Advances in wireless networking and the Internet move us toward ubiquitous and embedded computing. Ubiquitous and embedded computing enhances computer use by making computers available throughout the physical environment while making them effectively invisible to the user. In the ubiquitous and embedded computing era, computers in the traditional sense gradually fade, and information mediated by computers is available anywhere and anytime through devices that are embedded in the environment. Radio Frequency Identification (RFID) is one of the key technologies of the ubiquitous and embedded computing era. RFID is a technology used to identify, track, and trace a person or object and enables the automated collection of important business information. RFID minimizes human intervention in the person and object identification process by using electronic tags and is expected to complement or replace traditional barcode technology. RFID is a highly beneficial technological advancement which ultimately may change the way of doing business. This study examines the RFID adoption decision process and proposes a model predicting the likelihood of adopting RFID within organizations in the healthcare industry. A considerable number of studies have been conducted regarding organizational information technology (IT) adoption, but the nature of the organizational IT adoption process is still not well understood. It is even posited that the only consistency found in the organizational adoption literature is the inconsistency of research results. The inconsistency of results is partially explained by changes in technological, organizational, and environmental statuses. Therefore, factors explaining traditional IT adoption may not justify RFID adoption and should be revisited and revalidated. In addition, given the ongoing importance of RFID, it is very important to identify the unique factors that contribute to the likelihood of adopting RFID. In this study, an organizational RFID adoption model is proposed and empirically tested by a survey using a sample of 865 senior executives in U. S. hospitals. The model posits that three categories of factors, technology push, need pull, and decision maker characteristics, determine the likelihood of adopting RFID within organizations. The relationships between those three categories and the likelihood of adopting RFID are strengthened or weakened by organizational readiness. This study may serve as the theoretical and empirical basis for research on other forms of ubiquitous and embedded computing systems.

Oriented strandboard (OSB) is a wood-based composite product with the largest market share for residential and commercial construction. OSB composite products have introduced variability in their physical and mechanical properties due to their raw material and process variation. Reliable in-line non-destructive evaluation (NDE) devices are needed to rapidly determine OSB panel product quality during and after the manufacturing process. Wood specific gravity (SG) and moisture content (MC) play an important role in the wood composite manufacturing process. A real-time after-press monitoring device for locating SG and MC variations can supply information needed to control and improve mat formation, hot press schedules, detect MC-related problems, reduce product variation, and perform final product quality inspection. No real-time non-contact NDE methods are available for simultaneous detection of MC and SG variation. In this research, the radio frequency (RF) scanning technique was used to evaluate the MC and SG of OSB. The numerical simulation method assisted in developing RF sensors to nondestructively evaluate MC and SG of OSB composite specimens. MC and SG prediction models were derived based on RF testing results. The model behavior between relative humidity conditioned method and oven-drying conditioning method were compared. The results indicated the RF scanning technique can be successfully used as a NDE tool to measure MC and SG of OSB panel products. Numerical simulation can help deciding RF sensor geometry successfully and accurately. The MC and SG of OSB can be predicted with the models developed with the procedure used in this study. The RF scanning results are not only influenced by material physical properties, but also influenced by their MC conditioning method, such as relative humidity conditioned method and oven-drying conditioning method.

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Accelerator Mass Spectrometry (AMS) studies of rare isotopes with abundant isobars that form negative ions often require the use of large accelerators to achieve high sensitivity measurements. The Isobar Separator for Anions (ISA) is a radiofrequency quadrupole (RFQ) reaction cell system that provides selective isobar suppression for many of these isotopes in the low energy system, prior to injection into an accelerator. The ISA can then facilitate the measurement of these ions using smaller accelerators. A commercial version from Isobarex Corp. was installed in a separate low energy injection line of the 3 MV accelerator system at the A. E. Lalonde AMS Laboratory in the University of Ottawa and was tested using the measurement of 36Cl, suppressing its stable isobar 36S. The ISA includes a DC deceleration region, a combined cooling and reaction cell, and a DC acceleration region. The deceleration region reduces the beam energy from the ion source (20-35 keV) to a level that chemical reactions can occur, scattering is minimized, and that the reaction cell can accept and contain. RFQ segments along the length of the cell create a potential well, which limits the divergence of the traversing ions. DC offset voltages on these RFQ segments maintain a controlled ion velocity through the cell. Helium was used as a cooling gas to further decelerate the ions, facilitating charge exchange between 36S and a reaction gas. Helium provided the highest transmission of 30-80% for chlorine anions. The reaction gas NO2 was chosen to preferentially react with sulfur. Over seven orders of magnitude reduction of sulfur to chlorine was observed. After exiting the cell, the beam is reaccelerated prior to injection into the tandem accelerator for AMS analysis. Using 36Cl reference materials, it was determined that linear transmission results could be obtained for a 36Cl/Cl ratio ranging from 10−11 to 10−15. The measurements were stable over more than 24 hours of continuous measurement. A blank level on the order of 10−15 was observed. The ISA was used to measure unknown 36Cl /Cl ratio groundwater samples and the results are compared to external AMS measurements.

This dissertation explores the realm of high-accuracy localization through the utilization of ranging-based techniques, encompassing a spectrum of signals ranging from low-frequency ultrasound acoustic signals to more intricate high-frequency signals like Wireless Fidelity (Wi-Fi) IEEE 802.11az, 5G New Radio (NR), and 6G. Moreover, another contribution is the conception of a novel timing mechanism and synchronization protocol grounded in tunable quantum photonic oscillators. In general, our primary focus is to facilitate precise indoor localization, where conventional GPS signals are notably absent. To showcase the significance of this innovation, we present two vital use cases at the forefront: drone localization and metaverse user positioning. In the context of indoor drone localization, the spectrum of applications ranges from recreational enthusiasts to critical missions requiring pinpoint accuracy. At the hobbyist level, drones can autonomously navigate intricate indoor courses, enriching the recreational experience. As a finer illustration of a hobbyist application, consider the case of ``follow me drones". These specialized drones are tailored for indoor photography and videography, demanding an exceptionally accurate autonomous flight capability. This precision is essential to ensure the drone can consistently track and capture its designated subject, even as it moves within the confined indoor environment. Moving on from hobby use cases, the technology extends its profound impact to more crucial scenarios, such as search and rescue operations within confined spaces. The ability of drones to localize with high precision enhances their autonomy, allowing them to maneuver seamlessly, even in environments where human intervention proves challenging. Furthermore, the technology holds the potential to revolutionize the metaverse. Within the metaverse, where augmented and virtual realities converge, the importance of high-accuracy localization is amplified. Immersive experiences like Augmented/Virtual/Mixed Reality (AR/VR/MR) gaming rely heavily on precise user positioning to create seamless interactions between digital and physical environments. In entertainment, this innovation sparks innovation in narrative design, enhancing user engagement by aligning virtual elements with real-world surroundings. Beyond entertainment, applications extend to areas like telemedicine, enabling remote medical procedures with virtual guidance that matches physical reality. In light of all these examples, the imperative for an advanced high-accuracy localization system has become increasingly pronounced. The core objective of this dissertation is to address this pressing need by engineering systems endowed with exceptional precision in localization. Among the array of potential techniques suitable for GPS-absent scenarios, we have elected to focus on ranging-based methods. Specifically, our methodologies are built upon the fundamental principles of time of arrival, time difference of arrival, and time of flight measurements. In essence, each of our devised systems harnesses the capabilities of beacons such as ultrasound acoustic sensors, 5G femtocells, or Wi-Fi access points, which function as the pivotal positioning nodes. Through the application of trilateration techniques, based on the calculated distances between these positioning nodes and the integrated sensors on the drone or metaverse user side, we facilitate robust three-dimensional localization. This strategic approach empowers us to realize our ambition of creating localization systems that not only compensate for the absence of GPS signals but also deliver unparalleled accuracy and reliability in complex and dynamic indoor environments. A significant challenge that we confronted during our research pertained to the disparity in z-axis localization performance compared to that of the x-y plane. This nuanced yet pivotal concern often remains overlooked in much of the prevailing state-of-the-art literature, which predominantly emphasizes two-dimensional localization methodologies. Given the demanding context of our work, where drones and metaverse users navigate dynamically across all three dimensions, the imperative for three-dimensional localization became evident. To address this, we embarked on a comprehensive analysis, encompassing mathematical derivations of error bounds for our proposed localization systems. Our investigations unveiled that localization errors trace their origins to two distinct sources: errors induced by ranging-based factors and errors stemming from geometric considerations. The former category is chiefly influenced by factors encompassing the quality of measurement devices, channel quality in which the signal communication between the sensor on the user and the positioning nodes takes place, environmental noise, multipath interference, and more. In contrast, the latter category, involving geometry-induced errors, arises primarily from the spatial configuration of the positioning nodes relative to the user. Throughout our journey, we dedicated efforts to mitigate both sources of error, ensuring the robustness of our system against diverse error origins. Our approach entails a two-fold strategy for each proposed localization system. Firstly, we introduce innovative techniques such as Frequency-Hopping Spread Spectrum (FHSS) and Frequency-Hopping Code Division Multiple Access (FH-CDMA) and incorporate devices such as Reconfigurable Intelligent Surfaces (RIS) and photonic oscillators to fortify the system against errors stemming from ranging-related factors. Secondly, we devised novel evolutionary-based optimization algorithms, adept at addressing the complex NP-Hard challenge of optimal positioning node placement. This strategic placement mitigates the impact of geometry-induced errors on localization accuracy across the entire environmental space. By meticulously addressing both these sources of error, our localization systems stand as a testament to comprehensive robustness and accuracy. Our methodologies not only extend the frontiers of three-dimensional localization but also equip the systems to navigate the intricacies of indoor environments with precision and reliability, effectively fulfilling the evolving demands of drone navigation and metaverse user interaction. / Doctor of Philosophy / In this dissertation, we first explore some promising substitutes for the Global Positioning System (GPS) for the autonomous navigation of drones and metaverse user positioning in indoor spaces. Then, we will make the scope of research more comprehensive and try to explore substitutes to GPS for autonomous navigation of drones in general, both in indoor environments and outdoors. For the first part, we make our small indoor GPS. Similar to GPS, in our system, a receiver onboard the drone or the metaverse user can receive signals from our small semi-satellites in the room, and with that, it can localize itself. The idea is very similar to how the well-known GPS works, with some modifications. Unlike the GPS, we are using acoustic ultrasound signals or some RF signal based on 5G or Wi-Fi for transmission. Also, we have more freedom compared to GPS because, in GPS, they have to transmit signals from far ahead distances, whereas, in our scenario, it is just a room in which we put all of our semi-satellite transmitters. Moreover, we can put them anywhere we want in the room. This is, in fact important, because the positions of these semi-satellites have a huge effect on the accuracy of our system. Also, we can decide how many of them we need to cover every point in the room and not have any blind spots. We propose our novel techniques for finding the optimal placement to improve localization accuracy. In GPS, they propose a technique that is suitable for the case of those satellites and their distance to the targets. Similarly, we offer our novel techniques to have a robust transmission against noise and other factors and guarantee a localization scheme with high accuracy. All being said, our proposed system for indoor localization of drones and metaverse users in three dimensions has considered all the possible sources of error and proposed solutions to conquer them; hence a robust system with high accuracy in three-dimensional space.

<p> This thesis contributes to the computer-aided design (CAD) of spiral inductors and LC resonators with spiral inductors exploiting full-wave electromagnetic (EM) analysis.</p> <p> The spiral inductor is widely used in radio frequency integrated circuits (RF ICs), such as low noise amplifiers (LNA) and voltage controlled oscillators (VCO). The design of spiral inductors has a direct influence on the performance of these circuits. Recently proposed optimization methods for spiral inductors are usually based on circuit models, which are computationally efficient but inaccurate compared with full-wave electromagnetic (EM) simulations.</p> <p> For the first time, we develop an optimization technique for the design of spiral inductors and LC resonators exploiting both the computational efficiency of a (cheap) circuit model and the accuracy of a full-wave EM analysis, based on geometric programming (GP) and space mapping (SM). With the new technique, we can efficiently obtain EM-validated designs with considerable improvement over those obtained with traditional optimization methods.</p> / Thesis / Master of Applied Science (MASc)