Thesis (M.S.)--Rochester Institute of Technology, 2006. / Typescript. Includes bibliographical references (leaves i-xvi).

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 85-92). Also available in electronic version.

Location tracking on mobile devices like smartphones has already begun to revolutionize personal navigation. Unfortunately, these services perform poorly indoors when GPS signals are no longer available. Highly accurate indoor location tracking would enhance a wide variety of applications including: building navigation (malls, factories, airports), augmented reality, location-aware pervasive computing, targeted advertising, social networking, participatory sensing and could even support next generation beam forming MIMO wireless networks. Current indoor localization systems for smartphones often use RF signal strength from WiFi access points or Bluetooth Low Energy (BLE) beacons to fingerprint indoor locations. Such systems are sensitive to environmental changes and obstructions, require extensive training procedures and are limited in both absolute as well as semantic localization accuracy. We propose using audio signals in the ultrasound spectrum, just above the human hearing range, to provide ranging and localization for many off-the-shelf mobile devices that are equipped with microphones. Ultrasonic ranging provides several advantages over RF-based ranging and fingerprinting approaches, which make it attractive for indoor localization. A relatively low propagation speed and carrier frequency allow for precise propagation time measurements in software using commodity hardware. Acoustic signals also have a low penetration depth, which confines them to target areas for accurate semantic localization. In this dissertation we address several challenges related to acoustic localization, including system scalability, ranging and localization accuracy, energy efficiency, robustness to noise, elimination of human perceivable audio artifacts, efficient use of limited acoustic bandwidth and rapid deployment strategies.

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Exposure to aerosol particles can cause health issues such as respiratory and cardiovascular diseases. Nevertheless, aerosol particle exposure with size-resolved information was seldom investigated in real-life European houses in the long term, especially for the ultrafine size range. In this work, indoor and outdoor measurements were conducted from December 2016 to March 2019. A standard of high-quality indoor and outdoor particle measurements in multi-homes was established for the first time. For more than 500 days, measurement data were collected in 40 German homes, including particle mass concentrations (PMC) of PM10, PM2.5, and PM1, particle number concentration (PNC) and size distribution, equivalent black carbon (eBC) mass concentration, CO2 concentration, and the residents’ activity log. With such novel dataset, representative patterns of diurnal and seasonal variation of particle concentration and size distribution as well as eBC mass concentration have been captured. In the warm season, diurnal cycles of indoor PMC and PNC showed weaker variation and less intense peaks (around the time of breakfast, lunch and dinner) than in the cold season, due to ventilation reducing the effect of indoor sources. To better understand the key dynamic processes of indoor particles (i.e. ventilation, building shell penetration, particle losses and emission), two commonly used quantitation methods, single parameter approach (SPA) and Indoor Aerosol Model approach (IAM), were compared and evaluated for the first time. Correction factors were derived to adjust the emission rates calculated from the simplified SPA approach, making emission rates derived from different levels of analysis mutually comparable. Results show that indoor source was the major contributor (56%) to indoor particle number exposure in investigated German residences. For the contribution of outdoors, penetration through the building envelope (26%) was higher than infiltration through open windows (15%). Burning candles and opening of window(s) led to seasonal differences in the contributions of indoor sources to residential exposure (70% and 40% in the cold and warm season, respectively). Indoor sources should be taken into consideration in future epidemiological studies and risk assessment of exposure to particle air pollution. / Aerosolpartikel können gesundheitliche Probleme wie Atemwegs- und Herz-Kreislauf-Erkrankungen verursachen. Jedoch wurde die Aerosolpartikelexposition, insbesondere für ultrafeine Partikel, in realen Europäischen Haushalten bisher nicht langfristig untersucht. Für diese Arbeit wurden Messungen im Innen- und Außenbereich von Dezember 2016 bis März 2019 durchgeführt. Erstmals wurde ein Standard für qualitativ hochwertige Innen- und Außenmessungen von Aerosolpartikeln in Wohnhäusern etabliert. An mehr als 500 Tagen wurden Messdaten verteilt über 40 deutsche Haushalte gesammelt, darunter die Partikelmassekonzentration (PMC) von PM10, PM2,5 und PM1, die Partikelanzahlkonzentration (PNC) und deren Größenverteilung, die Massekonzentration des äquivalenter schwarzer Kohlenstoff (eBC – equivalent Black Carbon) und ein Aktivitätsprotokoll der Bewohner. Mit diesem neuartigen Datensatz wurden repräsentative Muster der tages- und jahreszeitlichen Variation der Partikelkonzentration und Partikelgrößenverteilung sowie der eBC-Konzentration erstellt. Im Sommerhalbjahr zeigen sich schwächere Variationen und weniger intensive Spitzen (während der Frühstücks-, Mittags- und Abendessenszeit) in den Tageszyklen von PMC und PNC als im Winterhalbjahr. Dies ist auf die die Belüftung zurückzuführen, die den Effekt von Innenraumquellen reduziert. Zwei häufig verwendete Quantifizierungsmethoden der Schlüsselprozesse, der Single-Parameter-Ansatz (SPA) und der Indoor-Aerosol-Modellansatz (IAM), wurden zum ersten Mal quantitativ verglichen und bewertet. Es wurden Korrekturfaktoren abgeleitet, um die mit dem vereinfachten SPA-Ansatz berechneten Emissionsraten anzupassen, so dass die aus verschiedenen Analyseebenen abgeleiteten Emissionsraten miteinander vergleichbar sind. Die Ergebnisse zeigen, dass Quellen in Innenräumen den größten Beitrag (56%) zur Partikelanzahlexposition der Bewohner der untersuchten deutschen Haushalte leisten. Der Beitrag des Außenbereichs durch das Eindringen durch die Gebäudehülle (26%) ist höher als der durch offene Fenster (15%). Brennende Kerzen und das Öffnen von Fenster(n) hatten den größten Einfluss auf die saisonalen Unterschiede in der Partikelexposition. Innenraumquellen sollten in zukünftigen epidemiologischen Studien und bei der Risikobewertung Aerosolpartikelexposition berücksichtigt werden.

GIS are geoprocessing programs that are commonly used to store and perform calculations on terrain data, maps, and other geospatial data. GIS offers the latest terrain and building data as well as tools to process this data. This thesis considers three applications of GIS data and software: a Large Scale Radio Frequency (RF) Model, a Medium Scale RF Model, and Indoor Positioning. The Large Scale RF Model estimates RF propagation using the latest terrain data supplied in GIS for frequencies ranging from 500 MHz to 5 GHz. The Medium Scale RF Model incorporates GIS building data to model WiFi systems at 2.4 GHz for a range of up to 300m. Both Models can be used by city planners and government offcials, who commonly use GIS for other geospatial and geostatistical information, to plan wireless broadband systems using GIS. An Indoor Positioning Experiment is also conducted to see if apriori knowledge of a building size, location, shape, and number of floors can aid in the RF geolocation of a target indoors. The experiment shows that correction of a target to within a building's boundaries reduces the location error of the target, and the vertical error is reduced by nearly half. / Master of Science

This thesis presents the results of a research project that involved laboratory experimentation for gathering data on how the concentration of a dense gas disperses in indoor air. A box was built and designed as a model of a typical indoor environment and a dense contaminant, carbon dioxide, was injected into the box at a known flow rate through either a high or a low injection port. The gases left the box through either a high or a low exit port. The concentration of the contaminant inside the box was measured with an indoor air quality monitor at sampling ports at different locations and at different times during the experiments. The time and place dependent data were organized to observe the pattern of dispersion of a dense indoor air contaminant. It was determined that CO2 tended to sink down and move away from the point of injection at lower elevations, while gradually diffusing upwards. It was concluded that the dispersion of carbon dioxide in the box was driven mainly by the density gradient and to a less extent by diffusivity.

Infrastructure-based indoor geolocation systems utilizing a regular grid arrangement of sensors are being investigated for many applications in indoor wireless networks. One of the factors affecting the Quality of Estimation (i.e. location estimation accuracy) of these systems is node density. In this dissertation we study the effects of node density on indoor geolocation systems based on time of arrival (TOA). The effects of node density on the performance of various indoor communication networks (e.g. wireless LANs) in the presence of realistic indoor radio propagation models has been analyzed and reported in the literature. However, we have noted the lack of an equivalent analysis on the effects of node density on the performance of infrastructure-based indoor geolocation systems. The goal of this dissertation is to address this knowledge gap. Due to the complicated behavior of the indoor radio channel, the relationship between the node density and Quality of Estimation (QoE) is not straightforward. Specifically, QoE depends on factors such as the bandwidth used to make the TOA-based distance measurements, the existence of undetected direct path (UDP) conditions, and coverage. In this dissertation, we characterize these dependencies. We begin by characterizing the Quality of Estimation for closest-neighbor (CN), least-squares (LS) and weighted LS techniques in the presence of different node densities and a distance measurement error (DME) model based on ray tracing (RT) that was recently proposed in the literature. Then, we propose a new indoor geolocation algorithm, Closest Neighbor with TOA Grid (CN-TOAG), characterize its performance and show that it outperforms the existing techniques. We also propose an extension to this algorithm, known as Coverage Map Search (CMS) that allows it to be used in suboptimal coverage conditions (which we refer to as partial coverage conditions) that may prevent other TOA-based geolocation techniques from being used. We treat the partial coverage case by defining coverage probabilities and relating them to the average radius of coverage and dimensions of the indoor area. Next, we characterize the effects of node density on the performance of the CN-TOAG algorithm using a DME model based on UWB measurements, and show that node density and partial coverage are intimately linked together. Since this second DME model also allows for the effects of UDP conditions (which affect the quality of the link or QoL), we also characterize the effects of varying UDP conditions on the performance. Finally, we conclude the dissertation by presenting an analysis of fundamental performance bounds for infrastructure-based indoor geolocation, specifically focusing on the Cramer-Rao Lower Bound (CRLB).

"Recently considerable attention has been paid to indoor geolocation using wireless local area networks (WLAN) and wireless personal area networks (WPAN) devices. As more applications using these technologies are emerging in the market, the need for accurate and reliable localization increases. In response to this need, a number of technologies and associated algorithms have been introduced in the literature. These algorithms resolve the location either by using estimated distances between a mobile station (MS) and at least three reference points (via triangulation) or pattern recognition through radio frequency (RF) fingerprinting. Since RF fingerprinting, which requires on site measurements is a time consuming process, it is ideal to replace this procedure with the results obtained from radio channel modeling techniques. Localization algorithms either use the received signal strength (RSS) or time of arrival (TOA) of the received signal as their localization metric. TOA based systems are sensitive to the available bandwidth, and also to the occurrence of undetected direct path (UDP) channel conditions, while RSS based systems are less sensitive to the bandwidth and more resilient to UDP conditions. Therefore, the comparative performance evaluation of different positioning systems is a multifaceted and challenging problem. This dissertation demonstrates the viability of radio channel modeling techniques to eliminate the costly fingerprinting process in pattern recognition algorithms by introducing novel ray tracing (RT) assisted RSS and TOA based algorithms. Two sets of empirical data obtained by radio channel measurements are used to create a baseline for comparative performance evaluation of localization algorithms. The first database is obtained by WiFi RSS measurements in the first floor of the Atwater Kent laboratory; an academic building on the campus of WPI; and the other by ultra wideband (UWB) channel measurements in the third floor of the same building. Using the results of measurement campaign, we specifically analyze the comparative behavior of TOA- and RSS-based indoor localization algorithms employing triangulation or pattern recognition with different bandwidths adopted in WLAN and WPAN systems. Finally, we introduce a new RT assisted hybrid RSS-TOA based algorithm which employs neural networks. The resulting algorithm demonstrates a superior performance compared to the conventional RSS and TOA based algorithms in wideband systems."

Ce travail a consisté à analyser et de simuler à l’aide du modèle INCA-Indoor la qualité de l’air intérieur, et de développer une nouvelle méthodologie pour étudier les contributions des différents processus aux concentrations de polluants. Cette nouvelle méthodologie se base sur un nouveau programme de sensibilité INCA-Indoor-D, permet d’identifier rapidement les paramètres les plus sensibles qui peuvent influencer la qualité de l’air intérieur. Le modèle INCA-Indoor a été validé expérimentalement en utilisant les données mesurées lors de la campagne MERMAID (2014-2015). L’application du programme de sensibilité INCA-Indoor-D est pour analyser des sensibilité des concentrations de OH par rapport aux divers paramètres. Une classification de l’importance de ces paramètres en fonction de la sensibilité a ainsi été effectuée. Ce travail de thèse offre une nouvelle analyse de la pollution de l’air ainsi que de nouvelles perspectives d’études possibles dans un bâtiment basse consommation. / This study consisted in the study of indoor air quality with INCA-Indoor model, and especially the development of a fast methodology to identify the most sensitive parameters influencing indoor air quality. The methodology is based on a sensitivity program INCA-Indoor-D, which was built to identify the most important parameters affecting pollutant concentrations. With measurement data from MERMAID (2014-2015), it is intended to continue to evaluate the INCA-Indoor model, which was used to analyze the indoor air quality of a low energy building. The first application of the sensitivity program INCA-Indoor-D is performed to develop a comprehensive sensitivity analysis of indoor [OH] with respect to diverse parameters. Sensitivity has been settled with a classification of the parameters. The results in this study provide useful information about roles of different processes controlling indoor air quality and the effects of different parameters on indoor pollutant concentrations.