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Background: Microwave Imaging (MWI) provides a method for imaging and quantitative reconstruction of permittivity and permeability using antennas and electromagnetic waves. MWI has been used for breast-cancer detection amongst many other applications. A possible way of enhancing MWI with a contrast agent is through the use of Magnetic Nanoparticles (MNPs). MNPs will change their permeability with the application of an external DC magnetic field, and thus placing an MWI chamber inside of an electromagnet allows us to generate differential scattered fields by turning the magnet ON and OFF. Objective: This thesis focuses on collecting the raw data from, and experimentally imaging MNP in an MWI system. Methods: Using a pre-existing MWI system based on quasi-resonant flat faceted imaging chamber, 24 monopole antennas were used to create a system capable of both detecting MNP and imaging the resulting data. The MWI system was placed in the bore of an electromagnet and experimental data using breast phantoms with MNP tumors (spheres filled with MNP and carrier fluid) was collected. The system at various frequencies was simulated using a Discontinuous Galerkin forward solver, and the collected data was calibrated with the forward solver output and inverted using the Contrast Source Inversion method, showing successful inversion of the expected permeability changes at the frequencies of interest. Results: Experimental signal from the magnetic contrast of the MNPs was observed at and around 2GHz when the MNP tumors were in the breast phantom (for various positions and concentrations of the MNP). Quantitative 3D imaging of the permeability was attempted for various frequencies where the MNP signal was strong. While some images showed the permeability expected from the MNP, most did not. First, we showed successful image reconstruction of permittivity at several other frequencies than achievable before. An analysis of our data and inversion processes showed that:(1) The raw MNP

Drone systems are being used everyday for many applications including delivery of goods and surveillance systems, which require a reliable navigation system to allow for a safe and successful mission. Global Navigation Satellite Systems have been the main means of navigation for decades, however, regions with poor GNSS coverage, such as the Arctic or urban centers, are challenging environments to navigate. In this thesis, the design and implementation of a low-cost portable beacon system that can provide position information using an extended Kalman filter (EKF) is presented. For the beacon system, an angle beacon prototype and two range beacon prototypes, one based on the received signal strength and the other based on the signal’s time of flight, were developed. The positioning performance of the EKF and beacons system in different scenarios was evaluated using Matlab simulations. The simulation results indicate that the range beacons offered the best positioning performance compared to the angle beacons or a mixture of both. Additionally, particle swarm optimization was used to optimally place an additional range/angle beacon, improving the overall system performance. Finally, the integrated system was tested in indoor and outdoor scenarios and the system output was compared with the ground truth data. The experimental tests resulted in a minimum error of 10.1 cm and 31.8 cm for indoor and outdoor tests, respectively. The steady state error was in orders of ≈ ±25 cm for the indoor tests and ≈ ±40 cm for the outdoor tests. Overall, the custom beacon designs provided reliable performance for drone navigation. With future hardware improvements, the system could be a viable option for low-cost portable navigation systems.

Tailoring electromagnetic waves has various applications in wireless communications, medical imaging, and remote sensing. However, to be suitable for a given application, the spatial properties of electromagnetic waves need to be manipulated to fit that application. This is generally done by designing antennas based on the required performance criteria. Designing antennas for complicated performance requirements can be quite time-consuming as it may require extensive full-wave optimization. Alternatively, a metasurface can be used as it can systematically transform electromagnetic waves to what the designer desires. Electromagnetic metasurfaces are sheet-like structures consisting of artificial “atoms” called meta-atoms or unit cells, that are small compared to the operational wavelength. When electromagnetic waves interact with these unit cells, they are transformed based on the properties of the unit cells. The design of lossless and passive metasurfaces requires enforcing total power conservation (TPC) and local power conservation (LPC) to make sure that the power is conserved in the whole metasurface and at each individual unit cell. If a single metasurface cannot satisfy the required constraints, a metasurface pair, also known as cascaded metasurfaces, can be considered. In this thesis, a fully automated design approach is developed for cascaded metasurface system designs with external sources based on an existing design approach. The overall design is divided into macroscopic and microscopic steps. In the macroscopic step, an automated conjugate gradient (CG) optimization algorithm is developed with analytical expressions to arrive at the required surface properties of the cascaded metasurface system. In the microscopic step, the unit cells are physically realized by copper traces determined using a lookup table which was further optimized to achieve the desired forward scattering parameters for each unit cell. The electromagnetic wave transformation is then v

Drone systems are being used everyday for many applications including delivery of goods and surveillance systems, which require a reliable navigation system to allow for a safe and successful mission. Global Navigation Satellite Systems have been the main means of navigation for decades, however, regions with poor GNSS coverage, such as the Arctic or urban centers, are challenging environments to navigate. In this thesis, the design and implementation of a low-cost portable beacon system that can provide position information using an extended Kalman filter (EKF) is presented. For the beacon system, an angle beacon prototype and two range beacon prototypes, one based on the received signal strength and the other based on the signal’s time of flight, were developed. The positioning performance of the EKF and beacons system in different scenarios was evaluated using Matlab simulations. The simulation results indicate that the range beacons offered the best positioning performance compared to the angle beacons or a mixture of both. Additionally, particle swarm optimization was used to optimally place an additional range/angle beacon, improving the overall system performance. Finally, the integrated system was tested in indoor and outdoor scenarios and the system output was compared with the ground truth data. The experimental tests resulted in a minimum error of 10.1 cm and 31.8 cm for indoor and outdoor tests, respectively. The steady state error was in orders of ≈ ±25 cm for the indoor tests and ≈ ±40 cm for the outdoor tests. Overall, the custom beacon designs provided reliable performance for drone navigation. With future hardware improvements, the system could be a viable option for low-cost portable navigation systems.

Tailoring electromagnetic waves has various applications in wireless communications, medical imaging, and remote sensing. However, to be suitable for a given application, the spatial properties of electromagnetic waves need to be manipulated to fit that application. This is generally done by designing antennas based on the required performance criteria. Designing antennas for complicated performance requirements can be quite time-consuming as it may require extensive full-wave optimization. Alternatively, a metasurface can be used as it can systematically transform electromagnetic waves to what the designer desires. Electromagnetic metasurfaces are sheet-like structures consisting of artificial “atoms” called meta-atoms or unit cells, that are small compared to the operational wavelength. When electromagnetic waves interact with these unit cells, they are transformed based on the properties of the unit cells. The design of lossless and passive metasurfaces requires enforcing total power conservation (TPC) and local power conservation (LPC) to make sure that the power is conserved in the whole metasurface and at each individual unit cell. If a single metasurface cannot satisfy the required constraints, a metasurface pair, also known as cascaded metasurfaces, can be considered. In this thesis, a fully automated design approach is developed for cascaded metasurface system designs with external sources based on an existing design approach. The overall design is divided into macroscopic and microscopic steps. In the macroscopic step, an automated conjugate gradient (CG) optimization algorithm is developed with analytical expressions to arrive at the required surface properties of the cascaded metasurface system. In the microscopic step, the unit cells are physically realized by copper traces determined using a lookup table which was further optimized to achieve the desired forward scattering parameters for each unit cell. The electromagnetic wave transformation is then v

This thesis presents a novel use of a six-port reflectometer (SPR) to build an SPR based network analyzer (SPNA) operating with two independent radio frequency (RF) power sources. The SPNA was first introduced by Hoer and Engen in the early 1970s using a common RF power source, along with power attenuators and a phase shifter. The basic description of a six-port reflectometer is having four side arms whose output is proportionally related to the incident voltage waves and the reflected ones at a measuring plane, i.e. measuring reflection coefficient of a device under testing (DUT) using only scalar power measurements at those four side arms. Throughout the research, a novel approach to the explicit calibration technique was developed apart from the traditional technique of using power measurements for several calibration standards, and the results confirmed its validity. Presented herein are the theory of SPR, calibration techniques, and SPNA, as well as simulated data which validate the main objective of this thesis. Physical realization of an SPR prototype for the chosen design, and results of the use of an arbitrarily DUT (Low Pass Filter – SLP 2400+) are presented in this report. Further work is required to confirm agreement between physical results of an SPNA with two independent RF power sources and the simulated.

One of the techniques to enhance the performance of microwave imaging (MWI) systems is to increase the amount of independent measured data that is collected when interrogating the object-of-interest (OI). A novel method is introduced which employs field perturbing elements (FPEs) to achieve this goal. The research reported herein utilizes a cylindrical metallic enclosure having an octagonal cross-section, i.e. with at-faceted walls on which a set of electrically controlled field perturbing elements (FPEs) that project into the chamber. The FPEs are implemented as printed circuit boards that include diodes that can be forward or reversed biased. Transmit and receive antennas are placed on the walls of the chamber and for each transmitter the interrogating eld can be changed by turning one or more of the recon figurable plates ON or OFF. This allows one to reduce the number of transmit antennas that are used. Our current system consists of an octogonally shaped chamber with four transmitter/receiver antennas. The size of the metallic chamber is on the order of a few wavelengths and with the entirety of the enclosure being made up of a conductive metallic material, it constitutes a quasi-resonant chamber (we utilize an open top adding some loss to the chamber). The fi eld perturbing elements include a pattern of segmented PCB traces with multiple diodes connecting the trace segments. To reduce the computational resources required to model these reconfi gurable plates, the design criteria is to choose a pattern of the segmented PCB traces that minimally perturb the fi eld within the chamber when the diodes are \OFF" (i.e. in reverse-biased mode) by choosing the frequencies that match closely with the numerical model. This allows us to remove the plates from the numerical model when in the \OFF" mode with small effects on the accuracy. In the \ON" mode, the plates are modelled as PEC surfaces. To evaluate the performance of the proposed approach, a number of imaging expe

This thesis focuses on monitoring stored grain through the use of electromagnetic inversion. Electromagnetic inversion is the process of illuminating a target with electromagnetic waves, and from limited field measurements attempting to reconstruct the target. Specifically, we aim to obtain an image of grain/air interface inside metallic-walled bins. Finding the grain/air interface is a starting point for full 3-D imaging of stored grain. The methods currently used to estimate the grain/air interface are computationally expensive, and have not been designed to image non-symmetric grain surfaces. To obtain the grain/air interface we studied and applied linear inversion methods, which are typically good at locating material boundaries (but not the exact permittivity of the materials). We use the linear Orthogonality Sampling Method (OSM), which we show is a specific form of back-propagated imaging. The identification of OSM as a back-propagation technique allows us to formulate the linear imaging problem with an arbitrary Green's function, which is a necessary step as grain bins are made of metallic shapes that do not have analytic Green's functions. To test our proposed technique for finding the grain/air interface, we consider a series of 2-D scalar simulations on both simple rectangular and grain bin-cross section-shaped boundaries. To compute our fields and numerical Green's functions, we use a Discontinuous Galerkin Method (DGM) solver. Initially applying the back-propagation technique in a background material of free space does not lead to reasonable results: images that should show the grain/air interface instead closely resembled the resonant modes of the electromagnetic fields inside the bin. To address this problem, we switch the background medium used in the back-propagation algorithm to closely resemble the permittivity of the grain. In this case, we could obtain reasonable images of the grain/air interface in our simulations. In brief, we show that using

This thesis focuses on monitoring stored grain through the use of electromagnetic inversion. Electromagnetic inversion is the process of illuminating a target with electromagnetic waves, and from limited field measurements attempting to reconstruct the target. Specifically, we aim to obtain an image of grain/air interface inside metallic-walled bins. Finding the grain/air interface is a starting point for full 3-D imaging of stored grain. The methods currently used to estimate the grain/air interface are computationally expensive, and have not been designed to image non-symmetric grain surfaces. To obtain the grain/air interface we studied and applied linear inversion methods, which are typically good at locating material boundaries (but not the exact permittivity of the materials). We use the linear Orthogonality Sampling Method (OSM), which we show is a specific form of back-propagated imaging. The identification of OSM as a back-propagation technique allows us to formulate the linear imaging problem with an arbitrary Green's function, which is a necessary step as grain bins are made of metallic shapes that do not have analytic Green's functions. To test our proposed technique for finding the grain/air interface, we consider a series of 2-D scalar simulations on both simple rectangular and grain bin-cross section-shaped boundaries. To compute our fields and numerical Green's functions, we use a Discontinuous Galerkin Method (DGM) solver. Initially applying the back-propagation technique in a background material of free space does not lead to reasonable results: images that should show the grain/air interface instead closely resembled the resonant modes of the electromagnetic fields inside the bin. To address this problem, we switch the background medium used in the back-propagation algorithm to closely resemble the permittivity of the grain. In this case, we could obtain reasonable images of the grain/air interface in our simulations. In brief, we show that using

One of the techniques to enhance the performance of microwave imaging (MWI) systems is to increase the amount of independent measured data that is collected when interrogating the object-of-interest (OI). A novel method is introduced which employs field perturbing elements (FPEs) to achieve this goal. The research reported herein utilizes a cylindrical metallic enclosure having an octagonal cross-section, i.e. with at-faceted walls on which a set of electrically controlled field perturbing elements (FPEs) that project into the chamber. The FPEs are implemented as printed circuit boards that include diodes that can be forward or reversed biased. Transmit and receive antennas are placed on the walls of the chamber and for each transmitter the interrogating eld can be changed by turning one or more of the recon figurable plates ON or OFF. This allows one to reduce the number of transmit antennas that are used. Our current system consists of an octogonally shaped chamber with four transmitter/receiver antennas. The size of the metallic chamber is on the order of a few wavelengths and with the entirety of the enclosure being made up of a conductive metallic material, it constitutes a quasi-resonant chamber (we utilize an open top adding some loss to the chamber). The fi eld perturbing elements include a pattern of segmented PCB traces with multiple diodes connecting the trace segments. To reduce the computational resources required to model these reconfi gurable plates, the design criteria is to choose a pattern of the segmented PCB traces that minimally perturb the fi eld within the chamber when the diodes are \OFF" (i.e. in reverse-biased mode) by choosing the frequencies that match closely with the numerical model. This allows us to remove the plates from the numerical model when in the \OFF" mode with small effects on the accuracy. In the \ON" mode, the plates are modelled as PEC surfaces. To evaluate the performance of the proposed approach, a number of imaging expe

This thesis presents the development, testing, and verification of transmission tomography software for the applications of small-scale biological imaging and large-scale stored-grain imaging. Transmission tomography is a non-invasive technique for producing quantitative images of an object's physical properties. This is done by interrogating the object with waves and measuring the resulting response at a set of measurement positions. The properties of the received waves are analyzed, and used to calculate the properties of the object which is interrogated. First, a mathematical formulation of transmission tomography is presented and explained. The formulation is then used to build a numerical model of the physical properties which dictate wave transmission in two systems of linear algebraic equations. The algorithms that are required for building the numerical model are then explained. The rest of this thesis is devoted to a series of experiments. These experiments show the usefulness of transmission tomography in some particular applications. They also motivate the incremental development of features of the transmission tomography algorithm that was developed for this thesis. The first experiment uses acoustic data to perform two-dimensional transmission tomography. The first experiment shows that two-dimensional transmission tomography of acoustic data produces useful images of an object's wave speed. The successful two-dimensional experiments motivate the two following experiments, which use electromagnetic data to perform three-dimensional transmission tomography. In order to perform three-dimensional tomography of electromagnetic data, a modification is made to the numerical model. The successful three-dimensional transmission tomography algorithm is then applied to track the volume of grain stored in a bin. The final experiment uses acoustic data and the modified numerical model to perform three-dimensional transmission tomography to identify two distinct sph

Passive resonators have been used for materials characterization, and in many different sensing applications. The passive resonator approach utilizes the frequency shift of the resonator due to the change in the material's dielectric property. Passive resonators, however, suffer from the effects of low-quality factor, and thus they do not provide high sensitivity to small changes in material parameters. In this thesis, an active resonator for real-time material sensing for bio-media applications is presented. The resonator presented provides a high-quality factor based on a passive planar resonator built with an active feedback loop to dramatically increase the resonator's quality factor. The designed resonator operates at 1.5 GHz and provides a quality factor of 40,000. This is an improvement of about 50 times over that of a passive resonator. Changes in the permittivity of a material were measured by placing the material in the resonator's gap capacitor region, and therefore changing the resonance frequency of the resonator. It is demonstrated that this resonator can detect different material permittivity with nanoliter sample amounts. The active resonator is able to detect liquid permittivity changes up to 1.5% IPA concentration difference.

This thesis presents the development, testing, and verification of transmission tomography software for the applications of small-scale biological imaging and large-scale stored-grain imaging. Transmission tomography is a non-invasive technique for producing quantitative images of an object's physical properties. This is done by interrogating the object with waves and measuring the resulting response at a set of measurement positions. The properties of the received waves are analyzed, and used to calculate the properties of the object which is interrogated. First, a mathematical formulation of transmission tomography is presented and explained. The formulation is then used to build a numerical model of the physical properties which dictate wave transmission in two systems of linear algebraic equations. The algorithms that are required for building the numerical model are then explained. The rest of this thesis is devoted to a series of experiments. These experiments show the usefulness of transmission tomography in some particular applications. They also motivate the incremental development of features of the transmission tomography algorithm that was developed for this thesis. The first experiment uses acoustic data to perform two-dimensional transmission tomography. The first experiment shows that two-dimensional transmission tomography of acoustic data produces useful images of an object's wave speed. The successful two-dimensional experiments motivate the two following experiments, which use electromagnetic data to perform three-dimensional transmission tomography. In order to perform three-dimensional tomography of electromagnetic data, a modification is made to the numerical model. The successful three-dimensional transmission tomography algorithm is then applied to track the volume of grain stored in a bin. The final experiment uses acoustic data and the modified numerical model to perform three-dimensional transmission tomography to identify two distinct sph

This thesis presents deep learning techniques applied to the modeling of geophysical quantities with remote sensing data to estimate soil moisture and classify sea ice type. The objective of the thesis is to determine if it is possible to estimate soil moisture and classify sea ice type from satellite data using deep learning. Soil moisture is an important metric used for agriculture, hydrology, and meteorology, while accurate maps of sea ice type are needed to address increased interest in commercial marine transportation through the Arctic. Satellites equipped with Synthetic Aperture Radar (SAR) sensors operating in the microwave band provide the remote sensing data used to predict these two geophysical quantities since SAR is capable of measuring the Earth's surface in all weather conditions and in darkness. Herein, the primary deep learning technique used to model soil moisture and sea ice types from SAR data is the Convolutional Neural Network (CNN). The best performing experiment for estimating soil moisture was achieved by U-Net on a dataset of 1,034 images to achieve a testing mean square error (MSE) of 3.82e-03 and correlation of 79.2%. For classifying sea ice type, DenseNet achieved the highest overall classification accuracy of 94.0% including water and the highest ice classification accuracy of 91.8% on a three class dataset using a fusion of HH and HV SAR polarizations for the input data. These results are contextualized and are shown to be comparable to other similar studies thereby supporting that the objective of the thesis has been achieved.

This thesis presents research into microwave remote sensing technologies for Arctic Science applications. In the first part of my thesis, I created and verified procedures for measuring the dielectric of a small volume of oil with cavity resonator and dielectric probe techniques. I designed and fabricated a cavity resonator and utilized a dielectric probe to demonstrate the technique and to generate new measurement results for three types of oil products. The measurement results are needed in dielectric mixture modelling and simulations of the normalized radar cross-section (NRCS) of oil-contaminated sea ice. I utilized five existing dielectric mixture models and the measured dielectric constants of three types of oil to model both clean and oil-contaminated sea ice. I simulated and compared their NRCS (three different types of contamination with three different amounts). The simulation results showed that I could distinguish between the clean and oil-contaminated sea ice based on the NRCS magnitudes. The simulation results demonstrate that for higher incidence angles, distinguishing clean sea ice from oil-contaminated sea ice with VV polarization is easier than HH polarization. In the second part of my thesis, I designed C-band Short Back-fire antennas that can be used for remote sensing applications. I simulated and characterized six different designs of Short Back-Fire antennas. The first design, SBF-1, is an optimum case of the conventional SBF antenna. In SBF-2, I improved the realized gain and bandwidth of the first one by adding a cylindrical choke to the main reflector. In the third design, I filled the choke with Teflon. In the subsequent designs, I increased the bandwidth and realized gain of SBF-1 and SBF-2 by adding a ring beside the rim and by adding a choke to the rim. To validate the simulations, I fabricated and tested SBF-1 and SBF-2. The measurement results show good agreement between the simulations and the actual measurements.

This thesis presents the development, testing, and verification of transmission tomography software for the applications of small-scale biological imaging and large-scale stored-grain imaging. Transmission tomography is a non-invasive technique for producing quantitative images of an object's physical properties. This is done by interrogating the object with waves and measuring the resulting response at a set of measurement positions. The properties of the received waves are analyzed, and used to calculate the properties of the object which is interrogated. First, a mathematical formulation of transmission tomography is presented and explained. The formulation is then used to build a numerical model of the physical properties which dictate wave transmission in two systems of linear algebraic equations. The algorithms that are required for building the numerical model are then explained. The rest of this thesis is devoted to a series of experiments. These experiments show the usefulness of transmission tomography in some particular applications. They also motivate the incremental development of features of the transmission tomography algorithm that was developed for this thesis. The first experiment uses acoustic data to perform two-dimensional transmission tomography. The first experiment shows that two-dimensional transmission tomography of acoustic data produces useful images of an object's wave speed. The successful two-dimensional experiments motivate the two following experiments, which use electromagnetic data to perform three-dimensional transmission tomography. In order to perform three-dimensional tomography of electromagnetic data, a modification is made to the numerical model. The successful three-dimensional transmission tomography algorithm is then applied to track the volume of grain stored in a bin. The final experiment uses acoustic data and the modified numerical model to perform three-dimensional transmission tomography to identify two distinct sph

Passive resonators have been used for materials characterization, and in many different sensing applications. The passive resonator approach utilizes the frequency shift of the resonator due to the change in the material's dielectric property. Passive resonators, however, suffer from the effects of low-quality factor, and thus they do not provide high sensitivity to small changes in material parameters. In this thesis, an active resonator for real-time material sensing for bio-media applications is presented. The resonator presented provides a high-quality factor based on a passive planar resonator built with an active feedback loop to dramatically increase the resonator's quality factor. The designed resonator operates at 1.5 GHz and provides a quality factor of 40,000. This is an improvement of about 50 times over that of a passive resonator. Changes in the permittivity of a material were measured by placing the material in the resonator's gap capacitor region, and therefore changing the resonance frequency of the resonator. It is demonstrated that this resonator can detect different material permittivity with nanoliter sample amounts. The active resonator is able to detect liquid permittivity changes up to 1.5% IPA concentration difference.

This thesis presents deep learning techniques applied to the modeling of geophysical quantities with remote sensing data to estimate soil moisture and classify sea ice type. The objective of the thesis is to determine if it is possible to estimate soil moisture and classify sea ice type from satellite data using deep learning. Soil moisture is an important metric used for agriculture, hydrology, and meteorology, while accurate maps of sea ice type are needed to address increased interest in commercial marine transportation through the Arctic. Satellites equipped with Synthetic Aperture Radar (SAR) sensors operating in the microwave band provide the remote sensing data used to predict these two geophysical quantities since SAR is capable of measuring the Earth's surface in all weather conditions and in darkness. Herein, the primary deep learning technique used to model soil moisture and sea ice types from SAR data is the Convolutional Neural Network (CNN). The best performing experiment for estimating soil moisture was achieved by U-Net on a dataset of 1,034 images to achieve a testing mean square error (MSE) of 3.82e-03 and correlation of 79.2%. For classifying sea ice type, DenseNet achieved the highest overall classification accuracy of 94.0% including water and the highest ice classification accuracy of 91.8% on a three class dataset using a fusion of HH and HV SAR polarizations for the input data. These results are contextualized and are shown to be comparable to other similar studies thereby supporting that the objective of the thesis has been achieved.

This thesis presents research into microwave remote sensing technologies for Arctic Science applications. In the first part of my thesis, I created and verified procedures for measuring the dielectric of a small volume of oil with cavity resonator and dielectric probe techniques. I designed and fabricated a cavity resonator and utilized a dielectric probe to demonstrate the technique and to generate new measurement results for three types of oil products. The measurement results are needed in dielectric mixture modelling and simulations of the normalized radar cross-section (NRCS) of oil-contaminated sea ice. I utilized five existing dielectric mixture models and the measured dielectric constants of three types of oil to model both clean and oil-contaminated sea ice. I simulated and compared their NRCS (three different types of contamination with three different amounts). The simulation results showed that I could distinguish between the clean and oil-contaminated sea ice based on the NRCS magnitudes. The simulation results demonstrate that for higher incidence angles, distinguishing clean sea ice from oil-contaminated sea ice with VV polarization is easier than HH polarization. In the second part of my thesis, I designed C-band Short Back-fire antennas that can be used for remote sensing applications. I simulated and characterized six different designs of Short Back-Fire antennas. The first design, SBF-1, is an optimum case of the conventional SBF antenna. In SBF-2, I improved the realized gain and bandwidth of the first one by adding a cylindrical choke to the main reflector. In the third design, I filled the choke with Teflon. In the subsequent designs, I increased the bandwidth and realized gain of SBF-1 and SBF-2 by adding a ring beside the rim and by adding a choke to the rim. To validate the simulations, I fabricated and tested SBF-1 and SBF-2. The measurement results show good agreement between the simulations and the actual measurements.

This thesis presents the development, testing, and verification of transmission tomography software for the applications of small-scale biological imaging and large-scale stored-grain imaging. Transmission tomography is a non-invasive technique for producing quantitative images of an object's physical properties. This is done by interrogating the object with waves and measuring the resulting response at a set of measurement positions. The properties of the received waves are analyzed, and used to calculate the properties of the object which is interrogated. First, a mathematical formulation of transmission tomography is presented and explained. The formulation is then used to build a numerical model of the physical properties which dictate wave transmission in two systems of linear algebraic equations. The algorithms that are required for building the numerical model are then explained. The rest of this thesis is devoted to a series of experiments. These experiments show the usefulness of transmission tomography in some particular applications. They also motivate the incremental development of features of the transmission tomography algorithm that was developed for this thesis. The first experiment uses acoustic data to perform two-dimensional transmission tomography. The first experiment shows that two-dimensional transmission tomography of acoustic data produces useful images of an object's wave speed. The successful two-dimensional experiments motivate the two following experiments, which use electromagnetic data to perform three-dimensional transmission tomography. In order to perform three-dimensional tomography of electromagnetic data, a modification is made to the numerical model. The successful three-dimensional transmission tomography algorithm is then applied to track the volume of grain stored in a bin. The final experiment uses acoustic data and the modified numerical model to perform three-dimensional transmission tomography to identify two distinct sph

In this thesis, I performed a study on the electromagnetic behaviors of an Electromagnetic Bandgap (EBG) structure with a specific set of parameters, and explored the effects of the geometrical parameters on its bandgap, which can be a stepping stone for designing future EBG structures with desired electromagnetic bandgaps. Later, the performance of a microstrip patch antenna when its ground plane is replaced by a lattice of the studied EBG units is investigated. This led to a noticeable bandwidth and gain enhancement, which motivated me to perform a parameter study to seek further bandwidth improvement. No restriction was placed on the range of parameters, and wider band antennas were observed when parameters fell outside the band gap requirements. Furthermore, I developed methods and designs that can be used to reduce the antenna size and weight by applying some structural modifications. I introduced two antennas, which I named “the puck antenna” and “suspended EBG-backed antenna”. The presented simulation results describe the performance of these antennas. The use of an EBG structure as the ground plane of a microstrip patch antenna is shown here to enhance the gain of the antenna. This motivated me to look for further gain enhancement by placing a dielectric cover on top of the antenna, referred to as “superstrate”. In the literature, the gain enhancement effect of the superstrate on different antennas including microstrip patch antennas has been confirmed. But using a superstrate on an EBG-backed microstrip patch antenna can be considered as another novelty of this thesis project, which led to further gain enhancement.

In this thesis, I performed a study on the electromagnetic behaviors of an Electromagnetic Bandgap (EBG) structure with a specific set of parameters, and explored the effects of the geometrical parameters on its bandgap, which can be a stepping stone for designing future EBG structures with desired electromagnetic bandgaps. Later, the performance of a microstrip patch antenna when its ground plane is replaced by a lattice of the studied EBG units is investigated. This led to a noticeable bandwidth and gain enhancement, which motivated me to perform a parameter study to seek further bandwidth improvement. No restriction was placed on the range of parameters, and wider band antennas were observed when parameters fell outside the band gap requirements. Furthermore, I developed methods and designs that can be used to reduce the antenna size and weight by applying some structural modifications. I introduced two antennas, which I named “the puck antenna” and “suspended EBG-backed antenna”. The presented simulation results describe the performance of these antennas. The use of an EBG structure as the ground plane of a microstrip patch antenna is shown here to enhance the gain of the antenna. This motivated me to look for further gain enhancement by placing a dielectric cover on top of the antenna, referred to as “superstrate”. In the literature, the gain enhancement effect of the superstrate on different antennas including microstrip patch antennas has been confirmed. But using a superstrate on an EBG-backed microstrip patch antenna can be considered as another novelty of this thesis project, which led to further gain enhancement.

Inaccuracies in sea ice observations from passive microwave satellite sensors increase during the summer melt period due to the evolution of sea ice thermophysical properties driving complexity in ice emissivity. Research from this thesis examines variations in sea ice thermophysical properties in Hudson Bay throughout summer melt and relates them to ice surface emissivity. This is achieved through the collection and analysis of a time-series of in situ passive microwave and unmanned aerial vehicle measurements of sea ice. Contributions from this thesis are made under two overarching categories: 1) the influence of sediment presence on sea ice passive microwave signature and; 2) the evolution of in situ and satellite-based sea ice emissivity throughout the melt period in Hudson Bay. Results from this research link non-uniform distribution of sediment across the ice surface to increased surface topography, as a result of enhanced melt rates from decreased surface albedo. The in situ passive microwave signature of sediment-laden ice is then examined, in relation to the surface roughness and liquid water presence on the ice surface. This thesis also verifies the evolution of in situ sea ice emissivity during the melt period in relation to the existing literature, and distinct periods of ice emissivity during ice melt are highlighted. In situ and satellite-based microwave brightness temperatures are compared, facilitated by a multi-sensor approach. To the authors' knowledge, these results contribute the first multi-sensor in situ observations of sediment laden sea ice, and the first comprehensive analysis of the emissive properties of Hudson Bay sea ice throughout the summer melt period.

Inaccuracies in sea ice observations from passive microwave satellite sensors increase during the summer melt period due to the evolution of sea ice thermophysical properties driving complexity in ice emissivity. Research from this thesis examines variations in sea ice thermophysical properties in Hudson Bay throughout summer melt and relates them to ice surface emissivity. This is achieved through the collection and analysis of a time-series of in situ passive microwave and unmanned aerial vehicle measurements of sea ice. Contributions from this thesis are made under two overarching categories: 1) the influence of sediment presence on sea ice passive microwave signature and; 2) the evolution of in situ and satellite-based sea ice emissivity throughout the melt period in Hudson Bay. Results from this research link non-uniform distribution of sediment across the ice surface to increased surface topography, as a result of enhanced melt rates from decreased surface albedo. The in situ passive microwave signature of sediment-laden ice is then examined, in relation to the surface roughness and liquid water presence on the ice surface. This thesis also verifies the evolution of in situ sea ice emissivity during the melt period in relation to the existing literature, and distinct periods of ice emissivity during ice melt are highlighted. In situ and satellite-based microwave brightness temperatures are compared, facilitated by a multi-sensor approach. To the authors' knowledge, these results contribute the first multi-sensor in situ observations of sediment laden sea ice, and the first comprehensive analysis of the emissive properties of Hudson Bay sea ice throughout the summer melt period.

In this thesis, a passive electric field sensor based on a substrate-integrated waveguide (SIW) resonator is designed, fabricated, and evaluated in the measurement of electric field of high voltage produced by two parallel plates. The proposed sensor is potentially applicable to measure the electric field of the high voltage apparatus remotely. When exposed to an external electric field, the resonance frequency of the SIW resonator-based sensor changes due to the variation of the capacitance of diode varactors that are mounted on the sensor. The capacitance of the varactor diode is a function of the voltage induced in its terminals. The sensor is designed to work in the industrial, scientific and medical (ISM) radio frequency band and demonstrates a high loaded quality factor of more than 200. An interrogation system has been developed that is capable of measuring the time-varying resonance frequency of the proposed sensor and deriving the waveform of the external electric field. The interrogation system transmits pulses of radio frequency (RF) signals to the sensor and the sensor re-emits the decaying ring back signals. The recorded ring back signals are downconverted and analyzed to derive the resonance frequency of the sensor.

In this thesis, a passive electric field sensor based on a substrate-integrated waveguide (SIW) resonator is designed, fabricated, and evaluated in the measurement of electric field of high voltage produced by two parallel plates. The proposed sensor is potentially applicable to measure the electric field of the high voltage apparatus remotely. When exposed to an external electric field, the resonance frequency of the SIW resonator-based sensor changes due to the variation of the capacitance of diode varactors that are mounted on the sensor. The capacitance of the varactor diode is a function of the voltage induced in its terminals. The sensor is designed to work in the industrial, scientific and medical (ISM) radio frequency band and demonstrates a high loaded quality factor of more than 200. An interrogation system has been developed that is capable of measuring the time-varying resonance frequency of the proposed sensor and deriving the waveform of the external electric field. The interrogation system transmits pulses of radio frequency (RF) signals to the sensor and the sensor re-emits the decaying ring back signals. The recorded ring back signals are downconverted and analyzed to derive the resonance frequency of the sensor.

This thesis explores the effects of crude oil spilled beneath young sea ice on the dielectric properties and normalized radar cross-section of the ice at microwave (C-band) frequencies. The dielectric profile, also referred to as the complex permittivity profile, of sea ice is a governing factor in how the ice scatters incident electromagnetic waves, thus affecting its associated normalized radar cross-section. The dielectric profile of sea ice is dependent on both the geophysical and thermal properties of the ice. Crude oil contained beneath, within, or on the surface of young sea ice was expected to change these properties, thus impacting both the dielectric profile and normalized radar cross-section of the ice. Differences between the dielectric profile and normalized radar cross-section of uncontaminated and oil-contaminated sea ice is expected to facilitate detection of oil-contaminated sea ice through active microwave remote sensing technologies. As such, a discussion of the geophysical, thermodynamic, and electromagnetic properties of sea ice is presented, as well as an overview of the behaviour of oil in ice infested environments and the efforts undertaken to detect crude oil-contaminated sea ice. Following this, the details of a preliminary laboratory experiment conducted to explore the differences in the dielectric profiles of uncontaminated and contaminated sea ice are presented. Next, a comprehensive description of the meso-scale crude oil-in-sea ice experiment and the results, including the modelled dielectric profiles and measured normalized radar cross-sections of uncontaminated and oil-contaminated sea ice, is provided. Finally, a simulation study comparing the normalized radar cross-section for the uncontaminated and oil-contaminated cases is presented. Based on the experimental and simulation work conducted, the observed differences in the temperature profiles and normalized radar cross-sections of uncontaminated and oil-contaminated sea ice sugges

The need to monitor geophysical properties of first year ice (FYI) in the Arctic is increasing as this type of sea ice becomes more prevalent. One such method of monitoring the Arctic is the use of electromagnetic remote sensing techniques. These methods determine dielectric properties of the illuminated sea ice by interpreting how the electromagnetic waves interact within the medium. In the literature, there are empirical formulas relating these dielectric properties to the geophysical properties of the sea ice. The contributions of this research are the development and testing of a surface based active microwave remote sensor to monitor sea ice growth in the winter through the reconstruction of the time series complex permittivity profile of FYI.

This thesis explores the effects of crude oil spilled beneath young sea ice on the dielectric properties and normalized radar cross-section of the ice at microwave (C-band) frequencies. The dielectric profile, also referred to as the complex permittivity profile, of sea ice is a governing factor in how the ice scatters incident electromagnetic waves, thus affecting its associated normalized radar cross-section. The dielectric profile of sea ice is dependent on both the geophysical and thermal properties of the ice. Crude oil contained beneath, within, or on the surface of young sea ice was expected to change these properties, thus impacting both the dielectric profile and normalized radar cross-section of the ice. Differences between the dielectric profile and normalized radar cross-section of uncontaminated and oil-contaminated sea ice is expected to facilitate detection of oil-contaminated sea ice through active microwave remote sensing technologies. As such, a discussion of the geophysical, thermodynamic, and electromagnetic properties of sea ice is presented, as well as an overview of the behaviour of oil in ice infested environments and the efforts undertaken to detect crude oil-contaminated sea ice. Following this, the details of a preliminary laboratory experiment conducted to explore the differences in the dielectric profiles of uncontaminated and contaminated sea ice are presented. Next, a comprehensive description of the meso-scale crude oil-in-sea ice experiment and the results, including the modelled dielectric profiles and measured normalized radar cross-sections of uncontaminated and oil-contaminated sea ice, is provided. Finally, a simulation study comparing the normalized radar cross-section for the uncontaminated and oil-contaminated cases is presented. Based on the experimental and simulation work conducted, the observed differences in the temperature profiles and normalized radar cross-sections of uncontaminated and oil-contaminated sea ice sugges

The need to monitor geophysical properties of first year ice (FYI) in the Arctic is increasing as this type of sea ice becomes more prevalent. One such method of monitoring the Arctic is the use of electromagnetic remote sensing techniques. These methods determine dielectric properties of the illuminated sea ice by interpreting how the electromagnetic waves interact within the medium. In the literature, there are empirical formulas relating these dielectric properties to the geophysical properties of the sea ice. The contributions of this research are the development and testing of a surface based active microwave remote sensor to monitor sea ice growth in the winter through the reconstruction of the time series complex permittivity profile of FYI.