Browsing by Author "Beko, Marko"
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- Analysis of MQTT-SN and LWM2M communication protocols for precision agriculture IoT devicesPublication . PEREIRA DOS SANTOS, ROGERIO; LEITHARDT, VALDERI; Beko, MarkoA Internet das Coisas (IoT) tem se tornado parte integrante do estilo de vida da sociedade moderna e uma ferramenta importante em muitas áreas de negócios. Nos últimos anos, verificou-se uma grande necessidade de conectar novos dispositivos IoT à agricultura de precisão. Conhecidos como objetos conectados, esses vêm ganhando cada vez mais força. Assim como a adoção da IoT para agricultura, residências, cidades inteligentes, logística, saúde, manufatura e outros. Existe também inúmeras preocupações em relação à comunicação destes dispositivos. Com a capacidade de coletar dados, a tecnologia IoT se torna um recurso valioso e cuidados devem ser tomados na busca por mecanismos eficazes de comunicação. Nesse sentido, este trabalho tem como objetivo apresentar uma análise dos protocolos de comunicação MQTT-SN e LWM2M, comparando seus desempenhos na transmissão de mensagens. O modelo foi desenvolvido com auxílio da ferramenta Node-RED, que consiste em programação baseada em fluxo na avaliação e desempenho implementado em tempo de execução. Ao final das simulações, foi possível avaliar que o protocolo MQTT-SN apresentou melhores resultados nos testes realizados.
- A Feed-Forward Neural Network Approach for Energy-Based Acoustic Source LocalizationPublication . Correia, S. D.; Tomic, Slavisa; Beko, Marko
- A Multi-Start Algorithm for Solving the Capacitated Vehicle Routing Problem with Two-Dimensional Loading ConstraintsPublication . Fava, Leandro; Furtado, João; Helfer, Gilson Augusto; Barbosa, Jorge; Beko, Marko; Correia, Sérgio; LEITHARDT, VALDERI
- Multispectral Cameras and Machine Learning Integrated into Portable Devices as Clay Prediction TechnologyPublication . Helfer, Gilson Augusto; Barbosa, Jorge; Alves, Douglas; Costa, Adilson Ben da; Beko, Marko; LEITHARDT, VALDERI
- Package Proposal for Data Pre-Processing for Machine Learning Applied to Precision IrrigationPublication . Santos, Rogério Pereira Dos; Beko, Marko; Leithardt, Valderi R. Q.The evolution of the Internet of Things (IoT) devices for precision agriculture is directly linked to the needs and interests of humanity. These advances include migration to cloud computing, data engineering, and the democratization of tools. These changes allow for better management, data quality, security, and scalability, reducing operational costs. The objective of this research was to present a proposal for a data pre-processing package for meteorological stations classified as conventional. Among the main findings of this research is the need for data pre-processing for Machine Learning applications focused on precision irrigation, controlled by IoT devices; the use of data from conventional weather stations for Machine Learning applications; the availability of applications developed in Open Source repositories, and the proposal of a data pre-processing package to help professionals from different areas. The systematic review examined the various machine-learning applications for precision irrigation. Different models and mechanisms used to apply Machine Learning in precision irrigation projects were identified. In addition, we look at the challenges faced when using Machine Learning for precision irrigation, including the lack of data, the need for efficient data pre-processing, and the need to tune the model to get the best possible result. At the end of the article, we propose a data pre-processing package for conventional meteorological stations. This package includes normalization, noise removal, and outliers to improve the reliability of the input data.
- A Review of Techniques for Implementing Elliptic Curve Point Multiplication on HardwarePublication . Verri Lucca, Arielle; Sborz, Guilherme; LEITHARDT, VALDERI; Beko, Marko; Zeferino, Cesar Albenes; D. Parreira, Wemerson
- Target localization in NLOS environments using RSS and TOA measurementsPublication . Tomic, Slavisa; Beko, Marko; Tuba, Milan; Franco Correira, V.This letter addresses the problem of target localization in adverse non-line-of-sight environments. By utilizing integrated received signal strength and time of arrival measurements, a novel alternating algorithm is proposed. The new algorithm is derived by converting the original nonconvex problem into a generalized trust region sub-problem framework, which can be solved exactly by just a bisection procedure. Therefore, the proposed algorithm is very light in terms of computational cost, and its excellent estimation accuracy is validated through computer simulations.