Digital Commons Network^™

Non-Linearity Modeling And Quantifications For Practical Rf Interference Control, Shengxuan Xia

Doctoral Dissertations

"Radio frequency (RF) interference can degrade the receiving sensitivity of antennas (desense problem). It is essential to model the nonlinearity as it is the root-cause of the unwanted frequency components. Understanding the electromagnetic (EM) coupling or radiated emissions is also important.

Nonlinearity causes modulation-involved desense problems, and it consists of two categories: upconvertion of the baseband noise by the transmitting (TX) signal, and the passive intermodulation (PIM) of the transmitting signal itself. The upconvertion caused desense can be modeled and analyzed with the dipole-moment based coupling framework. PIM has been identified as another nonlinear distortion mechanism, specifically in the metallic …

Methodology For Parameter Determination For Simulation Software, Aaron Flood

Doctoral Dissertations

"This research aims to present a methodology for optimizing input datasets to increase the accuracy of mathematical models and accelerate their application to engineering problems. To accomplish this goal, this work focused on the application of mathematical models to metal additive manufacturing (AM), specifically the thermal history of laser directed energy deposition (DED) of aluminum alloys. The initial steps of this body of work were to develop a mathematical model that is capable of simulating the metal AM process and applying it to the laser DED of aluminum. It was validated using the well characterized material Ti-64 and shown to …

A Lightweight Machine-Learning Framework For Enhancing Security In Iot Blockchain Networks, Charles Connor Rawlins

Doctoral Dissertations

"Blockchain is one of the fastest technologies that rivals the Internet in terms of adoption speed. This security method is applicable to data-centric environments for validating data in the presence of faults. However, traditional blockchain implementation introduces bottlenecks with computationally intense security measures to prevent malicious spam and resolve conflicts. This dissertation explores a new direction for blockchain technology that allows limited nodes, like IoT devices, to make independent decisions with compressed knowledge of past blockchain history through the use of machine-learning for active decisions (or the first machine-intelligent blockchain protocol). Proposing to introduce machine-intelligence into the rapidly evolving paradigm …

Development And Testing Of High Strength Scalable High Entropy Alloys, Hans Jakob Pommerenke

Doctoral Dissertations

"High entropy alloys have shown promise through their ability to be tailored to specific scenarios. From highly corrosive environments to precipitation strengthened alloys to radiation environments high entropy alloys allow tailor made alloys for every solution. New compositions are developed and tested but little work focuses on the processing effect choosing rather than using processing as a steppingstone to achieve properties without investigating the processing effects. This work focuses on single phase face-centered cubic alloys strengthened by processing and precipitation strengthening. In conjunction with the development of these alloys, the ability to scale production will be investigated and characterized to …

Communication Modality And Activity Recognition In Smart Manufacturing, Haodong Chen

Doctoral Dissertations

"Advancements in sensors, computational intelligence, and big data have been rapidly transforming and revolutionizing the manufacturing sector, leading to robot-rich and digitally connected factories. To facilitate such a transformative process, workforce training and coordination between the human workforce and robots have remained a major challenge. This study aims to create intelligent workforce training systems for human-computer interaction and human-robot collaboration. It proposes the use of dynamic gestures and speech commands for seamless communication between a worker and a robot, leveraging Convolutional Neural Networks (CNN) and multiple threading for recognition and integration. Additionally, a fine-grained activity recognition model has been crafted …

Modeling And Control For Precision Robotic Machining, Patrick Bazzoli

Doctoral Dissertations

"Robots are used in a wide variety of manufacturing applications, but machining applications in which robots can excel are limited by their lower accuracy and stiffness relative to traditional CNC machines. This work is composed of two parts: one to evaluate a robot’s accuracy and one to compensate for the vibrations of the robot due to its lower stiffness.

In order to evaluate whether a robot has the necessary accuracy to perform a given machining task, Paper 1 discusses a novel Model Invalidation method. This methodology provides a statistical framework as well as a measurement strategy for determining if a …

Applications Of Computational Intelligence And Data Fusion Techniques For Biomedical Images, Anand Krishnadas Nambisan

Doctoral Dissertations

"The realm of melanoma diagnosis has been significantly advanced by deep learning (DL) techniques, yet the current approaches are not without limitations, including missed diagnoses and the challenge of interpreting these "black box" models. The research is comprised of three studies, each contributing uniquely towards advancing melanoma detection accuracy and interpretability. The first study focuses on improving the detection of specific dermoscopic structures through a deep learning-based segmentation approach, while the second study builds upon this by employing a fusion technique that combines traditional image features with advanced deep learning models. This method significantly improves melanoma detection, particularly in recall …

Mission-Driven Inverse-Design Of Conventional And Unconventional Airplane Configurations Using Machine Learning, Rohan Srinivas Sharma

Doctoral Dissertations

"A key challenge in airplane design has been the long-desired ability to quickly create an airplane design with specific performance properties. To tackle this problem, researchers have traditionally leveraged design space exploration methodologies to iteratively explore a design space via surrogate models. On the other hand, inverse design is a process wherein performance objectives are fixed while a suitable design for satisfying these requirements is calculated. In recent years, artificial neural networks (ANNs) have been explored as viable surrogate models because traditional surrogate models struggle with the large number of output to input mapping needed for inverse design problems. In …

Lithospheric Drip And Mantle Thermal Upwelling Beneath Central Asia And The Southern Great Plains Of North America: Evidence From Receiver Function Imaging, Yangyang Liao

Doctoral Dissertations

"Investigating the depth of mantle transition zone (MTZ) discontinuities provides insights into mantle dynamics, including regional-scale downwelling of mantle lithosphere (i.e., lithospheric drip) and upwelling of thermal materials (i.e., mantle plume). In our study, we employ receiver functions to image the 410 km (d410) and 660 km (d660) mantle discontinuities and to ascertain thickness of the MTZ beneath Central Asia and the Great Plains in United States. The findings unveil a significantly thickened transition zone, exhibiting an average thickness of 284 km beneath East Sayan. When considered alongside geochemical observations, our results indicate that the descent of the lithosphere is …

Exploring Seismic Anisotropy In The Crust And Mantle Of The Tibetan Plateau And Analyzing Seismic Velocity Variations In Yellowstone, Cong Shen

Doctoral Dissertations

"Seismology is a scientific discipline that utilizes seismic waves to investigate the deep structures within the Earth's interior, which are otherwise inaccessible to direct observation by humans. These seismic waves can be categorized into two types: body waves and surface waves, each with distinct propagation modes and behaviors, carrying valuable information about their travel paths. The first paper concentrates on the analysis and inversion of body waves produced by large seismic events. It employs shear wave splitting and receiver function methods to explore the geodynamic structure of the south-central Tibetan Plateau, near the plate boundary. The investigation covers the depth …

Efficient Modeling And Simulation For The Aerothermal Analysis Of Particle-Laden Hypersonic Flows, Andrew Hinkle

Doctoral Dissertations

"Standard approaches for simulating hypersonic flows in particle-laden environments have key limitations in practice, such as high cost and numerical noise. In this work, efficient solution strategies for particle-laden hypersonic flows were developed using both Lagrangian and Eulerian models for the particle phase with the focus on investigating spacecraft surface erosion, as well as convective and radiative heat flux augmentation due to atmospheric particulate encountered during planetary entry.

First, an efficient integration strategy for Lagrangian methods, the Trajectory Control Volume (TCV) method, was developed for steady analysis of dilute particle phase problems on general geometries. The TCV method was shown …

Optimization Of Fire Emergency Preparedness In Underground Mining Environments: An Agent-Based Evacuation Approach, Oluwafemi Babatunde Salami

Doctoral Dissertations

"Underground mining environments present a complex interplay of thermal and non-thermal hazards, with fires constituting a significant risk due to the release of heat smoke from incomplete combustion. The propagation of these hazards through the ventilation networks underscores the critical need for understanding fire scenarios and their interaction with the underground environment. Existing knowledge regarding past mine fire disasters, and quantification of associated hazards and risks are insufficient for optimizing emergency evacuation strategies in the event of an underground fire emergency.

This research endeavors to bridge this gap by developing advanced quantitative risk assessment and evacuation models tailored specifically for …

In Situ Infrared Thermographic Inspection In Additive Manufacturing, Tao Liu

Doctoral Dissertations

"Laser Powder Bed Fusion (LPBF) is a key technique in metal additive manufacturing and has undergone extensive research over the past few decades. It enables the fabrication of complex geometries directly from digital design models. The solidification process in LPBF is heavily influenced by the characteristics of the powder layer, including its thickness and thermal transport properties. Additionally, the performance of the printed part is impacted by the temperature history during solidification and the final residual stress field. While these topics have been the focus of many studies, almost all research has concentrated on ex-situ measurement and testing. These ex-situ …

Topography Of The 410 And 660 Km Discontinuities Beneath The Tibetan Plateau And Adjacent Areas, Zexin Miao

Doctoral Dissertations

"The Tibetan Plateau represents the ongoing geotectonic aftermath of a continental collision initiated approximately 65 million years ago. Despite numerous seismological studies, controversies remain regarding the structure, evolution, and dynamics of the crust and mantle beneath Tibetan Plateau. In this study, we used teleseismic broadband data to investigate the topography of the mantle transition zone (MTZ) discontinuities. A total of ~655,000 receiver functions from 735 stations was utilized to image the 410 and 660 km discontinuities bordering the MTZ. Combining the observed and velocity corrected results, several areas in the central Tibet exhibit a typical d410 and an anomalously deep …

Critical Behavior And Dynamics Of The Superfluid-Mott Glass Transition, Jack Russell Crewse

Doctoral Dissertations

This work studies the effects of disorder on the thermodynamic critical behavior and dynamical properties of the superfluid-Mott glass quantum phase transition. After a brief introduction covering relevant fundamentals, we present the dissertation in the form of four separate but related publications. In the first two publications, we calculate the thermodynamic critical exponents of the superfluid-Mott glass quantum phase transition in both two and three spatial dimensions. The undiluted transition exhibits critical exponents that violate the Harris criterion, and thus the critical behavior is expected to change upon introducing disorder. We confirm this behavior via Monte Carlo simulation of a …

High-Resolution Spectroscopy Of Interstellar Lines And Comets, Chemeda Tadese Ejeta

Doctoral Dissertations

"The study of interstellar molecules such as CO is crucial because interstellar ices in the core of a pre-solar molecular cloud provide the starting point for volatile evolution in the protoplanetary disk. A record of the initial volatile composition of the protoplanetary disk can be obtained from the study of the chemical composition of cometary nuclei. Because of their long residence in the Oort cloud and infrequent passage through the inner solar system, long-period comets are one of the most primitive bodies in our solar system that can tell us about the composition of the early solar system. High-resolution infrared …

Adversarial Transferability And Generalization In Robust Deep Learning, Tao Wu

Doctoral Dissertations

Despite its remarkable achievements across a multitude of benchmark tasks, deep learning (DL) models exhibit significant fragility to adversarial examples, i.e., subtle modifications applied to inputs during testing yet effective in misleading DL models. These meticulously crafted perturbations possess the remarkable property of transferability: an adversarial example that effectively fools one model often retains its effectiveness against another model, even if the two models were trained independently. This research delves into the characteristics influencing the transferability of adversarial examples from three distinct and complementary perspectives: data, model, and optimization. Firstly, from the data perspective, we propose a new method of …

Deep Reinforcement Learning Based Strategies For Inverter Dominated Microgrids, Oroghene Oboreh-Snapps

Doctoral Dissertations

The integration of inverter-based distributed generators (IBDGs) in modern power systems has ushered in a new era of renewable energy utilization, prompting the rise of Microgrid (MG) architectures. Nevertheless, as IBDG penetration increases, a host of challenges surface. Foremost among these challenges are issues related to frequency stability stemming from the inherent lack of inertia in IBDGs, and inaccurate sharing of reactive and active power, particularly evident in parallel IBDG networks with mismatched feeder impedance. This research presents novel solutions to these challenges by adopting deep reinforcement learning (DRL), specifically, the twin delayed deep deterministic policy gradient (TD3) algorithm, to …

Crystal Structure Prediction Of Metal Chalcogenides, Qi Zhang

Doctoral Dissertations

A novel crystal structure prediction (CSP) method has been developed to predict energetically favorable (stable) structures based on targeted chemical compositions. It leverages the structural characteristics of recurring motifs featured in many crystals and symmetry restrictions from space groups to effectively lower the degrees of freedom of a system when conducting CSP simulations. The proposed method is applied to predicting low-energy structures of two metal chalcogenide systems: Li₃PS₄ and Na₆Ge₂Se₆. Both systems feature rigid bodies in their structures as building blocks, making them particularly suited to the proposed method. The validity …

High Viscosity Friction Reducer Evaluation At High Temperature And High Tds Environments, Ghith Ali Ahmed Biheri

Doctoral Dissertations

"In unconventional reservoirs, proppant transport during hydraulic fracturing treatments could be successful when the proppant is placed further within the reservoir's fracture networks. This could increase the reservoir formation conductivities and improve the productivity of the wells. This job of improving the reservoir conductivity by distributing and transporting proppant can be conducted by reliable fracture fluids with excellent fluid rheology (e.g., viscosity and elasticity). However, several challenges, including the high temperatures and the presence of high TDS in produced water, may limit the capability of fracture fluids to transport proppant. Additionally, the use of fracture fluid has raised fears regarding …

Adaptive Critic Optimal Control Of An Uncertain Robot Manipulator With Applications, Ravi Prakash, Laxmidhar Behera, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Realistic manipulation tasks involve a prolonged sequence of motor skills in varying control environments consisting of uncertain robot dynamic models and end-effector payloads. To address these challenges, this article proposes an adaptive critic (AC)-based basis function neural network (BFNN) optimal controller. Using a single neural network (NN) with a basis function, the proposed optimal controller simultaneously learns task-related optimal cost function, robot internal dynamics, and optimal control law. This is achieved through the development of a novel BFNN tuning law using closed-loop system stability. Therefore, the proposed optimal controller provides real-time, implementable, cost-effective control solutions for practical robotic tasks. The …

Towards Explainability Of Dimension Reduction Plots Of Unsupervised Learning Model Outcomes, Tony E.Astuhuaman Davila, Daniel B. Hier, Tayo Obafemi-Ajayi

Electrical and Computer Engineering Faculty Research & Creative Works

Dimension reduction methods are used to visualize the output of unsupervised learning models when applied to complex data. These techniques improve interpretability by transforming a high-dimension space to a lower-dimension space (usually 2D or 3D). The results are typically viewed as 2D scatter plots, and class centroids may be added to increase interpretability. Although useful, the relationship of these class centroids to the underlying feature space remains opaque. The innovative aspect of this work is to create a strong link between the dimension-reduced space and the underlying high-dimension feature space by adding selected feature centroids to the 2D scatter plots. …

Evaluation Of Transfer Learning Models On Traumatic Brain Injury Severity Classification, Ngoc Do, Daniel B. Hier, Tayo Obafemi-Ajayi

Electrical and Computer Engineering Faculty Research & Creative Works

After traumatic brain injury (TBI), clinicians use the Glasgow Coma Scale (GCS) to classify patients by severity and radiologists use the Rotterdam score and the Marshall score to classify CT scans by severity. This work investigates a viable efficient low-cost transfer learning model to use MRI images to predict GCS, Rotterdam, and Marshall severity class after TBI. The enhanced transfer learning model architecture integrates multiple fine-tuning steps in which a few layers of the pre-trained model is unfrozen in each iteration so that the neural network is able to learn layer by layer further aspects of the image for better …

Mechanisms For Unwanted Magnetic Field Coupling To A Shielded Magnetic Near-Field Probe, Xin Yan, Wei Zhang, Sajjad Sadeghi, Mehdi Gholizadeh, David J. Pommerenke, Daryl G. Beetner

Electrical and Computer Engineering Faculty Research & Creative Works

Unwanted field coupling is a major concern when using magnetic near-field probes for high-frequency scanning. The unwanted coupling of the tangential electric field to a tangential magnetic field probe has been studied extensively. This article shows a second unwanted coupling mechanism: coupling from a vertical magnetic field. The standing wave of a shorted pure TEM microstrip in air is utilized to separate the magnetic and electric field coupling. Simulations show that the eddy currents induced within the probe by the vertical magnetic field couple inductively to the pickup loop if the layer stack up is not symmetric. The unwanted magnetic …

Virtual Inertia Scheduling (Vis) For Microgrids With Static And Dynamic Security Constraints, Buxin She, Fangxing Li, Jinning Wang, Hantao Cui, Xiaofei Wang, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Microgrids feature a high penetration of inverter-interfaced distributed energy resources (DERs). The low inertia characteristic and fast dynamics of DERs pose challenges to conventional decoupled static economic operation and dynamic control design within microgrids. Hence, this paper proposed virtual inertia scheduling (VIS) for microgrids, aiming to ensure both economy and security. First, a unified framework for device-level control and grid-level operation is introduced, with VIS serving as a key application to address low inertia issues. VIS actively harnesses the controllability and flexibility of DERs to effectively manage microgrid inertia. It updates the conventional economic operation framework by incorporating the virtual …

Coupling-Informed Data-Driven Scheme For Joint Angle And Frequency Estimation In Uniform Linear Array With Mutual Coupling Present, Yanming Zhang, Wenchao Xu, A. Long Jin, Min Li, Peifeng Ma, Lijun Jiang, Steven Gao

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes a novel coupling-informed data-driven algorithm tailored for the concurrent estimation of frequency and angle within a uniform linear array (ULA), while addressing the complicating influence of mutual coupling. Leveraging the hybrid dynamic mode decomposition (DMD) methodology, termed as averaged DMD, we incorporate moving average techniques to achieve effective denoising. The averaged DMD further decomposes the received signal into eigenvalues and corresponding eigenvectors. The frequency information is derived from the eigenvalues and the corresponding eigenvectors represent the steering vectors of sources. Subsequently, mutual coupling is informed into the calibration of the steering vector for each source. Specifically, the …

Design Of The Tm010 Mode Cylindrical Cavity Resonator For Pcb Dielectric Characterization, Reza Asadi, Chaofeng Li, Seyedmehdi Mousavi, Seyed Moastafa Mousavi, Reza Vahdani, Xiaoning Ye, Donghyun Kim

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents the study of the TM₀₁₀ mode cylindrical resonator, which can be used for printed circuit board (PCB) material properties extraction, e.g., the dielectric constant (Dk) and the loss tangent (Df) extraction. The theoretical formulas of the resonance frequency and Q-factor of the resonator are presented. In real measurement, the TM₀₁₀ mode cylindrical cavity resonator needs to be excited by the probe. The study emphasizes the impact of probe orientation, location, and field distribution on the accuracy of material property extraction. The relationship between cavity dimensions and resonance frequency is explored, highlighting the influence of cavity …

A Tensor-Based Data-Driven Approach For Multidimensional Harmonic Retrieval And Its Application For Mimo Channel Sounding., Yanming Zhang, Wenchao Xu, A. Long Jin, Min Li, Ping Yuan, Lijun Jiang, Steven Gao

Electrical and Computer Engineering Faculty Research & Creative Works

In wireless channel sounding, accurately estimating multiple parameters within a multipath signal, such as azimuth, elevation, Doppler shift, and delay, necessitates addressing the challenges posed by the multidimensional harmonic retrieval (MHR) problem. To overcome these complexities, we propose a framework based on high-order dynamic mode decomposition (HODMD) that designed for robustly estimating frequencies of interest from high-dimensional sinusoidal signals, particularly in additive white Gaussian noise conditions. The HODMD approach, a hybrid algorithm amalgamating high-order singular value decomposition (HOSVD) and dynamic mode decomposition (DMD), operates by initially decomposing observed tensorial data into a core tensor and R mode matrices through HOSVD. …

Modeling Of Power Distrubition Network (Pdn) Noise Coupling Induced Clock Phase Noise, Zhekun Peng, Junyong Park, Chaofeng Li, Joey Stecher, Srinivas Venkataraman, Xu Wang, Granthana Rangaswamy, Donghyun Kim

Electrical and Computer Engineering Faculty Research & Creative Works

Phase noise analysis is important to clock design. Noise sources of spurs shown on the phase noise result are challenging to find out due to the unknown source locations and coupling mechanisms. Noise from power distribution network (PDN) is one of the most troublesome sources. A behavioral modeling methodology is proposed to simulate the clock phase noise induced by PDN noise coupling for two different mechanisms: PDN-to-clock additive coupling and PDN-to-PDN up-conversion modulation. The thermal noise and 1/f flicker noise are simplified to provide a straightforward view of spur level. The model can be applied to both single-ended clock and …

Cascading Of 2d And 3d Simulations Of Asic Substrate Interconnect Up To 100 Ghz, Zhekun Peng, Junyong Park, Sathvika Bandi, Santosh Pappu, Srinivas Venkataraman, Xu Wang, Granthana Ranzaswamy, Donghyun Kim

Electrical and Computer Engineering Faculty Research & Creative Works

A method of cascading 3D models and 2D models to model the full channel of ASIC package substrate interconnects is proposed, showing good match to full-wave results in S-parameter and TDR up to 100 GHz.