https://repositorio.ufu.br/handle/123456789/20862 2026-08-14T20:15:39Z https://repositorio.ufu.br/handle/123456789/49555 Title: Telemetry performance comparison between gNMI and SNMP in IP/MPLS network architecture Abstract: The exponential growth of data traffic in Service Provider environments demands highly accurate and real-time network observability. Traditionally, the Simple Network Management Protocol (SNMP) has been the standard for network monitoring; however, its pull-based polling architecture presents limitations in detecting transient anomalies. This study presents a comparative performance analysis between SNMP and the modern, push and Remote Procedure Call (RPC) based gRPC Network Management Interface (gNMI). By using Infrastructure as Code (IaC) principles, a multi-vendor routing environment comprising Juniper and Arista nodes was virtualized using the Containerlab orchestration framework. The methodology subjected the observability pipeline to deterministic control-plane and data-plane anomalies, including volumetric Distributed Denial of Service (DDoS) bursts, Border Gateway Protocol (BGP) route flapping, and Label Distribution Protocol (LDP) intermittency. The research concludes that gNMI provides high-fidelity visibility (better spikes view) and lower operational overhead (39%-50% less CPU usage) for modern autonomous networks, recommending a phased migration strategy for production environments. 2026-08-05T00:00:00Z https://repositorio.ufu.br/handle/123456789/49344 Title: Estimação da relação sinal ruído óptica baseada em aprendizado profundo utilizando diagramas de constelação das modulações dp-mpsk e dp-mqam em receptores ópticos coerentes flexíveis Abstract: The Optical Signal to Noise Ratio (OSNR) is a critical parameter for the operation, management, and maintenance of coherent optical communication networks. Given its importance, this work proposes and evaluates a deep learning (DL) based approach for OSNR estimation from constellation diagrams. For the development of the models, a diverse dataset comprising over 19,000 images was generated through the simulation of signals with DP m-PSK and DP m-QAM modulation formats under different visual configurations. In total, 15 representative state-of-the-art Convolutional Neural Network (CNN) architectures were evaluated, including the MobileNetV3, ConvNeXt, DenseNet, and EfficientNet families. The ConvNeXtBase architecture achieved the best predictive performance in the study, with a Mean Absolute Error (MAE) below 0.43 dB and a coefficient of determination (R²) above 0.98. The obtained results confirm the high potential of computer vision techniques for the continuous and non- intrusive performance monitoring of next-generation optical networks. 2026-03-16T00:00:00Z https://repositorio.ufu.br/handle/123456789/49199 Title: Modelagem de enlaces de comunicação com nanossatélites Abstract: This work presents an analysis of the communication link performance between low Earth orbit (LEO) nanosatellites and a ground station located at the Santa Mônica campus of the Federal University of Uberlândia. Given the increasing use of CubeSats for a wide range of applications, understanding how link losses behave is essential to ensure the reliability of the communication system. The study investigates the main factors that influence link performance, including the satellite antenna gain, atmospheric conditions, data rate, and elevation angle. The methodology relied on classical link budget modeling and MATLAB-based simulations, which made it possible to observe how each parameter affects the signal-to-noise ratio and the overall link margin. The results indicate that the combination of intense rainfall, low elevation angles, and high data rates forms the most critical scenario for communication, potentially making the link unfeasible when the margin is insufficient. The findings highlight that selecting appropriate CubeSat parameters, together with understanding the local environmental conditions, is crucial for the efficient design of nanosatellite communication systems. 2026-02-12T00:00:00Z https://repositorio.ufu.br/handle/123456789/49170 Title: Sistema de autodevolução de livros utilizando tecnologia RFID Abstract: With the popularization of RFID (Radio Frequency Identification) technology, it has become a trend to use it in libraries to replace and streamline traditional management. With this technology, the routine of book borrowing activities in libraries becomes much faster, as well as inventory management, as the technology enables the use of self-service equipment and automatic periodic inventories. The Santa Mônica campus library already uses RFID technology, but with the malfunctioning of the self-return system a few months ago, the system has been experiencing issues. In this work, a prototype of a new book self-return system was developed for the Santa Mônica campus library using RFID sensors, aiming to provide a solution to the problem at a low cost. 2024-02-29T00:00:00Z