Mestrado em Engenharia Elétrica

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http://repositorio.ufes.br/handle/10/17619

Nível: Mestrado Acadêmico
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Navegando Mestrado em Engenharia Elétrica por Assunto "Acesso remoto"

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    Desenvolvimento e implementação de um sistema de monitoramento em tempo real da tensão da rede com acesso remoto
    (Universidade Federal do Espírito Santo, 2009-10-05) Colnago, Guilherme Piazentini; Sousa, Gilberto Costa Drumond; Vieira, José Luiz Freitas; Marafão, Fernando Pinhabel; Simonetti, Domingos Sávio Lyrio
    Some years ago in Brazil the power quality was related, basically, with interruption of supplied energy and certain special loads of industry. In recent years, however, under controls of the regulating agency ANEEL (Agência Nacional de Energia Elétrica) and with specialists, the power quality area received the due attention and was legislated and acquired regulations. So, finally, the area of Power Quality was formally created and now it embraces several electrical phenomena and events. Because of the new regulations, this work presents a project of a power quality meter. One of meter s focuses is to be a low cost system and becomes able to be used in large scale. This power quality meter is an electronic system that processes the voltage signal of electrical network and extracts data related to power quality; the data are locally stored and after they are remotely accessed and transmitted to a data base to be analyzed.
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    Monitoramento da frequência respiratória usando sensores de fibras ópticas de polímero integrados ao smartphone com conectividade em nuvem
    (Universidade Federal do Espírito Santo, 2023-10-26) Gomes, Lívia Gonçalves; Leal Junior, Arnaldo Gomes; https://orcid.org/0000000290750619; http://lattes.cnpq.br/7246557168481527; http://lattes.cnpq.br/9570544321186212; Diaz, Camilo Arturo Rodriguez; https://orcid.org/0000000196575076; http://lattes.cnpq.br/2410092083336272; Mello, Ricardo Carminati de; https://orcid.org/0000000304204273; http://lattes.cnpq.br/1569638571582691; Marques, Carlos Alberto Ferreira
    Respiratory rate is one of the important physiological signals used to monitor human health in disease and physical activity areas. In this context, there are multiparameter sensors that are commercially known and usually applied in hospital settings. Their design does not provide great mobility for the patient, in addition to the high cost, making accessibility to other environments difficult. On the other hand, the advancement of technologies has led to the development of various types of electrical sensors for measuring these signals; however, the majority of them are electrically and electronically based, not being suitable for environments with electromagnetic interference. This work presents a respiratory rate system composed of a polymer optical fiber sensor using a smartphone as the interrogator system, which acts in the emission and acquisition of the optical signal. Additionally, integration with the Internet of Things was carried out, using Edge Computing techniques for local signal processing in an application developed on the AndroidStudio platform. The ThingSpeak platform is used for cloud storage and the ThingView mobile application allows online viewing of information, providing the remote access. To verify the accuracy of the sensor, a metronome was set to specific frequency rates, used as a reference. The sensor underwent extension and retraction movements, carried out manually, at the frequency of the metronome’s beats at these rates. The sensory system presents a maximum percentage error of 4.5% (1.35 BPM - Breaths per Minute) when comparing the values obtained with the reference frequencies used. Furthermore, tests were performed on volunteers to verify the system’s performance in a real environment, where they were asked to breathe normally, at rest, and also simulating the practice of physical exercises at three known breathing rates. The highest percentage error verified for the resting state is 3.63% (0.8 BPM) and for the moving state 5.35% (1.88 BPM). To verify the remote access to information, the visualization of respiratory rate data measured by the local system in ThingView was analyzed. The frequency read in the application is the same measured by the sensor, presented instantly, it is also visualized the measurement information of the week and month, showing the efficiency of the proposed approach for remote sensing applications with cloud integration.