Physical Sciences and Mathematics Commons^™

A New Proper Orthogonal Decomposition Method With Second Difference Quotients For The Wave Equation, Andrew Calvin Janes

Masters Theses

"Recently, researchers have investigated the relationship between proper orthogonal decomposition (POD), difference quotients (DQs), and pointwise in time error bounds for POD reduced order models of partial differential equations. In \cite {Sarahs}, a new approach to POD with DQs was developed that is more computationally efficient than the standard DQ POD approach and it also retains the guaranteed pointwise in time error bounds of the standard method. In this thesis, we extend the new DQ POD approach from \cite {Sarahs} to the case of second difference quotients (DDQs). Specifically, a new POD method utilizing DDQs and only one snapshot and …

The Geodetic Strain Rates From Gnss In The North American-Carribean-Cocos Triple Junction In Guatemala And Implications For Seismic Hazard, Tanaya Kashyap

Masters Theses

"Geodetic velocity data are pivotal for deciphering seismic risks, providing initial constraints for crustal surface strain rates. Gaining an understanding of the 2D surface strain tensor across an entire area necessitates knowledge of the surface velocity field, yet geodetic data such as measurements from the Global Navigation Satellite System (GNSS) are dispersed across the Earth’s surface. In the current scope of study, we have employed various methodologies to estimate strain rates in Guatemala, utilizing GNSS velocity data and leveraging the open-source Python tool Strain_2D. Strain_2D facilitates the computation of strain rates through diverse methods applied to the same dataset and …

Cryptographic Algorithms, Cryptocurrencies, And A Predictive Model Of Bitcoin Value By Pls Regression, Paul Kenneth O'Connor

Masters Theses

"With the invention of Bitcoin in 2009, as a seemingly timed response to the ongoing financial crisis, the popularity of the cryptocurrency has since continued to grow. Just this year, the Security Exchange Commission approved Bitcoin for exchange traded funds, allowing major investment firms to begin product trading. With this approval, and during this very moment of writing, Bitcoin has entered a bull market and reached a record value of over 72,000 USD. In addition, the Bitcoin halving event in April of 2024 is expected to increase demand even further. It has been anticipated that Bitcoin and other cryptocurrencies will …

Learn From The Past: Using Peer Data To Improve Course Recommendations In Personalized Education, Colton Walker

Masters Theses

"This work describes a recommendation approach designed to enhance student success by identifying semester schedules and graduation paths. The primary objective is to provide personalized graduation path recommendations rooted in individual student performance and draw insights from the academic journeys of similar students who successfully graduated. The original research contribution of this work lies in the development of a graduation path recommender system that leverages a combination of Markov Decision Process, Q-Learning, and collaborative filtering techniques to pinpoint graduation paths with a higher likelihood of leading students to success based on their academic progress thus far. The effectiveness of the …

Chirality Determination Using Three-Wave Mixing Microwave Spectroscopy, Nicole Taylor Moon

Doctoral Dissertations

"Rotational spectroscopy has established itself as a reliable gas-phase spectroscopic technique for the structural determination of molecules. This reliability has stemmed from both advancements in microwave technology and a willingness from the community to push the boundaries of the field. In this dissertation, the boundaries are tested in both how well the technique can determine the structure of molecules exhibiting large amplitude motion and through chirality determination. The first half of this dissertation explores the use of deep averaging to determine the structure of silicon containing molecules in collaboration with Dr. Guirgis from the College of Charleston. For each of …

The Science Of Gravitational-Wave Sources And Beyond Compact Binary Coalescences, Yanyan Zheng

Doctoral Dissertations

"This work focuses on the field of gravitational-wave astronomy by extending the scope of detectable sources beyond compact binary coalescences, All the gravitational-wave detections so far come from compact binary coalescences. Focusing on core-collapse supernovae as promising sources for short gravitational-wave transients, this work reports optically targeted searches for gravitational-wave emitted by core-collapse supernovae during the third observing run of the LIGO and Virgo detectors. It also predicts the search sensitivity for the ongoing fourth and forthcoming fifth observing runs. Moreover, the work introduces a novel computational framework for testing the spatial distribution of binary black hole sources, allowing for …

Secure And Privacy-Preserving Federated Learning With Rapid Convergence In Leo Satellite Networks, Mohamed Elmahallawy

Doctoral Dissertations

"The advancement of satellite technology has enabled the launch of small satellites equipped with high-resolution cameras into low Earth orbit (LEO), enabling the collection of extensive Earth data for training AI models. However, the conventional approach of downloading satellite-related data to a ground station (GS) for training a centralized machine learning (ML) model faces significant challenges. Firstly, the transmission of raw data raises security and privacy concerns, especially in military applications. Secondly, the download bandwidth is limited, which puts a stringent limit on image transmissions to the GS. Lastly, LEO satellites have sporadic visibility with the GS, and orbit the …

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 …

Re-Evaluating Missouri’S Strategic Element Potential: A Geochemical Study Of The Mesoproterozoic Fe-Cu-Co-Ree Deposits In Southeast Missouri, Usa, Brandon James Sullivan

Doctoral Dissertations

"Iron-oxide-copper-gold (IOCG) deposits are poorly understood mineral systems. For example, we do not know why Cu- and Co-rich IOCG deposits typically occur proximal to Fe ore deposits that are notably Cu and Co-poor, such as Iron Oxide Apatite (IOA) deposits. To better understand the formation of IOA and IOCG deposits in Missouri, USA, this PhD thesis examines the genesis of the Kratz Spring IOA and the Boss Central Dome IOCG deposits. This study presents the first constraints on formation conditions and fluid sources in the studied deposits using integrated petrographic, mineral composition, and Fe isotope analyses of oxide minerals. Observations …

Design, Synthesis, And Characterization Of Complex Chalcogenides For Energy Storage And Energy Conversion Applications, Srikanth Balijapelly

Doctoral Dissertations

"Through this investigation, complex chalcogenides with the combination of main group metals, transition metals, and rare earth metals have been synthesized using the building block approach and their structure-property relationships are evaluated. The main emphasis of research is on rationally designing new materials for applications in sodium and lithium ion conducting solid electrolytes, cathodes, thermoelectrics, and nonlinear optics. Along with the experimental studies, theoretical calculations are also employed to better understand the physicochemical properties of the synthesized compounds.

The first part of the research will discuss designing alkali ion containing complex chalcogenides using the building block approach. This investigation resulted …

Dynamics And Inverse Problems For Nonlinear Schrödinger Equations, Christopher Hogan

Doctoral Dissertations

"The cubic nonlinear Schrödinger equation (NLS) is a model of interest in the study of physical problems including nonlinear optics and Bose-Einstein condensates. Of particular interest is the study of cubic NLS with inhomogeneities such as localizations of the nonlinearity or terms introducing potential barriers. We first address some preliminaries and techniques useful in the study of the cubic NLS and its variations. We then consider the cubic NLS with a localized nonlinearity in dimensions d ≥ 2. We show that solutions with data given by small-amplitude wave packets accrue a nonlinear phase that determines the X-ray transform of the …

Analytical Methods For Monitoring Traumatic Brain Injury Biomarkers/Treatment And Pharmaceutical Residual Solvents, Olajide Philip Adetunji

Doctoral Dissertations

"The development of highly sensitive and efficient analytical methods utilizing advanced instrumentation is necessary to help improve disease diagnosis and therapeutics. A major neuro-consequence of traumatic brain injury (TBI) is oxidative stress from the generation of reactive oxygen species and the depletion of antioxidant defenses. Alteration in concentrations of certain small molecules also occurs with the disease progression and can help understand TBI pathophysiology. Two analytical methods employing liquid chromatography with tandem mass spectrometry (LC-MS/MS) were developed and validated to monitor the potential small-molecule TBI biomarkers at sub-ppb levels. Subsequently, the neuroprotective effect of an antioxidant prodrug, N-acetylcysteine amide (NACA), …

Catalytic Control Of The Nanomorphology And Mechanical Properties Of Aliphatic Shape-Memory Aerogels, A B M Shaheen Ud Doulah

Doctoral Dissertations

"Shape-memory poly(isocyanurate-urethane) (PIR-PUR) aerogels are nanoporous solids that can return to their original shape after being compressed, through a heating actuation step. This thesis compares the effectiveness of various metal ions as catalysts in the formation of PIR-PUR aerogels, and explores the correlation between catalytic activity, nanomorphology, and mechanical properties of the resulting aerogels. The gelation rate was found to increase from Fe to Cu and then decline from Cu to Ga in the periodic table. CuCl₂ was found to be the fastest catalyst, and FeCl₃ the slowest. The morphology of the aerogels changed from bicontinuous to spheroidal …

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 …

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 …

Adaptive Resilient Control For A Class Of Nonlinear Distributed Parameter Systems With Actuator Faults, Hasan Ferdowsi, Jia Cai, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a new model-based fault resilient control scheme for a class of nonlinear distributed parameter systems (DPS) represented by parabolic partial differential equations (PDE) in the presence of actuator faults. A Luenberger-like observer on the basis of nonlinear PDE representation of DPS is developed with boundary measurements. A detection residual is generated by taking the difference between the measured output of the DPS and the estimated one given by the observer. Once a fault is detected, an unknown actuator fault parameter vector together with a known basis function is utilized to adaptively estimate the fault dynamics. A novel …

Meta-Icvi: Ensemble Validity Metrics For Concise Labeling Of Correct, Under- Or Over-Partitioning In Streaming Clustering, Niklas M. Melton, Sasha Petrenko, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Understanding the performance and validity of clustering algorithms is both challenging and crucial, particularly when clustering must be done online. Until recently, most validation methods have relied on batch calculation and have required considerable human expertise in their interpretation. Improving real-time performance and interpretability of cluster validation, therefore, continues to be an important theme in unsupervised learning. Building upon previous work on incremental cluster validity indices (iCVIs), this paper introduces the Meta- iCVI as a tool for explainable and concise labeling of partition quality in online clustering. Leveraging a time-series classifier and data-fusion techniques, the Meta- iCVI combines the outputs …

Lifelong Learning-Based Optimal Trajectory Tracking Control Of Constrained Nonlinear Affine Systems Using Deep Neural Networks, Irfan Ganie, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article presents a novel lifelong integral reinforcement learning (LIRL)-based optimal trajectory tracking scheme using the multilayer (MNN) or deep neural network (Deep NN) for the uncertain nonlinear continuous-time (CT) affine systems subject to state constraints. A critic MNN, which approximates the value function, and a second NN identifier are together used to generate the optimal control policies. The weights of the critic MNN are tuned online using a novel singular value decomposition (SVD)-based method, which can be extended to MNN with the N-hidden layers. Moreover, an online lifelong learning (LL) scheme is incorporated with the critic MNN to mitigate …

Online Continual Safe Reinforcement Learning-Based Optimal Control Of Mobile Robot Formations, Irfan Ganie, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In this work, a leader-follower tracking and formation control strategy for mobile robots (MRs) with uncertain dynamics is proposed. This strategy utilizes a continual lifelong safe reinforcement learning (CLSRL) framework based on multilayer neural networks (MNNs). The proposed design employs actor-critic MNNs, incorporating a barrier function. This function is derived from the Bellman optimality principle. It addresses the state constraints throughout the control design process. A novel online continual lifelong learning (CLL) method is introduced for MR formation. This method leverages the Bellman residual error for weight significance in MNNs. It addresses catastrophic forgetting and interlayer dependence through layer-specific regularizers. …

Reinforcement Learning-Based Constrained Optimal Control Of Strict-Feedback Nonlinear Systems: Application To Autonomous Underwater Vehicles, Behzad Farzanegan, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper addresses a constrained neural network (NN)-based optimal tracking scheme for a class of uncertain nonlinear discrete-time systems in strict-feedback form by using a control barrier function (CBF). First, a modified barrier-type cost function is introduced for each subsystem, guiding the actual system trajectory toward the safe set or desired trajectory while avoiding unwanted sets. To address the tracking problem, an augmented system is employed to convert the time-varying optimal tracking to a time-invariant optimal regulation. Then, an actor-critic framework is employed with the backstepping technique to obtain both virtual and actual optimal control policies for each subsystem to …

Learning From The Past: Using Peer Data To Improve Course Recommendations In Personalized Education, Colton Walker, Sahra Sedigh Sarvestani, Ali R. Hurson

Electrical and Computer Engineering Faculty Research & Creative Works

This research introduces a recommendation system designed to enhance student success by intelligently personalizing the semester schedules and graduation path based on the student's performance, interests, and background; and inspired by the academic journeys of similar students who have successfully graduated in the past. The proposed recommender system leverages a combination of Markov decision processes, Q-Learning, and collaborative filtering techniques to identify graduation paths with a higher likelihood of success for the student. The proposed model is versatile and generic and can be adapted to various disciplines if sufficient past historical data is available. The proposed model has been prototyped …

Survey Of Hidden Markov Models (Hmms) For Sign Language Recognition (Slr), Iwan Sandjaja, Ahmad Alsharoa, Donald Wunsch, Jian Liu

Electrical and Computer Engineering Faculty Research & Creative Works

This paper surveyed several significant papers on specific topics applying the Hidden Markov Models (HMMs) for Sign Language Recognition (SLR), divided into five main episodes: Classical HMMs, Extended HMMs, HMMs and Machine Learning, HMMs and Sensor Fusion, and HMMs and Big Data. This stringent survey would contribute significantly to advanced research on unification brain models such as neural networks, adaptive resonance theory, and confabulation theory. First, the HMM was introduced as one of the popular methods of performing SLR, and each episode of its development was expounded. In each episode, a main paper and several supporting papers were summarized. Next, …

Relative Altitude Estimation Of Infrared Thermal Uav Images Using Sift Features, Shirin Nasr Esfahani, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

Unmanned Aerial Vehicles (UAVs) have become indispensable in various applications, including surveillance, urban scene analysis, and agricultural monitoring. Accurate altitude estimation is critical for UAV operations, especially in environments where traditional sensors like GPS, pressure altimeters, and radar may fail. This paper explores the use of infrared and thermal imaging for relative altitude estimation of UAVs, highlighting their significant advantages over traditional RGB images. Infrared and thermal imaging offer superior performance in low-light and adverse weather conditions, providing clearer visibility and more reliable feature detection. By leveraging the Scale-Invariant Feature Transform (SIFT) features, this approach utilizes the inherent benefits of …

Lifelong Direct Error-Driven Learning For Uav Altitude Estimation In Different Weather Conditions, Shirin Nasr-Esfahani, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

While deep neural networks achieve remarkable visual perception capabilities for UAV position and orientation estimation, their resilience to different weather conditions still needs improvement. These models often suffer from catastrophic forgetting when adapted to new environments, losing previously acquired knowledge. Lifelong learning methods aim to balance learning flexibility and memory stability. In this paper, we present an image-based approach to estimate the relative altitude of a UAV using 2D images under varying weather conditions, including sunny, sunset, and foggy scenarios. Our experiments demonstrate significant performance degradation when the model is trained sequentially on different weather datasets, especially when new images …

Optimal Trajectory Tracking For Uncertain Linear Discrete-Time Systems Using Time-Varying Q-Learning, Maxwell Geiger, Vignesh Narayanan, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This Article Introduces a Novel Optimal Trajectory Tracking Control Scheme Designed for Uncertain Linear Discrete-Time (DT) Systems. in Contrast to Traditional Tracking Control Methods, Our Approach Removes the Requirement for the Reference Trajectory to Align with the Generator Dynamics of an Autonomous Dynamical System. Moreover, It Does Not Demand the Complete Desired Trajectory to Be Known in Advance, Whether through the Generator Model or Any Other Means. Instead, Our Approach Can Dynamically Incorporate Segments (Finite Horizons) of Reference Trajectories and Autonomously Learn an Optimal Control Policy to Track Them in Real Time. to Achieve This, We Address the Tracking Problem …

Multiple Imputation For Robust Cluster Analysis To Address Missingness In Medical Data, Arnold Harder, Gayla R. Olbricht, Godwin Ekuma, Daniel B. Hier, Tayo Obafemi-Ajayi

Mathematics and Statistics Faculty Research & Creative Works

Cluster Analysis Has Been Applied To A Wide Range Of Problems As An Exploratory Tool To Enhance Knowledge Discovery. Clustering Aids Disease Subtyping, I.e. Identifying Homogeneous Patient Subgroups, In Medical Data. Missing Data Is A Common Problem In Medical Research And Could Bias Clustering Results If Not Properly Handled. Yet, Multiple Imputation Has Been Under-Utilized To Address Missingness, When Clustering Medical Data. Its Limited Integration In Clustering Of Medical Data, Despite The Known Advantages And Benefits Of Multiple Imputation, Could Be Attributed To Many Factors. This Includes Methodological Complexity, Difficulties In Pooling Results To Obtain A Consensus Clustering, Uncertainty Regarding …

Advanced Detection Of Sars-Cov-2 And Omicron Variants Via Mxene-Graphene Hybrid Biosensors Utilizing Nucleic Acid Probes, Jiaoli Li, Yuwei Zhang, Congjie Wei, Yanxiao Li, Zhekun Peng, Hsin Yin Chuang, Logan Pearce, Adrianus Boon, Yue-Wern Huang, Dong Hyun Kim, Risheng Wang, Chenglin Wu

Biological Sciences Faculty Research & Creative Works

Low-cost biosensors that can rapidly and widely detect viruses are critical for faster diagnosis and treatment decision-making, especially for infections. The commonly used field-effect transistor is sensitive to the biomarker's detection but struggles with precise detection, particularly of nontargets such as ions and proteins. To overcome this limitation, we developed a field-effect transistor biosensor design based on MXene-graphene materials to increase the accuracy and sensitivity of virus detection. Based on the hybridization process between two complementary DNA strands, single-stranded nucleic acids were immobilized on the sensing surface via 3-aminopropyltriethoxysilane and glutaraldehyde to capture the nucleic acids of the target virus. …

Mobility Management In Tsch-Based Industrial Wireless Networks, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi

Computer Science Faculty Research & Creative Works

Wireless Sensor and Actuator Networks (WSANs) are an effective technology for improving the efficiency and productivity in many industrial domains and are also the building blocks for the Industrial Internet of Things (IIoT). To support this trend, the IEEE has defined the 802.5.4 Time-Slotted Channel Hopping (TSCH) protocol. Unfortunately, TSCH does not provide any mechanism to manage node mobility, while many current industrial applications involve Mobile Nodes (MNs), e.g., mobile robots or wearable devices carried by workers. In this article, we present a framework to efficiently manage mobility in TSCH networks, by proposing an enhanced version of the Synchronized Single-hop …

Energy Consumption Optimization Of Uav-Assisted Traffic Monitoring Scheme With Tiny Reinforcement Learning, Xiangjie Kong, Chenhao Ni, Gaohui Duan, Guojiang Shen, Yao Yang, Sajal K. Das

Computer Science Faculty Research & Creative Works

Unmanned Aerial Vehicles (UAVs) can capture pictures of road conditions in all directions and from different angles by carrying high-definition cameras, which helps gather relevant road data more effectively. However, due to their limited energy capacity, drones face challenges in performing related tasks for an extended period. Therefore, a crucial concern is how to plan the path of UAVs and minimize energy consumption. To address this problem, we propose a multi-agent deep deterministic policy gradient based (MADDPG) algorithm for UAV path planning (MAUP). Considering the energy consumption and memory usage of MAUP, we have conducted optimizations to reduce consumption on …

Stitching Satellites To The Edge: Pervasive And Efficient Federated Leo Satellite Learning, Mohamed Elmahallawy, Tony Tie Luo

Computer Science Faculty Research & Creative Works

In the Ambitious Realm of Space AI, the Integration of Federated Learning (FL) with Low Earth Orbit (LEO) Satellite Constellations Holds Immense Promise. However, Many Challenges Persist in Terms of Feasibility, Learning Efficiency, and Convergence. These Hurdles Stem from the Bottleneck in Communication, Characterized by Sporadic and Irregular Connectivity between LEO Satellites and Ground Stations, Coupled with the Limited Computation Capability of Satellite Edge Computing (SEC). This Paper Proposes a Novel FL-SEC Framework that Empowers LEO Satellites to Execute Large-Scale Machine Learning (ML) Tasks Onboard Efficiently. its Key Components Include I) Personalized Learning Via Divide-And-Conquer, Which Identifies and Eliminates Redundant …