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Many methods of single-cell analysis are currently used, and each method provides different characteristics of the cell, such as dielectric and physical properties. In this thesis we investigate the use of optical analysis in tandem with dielectrophoresis analysis in a high throughput flow cytometer system. To examine the viability of combining optical and dielectrophoretic analysis methods, this thesis proposes an optical model using Mie scattering theory, and a data extraction and particle classification algorithm that employs both analysis modalities. The optical model presented here examines the effects of different experimental variables such as particle size and refractive index, the refractive index of the medium, and cytometer components like LED wavelength. The data extraction and particle classification algorithm presented extracts both optical and DEP data and is used to identify and sort particles in mixed population experiments. Combining the information gathered from examining the optical data from particles as well as the dielectric properties from dielectrophoretic measurements provides a more robust and confident classification of particles. In this thesis, the combination of the two methods was applied to both polystyrene polymer sphere (PSS) beads and Chinese hamster ovary (CHO) cell experiments. Particle size data was successfully extracted using optical analysis and used to provide more confidence and robustness to the particle classification conducted using DEP analysis. The findings of this thesis provide a starting point for further use of optical analysis in conjunction with DEP analysis in a high through put flow cytometer system.

Compressed sensing is an exciting, quickly developing field, pulling in significant consideration in electrical engineering, applied mathematics, statistics, and software engineering. It can likewise serve computer vision, coding theory, signal processing, image processing, and algorithms for efficient data processing. Compressed sensing gives an exquisite framework for recuperating signals from compressed measurements. In this regard, our main focus in this thesis is the problem of bilinear factorization of sub-sampled matrices, also known as matrix factorization (MF), which has different variations, such as unconstrained MF, integer MF (IMF), non-negative MF (NMF), probabilistic MF (PMF), to cite a few. In this thesis, we introduce a new Bayesian quantized MF solution based on the approximate message passing (AMP) framework to solve the discrete matrix completion (MC) problem. Specifically, we resort to the bilinear generalized vector AMP (BiG-VAMP) algorithm along with a quantizer, and an expectation-maximization (EM) learning procedure to optimize the quantization thresholds. This approach enables our algorithm to jointly recover two matrices, denoted as U and V, as well as their real-valued and quantized product matrices from an incomplete discrete low-rank matrix Y, observed through a component-wise selection transformation. Extensive computer simulations, based on synthetic and real-world discrete datasets, show that the proposed method outperforms the state-of-the-art techniques for MF with discrete-valued factors in terms of reconstruction performance. Moreover, we conduct a theoretical performance analysis of the proposed method, based on the state evolution (SE) framework, and show its consistency with the empirical MSE performance obtained by computer simulations.

The complexity of power networks is increasing rapidly due to the growing use of power electronic devices and the expansion of new energy sources. As a result, ensuring the stability of power systems has become increasingly important. Grid-connected inverter systems play a crucial role in modern power systems, and studying their stability can aid in developing new tools for stability analysis. Transient stability assessment (TSA) involves analyzing the behavior of system variables during and after disturbances. It's preferred to express stability boundaries as nonlinear functions of system variables, which traditional analytical methods struggle to achieve. In recent decades, Machine Learning (ML) techniques have been proposed. However, most studies focus on synchronous generator-based systems, utilizing physical data such as power flow, voltage, and current. In addition to using physical quantities, the stability status of the entire power system can be determined by either the state variables or a subset thereof. This thesis introduces an analysis method based on statistical method and machine learning to determine the stability status of the inverter connected to the gird. The analysis method focuses on the examination and selection of appropriate state variables in inverter that can be used for rapid and accurate predict the stability. The dataset was generated by simulating disturbances and capturing the variables at the disturbance's clearance and the multiple simulations of a modified 12-bus system using the electromagnetic transient simulation tool, PSCAD/EMTDC. The research framework, established through a detailed literature review, includes describing the test system and modifications for inverter-based system. It explores gird-following inverter modeling and uses statistical methods for effective transient stability assessment.. The decision tree classifier is the machine learning approach utilized in this research. The constructed classifier correlates

This thesis introduces a new algorithmic solution to the massive unsourced random access (mURA) problem. The proposed uncoupled compressed sensing (UCS)-based scheme relies on slotted transmissions and takes advantage of the inherent coupling provided by the users’ spatial signatures in the form of channel correlations across slots to completely eliminate the need for concatenated coding. As opposed to all existing methods, the proposed solution combines the steps of activity detection, channel estimation, and data decoding into a unified mURA framework. It capitalizes on the bilinear generalized vector approximate message passing (BiG-VAMP) algorithm, tailored to fit the inherent constraints of mURA. To account for the quantization effects, we incorporate an output denoising module into the algorithm. Furthermore, this work presents a novel approach for handling a coarse quantized massive MIMO system by integrating activity detection, channel estimation, and data decoding within a unified framework. The proposed quantized mURA algorithm is evaluated using low-precision analog-to-digital converters (ADCs). Additionally, a state evolution algorithm is developed to validate the performance of both the proposed unquantized and quantized algorithms, demonstrating their consistency in the asymptotic regime. Furthermore, exhaustive computer simulations reveal that the proposed scheme shows promising results even in challenging scenarios.

Power transformers are critical assets in the power system, and routine condition assessment of them is necessary to ensure reliable and cost-effective energy trans- mission. As a transformer operates, thermal and electrical stresses degrade its electrical insulation and remaining lifespan, and these degradation processes pro- duce gas compounds that are dissolved in its insulating oil. Dissolved gas analysis (DGA) assesses a transformer’s condition by measuring these gasses, and numerous interpretation techniques exist based on physical mechanisms and experimental data to determine the associated faults that caused the gassing. Artificial neural net- works (ANN) are a deep learning topology, which is a type of artificial intelligence (AI). ANNs are proven to be useful tools for determining complex relationships in data — particularly for classification purposes — and lend themselves suitable for DGA interpretation. Due to the dependence of AI models on their provided datasets, uncertainty regarding their accuracies when predicting new assessments is expected. This thesis explores the use of ANNs to interpret DGA for improved certainty in fault classification, and postulates that the existing DGA interpretation methods encode relationships that are not easily learned by ANNs. This work proposes a methodology that combines select methods (e.g., Duval’s Triangle 1, Duval’s Pentagon 1, and IEC Ratios) as input features along with a structure that also learns novel gassing-fault relationships in an attempt to improve performance and trust with DGA interpretation. Three models with varied input features are optimized with respect to their hidden-layer dimensions and training parameters, and are evaluated using repeated random subsampling with K-fold validation. The results from this work demonstrate that using ANN’s for DGA interpretation offers improvements over conventional methods — notably in low-temperature fault classification — and shows the best-performing model

The designs for a remote managed honey level and temperature control system for a beehive are presented in this thesis. These designs can be useful in beekeeping, as apiarists can have better understanding of environmental factors and stresses affecting the honeybee population. For monitoring honey level in the hive boxes, interdigitated capacitor sensors fabricated up on the frames in the hive box are presented. Simulations and experimental testing of prototypes are presented for measuring the level of honey on top of the frames. For the temperature control in the hive, various heater designs with performance simulations are presented to heat the brood in the hive to 35 ℃ regularly, and further increase temperature to kill Varro mites. This system will measure the temperature in each region of the frame board. If any mite is detected, the temperature will be increased on that region to kill the mite, and this can protect other honey regions from destroying and save energy. The prototype interdigitated capacitor sensors were built and experimentally tested in the lab to measure the level of water on the frames. They showed that the capacitance would increase with the level of water on top of the sensor from 1 mm to 10 mm. Electronics were constructed to record the data from the interdigitated capacitor sensors and record the data on an SD card.

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

High voltage direct current (HVdc) transmission systems are widely used for bulk energy transmission over large distances and the impact of short circuit faults on dc lines in these systems have been extensively studied. However, a type of fault that has hitherto not widely been investigated is the open circuiting of a conductor, as its occurrence is relatively rare. None of the available literature provides a reasonable discussion on the impact of HVdc open conductor faults on an HVdc system. Using electromagnetic transient (EMT) simulations, this study addresses the void in HVdc fault analysis by giving useful insight into the impact of HVdc open conductor faults on current and voltage transients and their potential impact on the converter operation. A modified empirical arc model with arc length dynamics is developed to model the series open air arc resulting from conductor breaking. The model is implemented as a custom component in the PSCAD simulation platform for conducting simulation studies. Due to the lack of experimental data for HVdc open circuiting faults, the fault induced transients in terminal voltages and currents are studied considering reasonable ranges of arc parameters. Using an EMT model of a point-to-point LCC-HVdc system, the study shows that the dc line overvoltage is highest on the rectifier side and occurs for a conductor break close to the rectifier end. This study also shows that the breaking of conductors generates significant voltage and current travelling waves, which are sufficient to successfully apply the conventional travelling waves-based fault location algorithms to locate HVdc open conductor faults. After evaluating the applicability of the travelling waves-based fault location technique, the achievable accuracy of estimating the location of pole to ground and open conductor faults are evaluated. An improved algorithm is introduced to locate both types of faults with an overall accuracy of ±375 m which is specific to the 1400 km

Electromagnetic metasurfaces are electrically thin structures with small inclusions that allow for electromagnetic wave manipulation beyond the capabilities of conventional thin materials. Using the often desired passivity and losslessness attributes, single metasurfaces fail to provide certain electromagnetic wave transformations. To handle this, cascaded metasurface systems, consisting of two separated metasurfaces, are considered to overcome the power conservation limitations created by these attributes. This thesis focuses on the design optimization and validation of electromagnetic cascaded metasurfaces with internal sources, with an exploration of many different use cases. It starts with an overview of the conceptual framework that guides the entire process with a methodology overview. It covers the topic of metasurfaces in general, starting from a qualitative introduction to metamaterials, their historical context, and evolution, before advancing to a quantitative examination that includes the mathematical formulations and theoretical underpinnings. This part of the thesis establishes a foundation for metasurfaces in general before transitioning into the core of the thesis. The core of the thesis casts the design variables, originally formulated in an earlier work, in the forms of matrices and vectors, and then optimizes a design cost functional through the nonlinear conjugate gradient (CG) scheme. This CG scheme is also adapted to a more affordable system where one of the metasurfaces is replaced by a simple metallic sheet. The optimization scheme is considered in both complex and real domains to show the equivalency of optimization in both domains. The results chapter presents an analysis of various setups and simulations for cascaded metasurfaces with an internal source for beam shaping and matching scenarios. This draws comparisons between the nonlinear CG method and previously implemented gradient descent approaches. It explores different user-defined sp

Sub-Synchronous Oscillations (SSOs) pose significant challenges to both conventional and modern power grids. For years, the power industry has relied on Fixed Series Compensators (FSCs) for long-distance AC power transmission, but heavily compensated lines are a major cause for SSOs. Most Thyristor Controlled Series Compensator (TCSC) applications use Sub-Synchronous Damping Controllers (SSDCs) to avoid SSR. The TCSC inherently provides some level of damping in the sub synchronous frequency range even without SSDCs. However, the mechanism behind the inherent damping capability of the TCSC and its application of it to mitigate SSR is not well established. Furthermore, there remains uncertainty regarding the extent to which the inherent damping capability can be relied upon to mitigate SSOs and the need for SSDCs. This thesis investigates the role of TCSC in damping SSOs in power systems, mainly considering its inherent damping nature, while accommodating SSDCs when required. A simplified Dynamic Phasor (DP) small signal model of the TCSC is proposed for SSO studies, which accurately represent its inherent damping capability in the sub-synchronous frequency range. The proposed model is used to evaluate the inherent damping capability of the TCSC to mitigate Induction Generator Effect (IGE) and Torsional Interactions (TIs) in conventional power systems and Wind Sub-Synchronous Controller Interactions (W-SSCIs) in systems with Type 3 Wind power plants, through DP based small signal stability analysis. The study reveals that proper selection of TCSC parameters and operation close to its highly non-linear range improves its inherent damping capability. The inherent damping capability of the TCSC is adequate to avoid IGE and W-SSCIs even at low levels of TCSC. However, to mitigate TIs in Conventional Turbine Generator (CTG) systems with low mechanical damping, high levels of TCSC and operation near highly non-linear region are necessary. Consequently, the utilization of SS

This thesis presents the simulation and experiments performed on a Micro-Electro-Mechanical System (MEMS) electrostatic field sensor with a virtual ground. Because of their small form factor and low power consumption, MEMS electrostatic field sensors offer a promising alternative to commonly used devices such as field mills. Four separate simulations were conducted on the device, each with varying parameters to gain an understanding of how the device behaves when exposed to a 10kV DC voltage carrying wire. The simulation environment was designed to mimic the experimental conditions as close as possible. That being, simulation #1 tested the device moving towards the high voltage wire from the ground plane, the second simulation preformed had the device moving away from the wire while in contact with the ground plane. The third and fourth simulations added a wooden pole to act as a handle, this was done to mimic a possible real-world application. In these two simulations the device was tested at different heights from the ground plane and at different distances from the high voltage carrying wire. The results of these simulations were then compared to analytically obtained values, of which the simulated values agreed. Five experiments were conducted in the University of Manitoba’s High Voltage Laboratory of which four were completed with the same environment as the simulations to ensure the results have as little discrepancy as possible. Experiment #1 was setup the same as simulation #1 and so on. The fifth experiment was conducted to see the behavior of the device in a potential real-world environment where a surface has become energized. A close correlation between the simulated, calculated, and experimental values were achieved. In addition, control electronics and a Printed Circuit Board (PCB) were developed for the sensor that allowed it to be used in the experiments to collect data and have it perform as it would in real world setting. This system included a wir

In a Reconfigurable Intelligent Surface (RIS), Mutual Coupling (MC) is unavoidable as a result of the sub-wavelength structure of its unit elements. In addition, MC inherently leads to multiple reflection effects that are ignored in conventional (approximative) RIS models. In this thesis, we examine the problem of joint active and passive beamforming in a controllable multi-user reconfigurable intelligent surface (RIS)-assisted downlink and uplink wireless communication system, considering the MC among RIS elements. We formulate a problem to optimize active and passive beamforming jointly under the MMSE criterion. Given the non-convex nature of the problem, we use alternating optimization to decompose the problem into two sub-problems for downlink and uplink transmissions separately. In both transmissions, one sub-problem involves optimizing the phase-shift matrix of RIS. In downlink, the other sub-problem is the optimization of active precoding for the base station (BS), while the equivalent sub-problem in uplink is the optimization of the linear receiver matrix. We optimize the phase shift matrix under a physically-consistent model using the gradient descent algorithm for both transmissions. We use the Lagrange multiplier method to optimize active precoding in the downlink and apply the First Order Necessary Condition (FONC) to optimize the linear receiver in the uplink. Simulation results are represented for both lossless and lossy RIS scenarios under perfect and imperfect channel state information. We discuss the impact of changing the number of RIS elements and the RIS element spacing on system performance. The results show that, with optimized phase shifts and active precoding, the inherent multiple reflection effect can improve the performance of RIS-aided wireless communications systems. In our previous analysis, we focused on optimizing joint beamforming within a physically consistent environment by incorporating MC effects. However, we did not explicitly a

This thesis investigates the uplink scenario in millimetre-wave (mmWave) mas sive multiple-input multiple-output (MIMO) communication systems characterized by dense, uniform linear arrays (ULAs) of antenna elements tightly packed within a con fined space and equipped with low-resolution Analog-to-Digital Converters (ADCs). The primary focus of our study is to address the critical challenges of power con sumption reduction and hardware simplification while simultaneously improving the performance of quantized systems by exploring spatial oversampling. Due to the con sideration of subwavelength inter-element spacing in the ULA, extrinsic spatial thermal noise correlations arise due to significant coupling between adjacent antenna terminals. In addition to this correlated noise, the noise figure caused by hardware imperfections profoundly impacts signal recovery and cannot be dismissed as a negligible factor in system performance analysis. To tackle the problem of signal recovery in such high density ULAs with low-resolution ADCs, we propose a non-linear inference method based on Vector Approximate Message Passing (VAMP) and Belief Propagation. Addi tionally, we employ a state evolution analysis to investigate the algorithm’s asymptotic behaviour. The main objective of this algorithm is to reconstruct transmitted signals from the quantized measurements obtained by the coupled antennas. In this work, we demonstrate that employing oversampling techniques in the context of low-bit quan tized systems can substantially enhance system performance, bringing it closer to the ideal scenario with infinite-resolution ADCs. Remarkably, this performance improve ment persists even when the system is oversampled, emphasizing the potential of spatial oversampling as an effective strategy for enhancing the performance of low-resolution ADCs. We also analyze how the noise figure impacts the recovery in the context of oversampling, highlighting its significance in system design considera

The rising frequency of extreme weather events associated with climate change has made the resilient design an important feature for power systems. Incorporating the capability of parts of the power system to operate as active decentralized systems is one way of increasing the resiliency and enhancing the system reliability. This thesis addresses two significant issues related to decentralized active networks. The first issue concerns the challenge of maintaining the balance between generation and load within an islanded system through under-frequency load shedding (UFLS) during islanding events. The optimal design of UFLS schemes in active networks presents a complex problem, especially with the high penetration of inverter-based resources (IBR) that exhibit complex dynamics and low inertia. It is also important to ensure that the system operates effectively at any given operating point, necessitating the consideration of multiple operating points in the design process. Conventional schemes and control methods may not be sufficient and identifying a suitable scheme can be both time-consuming and labor-intensive, often requires conducting many simulations at various operating points. This thesis presents an optimal load shedding method in active decentralized power systems using reinforcement learning. The effectiveness of these proposed methodologies is demonstrated through the implementation of a UFLS system on a segment of the Manitoba Hydro power network. The second significant issue addressed in this context is the tuning of controllers for active, decentralized systems. These controllers need to be adjusted to function optimally across a wide range of operating conditions, both in grid-connected and islanded modes. This poses numerous challenges due to the systems' complexity and dynamic nature and traditional heuristic controller tuning methods often prove inadequate in managing such complexities. In response, this thesis proposes a novel approach that extend