Environmental factors can have a significant impact on an individual's health and well-being, and a primary characteristic of environments is air quality. Air sensing equipment is available to the public, but it is often expensive,stationary, or unusable for persons without technical expertise. The goal of this project is to develop an inexpensive and portable sensor module for public use. The system is capable of measuring temperature in Celsius and Fahrenheit, heat index, relative humidity, and carbon dioxide concentration. The sensor module, referred to as the "sniffer," consists of a printed circuit board that interconnects a carbon dioxide sensor, a temperature/humidity sensor, an Arduino microcontroller, and a Bluetooth module. The sniffer is small enough to be worn as a pendant or a belt attachment, and it is rugged enough to consistently collect and transmit data to a user's smartphone throughout their workday. The accompanying smartphone app uses Bluetooth and GPS hardware to collect data and affix samples with a time stamp and GPS coordinates. The accumulated sensor data is saved to a file on the user's phone, which is then examined on a standard computer.

Thesis deals with the design of intelligent electrical socket. At the beginning of the thesis, there are described the characteristics of the distribution network in the Czech Republic and the ways of measuring these properties. The selected integrated circuit that realizes the measurement is described in detail. The third chapter deals with the current possibilities of power management, especially from the perspective of the distribution company and the concept of smart networks. The fourth chapter deals with the selection of communication interface suitable for smart socket as an element of smart home. The selected IEEE 802.15.4 standard and the microcontroller implementing the communication itself are described in more detail. The following part of the work is about design of the intelligent socket development board, on which the concept is tested and the design of the final hardware is described in this chapter. The last chapter describes the measurement results realized by the created device.

This study focuses on the development of a generalised multivariate beta type II distribution as well as the noncentral and bimatrix counterparts with positive domain. These models emanate from a sequential quality monitoring procedure with the normal and multivariate normal distributions as the underlying process distributions. Three different scenarios are considered, namely: 1. The variance is monitored from a normal process and the mean remains unchanged; 2. The above-mentioned scenario but the known mean also encounters a sustained shift; 3. The covariance structure of a multivariate normal distribution is monitored with the known mean vector unchanged. The statistics originating from the above-mentioned scenarios considered are constructed from different dependent chi-squared or Wishart ratios. Exact expressions are derived for the probability density functions of these statistics. These new distributions contribute to the statistical discipline in the sense that it can serve as alternatives to existing probability models, and can be used in determining the performance of the quality monitoring procedure. / Thesis (PhD)--University of Pretoria, 2013. / gm2014 / Statistics / unrestricted

Hazards associated with air pollution motivate the search for technologies capable of monitoring individual exposure to gaseous pollutants and particulate matter (PM). A Low-cost Optical Particle Counter (OPC), costing less than 50 USD, is an example of such technologies. Currently, OPCs are widely used to measure the concentration of particle matter in ambient air. While these low-cost air quality sensors are widely available, the accuracy and precision of these devices is highly uncertain. Consequently, the purpose of this thesis is to present an analytical model of two generic, low-cost OPCs based on the Laws of Conservation of Mass, Momentum, and Energy. These models utilize Mie scattering theory and Computational Fluid Dynamics models to quantify uncertainty and accuracy in low-cost OPCs based first principles. Modeling results indicate that the measurement of forward-scattered light may dramatically increase the accuracy of low-cost OPCs. These results also indicate that careful attention must be placed on the design of sensor flow passages so as to most efficiently transport particles to the scattering volume where they may be detected. A combination of careful attention to photodetector placement in the forward scattering regime as well as efficient transport to the scattering volume may increase low-cost OPC accuracy by magnitudes of order.

Although water is incredibly abundant on Earth, only 2.5 % of the water reserve is freshwater, and one third of the world’s population lacks safe drinking water. Water quality monitoring (WQM) is thus a crucial asset to safeguard this invaluable resource. Gaining popularity in the 1960s, WQM has since evolved from a lab-based, labour-intensive program to an increasingly automated operation. Today, autonomous sensors automatically assess water quality, albeit with surpassed technology, complex procedures, and expensive equipment. This thesis presents a next-generation, IoT-based WQM system, capable of unparalleled high-frequency data collection at a fraction of the cost of present-day solutions, that operates via The Thing Network’s public LoRaWAN connectivity. The system is validated through a lightweight prototype buoy, which is deployed on a 14-days-long campaign in the lakes of Stockholm. The study concludes that (i) The Things Network is a promising backbone for monitoring applications in the smart city, with an estimated current 57 % coverage of Stockholm; (ii) IoT devices can harvest sufficient solar energy to deliver 30–60 messages/h all year round even at high latitudes; (iii) IoT-based WQM has the potential for unprecedented resolution, energy-efficiency, and cost-effectiveness compared to traditional industrial-grade, cellular-based systems. / Även om vatten är otroligt rikligt på jorden, är bara 2,5 % av vattenreserven sötvatten, och en tredjedel av världens befolkning saknar säkert dricksvatten. Water quality monitoring (WQM) är således en avgörande tillgång för att skydda denna ovärderliga resurs. WQM blev allt populärare på 1960-talet och har sedan dess utvecklats från ett labbaserat, arbetskrävande program till en alltmer automatiserad verksamhet. Idag bedömer autonoma sensorer automatiskt vattenkvaliteten, om än med överträffad teknik, komplexa förfaranden och dyr utrustning. Denna avhandling presenterar ett nästa generations, IoT-baserade WQM-system, som kan möta högfrekvent datainsamling utan motstycke till en bråkdel av kostnaden för dagens lösningar, som fungerar via The Things Network offentliga LoRaWAN-anslutning. Systemet valideras genom en lätt prototypboj, som används på en 14 dagar lång kampanj i Stockholms sjöar. Studien drar slutsatsen att (i) The Things Network är en lovande ryggrad för övervakning av applikationer i den smart city, med en uppskattningsvis 57 % täckning av Stockholm; (ii) IoT-enheter kan skörda tillräckligt med solenergi för att leverera 30-60 meddelanden/timme året runt även på höga breddgrader; (iii) IoT-baserat WQM har potential för oöverträffad upplösning, energieffektivitet och kostnadseffektivitet jämfört med traditionella, industriella, cellbaserade system.

Water is an essential part of human life. According to the sustainable development goal (SDG), monitoring water quality is a fundamental goal of the international community. Therefore, there has been research on how the quality of water can be monitored and improved.  Data related to water quality can provide service opportunities such as to inform if the water is suitable for drinking by monitoring the quality of water resources. However, there has been limited research exploring opportunities and challenges of digital water quality monitoring services to fulfill requirements in rural areas, such as clean and safe water for drinking. Some challenges can be due to the geographical location of the area or avoiding certain regions for implementing a service due to political agenda or infrastructure, especially in less developed countries or areas. The lack of exploration of opportunities points to the need to further investigate service opportunities like to inform if the water is suitable for drinking in rural areas. It is crucial to monitor water quality and offer services because bad water quality can lead to health issues or poor yield in agriculture, some of the few consequences. Therefore, in my paper, using the Design Science Research (DSR) approach, I have explored various opportunities and challenges of digital water quality monitoring services in Indian rural areas. The result demonstrates opportunities related to the exploration of digital water quality monitoring services in rural areas and the challenges that need to be overcome in order to realize the opportunities. This overview can inform researchers, designers, and stakeholders to understand new service opportunities and challenges of digital water quality monitoring services in rural areas.

Water quality monitoring is vital in smart city planning for managing water resources. In the smart city, more data is being collected. In terms of data related to water quality, many data sources such as smart sensors attached to water quality monitoring systems have been continuously collecting a significant amount of data. The potential of collected data from these sources holds no value for smart cities, unless it is being used to provide digital services such as information about clean and safe drinking water, swimming, fishing, domestic uses, and water reuse. However, the knowledge on how to utilize water quality data for the benefits of smart cities is limited. So, in this paper, I propose digital water quality monitoring services for smart city residents. I explore this proposition through a design study engaging smart city residents, service designers, and developers of water quality monitoring systems. As a result, a service blueprint is presented to illustrate how such services can be designed to provide water quality information for different activities. The study aims to illustrate how opportunities of water quality monitoring system can be explored for smart cities. The study intends that the results are helpful to designers and researchers in designing and developing digital water quality monitoring services in smart cities.

This master thesis deals with service quality monitoring in libraries and it focuses especially on the situation in university libraries. There are foreign tools described in this thesis, namely the American LibQUAL+® which uses user satisfaction surveys, and German Bibliothekxindex which compares libraries according to recieved statistical data. From the Czech environment there is an initiative of the National Library of the Czech Republic described, "Benchmarking of Libraries", which is based on the German Bibliotheksindex but it is not applicable in university libraries. The thesis includes a self made suggestion on how to monitor service quality according to both of the foreign tools which was also applied to a selected university library. The results of this research indicate that these foreign tools are applicable in the Czech Republic and that the selected university library provides services which its users are overall very satisfied with.

No description available.

Background: Air pollution has become a serious environmental issue. It is responsible for hundreds of fatalities each year and it poses a serious threat to human health and environment. It leads to global warming, greenhouse effect and it also causes respiratory problems like asthma, lung cancer etc. It is important to predict the quality of the air to regulate air pollution. Air quality index (AQI) is a measure of air quality which describes the level of air pollution. Machine learning algorithms can help in predicting the AQI. Linear regression, LASSO regression, ridge regression, and SVR algorithms were used to forecast the AQI.  Objectives: The main objective of the thesis is to build and train a models using machine learning algorithms and find out the most accurate model in predicting the AQI.  Methods: Literature Review and Experimentation were chosen as methods to answer the research questions. There are a number of research papers written on prediction of AQI and literature review helped us a lot in research and references. Experimentation is also used to find out the most accurate machine learning model in predicting the air quality. In the experimentation phase, four machine learning algorithms were trained with air quality data to create predictive models for fore- casting AQI.  Results: Algorithms like Logistic Regression, Ridge Regression, LASSO Regression, and SVR are selected through literature review. Upon experimentation and training the algorithm with "Air Quality Data in India (2015-2020)" data set has showed that Ridge regression has the least MAE and RMSE and the highest R- square, which shows that it has the highest performance in predicting the AQI.  Conclusions: Four models are built by training with machine learning algorithms like Logistic Regression, Ridge Regression, LASSO Regression, and SVR and "Air Quality Data in India (2015-2020)" data set. After experimentation, it was found that Ridge Regression and LASSO regression has the better performance in the prediction of AQI.