctoral Thesis A novel deep neural network technique for drug-target interaction prediction(Universidade Federal do Rio Grande do Norte, 2024-12-10) Souza, Jackson Gomes de; Fernandes, Marcelo Augusto Costa; Barbosa, Raquel de Melo; https://orcid.org/0000-0001-7536-2506; http://lattes.cnpq.br/3475337353676349; https://orcid.org/0000-0003-0665-7153; http://lattes.cnpq.br/7022849614714429; Villén, Fátima García; Silva, Lucileide Medeiros Dantas da; Coutinho, Maria Gracielly FernandesDrug discovery (DD) is a time-consuming and expensive process. Thus, the industry employs strategies such as drug repositioning and drug repurposing, which allows the application of already approved drugs to treat a different disease, as occurred in the first months of 2020, during the COVID-19 pandemic. Prediction of drug-target interaction is an essential part of the DD process because it can accelerate it and reduce required costs. DTI prediction performed in silico have used approaches based on molecular docking simulation, similarity-based and network and graph based. This paper presents MPS2ITDTI, a DTI prediction model obtained from research conducted in the following steps: the definition of a new method for representing/encoding molecule and protein sequences into images; and the definition of a deep-learning approach based on a convolutional neuralnetwork in order to create a new method for DTI prediction. The results of this research indicate that the image-based representation of molecule and protein sequences is a viable alternative to the NLP-based approaches and, as such, does not adopt an embedding layer in the neural network. The training results conducted with the Davis and KIBA datasets show that MPS2IT-DTI is viable compared to other state-of-the-art (SOTA) approaches in terms of performance and complexity of the neural network model. Regarding the Davis dataset, the results of the experiments indicate a concordance index (CI) of 0.876 and a MSE of 0.276; with the KIBA dataset, 0.836 and 0.226, respectively. Finally, the experimental results utilizing the BindingDB dataset and six core proteins of SARS-CoV-2 suggest that MPS2IT-DTI performs comparably with state-of-the-art methodologies for the repurposing of clinically approved antiviral agents in the context of COVID-19 treatment.Doctoral Thesis Reconfigurable hardware architecture for SHA-256 hashing in blockchain and IoT applications(Universidade Federal do Rio Grande do Norte, 2024-12-11) Santos Júnior, Carlos Eduardo de Barros; Fernandes, Marcelo Augusto Costa; Silva, Sérgio Natan; https://orcid.org/0000-0001-7536-2506; http://lattes.cnpq.br/3475337353676349; http://lattes.cnpq.br/1334493042199015; Dias, Leonardo Alves; Silva, Lucileide Medeiros Dantas da; Coutinho, Maria Gracielly FernandesAs IoT device usage continues to expand, ensuring secure, low-latency data exchange has become essential, driving research into blockchain-based solutions to meet these requirements. Addressing this demand, this thesis presents a reconfigurable hardware architecture for the SHA-256 hash algorithm, focusing on blockchain and IoT applications, utilizing Field Programmable Gate Arrays (FPGAs) as the target hardware to maximize performance and efficiency in data security processes. The proposed FPGA implementation provides adaptability across various environments, from network servers to energyconstrained IoT devices. Key innovations in this proposal include a multicore parallelism system that optimizes the use of available FPGA resources and a structured analysis of resource consumption, considering both clock frequency and throughput. Additionally, the thesis provides a power consumption analysis, comparing power efficiency across different hardware architectures. The proposed design achieved the implementation of 16 parallel cores on a Xilinx Virtex 6 xc6vlx240t-1ff1156 FPGA, reaching a maximum throughput of 1.4Gbps and dynamic power consumption of 0.452W. This performance represents up to 16x speedup over previous FPGA models and a reduction of up to 234.52x in dynamic power consumption compared to implementations from prior research. Additional comparisons were conducted with other hardware architectures, such as 8- and 16-bit microcontrollers, general-purpose processors, and GPUs. The results underscore the versatility and scalability of FPGA-based SHA-256 implementations for applications requiring high throughput and power efficiency, establishing this work as a significant contribution to information security and computational performance in IoT environments within a blockchain context.Master Thesis Metodologia e plataforma baseadas em aprendizado de máquina para inspeção de defeitos na indústria têxtil(Universidade Federal do Rio Grande do Norte, 2025-01-27) Góes, Angelo Leite Medeiros de; Dória Neto, Adrião Duarte; https://orcid.org/0000-0002-5445-7327; http://lattes.cnpq.br/1987295209521433; https://orcid.org/0000-0003-1138-1403; http://lattes.cnpq.br/3037310998903708; Fontes, Aluisio Igor Rego; Florêncio, Heitor Medeiros; Barroca Filho, Itamir de MoraisQuality inspection (QI) in the textile industry is an essential yet challenging process, particularly due to the diversity of defects and reliance on subjective criteria from human inspectors. This work proposes a methodology based on computer vision and deep learning to optimize the QI process in textiles, alongside the development of an associated platform to demonstrate its feasibility and applicability in industrial environments. The platform integrates the proposed algorithms: grid-based detection, which segments images into patches for local defect classification, and ripple refinement, which improves detection quality by analyzing neighboring cells. The experimental results validate the effectiveness of the methodology. A benchmark of machine learning models was conducted, comparing eight approaches, including YOLOv5, YOLOv8 variants, and popular networks in the literature, using the TILDA 400 dataset, composed of five distinct defect classes. The YOLOv8 models stood out, especially YOLOv8 medium, which achieved 90.35% accuracy with an inference time of 0.5ms on a Tesla P100 GPU, surpassing the average precision of 70% from human inspectors. The YOLOv8 small also demonstrated remarkable performance, with a theoretical inspection capacity of up to 46.875 m/min and 86.96% accuracy, exceeding the manual maximum speed of 15 to 20 m/min. The application of this methodology, combined with the developed platform, demonstrates potential to enhance accuracy, speed, and organization in QI, while also reducing operational costs and promoting automation and efficiency in the textile industry.Master Thesis Blockchain no ecossistema tecnológico da Plataforma revELA: no contexto da esclerose lateral amiotrófica (ELA)(Universidade Federal do Rio Grande do Norte, 2025-01-13) Fonseca, Aleika Lwiza Alves; Valentim, Ricardo Alexsandro de Medeiros; https://orcid.org/0000-0002-9216-8593; http://lattes.cnpq.br/3181772060208133; http://lattes.cnpq.br/2621599224457086; Morais, Antonio Higor Freire de; Nagem, Danilo Alves Pinto; Barbalho, Ingridy Marina Pierre; Valentim, Janaína Luana Rodrigues da Silva; Oliveira, Luiz Affonso Henderson Guedes deThe National ALS Registry is a digital health solution that aims to collect epidemiological data from various patients with Amyotrophic Lateral Sclerosis (ALS) in Brazil. When using technological solutions such as this one, for electronic health data recording, concerns arise regarding security, data integrity, and the importance of an interoperable platform for sharing with other systems. To address these concerns, blockchain technology has been attracting a lot of attention in several areas that already see the possibilities for progress with its incorporation. Considering the needs for security, auditability, and interoperability, and to make the National ALS Registry increasingly secure and reliable, this paper presents the configuration of a blockchain network with the Hyperledger Fabric framework and the implementation of an API, developed with Node.js, used to integrate the blockchain with the National ALS Registry. To evaluate the performance of the proposed solution, the Hyperledger Caliper tool was used, which is a reference for benchmarking blockchain solutions. The results show the viability of using this technology and the relevance of its use is highlighted throughout the text. Although the performance analysis was not satisfactory, with average latency above 19s and throughput values below 100 transactions per second, the data obtained allows us to identify points for improvement in the implementation and optimization of the network.Doctoral Thesis Aplicação de superfície seletiva de frequência como refletor reconfigurável para otimização do desempenho de antena de microfita para aplicações em 5G(Universidade Federal do Rio Grande do Norte, 2024-11-29) Sousa, Thayuan Rolim de; Silva Neto, Valdemir Praxedes da; https://orcid.org/0000-0003-3867-3297; http://lattes.cnpq.br/4160231554601828; http://lattes.cnpq.br/3138846318611230; D'Assunção, Adaildo Gomes; Mendonça, Laercio Martins de; Gomes Neto, Alfredo; Silva, Jefferson Costa eThis work presents the development of reconfigurable frequency-selective surfaces (RFSS) as a reflector to optimize the performance of microstrip antenna for 5G applications. Frequency-selective surfaces (FSS) are structures widely used in communication systems, with applications in the microwave to terahertz range. The research in this doctoral thesis includes computation analysis and experimental characterizations to evaluate the effectiveness of RFSS in different propagation scenarios. The initial analysis is performed by characterizing the FSS in its ideal form to predict the behavior of the reconfigurable structure. Then the proposed RFSS is designed and evaluated. The proposed structure consists of a single dielectric substrate with a conductive layer. The RFSS unit cell is based on a V-shaped element and only a single PIN diode is used as an active device per unit cell. The PIN diode on each line of the RFSS is connected in parallel with a very simple biasing network. These structures were designed, optimized, manufactured and experimentally characterized to validate the numerical results, with good agreement between them. Three biasing state configurations are analyzed: off state, on state and intermediate state. Depending on the biasing state, the RFSS can exhibit single- or multi- resonance frequency(ies). The proposed RFSS exhibits independently switchable characteristics that make it suitable for a variety of applications, in its single-band or multi-band configuration. After this analysis, the RFSS were applied to a system with a dual-band rectangular microstrip antenna. The system consists of placing the RFSS in front of the microstrip antenna, acting as a reflector. The antenna used is of the microstrip type with a rectangular patch, which is modified by inserting two T-shaped slots to provide dual-band response at 1.7 GHz and 3.7 GHz. Numerical results were obtained through simulations using the commercial software ANSYS HFSS. By changing the state of the PIN diode, the system increases the antenna gain and reconfigures its radiation pattern, reaching values of 5.6-dB and 5.5-dB gain increase for the first and second resonance frequencies, respectively. The final results demonstrate that implementing RFSS can significantly increase the gain and steering capability of microstrip antennas. It is demonstrated that the proposed approach is viable and offers substantial improvements in antenna performance, making it a promising solution and contributing to the evolution of 5G networks.Doctoral Thesis Desenvolvimento de novos ressoadores para transmissão de energia sem fio aplicados em Dispositivos Médicos Implantáveis(Universidade Federal do Rio Grande do Norte, 2024-12-20) Duarte Júnior, José Garibaldi; Silva Neto, Valdemir Praxedes da; https://orcid.org/0000-0003-3867-3297; http://lattes.cnpq.br/4160231554601828; https://orcid.org/0000-0002-9708-3118; http://lattes.cnpq.br/4332534673634063; D'Assunção, Adaildo Gomes; Capovilla, Carlos Eduardo; Begmann, José Ricardo; Rodrigues, Márcio Eduardo da CostaThis work presents the analysis and design of new configurations of microwave planar resonators with the proposal of integrating systems for wireless power supply of implanted medical devices. Over the last few years, implantable medical devices (IMD) have become protagonists in assisting diagnostic, prognostic and medical treatment tasks. Most IMD require a form of power supply to operate, which is often done through the use of batteries, thus demanding periodic maintenance and invasive surgical procedures. The wireless power transfer (WPT) technology employed in this scenario presents itself as a promising alternative solution. This thesis is situated in the context of proposing new models of compatible resonators that can be integrated into WPT solutions for IMD power supply. In this work, two WPT techniques were explored: inductive coupling (non-radiative/near field) and planar antennas (radiative). The analysis and design were carried out through the analytical and parametric study of the proposed models using computational electromagnetic simulation tools. A series of technical parameters were verified, with emphasis on the operating frequency, physical dimensions, efficiency, positioning sensitivity, and safety parameters, such as the specific absorption rate (SAR) and admissible power range. Using low-loss dielectric substrates (RO3006 and RO3010), three prototypes were built, designed for operation in the 0,403, 0.915 and 2.45 GHz frequencies, which are part of the MedRadio and ISM bands. Via inductive coupling, it was possible to obtain a system with transfer efficiency greater than 50% at a separation of 8.0 mm of tissue, enabling an admissible power of up to 120 mW for a safety limit of 2.0 W/kg of SAR. Using a planar antenna, a model with dual operating band (0.9 and 2.45 GHz) and circular polarization was obtained, achieving efficiency levels of 0.57 and 1.12% with reduced physical dimensions. The experimentally measured results demonstrated good agreement with the computational analyses and led to promising conclusions regarding the performance of the proposed resonators.Master Thesis Detecção de falhas em operação de bombas elétricas utilizando TinyML e sinais acústicos(Universidade Federal do Rio Grande do Norte, 2024-11-22) Silva, Deivison Luan Xavier; Oliveira, Luiz Affonso Henderson Guedes de; Gendriz, Ignacio Sanchez; https://orcid.org/0000-0003-2690-1563; http://lattes.cnpq.br/7987212907837941; https://orcid.org/0009-0000-2388-0571; http://lattes.cnpq.br/3317633556320520; Bustos, Harold Ivan Ângulo; Silva, Ivanovitch Medeiros Dantas daMost production processes begin with the generation of kinetic energy by electric motors. In fluid-related operations, electric pumps are responsible for the movement and pressure variation of fluids. Given the importance of this equipment, failures in their operation can negatively impact the entire production chain, causing reduced efficiency, unforeseen stoppages, and potential safety risks for workers. To mitigate these issues, industrial asset monitoring strategies are implemented, assessing factors such as the condition and performance of pumps, aiding in corrective and preventive maintenance decisions. Traditionally, vibration and current sensors are used to monitor the condition of this equipment, but due to cost and the need for physical contact, their application may be unfeasible in some cases. Acoustic sensors are a low-impact alternative, capable of detecting anomalies at early stages without physical contact, offering an efficient and non-intrusive monitoring solution. For detecting and diagnosing defects in electric pumps, machine learning techniques, especially neural networks, are widely used. This study investigates the feasibility of using miniaturized machine learning, known as TinyML, in combination with acoustic sensors for detecting defects in electric pumps. Specifically, an audio classification model based on convolutional neural networks (CNN) was developed, implemented, and embedded on the Arduino Nano platform. To validate the proposal, the "MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection" was used, containing audio recordings of defects from different pump models. To ensure the applicability of the embedded system, the relationship between neural network model complexity and its accuracy in detecting anomalies was analyzed, using quantization techniques to reduce the CNN by approximately 50%. Enabling the creation, training, and deployment of machine learning models on edge devices, the Edge Impulse platform was used for developing the fault detection model using acoustic sensors and its compression into a reduced model optimized for the Arduino. The results indicate an accuracy of over 96% in defect detection, providing a low-cost, highly efficient, and easy-to-implement solution with potential applications in various industrial areas, significantly improving asset monitoring and reducing maintenance-related costs.Master Thesis Desenvolvimento de uma plataforma didática para experimento sísmico em caixa de areia(Universidade Federal do Rio Grande do Norte, 2025-01-16) Costa, Anderson Eugênio Silva da; Salazar, Andrés Ortiz; https://orcid.org/0000-0001-5650-3668; http://lattes.cnpq.br/7865065553087432; https://orcid.org/0000-0001-9469-5832; http://lattes.cnpq.br/5353061057885174; Maitelli, André Laurindo; Fonseca, Diego Antônio de Moura; Paiva, José Álvaro deThis work will present the design, development, and implementation of a didactic platform for seismic experiments in a sandbox, aimed at simulating seismic wave propagation and investigating geological features on a laboratory scale. To achieve this, 40 kHz piezoelectric transducers and an automated movement system will be implemented, utilizing an Arduino as the controller, three power drivers, and three stepper motors. The experiment will aim to capture seismic data that will subsequently be transformed into two-dimensional and three-dimensional images of the simulated structures. The platform will be designed to accurately reproduce geological conditions in a controlled manner, enabling the testing of seismic processing algorithms and the analysis of seismic responses in laboratory environments. The platform will provide a user-friendly and accessible system for studying complex geodynamic processes, contributing to the advancement of understanding crustal deformation and seismic wave propagation in natural environments.Master Thesis Planejamento de trajetórias baseado em espuma probabilística para sistemas robóticos autônomos em ambientes dinâmicos(Universidade Federal do Rio Grande do Norte, 2025-01-14) Lima, Alysson Paulo Holanda; Alsina, Pablo Javier; http://lattes.cnpq.br/3653597363789712; http://lattes.cnpq.br/7180434312110852; Medeiros, Adelardo Adelino Dantas de; http://lattes.cnpq.br/6787525856497063; Silva, Bruno Marques Ferreira da; Lins, Filipe Campos de Alcântara; Nascimento, Luís Bruno Pereira doThis study presents an enhanced version of the Probabilistic Foam Method (PFM), focused on motion planning for autonomous robots. In its original form, PFM is developed over a static and previously known configuration space. In this context, the free space is partially filled with overlapping convex bubbles that form a structure resembling foam, creating a safe zone for movement and ensuring safety during maneuvers. From this structure, a search tree is constructed over the bubbles, identifying a feasible path between the specified initial and final configurations. This work proposes improvements to the foam propagation strategy of the PFM to adapt the method to dynamic environments. To handle moving obstacles, the bubble expansion process is modified to account for both spatial and temporal requirements. Specifically, from the surface of a parent bubble, coordinates for the center of a new bubble are randomly generated, considering both position and time. The proposed method stands out for its ability to quickly determine efficient solutions for trajectory planning problems in dynamic environments, even in the presence of moving obstacles with varying but uniform speeds. The algorithm’s effectiveness is validated through functional simulations, where parameters such as the minimum bubble radius, the robot’s maximum speed, and the speeds and directions of the moving obstacles are defined. The simulation results demonstrate the feasibility and efficiency of the proposed method, highlighting its capability to generate safe trajectories in dynamic and previously known scenarios. Thus, the enhanced PFM provides a robust and adaptable planning framework for situations subject to changes over time.