Trabalho de Conclusão de Curso

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Identification of Flooding Incident Impacts Using Neural Networks
(2024) Santos, Alexandre Magno Maciel dos; Vaz, Eduardo Mendes; Cadorniga, João Lucas de Moraes Barros; Pertusi, Pedro Vaz de Moraes
In a world where flooding impacts are becoming increasingly common, such as the disaster in the Brazilian state of Rio Grande do Sul in 2024, the goal of this project is to develop an open-source flooding impact assessment pipeline. Preliminary technical evaluation by NVIDIA indicates that this tool could be integrated with technologies such as a flood simulation system, allowing for predictions in susceptible regions. The pipeline utilizes Convolutional Neural Networks (CNN), public population, and geographic data to process images extracted from the Sentinel-1 and Sentinel-2 satellites and generate metrics. This project classifies flooded regions prior to and after a crisis, providing, for example, estimations of the affected population by area to showcase the impact to assist urban planning professionals. The developed tool integrates a satellite data collection system from these satellites, as they are also open-source and include periodical data, and the CNNs in an intuitive and easily utilizable pipeline, inspired by UNOSAT’s Emergency Mapping which analyzed the impact of floodings in Nepal in 2021. Keywords:
Exploring RISC-V CPU for Aerospace Applications
(2024) Barreto, Arthur Martins de Souza; Barros, Eduardo Schneider Monteiro de; Patelli, Rodrigo Anciães; Assis, Victor Luis Gama de
This paper explores the potential of RISC-V CPUs for aerospace applications, focusing on the specific use case of Brushless Direct Current Motor (BLDC) control. The project aims to contribute to Brazil’s technological sovereignty by reducing reliance on foreign semiconductor technology. An initial implementation of a basic Six-Step algorithm demonstrated the feasibility of RISC-V for motor control. However, the limitations of this approach led to the investigation of more advanced Field- Oriented Control (FOC). While FOC offers superior performance, its implementation presented challenges related to timing constraints, current measurement accuracy, and rotor position feedback. The project successfully identified hardware requirements and constraints associated with BLDC motor control.
Estimating Room Temperature Arrival Time
(2024) Moura, Adney Costa; Drummond, Felipe Martins da Costa; Lopes, Lorran Caetano Machado; Alessi, Tomas Rodrigues
This project aims to develop a predictive model to estimate the temperature decay in a room equipped with air conditioning, using data collected by the Klima device, developed by Boldr, and integrating it into the company’s client environment for visualization. Klima is a device that integrates temperature and humidity sensors, allowing for both the control of air conditioning units and the transmission of data to the cloud, where it is stored and accessed through the company’s application. Based on this historical data, which includes variables such as the air conditioner’s operating mode and the temperature evolution over time, the project seeks to build a model capable of predicting the temperature decay curve of the environment, adapting to the specific behavior of each room. Besides developing the predictive model, the project also aims to integrate these predictions into Boldr’s application, making them accessible to customers, and to update the device’s firmware so that the predictions can also be displayed on a physical screen.
Dynamic Adaptation of Graphical User Interfaces in Augmented Reality Based on Environmental Factors: Enhancing User Experience and Safety
(2024) Santos, André Corrêa; Barão, Pedro Bittar; Lima, Rafael Melhado Araujo
Augmented Reality (AR) has been experiencing great steps in further blending real life and virtual environments, by bringing additional information and virtual controls to real world scenarios. As such, adequate and responsive positioning and integration of virtual elements are fundamental in AR to provide users a seamless experience in blending virtual elements to reality. This project aims to develop an Augmented Reality solution that makes graphical user interfaces (GUI), such as interactive panels, respond dynamically to their environment. The solution automatically adjusts the positioning and appearance of graphical interfaces based on conditions from the environment, such as changes in lighting, presence of important objects (such as warning signs), to the presence of people, and to the presence of possible safety risks to the user. To achieve this, multiple techniques in the area of computer vision have been used for identifying and classifying objects detected, while a Generative Artificial Intelligence (AI) model is used to interpret more nuanced contextual data, such as a user text input. This allows interfaces to adjust themselves to not block the vision of points of interest from the user, adjust colors and contrast for legibility and visual comfort. The development of the solution has been guided by user testing to ensure effectiveness and an intuitive experience. As of this report, a prototype has been developed that can adjust the positioning of the GUI to avoid occluding specific classes based on a text input. Additionally, it modifies the color of the GUI elements to complement the dominant colors behind GUIs in the camera image.

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