Browsing by Author "Pires, Ivan Miguel"
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- Analysis and real-time data of meteorologic impact on home solar energy harvestingPublication . Ferreira, João; Lourenço, Ismael; Henriques, João; Pires, Ivan Miguel; Caldeira, Filipe; Wanzeller, CristinaSolar energy production increased in the world from 0 TWh in 1965 to 724.09 TWh in 2019. Solar energy is adopted as a source for residential renewable energy sources because, besides Biomass sources, it’s the only one that can be installed and maintained at home. Operating efficiency is an important consideration when evaluating the application of photovoltaic panels (PV) technology. A real-time system monitoring is required to analyse the current production and understand the impact of the weather conditions on PV production. This paper extends the literature on the residence solar energy harvesting subject, by providing a scalable architecture that can be used as starting point on data analysis on PV panels efficiency and how weather conditions impact energy production. A dataset was collected related to PV panel energy production, the residence energy consumption and that’s reading weather conditions. Wind intensity and direction, temperature, precipitation, humidity, atmospheric pressure and radiation were weather conditions analysed. Moreover, this data was analysed and interpreted in order to evaluate the pros and cons of the architecture as well as how the weather impacted the energy production.
- Biometric Data Capture as a Way to Identify Lack of Physical Activity in Daily LifePublication . Marques, Luís; Lopes, Luca; Ferreira, Miguel; Henriques, João; Pires, Ivan Miguel; Caldeira, Filipe; Wanzeller, CristinaGiven the impact of the pandemic era, it is important the effects of physical activity on human beings, physically and mentally. The significant advance in the technology industry of biomedical sensors and mobile devices allowed the arrival of new health monitoring prototypes to improve people’s lives. This work implements a data capture system, using an electrocardiogram (ECG) and accelerometer (ACC) type sensor to collect a large volume of data for further analysis to obtain metrics to assess the activity level during this pandemic phase. Using a BITalino device that allows us to collect a large amount of information from various sensors, we, therefore, chose to use it as a platform to capture data from the sensors mentioned above. In the first phase, we will capture the largest possible amount of data from the subject in the test phase. Then, the collected data will be sent to a web server, where it will be processed. Finally, in a third phase, the data will be presented in a more summarized and graphical way. In this way, we will analyze the impact of movement/inactivity on the test subjects’ daily life with the referred sensors’ biometric data.
- COVID-19 Next Day Trend ForecastPublication . Costa, Marcelo; Rodrigues, Margarida; Baptista, Pedro; Henriques, João; Pires, Ivan Miguel; Wanzeller, Cristina; Caldeira, FilipeHistorically, weather conditions are depicted as an essential factor to be considered in predicting variation infections due to respiratory diseases, including influenza and Severe Acute Respiratory Syndrome SARS-CoV-2, best known as COVID-19. Predicting the number of cases will contribute to plan human and non-human resources in hospital facilities, including beds, ventilators, and support policy decisions on sanitary population warnings, and help to provision the demand for COVID-19 tests. In this work, an integrated framework predicts the number of cases for the upcoming days by considering the COVID-19 cases and temperature records supported by a kNN algorithm.
- Mobile 4G Network: Signal Power and Quality vs Bandwidth ThroughputPublication . Oliveira, Luis; Henriques, João; Pires, Ivan Miguel; Wanzeller, Cristina; Caldeira, FilipeOne of the significant challenges mobile telecom operators face is providing a good Quality Of Experience (QoE) to their customers. This QoE in mobile broadband is sometimes measured by throughput and latency that the customers observe when using their APPs or even when intentionally perform speed meter tests using, most of the time, the worldwide well-known APP Speedtest. Since the implementation, the 4G mobile broadband technology has been the network, where customer expectations are high when related to throughput and latency. Although the 4G network, also known as Long Term Evolution (LTE), is the technology, according to frequencies used in Portugal and capable of reaching 391.6Mbps Mbps on the downlink and 75 Mbps the Uplink, on LTE-Advanced with carrier aggregation. Thus, it possible to achieve must higher throughput, and users never observed these throughputs. The throughput obtained depends not only on the network and terminals’ capabilities and configurations but also on the radio conditions measured by signal strength and quality. This paper presents a study of these radio conditions, throughput, and obtained results. Moreover, the results are shown on an analytic and monitoring solution.