Low-cost sensors have the potential to create dense air monitoring networks that help enhance our understanding of pollution exposure and variability at the individual and neighborhood-level; however, sensors can be easily influenced by environmental conditions, resulting in performance inconsistencies across monitoring settings. During summer 2020, 20 low-cost particulate sensors were deployed with a reference PM2.5 monitor in Denton, Texas in preparation for calibration. However, from mid to late-summer, dust transported by the Saharan Air Layer moved through the North Texas region periodically, influencing the typical monitoring pattern exhibited between low-cost sensors and reference instruments. Traditional modeling strategies were adapted to develop a new approach to calibrating low-cost particulate sensors. In this study, data collected by sensors was split according to a novel dust filter into dust and non-dust subsets prior to modeling. This approach was compared with building a single model from the data, as is typically done in other studies. Random forest and multiple linear regression algorithms were used to train models for both strategies. The best performing split-model strategy, the multiple linear regression models split according to dust and non-dust subsets (combined R2 = 0.65), outperformed the best performing single-model strategy, a random forest model (R2 = 0.49). The results from this study indicate that low-cost sensor performance can be greatly influenced by the presence of dust, and that adaptive strategies, like the ones presented in this paper, are necessary when calibrating sensors in environments that may experience pollution from inconsistent sources throughout the year.

The construction industry is massive but not so advanced in adapting and utilizing technology. Sensors and sensing technology are critical elements that can enhance the digitalization of the construction industry. The need for low-cost sensors is rapidly increasing as the industry steps into the construction 4.0 phase. This research is an exploratory study on the potential of low-cost sensors, concentrating on the temperature and humidity sensors in the production stage of construction. The methodology used in the research is a qualitative approach consisting of literature review and interviews. The literature review identified the state of art of low-cost temperature and humidity sensors. From interviews, the constraints, adaptation factors were identified. The potential application of the low-cost temperature and humidity sensors were identified from the literatures and interviews. The conclusion of the research was that there is a knowledge gap in the field of low-cost temperature and humidity sensors. Furthermore, there is lack of adaptation of sensor technology in the production stage of the construction industry even though it has potential to improve the productivity and quality.

General Aviation (GA) accidents constitute the majority of aviation related accidents. In the United States, there have been over 7,000 GA accidents compared to 190 airline accidents in the last 8 years. Flight data analysis has helped reduce the accident rate in commercial aviation. Similarly, safety analysis based on flight data can help GA be safer. The FAA mandates flight data recorders for multi-engine and turbine powered aircraft, but nearly 80% of General Aviation consists of single engine, of which only a small portion contain any form of data recording device. GA aircraft flight data recorders are costly for operating pilots. Low-cost flight recorders are few and rarely used in GA safety analysis due to lack of accuracy compared to the certified on-board equipment. In this thesis, I investigate the feasibility of using a low-cost Attitude and Heading Reference System (AHRS) to detect hazardous states in GA aircraft. I considered the case of roll angles and found that the low-cost device has significant measurement errors. I developed models to correct the roll angle error as well as methods to improve the detection of hazardous roll angles. I devised a method to evaluate the time accuracy along with the angle accuracy and showed that despite the errors, the low-cost device can provide partial hazardous state detection information.

Orientador: André Luiz Andreoli / Resumo: Os transformadores são equipamentos de suma importância no sistema elétrico de potência. Qualquer falha destes equipamentos, pode acarretar prejuízos e consequências graves tanto para as concessionárias de energia, quanto aos seus consumidores. A ocorrência de descargas parciais em transformadores de potência está diretamente relacionada às condições de seu sistema de isolamento. Elas podem se desenvolver ao longo do tempo e culminar na perda total do sistema de isolamento. Desta forma, o monitoramento das descargas parciais torna-se essencial para o planejamento de uma manutenção e a garantia do funcionamento adequado destes equipamentos. Neste contexto, o objetivo desta pesquisa é apresentar um método de localização e identificação de descargas parciais em transformador de potência operando com carregamento nominal, através de sensores piezelétricos de baixo custo e ferramentas como a transformada wavelet e os sistemas de inferência neuro-difuso adaptativos. / Mestre

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This study has looked at two separate locations, Malmö in Sweden, and Medellin in Colombia, and what the possibilities of rainwater harvesting are in those locations. The paper investigated data gathered by low-cost sensors in Malmö region, as well as sampling and measurements of rainwater collected in Malmö and Medellin, analysed by laboratories in both countries. Important data for the paper are quantity, quality, as well as environmental benefits and the economical perspective of rainwater harvesting. There are several steps that can be taken towards a more sustainable use of the Earth’s resources, and rainwater harvesting is included in the possibilities already at hand. But there is need for further development of monitoring and ways to qualify water for different purposes. It is possible to utilize rainwater for many things, and with that, ease the pressure on the local water supply infrastructure. This study has shown some important parts of that process, and that even low-cost sensors can help in gathering essential data for further evaluation of the usability of the rainwater. By collecting parts of the rainwater, flooding due to extensive downpour can be mitigated to some extent. Moreover, considering the low price of the sensors tested in this study, it is possible to use several low-cost sensors to determine the usability to some extent. Had the measurements rendered in values being too low compared to the laboratory results, the risk would have been greater. The consequence is that usable water could be regarded as not usable since the sensors returned an overall higher value compared to the laboratory results. Furthermore, the research found that the environmental benefit can be substantial. But there are more positive implications when gathering water for usage locally. For example, the water supply can become more distributed and with that, less prone to cause events such as water leaks, polluted water, and extreme events that have rendered an unserviceable infrastructure.