Physical Sciences and Mathematics Commons^™

Stability Of Quantum Computers, Samudra Dasgupta

Doctoral Dissertations

Quantum computing's potential is immense, promising super-polynomial reductions in execution time, energy use, and memory requirements compared to classical computers. This technology has the power to revolutionize scientific applications such as simulating many-body quantum systems for molecular structure understanding, factorization of large integers, enhance machine learning, and in the process, disrupt industries like telecommunications, material science, pharmaceuticals and artificial intelligence. However, quantum computing's potential is curtailed by noise, further complicated by non-stationary noise parameter distributions across time and qubits. This dissertation focuses on the persistent issue of noise in quantum computing, particularly non-stationarity of noise parameters in transmon processors. It …

Experimental Quantum Key Distribution In Turbulent Channels, Kazi Mh Reaz

Doctoral Dissertations

Quantum Key Distribution (QKD) ensures security by relying on the laws of quantum physics rather than the mathematical intricacy of encryption algorithms. The transmission medium is a critical restricting factor for any quantum communication protocol. Fiber-based optical networks suffer great losses, making quantum communication impossible beyond metropolitan scales. Here free-space quantum communication can be a great alternative for long-distance communication. Even though modern Communications are mostly wireless the atmosphere poses a challenge for QKD. So QKD must be resistant to both atmospheric loss and variations in transmittance. In this thesis we conduct an experiment to strengthen the BB84 protocol's resistance …

Static And Dynamic State Estimation Applications In Power Systems Protection And Control Engineering, Ibukunoluwa Olayemi Korede

Doctoral Dissertations

The developed methodologies are proposed to serve as support for control centers and fault analysis engineers. These approaches provide a dependable and effective means of pinpointing and resolving faults, which ultimately enhances power grid reliability. The algorithm uses the Least Absolute Value (LAV) method to estimate the augmented states of the PCB, enabling supervisory monitoring of the system. In addition, the application of statistical analysis based on projection statistics of the system Jacobian as a virtual sensor to detect faults on transmission lines. This approach is particularly valuable for detecting anomalies in transmission line data, such as bad data or …

The Synthesis And Optimization Of Sulfide And Halide Solid Electrolytes For All Solid-State Batteries, Teerth Brahmbhatt

Doctoral Dissertations

Countries and organizations around the world have established ambitious targets to transition away from fossil fuel-based energy sources and devices. The transition is focused on cleaning up power generation by converting coal, natural gas, and oil-based power generation to renewables and nuclear energy. Decarbonizing other sectors of energy use, transportation for example, will require broader electrification. To drive this move away from fossil fuel powered transportation will require portable energy storage devices. Conventional lithium-ion batteries are a popular candidate to lead this shift. However, these batteries often rely on flammable liquid electrolytes and carbon anodes that suffer from low energy …

The Bracelet: An American Sign Language (Asl) Interpreting Wearable Device, Samuel Aba, Ahmadre Darrisaw, Pei Lin, Thomas Leonard

Chancellor’s Honors Program Projects

No abstract provided.

Evaluation Of Robust Deep Learning Pipelines Targeting Low Swap Edge Deployment, David Carter Cornett

Masters Theses

The deep learning technique of convolutional neural networks (CNNs) has greatly advanced the state-of-the-art for computer vision tasks such as image classification and object detection. These solutions rely on large systems leveraging wattage-hungry GPUs to provide the computational power to achieve such performance. However, the size, weight and power (SWaP) requirements of these conventional GPU-based deep learning systems are not suitable when a solution requires deployment to so called "Edge" environments such as autonomous vehicles, unmanned aerial vehicles (UAVs) and smart security cameras.

The objective of this work is to benchmark FPGA-based alternatives to conventional GPU systems that have the …

Characterization Techniques And Cation Exchange Membrane For Non-Aqueous Redox Flow Battery, Kun Lou

Doctoral Dissertations

The motivation of this work comes from one of the major problems of emerging non-aqueous flow battery (NAFB) that a separator or membrane which facilitates conductivity and blocks redox species crossover does not exist. Although many aspects of principles can be mirrored from mature fuel cell and aqueous flow battery, it is found that some well-defined membrane properties in aqueous systems such as swelling, transport and interactions are different in non-aqueous solvents to some extent. However, the approach of this work does follow the way perfluorosulfonate ion exchange membrane (PFSA) facilitated development of fuel cell and aqueous flow battery in …

Power System Stability Assessment With Supervised Machine Learning, Mirka Mandich

Masters Theses

Power system stability assessment has become an important area of research due to the increased penetration of photovoltaics (PV) in modern power systems. This work explores how supervised machine learning can be used to assess power system stability for the Western Electricity Coordinating Council (WECC) service region as part of the Data-driven Security Assessment for the Multi-Timescale Integrated Dynamics and Scheduling for Solar (MIDAS) project. Data-driven methods offer to improve power flow scheduling through machine learning prediction, enabling better energy resource management and reducing demand on real-time time-domain simulations. Frequency, transient, and small signal stability datasets were created using the …

Machine Learning With Topological Data Analysis, Ephraim Robert Love

Doctoral Dissertations

Topological Data Analysis (TDA) is a relatively new focus in the fields of statistics and machine learning. Methods of exploiting the geometry of data, such as clustering, have proven theoretically and empirically invaluable. TDA provides a general framework within which to study topological invariants (shapes) of data, which are more robust to noise and can recover information on higher dimensional features than immediately apparent in the data. A common tool for conducting TDA is persistence homology, which measures the significance of these invariants. Persistence homology has prominent realizations in methods of data visualization, statistics and machine learning. Extending ML with …

Approaches To Studying Bacterial Biofilms In The Bioeconomy With Nanofabrication Techniques And Engineered Platforms., Michelle Caroline Halsted

Doctoral Dissertations

Studies that estimate more than 90% of bacteria subsist in a biofilm state to survive environmental stressors. These biofilms persist on man-made and natural surfaces, and examples of the rich biofilm diversity extends from the roots of bioenergy crops to electroactive biofilms in bioelectrochemical reactors. Efforts to optimize microbial systems in the bioeconomy will benefit from an improved fundamental understanding of bacterial biofilms. An understanding of these microbial systems shows promise to increase crop yields with precision agriculture (e.g. biosynthetic fertilizer, microbial pesticides, and soil remediation) and increase commodity production yields in bioreactors. Yet conventional laboratory methods investigate these micron-scale …

Leveraging Conventional Internet Routing Protocol Behavior To Defeat Ddos And Adverse Networking Conditions, Jared M. Smith

Doctoral Dissertations

The Internet is a cornerstone of modern society. Yet increasingly devastating attacks against the Internet threaten to undermine the Internet's success at connecting the unconnected. Of all the adversarial campaigns waged against the Internet and the organizations that rely on it, distributed denial of service, or DDoS, tops the list of the most volatile attacks. In recent years, DDoS attacks have been responsible for large swaths of the Internet blacking out, while other attacks have completely overwhelmed key Internet services and websites. Core to the Internet's functionality is the way in which traffic on the Internet gets from one destination …

Early Warning Solar Storm Prediction, Ian D. Lumsden, Marvin Joshi, Matthew Smalley, Aiden Rutter, Ben Klein

Chancellor’s Honors Program Projects

No abstract provided.

Target Detection With Neural Network Hardware, Hollis Bui, Garrett Massman, Nikolas Spangler, Jalen Tarvin, Luke Bechtel, Kevin Dunn, Shawn Bradford

Chancellor’s Honors Program Projects

No abstract provided.

Interface And Morphology Engineering In Solution-Processed Electronic And Optoelectronic Devices, Sanjib Das

Doctoral Dissertations

The first part of this dissertation focuses on interface and morphology engineering in polymer- and small molecule-based organic solar cells. High-performance devices were fabricated, and the device performance was correlated with nanoscale structures using various electrical, spectroscopic and microscopic characterization techniques, providing guidelines for high-efficiency cell design.

The second part focuses on perovskite solar cells (PSCs), an emerging photovoltaic technology with skyrocketing rise in power conversion efficiency (PCE) and currently showing comparable PCEs with those of existing thin film photovoltaic technologies such as CIGS and CdTe. Fabrication of large-area PSCs without compromising reproducibility and device PCE requires formation of dense, …

Neuron Clustering For Mitigating Catastrophic Forgetting In Supervised And Reinforcement Learning, Benjamin Frederick Goodrich

Doctoral Dissertations

Neural networks have had many great successes in recent years, particularly with the advent of deep learning and many novel training techniques. One issue that has affected neural networks and prevented them from performing well in more realistic online environments is that of catastrophic forgetting. Catastrophic forgetting affects supervised learning systems when input samples are temporally correlated or are non-stationary. However, most real-world problems are non-stationary in nature, resulting in prolonged periods of time separating inputs drawn from different regions of the input space.

Reinforcement learning represents a worst-case scenario when it comes to precipitating catastrophic forgetting in neural networks. …

Versatile Three-Phase Power Electronics Converter Based Real-Time Load Emulators, Jing Wang

Doctoral Dissertations

This dissertation includes the methodology, implementation, validation, as well as real-time modeling of a load emulator for a reconfigurable grid emulation platform of hardware test-bed (HTB). This test-bed was proposed by Center of Ultra-wide-area Resilient Electric Energy Transmission Network (CURENT) at the University of Tennessee, at Knoxville in 2011, to address the transmission level system challenges posed by contemporary fast changing energy technologies.

Detailed HTB introduction, including design concept, fundamental units and hardware construction, is elaborated. In the development, current controlled three-phase power electronics converter based emulator unit is adopted to create desired power system loading conditions.

In the application, …

Cpas - Campus Parking Availability System, Jacob Lambert

Chancellor’s Honors Program Projects

No abstract provided.

Elucidating Fundamental Mechanisms In Focused Electron- And Ion-Beam Induced Synthesis, Carlos M. Gonzalez

Doctoral Dissertations

A focused electron beam deposition process (FEBID) coupled with in-situ infrared pulsed laser assist (LA-EBID) has been implemented for higher purity tungsten nanowires using W(CO)₆ [tungsten hexacarbonyl] as parent precursor gas. Nanowires made of Co from Co₂(CO)₈ [dicobalt octacarbonyl] and Pt from MeCpPtIVMe₃ [trimethyl methylcyclopentadienyl platinum] have also been realized by using inert focused ion beams of helium and helium and neon, respectively. In all cases, higher electrical conductivities, higher purities and larger grain sizes have been obtained when compared with preceding traditional additive edit techniques. These new approaches will make possible successful nanoscale direct-write …

Bayesian Dictionary Learning For Single And Coupled Feature Spaces, Li He

Doctoral Dissertations

Over-complete bases offer the flexibility to represent much wider range of signals with more elementary basis atoms than signal dimension. The use of over-complete dictionaries for sparse representation has been a new trend recently and has increasingly become recognized as providing high performance for applications such as denoise, image super-resolution, inpaiting, compression, blind source separation and linear unmixing. This dissertation studies the dictionary learning for single or coupled feature spaces and its application in image restoration tasks. A Bayesian strategy using a beta process prior is applied to solve both problems.

Firstly, we illustrate how to generalize the existing beta …

3design - Holographic Telecollaboration Interface, Thomas W. De Wit, Mark Gill, Scott Freemon, Preston Garland

Chancellor’s Honors Program Projects

No abstract provided.

Development And Experimental Analysis Of Wireless High Accuracy Ultra-Wideband Localization Systems For Indoor Medical Applications, Michael Joseph Kuhn

Doctoral Dissertations

This dissertation addresses several interesting and relevant problems in the field of wireless technologies applied to medical applications and specifically problems related to ultra-wideband high accuracy localization for use in the operating room. This research is cross disciplinary in nature and fundamentally builds upon microwave engineering, software engineering, systems engineering, and biomedical engineering. A good portion of this work has been published in peer reviewed microwave engineering and biomedical engineering conferences and journals. Wireless technologies in medicine are discussed with focus on ultra-wideband positioning in orthopedic surgical navigation. Characterization of the operating room as a medium for ultra-wideband signal transmission …

Cytongrasp: Cyton Alpha Controller Via Graspit! Simulation, Nicholas Wayne Overfield

Masters Theses

This thesis addresses an expansion of the control programs for the Cyton Alpha 7D 1G arm. The original control system made use of configurable software which exploited the arm’s seven degrees of freedom and kinematic redundancy to control the arm based on desired behaviors that were configured off-line. The inclusions of the GraspIt! grasp planning simulator and toolkit enables the Cyton Alpha to be used in more proactive on-line grasping problems, as well as, presenting many additional tools for on-line learning applications. In short, GraspIt! expands what is possible with the Cyton Alpha to include many machine learning tools and …

Analytical Computation Of Proper Orthogonal Decomposition Modes And N-Width Approximations For The Heat Equation With Boundary Control, Tasha N. Fernandez

Masters Theses

Model reduction is a powerful and ubiquitous tool used to reduce the complexity of a dynamical system while preserving the input-output behavior. It has been applied throughout many different disciplines, including controls, fluid and structural dynamics. Model reduction via proper orthogonal decomposition (POD) is utilized for of control of partial differential equations. In this thesis, the analytical expressions of POD modes are derived for the heat equation. The autocorrelation function of the latter is viewed as the kernel of a self adjoint compact operator, and the POD modes and corresponding eigenvalues are computed by solving homogeneous integral equations of the …

Universal Geometric Camera Calibration With Statistical Model Selection, Chistopher Paul Broaddus

Masters Theses

We propose a new universal camera calibration approach that uses statistical information criteria for automatic camera model selection. It requires the camera to observe a planar pattern from different positions, and then closed-form estimates for the intrinsic and extrinsic parameters are computed followed by nonlinear optimization. In lieu of modeling radial distortion, the lens projection of the camera is modeled, and in addition we include decentering distortion. This approach is particularly advantageous for wide angle (fisheye) camera calibration because it often reduces the complexity of the model compared to modeling radial distortion. We then apply statistical information criteria to automatically …