https://repositorio.ufu.br/handle/123456789/5142 Tue, 19 May 2026 08:06:48 GMT 2026-05-19T08:06:48Z https://repositorio.ufu.br:443/retrieve/9bc49516-3b2d-430b-b40d-367ebc2fccfc/logo-facom.png https://repositorio.ufu.br/handle/123456789/5142 https://repositorio.ufu.br/handle/123456789/48693 Title: Ponto de Troca de Internet do Futuro (FIXP) - habilitando Interconexão de arquiteturas de internet do futuro Abstract: The Internet was designed to meet needs different from those of today. Current applications impose new demands on the network that it cannot meet, given its limitations. One of the difficulties of the Internet is the lack of flexibility to deploy new network-layer services, which limits its evolution. This scenario motivated the development of several Future Internet Architectures (FIAs). These are disjoint architectures that propose, through their designs, to offer mechanisms that advance various aspects of communication, such as flexibility in the network layer, mobility, multicasting, quality of service (QoS), connectivity, quality of experience (QoE), security, among others. Despite the demand, the lack of implementation of these new architectures in high-performance networks makes it difficult to assess the maturity of their communication characteristics for specific scenarios and applications. On the other hand, due to the ubiquity of the traditional Internet, replacing the TCP/IP architecture is impractical. A single communication network or logical exchange point that integrates FIAs and the TCP/IP architecture without compromising its architectural principles (resources and mechanisms) is a promising approach to meeting the demands of current and future applications. This work presents the Future Internet Exchange Point (FIXP) and positions it as a candidate for this single network or logical exchange point that allows the coexistence of different Internet architectures; it also provides qualitative and quantitative analysis of FIXP: its capacity for evolution and incorporation of new architectures, the feasibility of implementing FIXP in the current networks, and the performance of application communication of IP and ETArch architectures in the FIXP infrastructure, taking into account the quality of user experience. Evaluations demonstrate the FIXP architecture conceptually by executing selected use cases in which applications from different FIAs coexist in the same communication environment. In addition, the work presents the details of the integration of the ETArch and IP architectures in the FIXP network, aiming to facilitate, in the future, the incorporation of other Internet architectures in this infrastructure. Regarding related work, the collection allows comparison with these works and with FIXP, positioning it as the state of the art, as the only infrastructure that does not add overhead to switching devices through interoperability processes and/or protocol stack development. Furthermore, its architectural principles differ from those of related work; therefore, the difficulty of integrating new FIAs into FIXP is low to medium relative to that of related work. These two facts, the lack of data plan overhead and the low or medium difficulty level of integrating new FIAs, facilitate the implementation of FIXP in current networks. The work also presents FIXP’s view of multi-architecture applications. These applications use different Internet architectures for their services. The use cases that demonstrate FIXP’s support for these applications simulate the execution of microservices within the same application or virtual machine, which is common nowadays. The difference is that microservices in a multi-architecture application can use frameworks from different FIAs. Notes: O documento inclui um apêndice (Apêndice A) que apresenta parte da implementação dos switches FIXP. Tue, 27 Jan 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/48693 2026-01-27T00:00:00Z https://repositorio.ufu.br/handle/123456789/48631 Title: Visualização e Interação no Tesouro Direto: Uma Análise de Usabilidade e Acessibilidade na Seção de Histórico de Preços e Taxas Fri, 20 Mar 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/48631 2026-03-20T00:00:00Z https://repositorio.ufu.br/handle/123456789/48612 Title: Modelo de otimização da grade horária para discentes da FACOM/UFU Wed, 25 Mar 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/48612 2026-03-25T00:00:00Z https://repositorio.ufu.br/handle/123456789/48556 Title: Aprendizado de redes neurais profundas para diagnóstico molecular rápido e não invasivo de COVID-19 pela saliva Abstract: The COVID-19 pandemic exposed limitations of conventional diagnostic methods, such as RT-PCR, which, despite their high sensitivity, are costly, time-consuming, and logistically complex. Vibrational spectroscopy emerges as a promising alternative, offering rapid, low-cost, and reagent-free analyses. However, the complexity of biochemical changes reflected in Raman and ATR-FTIR spectra demands advanced analytical methods that surpass traditional chemometric approaches in extracting nonlinear patterns. This work develops and evaluates models based on deep learning, specifically Convolutional Neural Network (CNN) architectures and Convolutional Neural Networks integrated with Long Short-Term Memory (CNN-BiLSTM), applied to the analysis of non-invasive biological sample spectra for COVID-19 detection. CNNs offer superior capability for extracting local spatial features from spectra through convolution operations, while BiLSTM networks complement this analysis by capturing bidirectional temporal dependencies in sequential spectral data, enabling a more comprehensive understanding of complex spectral patterns. Two architectures were developed: CNN-Spectra was designed to analyze Raman spectra from blood serum, achieving 96.8% accuracy; CNN-BiLSTM-Spectra was developed to analyze ATR-FTIR spectra from saliva, reaching an average accuracy of 80% and outperforming traditional and state-of-the-art models. The results confirm the effectiveness of the proposed models in extracting relevant features from complex spectral data, improving classification between positive and negative COVID-19 samples. It is concluded that the integration of deep learning with vibrational spectroscopy is a promising approach for biomedical diagnosis, contributing to faster, more accurate, and sustainable detection methods. Mon, 22 Sep 2025 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/48556 2025-09-22T00:00:00Z