Mestrado em Engenharia Elétrica
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- ItemDetecção de transtorno mental via EEG, microestados e redes neurais de grafos(Universidade Federal do Espírito Santo, 2025-05-27) Candeia, Daniel Ribeiro; Ciarelli, Patrick Marques ; https://orcid.org/0000-0003-3177-4028; http://lattes.cnpq.br/1267950518719423; https://orcid.org/0009-0001-4427-7496; http://lattes.cnpq.br/2696632870728316; Côco, Klaus Fabian ; https://orcid.org/0000-0001-7793-0693; http://lattes.cnpq.br/1374499533178055; Tello, Richard Junior Manuel Godinez ; https://orcid.org/0000-0003-1428-0990; http://lattes.cnpq.br/3966230569744918Electroencephalogram (EEG) is a non-invasive and cost-effective technique widely used to study brain activity and diagnose neurological disorders. However, visual analysis of EEG signals is complex and requires expertise, highlighting the need for automated diagnostic support systems. In this context, this study proposes a graph-based neural network model for detecting mental disorders using EEG signals, leveraging microstate analysis. The proposed model integrates graph neural networks (GNNs) with microstate analysis, which captures transient and stable patterns of brain activity. The TUH Abnormal EEG Corpus (TUAB) dataset, containing normal and abnormal EEG signals, was used. The process included the extraction of microstates, the construction of graphs based on Spearman correlation between EEG channels, the extraction of features from EEG signals, and the application of Principal Component Analysis (PCA) to reduce the dimensionality of these features. Three GNNs were trained, each associated with signals from each microstate, and their outputs were combined using an ensemble technique. The final model achieved an accuracy of 97.46% on the test set, outperforming existing results of methods in the literature. The results highlight the effectiveness of the proposed approach, demonstrating the potential of GNNs and microstate analysis for detecting mental disorders from EEG signals
- ItemDevelopment of a mobile service robot system: enhancing localization, guidance, and search tasks in indoor environments(Universidade Federal do Espírito Santo, 2025-08-06) Rodriguez, Elio David Triana; Jiménez-Hernández, Mario Fernando ; https://orcid.org/0000-0003-0965-277X; http://lattes.cnpq.br/6078067029625341; Frizera Neto, Anselmo; https://orcid.org/0000-0002-0687-3967; http://lattes.cnpq.br/8928890008799265; https://orcid.org/0009-0000-4454-987X; ttp://lattes.cnpq.br/3696912600039873; Santos, Thiago Oliveira dos; https://orcid.org/0000-0001-7607-635X; http://lattes.cnpq.br/5117339495064254; Avilés Sanchez, Oscar Fernando ; https://orcid.org/0000-0001-8676-9926; http://lattes.cnpq.br/7481150993160615Advancements in service robotic systems demand robust Human-Robot Interaction (HRI) strategies capable of operating in multilingual and dynamic environments. However, current HRI approaches based on Natural Language Processing (NLP) often face limitations related to scalability, ambiguity in communication, and dif f iculty in linking unstructured input to structured data, thereby reducing robotic adaptability. This study proposes an HRI framework that integrates NLP through Large Language Models, combined with a decision-making algorithm grounded in Generative Artificial Intelligence (Generative AI) and context-aware reasoning. The system adopts a modular architecture comprising request validation, map valida tion, and response generation, enabling the synthesis and association of structured andunstructured data. As a result, the robot is capable of navigating, guiding users, executing adaptive tasks, and responding to user requests through a chat-style in terface. The framework was implemented on a mobile robot that was structurally, electrically, and software-wise adapted, culminating in the development of an au tonomous system able to complete localization, guidance and search tasks in in door environments. The NLP-based interaction modules were then integrated, and the resulting autonomous responses to user requests were evaluated as satisfactory. Usability evaluations conducted with real users, using the System Usability Scale (SUS), yielded consistently high scores ranging from “good” to “excellent.” How ever, participants reported a slightly lower perception of accuracy and increased frustration when operating in fully autonomous LLM mode compared to a pre programmed control mode. On the other hand, validation experiments demon strated a 91% success rate, confirming the system’s capability to process user re quests andexecutetaskstypicalofguiderobots. Theseresultsvalidatethefeasibility of integrating LLMs into multilingual robotic systems, highlighting both the poten tial and current limitations of NLP in HRI. They also highlight the transformative role of LLMs in enhancing natural language understanding and decision-making in real-world scenarios. Future work should focus on improving the handling of ambiguous user requests and refining feedback mechanisms to enhance the overall user experience.
- ItemMPPT for photovoltaic systems using Fractional Power Processing Architecture(Universidade Federal do Espírito Santo, 2025-08-05) Leão, André Salume Lima Ferreira; Santos, Walbermark Marques dos; https://orcid.org/0000-0002-9871-6028; http://lattes.cnpq.br/5558697161842579; https://orcid.org/0009-0008-1649-3088; http://lattes.cnpq.br/0522526374396770; Simonetti, Domingos Sávio Lyrio; https://orcid.org/0000-0001-5920-2932; http://lattes.cnpq.br/1107005171102255; Coelho, Roberto Francisco; https://orcid.org/0000-0002-4672-0885; http://lattes.cnpq.br/9967005468124403This study presents a novel investigation, with conceptual contributions, into how a fractional power processing (FRPP) architecture can be used to track the maximum power point of a photovoltaic (PV) system while using PV modules as the auxiliary source. Two configurations are proposed for fractional power processing within the photovoltaic system. The first, referred to as coupled FRPP (CFRPP), utilizes only photovoltaic modules as the auxiliary energy source, with direct connection to a converter. The second, referred to as decoupled FRPP (DFRPP), incorporates an energy storage system (ESS) alongside the PV modules, allowing the two PV arrays to function independently. Simulations were conducted to validate the theoretical analysis of a PV system under these two FRPP architecture configurations and to compare their performance to different power processing architectures. The results indicate that the proposed model equations are valid. It is concluded that fractional power processing enables converters designed for lower power levels to regulate the generation of higher. When applied to photovoltaic systems, both types of FRPP have demonstrated the ability to achieve maximum power while using low-power, low-complexity converters, since they are non-isolated and can even adopt simple topologies, such as buck or boost. However, each type exhibited significantly different capabilities. The DFRPP configuration demonstrates its viability by delivering superior performance compared to other power processing architectures. In contrast, the CFRPP architecture exhibits performance limitations, making it less advantageous under comparable conditions
- ItemGesture recognition for prosthesis control using electromyography and force myography based on optical fiber sensors(Universidade Federal do Espírito Santo, 2025-09-17) Ramirez Cortés, Felipe; Segatto, Marcelo Eduardo Vieira ; https://orcid.org/0000-0003-4083-992X; http://lattes.cnpq.br/2379169013108798; Díaz, Camilo Arturo Rodríguez ; https://orcid.org/0000-0001-9657-5076; http://lattes.cnpq.br/2410092083336272; https://orcid.org/0009-0008-7465-3887; http://lattes.cnpq.br/0873671321556842; Silveira, Mariana Lyra ; https://orcid.org/0000-0002-0368-5629; http://lattes.cnpq.br/5307116832176112; Silva, Jean Carlos Cardozo da ; https://orcid.org/0000-0003-2310-9159; http://lattes.cnpq.br/9949032159595994Amputation is the partial or total loss of a limb. It is a challenging event that affects people worldwide, with an estimated prevalence of 552.45 million in 2019 and a growing rate. The loss of an upper limb, in particular, strongly affects a person’s ability to perform activities of daily living (ADL), communicate, and interact with their environment. To restore lost functionality, assistive devices known as prostheses have been developed. Modern active prostheses can be controlled by interpreting the user’s movement intention through various biological signals, such as Surface Electromyography (sEMG), which measures the electrical activity of muscles. While sEMG is an established and predominant control method, it has limitations. Forcemyography (FMG) is a technique that measures changes in muscle volume and pressure during contraction. It has emerged as a promising alternative, offering advantages such as greater signal stability and reduced sensitivity to skin conditions like sweat. This master’s dissertation proposes and evaluates a hybrid sensor system combining FMG and sEMG to create a more robust and precise method for hand gesture classification. The system integrates a custom-developed FMG sensor, which uses a Fiber Bragg Grating (FBG) embedded within a flexible 3D-printed structure, with a commercial sEMG sensor. The primary goal is to improve the control of real and virtual prosthetic hands for amputees. The study involved recording signals from able-bodied subjects while they performed tasks involving different hand angles and grip forces. Data from the sEMG, FMG, and the combined hybrid system were used to train and test seven different machine learning algorithms, with the dataset split into 80% for training and 20% for testing. Results showed that the optimal sensing strategy is task-dependent. For angle classification, the hybrid FMG-sEMG sensor achieved the highest accuracy of 85.62% with the K-Nearest Neighbors (KNN) classifier. For force classification, the sEMG sensor alone was superior, reaching an accuracy of 92.53% with a Support Vector Machine (SVM). Furthermore, the hybrid system’s feasibility for real-time application was validated in a Virtual Reality (VR) environment, where it achieved 99.83% accuracy in classifying binary open/close hand gestures. This research demonstrates the complementary nature of FMG and sEMG signals, concluding that a multimodal approach can be used to develop more sophisticated, reliable, and intuitive control systems for upper-limb prostheses by selecting the best sensing modality for the desired task
- ItemVirtualização de ambientes em tempo real para interação multimodal : teleoperação com uso de realidade mista e feedback háptico(Universidade Federal do Espírito Santo, 2025-09-06) Vieira, Igor Batista; Mello, Ricardo Carminati de; ttps://orcid.org/0000-0003-0420-4273; http://lattes.cnpq.br/1569638571582691; Frizera Neto, Anselmo; https://orcid.org/0000-0002-0687-3967; http://lattes.cnpq.br/8928890008799265; https://orcid.org/0009-0007-9547-4485; http://lattes.cnpq.br/; Rodríguez Díaz, Camilo Arturo; https://orcid.org/0000-0001-9657-5076; http://lattes.cnpq.br/2410092083336272; Alsina, Pablo Javier; https://orcid.org/0000-0002-2882-5237; http://lattes.cnpq.br/3653597363789712Conventionalteleoperationinterfaces, basedontwo-dimensionalmonitorsandnon immersive controllers, present significant limitations for human-robot interaction. The absence of depth perception, restricted field of view, and high cognitive load hinder the operator’s ability to build an accurate mental model of the remote envi ronment, reducing the effectiveness of robot control. In this context, the integration of immersive technologies and haptic devices emerges as an alternative to enhance the user’s sense of presence, overcome perceptual barriers, and make teleoperation more natural and efficient. To address these challenges, this dissertation proposes the development of a multi modal teleoperation system, composed of a mobile robotic platform equipped with perception sensors, a simultaneous localization and mapping (SLAM) module, and an immersive interface based on Virtual Reality integrated with a haptic device. The architecture was designed to operate in a distributed manner, with processing shared between the robot and the operator station, enabling the construction of a low-latency digital twin. Two experimental studies were conducted: the first vali dated the accuracy of the visual mapping system compared to classical approaches, while the second evaluated the haptic interface in user teleoperation tasks. The results obtained confirmed the hypothesis that the combination between Vir tual Reality and haptic feedback provides a telepresence experience superior to tra ditional solutions. The system demonstrated robustness in environment mapping, low response time in data transmission, and an increased sense of immersion re ported by the users. Specifically, the user study demonstrated that the immersive interface was able to reduce the average number of collisions from 3.00 to less than 0.3 and decrease the perceived workload (NASA-TLX) by more than 50%. These f indings highlight the potential of the proposed approach as a relevant contribution to the advancement of robotic teleoperation, with possible applications in remote inspection, hazardous environments, and human-robot collaboration systems.