Since its inception, Moore's Law has been a reliable predictor of computational power. This steady increase in computational power has been due to the ability to fit increasing numbers of transistors in a single chip. A consequence of increasing the number of transistors is also increasing the power consumption. The physical properties of CMOS technologies will make this powerwall unavoidable and will result in severe restrictions to future progress and applications. A potential solution to the problem of rising power demands is to investigate alternative low power nanotechnologies for implementing logic circuits. The intrinsic properties of these emerging nanotechnologies result in them being low power in nature when compared to current CMOS technologies. This thesis specifically highlights quantum dot celluar automata (QCA) and nanomagnetic logic (NML) as just two possible technologies. Designs in NML and QCA are explored for simple arithmetic units such as full adders and subtractors. A new multilayer 5-input majority gate design is proposed for use in NML. Designs of reversible adders are proposed which are easily testable for unidirectional stuck at faults.

Background subtraction is the algorithmic process that segments out the region of interest often known as foreground from the background. Extensive literature and numerous algorithms exist in this domain, but most research have focused on videos captured by static cameras. The proliferation of portable platforms equipped with cameras has resulted in a large amount of video data being generated from moving cameras. This motivates the need for foundational algorithms for foreground/background segmentation in videos from moving cameras. In this dissertation, I propose three new types of background subtraction algorithms for moving cameras based on appearance, motion, and a combination of them. Comprehensive evaluation of the proposed approaches on publicly available test sequences show superiority of our system over state-of-the-art algorithms. The first method is an appearance-based global modeling of foreground and background. Features are extracted by sliding a fixed size window over the entire image without any spatial constraint to accommodate arbitrary camera movements. Supervised learning method is then used to build foreground and background models. This method is suitable for limited scene scenarios such as Pan-Tilt-Zoom surveillance cameras. The second method relies on motion. It comprises of an innovative background motion approximation mechanism followed by spatial regulation through a Mega-Pixel denoising process. This work does not need to maintain any costly appearance models and is therefore appropriate for resource constraint ego-vision systems. The proposed segmentation combined with skin cues is validated by a novel application on authenticating hand-gestured signature captured by wearable cameras. The third method combines both motion and appearance. Foreground probabilities are jointly estimated by motion and appearance. After the mega-pixel denoising process, the probability estimates and gradient image are combined by Graph-Cut to produce the segmentation mask. This method is universal as it can handle all types of moving cameras.

Mechanical stresses placed on ferromagnetic materials while under the influence of a magnetic field are known to cause changes to the permanent magnetization of the material. Modeling this phenomenon is vital to the safety of ocean faring ships. In this thesis, a quasi-strip theory method of computing the nonlinear wave induced motion of a ship is developed, and the fluid pressure on the surface of the hull is used to determine the mechanical stresses. An existing magnetostatic volume integral equation code is used to evaluate the effects of the ship motion and hull stresses. The resulting changes in the magnetic field for various ship forms are presented to demonstrate the effects of given sea states.

Current transformer saturation will cause the secondary current distortion. When saturation occurs, the secondary current will not be linearly proportional to the primary current, which may lead to maloperation of protection devices. This thesis researches and tests two detecting methods: Fast Fourier Transform (FFT) and Wavelet Transform based methods. Comparing these two methods, FFT has a better performance in steady state saturation, and Wavelet Transform can determine singularity to provide the moment of distortion. The Jiles-Atherton (J-A) theory of ferromagnetic hysteresis is one approach used in electromagnetics transient modeling. With decades of development, the J-A model has evolved into different versions. The author summarizes the different models and implements J-A model in both MATLAB and Simulink.

From object tracking to 3D reconstruction, RGB-Depth (RGB-D) camera networks play an increasingly important role in many vision and graphics applications. With the recent explosive growth of Augmented Reality (AR) and Virtual Reality (VR) platforms, utilizing camera RGB-D camera networks to capture and render dynamic physical space can enhance immersive experiences for users. To maximize coverage and minimize costs, practical applications often use a small number of RGB-D cameras and sparsely place them around the environment for data capturing. While sparse color camera networks have been studied for decades, the problems of extrinsic calibration of and rendering with sparse RGB-D camera networks are less well understood. Extrinsic calibration is difficult because of inappropriate RGB-D camera models and lack of shared scene features. Due to the significant camera noise and sparse coverage of the scene, the quality of rendering 3D point clouds is much lower compared with synthetic models. Adding virtual objects whose rendering depend on the physical environment such as those with reflective surfaces further complicate the rendering pipeline. In this dissertation, I propose novel solutions to tackle these challenges faced by RGB-D camera systems. First, I propose a novel extrinsic calibration algorithm that can accurately and rapidly calibrate the geometric relationships across an arbitrary number of RGB-D cameras on a network. Second, I propose a novel rendering pipeline that can capture and render, in real-time, dynamic scenes in the presence of arbitrary-shaped reflective virtual objects. Third, I have demonstrated a teleportation application that uses the proposed system to merge two geographically separated 3D captured scenes into the same reconstructed environment. To provide a fast and robust calibration for a sparse RGB-D camera network, first, the correspondences between different camera views are established by using a spherical calibration object. We show that this approach outperforms other techniques based on planar calibration objects. Second, instead of modeling camera extrinsic using rigid transformation that is optimal only for pinhole cameras, different view transformation functions including rigid transformation, polynomial transformation, and manifold regression are systematically tested to determine the most robust mapping that generalizes well to unseen data. Third, the celebrated bundle adjustment procedure is reformulated to minimize the global 3D projection error so as to fine-tune the initial estimates. To achieve a realistic mirror rendering, a robust eye detector is used to identify the viewer's 3D location and render the reflective scene accordingly. The limited field of view obtained from a single camera is overcome by our calibrated RGB-D camera network system that is scalable to capture an arbitrarily large environment. The rendering is accomplished by raytracing light rays from the viewpoint to the scene reflected by the virtual curved surface. To the best of our knowledge, the proposed system is the first to render reflective dynamic scenes from real 3D data in large environments. Our scalable client-server architecture is computationally efficient - the calibration of a camera network system, including data capture, can be done in minutes using only commodity PCs.

The synchronous machine is one of the critical components of electric power systems. Modeling of synchronous machines is essential for power systems analyses. Electric machines are often interfaced with power electronic components. This work presents an advanced synchronous machine modeling, which emphasis on the modeling and simulation of systems that contain a mixture of synchronous machines and power electronic components. Such systems can be found in electric drive systems, dc power systems, renewable energy, and conventional synchronous machine excitation. Numerous models and formulations have been used to study synchronous machines in different applications. Herein, a unified derivation of the various model formulations, which support direct interface to external circuitry in a variety of scenarios, is presented. Selection of the formulation with the most suitable interface for the simulation scenario has better accuracy, fewer time steps, and less run time. Brushless excitation systems are widely used for synchronous machines. As a critical part of the system, rotating rectifiers have a significant impact on the system behavior. This work presents a numerical average-value model (AVM) for rotating rectifiers in brushless excitation systems, where the essential numerical functions are extracted from the detailed simulations and vary depending on the loading conditions. The proposed AVM can provide accurate simulations in both transient and steady states with fewer time steps and less run time compared with detailed models of such systems and that the proposed AVM can be combined with AVM models of other rectifiers in the system to reduce the overall computational cost. Furthermore, this work proposes an alternative formulation of numerical AVMs of machine-rectifier systems, which makes direct use of the natural dynamic impedance of the rectifier without introducing low-frequency approximations or algebraic loops. By using this formulation, a direct interface of the AVM is achieved with inductive circuitry on both the ac and dc sides allowing traditional voltage-in, current-out formulations of the circuitry on these sides to be used with the proposed formulation directly. This numerical AVM formulation is validated against an experimentally validated detailed model and compared with previous AVM formulations. It is demonstrated that the proposed AVM formulation accurately predicts the system's low-frequency behavior during both steady and transient states, including in cases where previous AVM formulations cannot predict accurate results. Both run times and numbers of time steps needed by the proposed AVM formulation are comparable to those of existing AVM formulations and significantly decreased compared with the detailed model.

In this dissertation, several volt-var optimization methods have been proposed to improve the expected performance of the distribution system using distributed renewable energy sources and conventional volt-var control equipment: photovoltaic inverter reactive power control for chance-constrained distribution system performance optimisation, integrated distribution system optimization using a chance-constrained formulation, integrated control of distribution system equipment and distributed generation inverters, and coordination of PV inverters and voltage regulators considering generation correlation and voltage quality constraints for loss minimization. Distributed generation sources (DGs) have important benefits, including the use of renewable resources, increased customer participation, and decreased losses. However, as the penetration level of DGs increases, the technical challenges of integrating these resources into the power system increase as well. One such challenge is the rapid variation of voltages along distribution feeders in response to DG output fluctuations, and the traditional volt-var control equipment and inverter-based DG can be used to address this challenge. These methods aim to achieve an optimal expected performance with respect to the figure of merit of interest to the distribution system operator while maintaining appropriate system voltage magnitudes and considering the uncertainty of DG power injections. The first method is used to optimize only the reactive power output of DGs to improve system performance (e.g., operating profit) and compensate for variations in active power injection while maintaining appropriate system voltage magnitudes and considering the uncertainty of DG power injections over the interval of interest. The second method proposes an integrated volt-var control based on a control action ahead of time to find the optimal voltage regulation tap settings and inverter reactive control parameters to improve the expected system performance (e.g., operating profit) while keeping the voltages across the system within specified ranges and considering the uncertainty of DG power injections over the interval of interest. In the third method, an integrated control strategy is formulated for the coordinated control of both distribution system equipment and inverter-based DG. This control strategy combines the use of inverter reactive power capability with the operation of voltage regulators to improve the expected value of the desired figure of merit (e.g., system losses) while maintaining appropriate system voltage magnitudes. The fourth method proposes a coordinated control strategy of voltage and reactive power control equipment to improve the expected system performance (e.g., system losses and voltage profiles) while considering the spatial correlation among the DGs and keeping voltage magnitudes within permissible limits, by formulating chance constraints on the voltage magnitude and considering the uncertainty of PV power injections over the interval of interest. The proposed methods require infrequent communication with the distribution system operator and base their decisions on short-term forecasts (i.e., the first and second methods) and long-term forecasts (i.e., the third and fourth methods). The proposed methods achieve the best set of control actions for all voltage and reactive power control equipment to improve the expected value of the figure of merit proposed in this dissertation without violating any of the operating constraints. The proposed methods are validated using the IEEE 123-node radial distribution test feeder.

This dissertation presents techniques for high-order simulation of electromagnetic fields, particularly for problems involving ships with ferromagnetic hulls and active corrosion-protection systems. A set of numerically constrained hexahedral basis functions for volume integral equation discretization is presented in a method-of-moments context. Test simulations demonstrate the accuracy achievable with these functions as well as the improvement brought about in system conditioning when compared to other basis sets. A general method for converting between a locally-corrected Nyström discretization of an integral equation and a method-of-moments discretization is presented next. Several problems involving conducting and magnetic-conducting materials are solved to verify the accuracy of the method and to illustrate both the reduction in number of unknowns and the effect of the numerically constrained bases on the conditioning of the converted matrix. Finally, a surface integral equation derived from Laplace’s equation is discretized using the locally-corrected Nyström method in order to calculate the electric fields created by impressed-current corrosion protection systems. An iterative technique is presented for handling nonlinear boundary conditions. In addition we examine different approaches for calculating the magnetic field radiated by the corrosion protection system. Numerical tests show the accuracy achievable by higher-order discretizations, validate the iterative technique presented. Various methods for magnetic field calculation are also applied to basic test cases.

The prevalence of wireless networks and the convenience of mobile cameras enable many new video applications other than security and entertainment. From behavioral diagnosis to wellness monitoring, cameras are increasing used for observations in various educational and medical settings. Videos collected for such applications are considered protected health information under privacy laws in many countries. Visual privacy protection techniques, such as blurring or object removal, can be used to mitigate privacy concern, but they also obliterate important visual cues of affect and social behaviors that are crucial for the target applications. In this dissertation, we propose to balance the privacy protection and the utility of the data by preserving the privacy-insensitive information, such as pose and expression, which is useful in many applications involving visual understanding. The Intellectual Merits of the dissertation include a novel framework for visual privacy protection by manipulating facial image and body shape of individuals, which: (1) is able to conceal the identity of individuals; (2) provide a way to preserve the utility of the data, such as expression and pose information; (3) balance the utility of the data and capacity of the privacy protection. The Broader Impacts of the dissertation focus on the significance of privacy protection on visual data, and the inadequacy of current privacy enhancing technologies in preserving affect and behavioral attributes of the visual content, which are highly useful for behavior observation in educational and medical settings. This work in this dissertation represents one of the first attempts in achieving both goals simultaneously.

This dissertation investigates the development and use of self-images in augmented reality systems for learning and learning-based activities. This work focuses on self- modeling, a particular form of learning, actively employed in various settings for therapy or teaching. In particular, this work aims to develop novel multimedia systems to support the display and rendering of augmented self-images. It aims to use interactivity (via games) as a means of obtaining imagery for use in creating augmented self-images. Two multimedia systems are developed, discussed and analyzed. The proposed systems are validated in terms of their technical innovation and their clinical efficacy in delivering behavioral interventions for young children on the autism spectrum.