Engineering Commons^™

Transfer Learning For Temporal Logic Objectives, Lucas M. Santana Rovira

McKelvey School of Engineering Graduate Student Theses & Dissertations

Reinforcement learning algorithms can enable autonomous systems to learn the control skills needed to accomplish a task specified by a linear temporal logic formula. However, they cannot be transferred to a new task, even when the two are very similar. For each new task, the policy must be redesigned from scratch, which is a common limitation of existing reinforcement learning methods for temporal logic tasks. A proposed solution to this problem leverages the similarity between past and new tasks to reuse already learned control skills to accomplish the new task, with minimal or no retraining.

Rather than learning a single …

Direct Computations Of Spatially Resolved Viscoelastic Moduli Of Biomolecular Condensates, Liwen Gu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Biomolecular condensates are viscoelastic materials formed by liquid-liquid phase separations (LLPS) of biopolymers. In this study, we develop a modified graph Laplacian-based collective model to characterize viscoelastic heterogeneity within condensates based on results of lattice-based Metropolis Monte Carlo (MMC) simulations. By integrating random graph models and simulations of A1-LCD, a type of intrinsically disordered protein, we examine how network topology influences storage modulus, loss modulus, crossover frequency, and relaxation time spectra. Our results reveal a strong correlation between topological features and mechanical response, with the condensate interior exhibiting higher stiffness and faster relaxation than the interface. The relaxation spectra provide …

New Signal Identification Algorithms For Enhanced Gamma-Ray Burst Detection In The Advanced Particle-Astrophysics Telescope, Longhao Huang

McKelvey School of Engineering Graduate Student Theses & Dissertations

This work presents a series of algorithmic advancements aimed at improving photon signal identification and gamma-ray burst (GRB) source localization for the Advanced Particle-astrophysics Telescope (APT) and its Antarctic Demonstrator (ADAPT). These advancements are aimed at identifying valid signals in noisy environments. Previous methods failed to effectively distinguish real photon signals from noise, prompting us to develop a new photon detection algorithm with peak counting. Instead of integrating all waveform data in the observation window, we use multiple thresholds to accurately identify single-photon and two-photon arrival events, minimizing false counts due to amplifier noise. The new peak count algorithm also …

The Role Of Piezo1 In Mediating Chondrocyte Cell Signaling And Senescence, Sophie Gretler Paradi

McKelvey School of Engineering Graduate Student Theses & Dissertations

Osteoarthritis (OA), the leading cause of disability, is driven by articular cartilage degeneration and inflammation. Mechanosensitive ion channels PIEZO1 and PIEZO2 have been implicated in OA progression via calcium signaling in chondrocytes. This study investigates the role of conditional Piezo channel knockout (cKO) in modulating calcium signaling and senescence in OA, particularly under the supra-physiologic inflammatory condition of a high-fat diet (HFD). Using calcium imaging, we found that Piezo1 cKO and dual Piezo1/2 cKO significantly reduced Yoda1-induced calcium signaling, while Piezo2 cKO alone had no effect. Under HFD conditions, Piezo1 cKO showed a non-significant trend toward reduced calcium influx, potentially …

Structural Controllability For Switched Linear Ensemble Systems, Yi Li

McKelvey School of Engineering Graduate Student Theses & Dissertations

No abstract provided.

Learning-Based Mri Reconstruction Method With Coil Sensitivity Estimation And Prior Adaptation, Haoyu Zhang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Magnetic Resonance Imaging (MRI) reconstruction from undersampled multi-coil k-space data is a challenging inverse problem, typically relying on fixed priors such as precom puted coil sensitivity maps. To improve adaptability, we propose a novel framework named Learning-based Coil sensitivity Estimation and Prior Adaptation (LCEPA). LCEPA formu lates reconstruction as a bilevel optimization, using a Joint Deep Equilibrium (Joint DEQ) model in the inner loop to simultaneously estimate images and coil sensitivity maps, while the outer loop adaptively fine-tunes image and coil priors using supervised data. Experiments demonstrate that LCEPA surpasses state-of-the-art methods in terms of PSNR and SSIM, showcasing robust …

Automated Beam Stitching And Segmentation Procedure For Space Division Multiplexing Optical Coherence Tomography Angiography, Andrew J. Song

McKelvey School of Engineering Graduate Student Theses & Dissertations

Optical Coherence Tomography Angiography (OCTA) has revolutionized ophthalmic imaging and its capability to produce high-resolution 3D maps of the retinal microvasculature is instrumental in diagnosing retinovascular diseases such as diabetic retinopathy and age-related macular degeneration; However, the existing OCTA devices often suffer from slow acquisition speed limiting the field-of-view (FOV) in the clinic. Space Division Multiplexing OCTA (SDM-OCTA) address these limitations by acquiring multiple beams simultaneously, achieving manyfold faster acquisition speeds than single beam OCTA systems. But as each beam contains only part of the image, SDM-OCTA requires additional processing steps to produce coherent wide-field images. Though manual stitching and …

Comparative Analysis Of Intrinsic Reward-Based Reinforcement Learning Algorithms, Chengyu Li

McKelvey School of Engineering Graduate Student Theses & Dissertations

Reinforcement learning agents often struggle in tasks with sparse or delayed rewards, since they receive little guidance about which actions to pursue. This thesis investigates how adding intrinsic rewards can help address that issue. We focus on three main methods: Count-Based bonuses, where states are hashed and infrequent states receive higher rewards; Random Network Distillation (RND), where a predictor network learns to match the output of a fixed random target; and the Intrinsic Curiosity Module (ICM), which uses an inverse and a forward model to highlight transitions the agent cannot yet predict.

We implement these approaches under a single Proximal …

Functional Devices Based On Freestanding 2d Materials, Shijue Xu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Two-dimensional (2D) materials have attracted extensive attention in the field of nanoelectronics due to their atomic-scale thickness, high surface-to-volume ratio, tunable electronic properties, and compatibility with low-temperature processing. These characteristics make them highly suitable for the construction of emerging device architectures, particularly in both ionic and electronic devices.

In this work, we investigate the application of 2D materials in two distinct classes of devices: ionically-driven memristors and electronically-dominated metal–semiconductor contacts. For the memristor study, we fabricate heterostructure-based resistive switching devices using h-BN and WSe₂ as active layers. These 2D material-based memristors exhibit stable power consumption loops and high linearity …

Soft, Adhesive Dry Electrodes For Improved Electrophysiological Signal Acquisition With Motion Tolerance And Long-Term-Recoverability, Ryan Andersen

McKelvey School of Engineering Graduate Student Theses & Dissertations

Soft electronic devices and sensors have seen substantial interest devoted to them for the purpose of recording and analyzing various electrophysiological signals including for the use of electrocardiography (ECG), electromyography (EMG), and electroencephalography (EEG). Dry electrodes specifically have enormous potential compared to traditional commercial electrodes for long-term, real-time monitoring of biopotential activity. Gel-based commercial electrodes have notable drawbacks such as limited flexibility, skin irritation that decreases comfort, inconsistent signal quality due to poor conformal contact, and degraded signal quality over long-term monitoring due to drying out.

Herein, we report a soft, adhesive dry electrode that utilizes chemical additives to promote …

Adaptive Noise Estimation And Denoising With Deep Learning For Nmr Spectroscopy, Naveen Asokan

McKelvey School of Engineering Graduate Student Theses & Dissertations

Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful analytical technique widely used for molecular structure elucidation in chemistry, biology, and medicine. However, spectral accuracy is often degraded by noise—particularly in low acquisition time settings—resulting in reduced resolution and obscured chemical features. While traditional noise reduction techniques such as signal averaging can improve spectral quality, they require longer acquisition times, limiting their utility in real-time and high-throughput applications.

This thesis presents a deep learning-based denoising framework designed to enhance the quality of complex-valued NMR spectra. The proposed model, built upon a U-Net architecture, incorporates both real and imaginary components of the …

Generic Fpga Preprocessing For Astrophysics Instruments In Hls, Qinzhou Song

McKelvey School of Engineering Graduate Student Theses & Dissertations

FPGAs are widely deployed on high-energy astroparticle physics instruments to preprocess large volumes of streaming data from various sensors. Increasingly, these deployments are finding their way to space-borne instruments, where constraints on size, weight, and power (SWaP) require careful balancing of speed and resource utilization. Although telescope designs vary widely, they often share common preprocessing elements, including channel-level readout, pedestal subtraction, waveform integration, and zero suppression from front-end ADCs, as well as identification and centroiding of signal islands across groups of multiple channels. High-Level Synthesis (HLS) tools allow these designs to be expressed at a conceptual level, which automates a …

Partially Supervised Reinforcement Learning For Gps-Denied Navigation, Ethan Weilheimer

McKelvey School of Engineering Graduate Student Theses & Dissertations

Navigating dynamic environments is a key challenge for autonomous agents, yet most existing research focuses on 3D settings or settings where the agent has full access to relevant semantics. In this work, we propose a learning framework for aerial navigation in the presence of changing dynamics and limited positional information. Specifically, we consider a drone navigation task where a drone at one time has access to GPS location information, which it has now lost and needs to navigate in the same area but at a future time with no GPS signal and differing transition dynamics. To address this, we introduce …

Training Safety Control Filters Using High-Dimensional And Un-Labeled Data, Yuxuan Yang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Synthesizing control policies that preserve the safety of autonomous systems is a challenge that remains to be solved. Towards that goal, control barrier functions (CBFs) have been developed as mathematical constructs that can be used in real-time to correct safety-violating nominal actions to ones which preserve the safety of control systems. However, synthesizing CBFs using correct-by-construction methods has not been scalable. Instead, recent research has proposed data-driven approaches for learning CBFs in the form of neural networks. Two main challenges face such approaches: (1) labeling states as unsafe or safe ones requires the knowledge of the states in the backward …

Strategic Reactor Allocation For Deadlock-Free Execution, Jeevan Sivamohan

McKelvey School of Engineering Graduate Student Theses & Dissertations

As it becomes harder to increase the computation power of a single machine, we are turning towards parallel and distributed systems to extract additional performance by breaking down the problem into pieces and solving it simultaneously. While this provides a great opportunity for increased performance,e it comes with additional problems not present in the sequential approach. One such problem is deadlock. Deadlock is defined as the state in which program execution stalls because the system has run out of resources to manage and execute the program properly, or there exists some circular dependency in the data between parallel or distributed …

The Little Diagram That Could: Geometric Properties And Statistical Applications Of Persistence Diagrams In Topological Data Analysis, Eugene Kler

McKelvey School of Engineering Graduate Student Theses & Dissertations

Topological Data Analysis (TDA) is a collection of techniques for data analysis that leverages topological invariants of spaces formed from data points. These methods excel at extracting useful information from noisy or sparse data, making them attractive to many mathematicians, statisticians, and scientists. In this thesis, we explore TDA on three fronts: algebraic foundations, statistical applications, and metric properties. Throughout, the central object of study is the Persistence Diagram (PD), a summary of the changes in homology that occur as one builds simplicial complexes from the data by increasing a parameter.

Reducing Measurement Error In Three-Point Bend Test Using Linear Gradient Correction Model, Juan J. Becerra-Garcia

McKelvey School of Engineering Graduate Student Theses & Dissertations

Endovascular devices like catheters feature non-uniform flexural rigidity (EI) profiles (flexi- ble tip, stiff shaft) crucial for navigation and pushability. Standard three-point bend analysis (assuming uniform stiffness) yields inaccurate apparent rigidity (B0) for these graded devices, especially in transition zones. This inaccuracy hinders device comparison, clinical selection, and predictive modeling. This thesis develops and validates a correction model for three-point bend tests aimed at improving local flexural rigidity accuracy for devices with varying stiffness, particularly those exhibiting transition zones similar to logistic profiles. The model assumes a linear gradient in rigidity (B(x) = Bm(1 + 2mx/L)) across the test span …

A Neuroimmune Circuit Mediates Chemo Brain Pathology, Daniel Ahmadi

McKelvey School of Engineering Graduate Student Theses & Dissertations

Chemotherapeutic agents, while effective in targeting malignant tumors, often reduce the patients’ quality of life by inducing fatigue, pain, and motivational deficits. These symptoms are collectively referred to as “chemo brain.” The mechanisms underlying these adverse effects, and specifically whether fatigue and motivational impairments share a common etiology, remain poorly understood. The neural–immune substrates of these symptoms remain obscure, in part because standard preclinical regimens either bypass key systemic signals or introduce local toxicity confounds. Here, I establish and validate a three‑day, low‑dose intravenous cisplatin protocol that avoids peritoneal inflammation and severe renal injury yet produces robust, reversible fatigue and …

Compartmental Disaggregation: Bridging Simulation And Sampling Methods For Synthetic Population Data Generation, Dylan Mack

McKelvey School of Engineering Graduate Student Theses & Dissertations

As agent-based models (ABMs) grow increasingly widespread in public health, their associated challenges have become all the more significant. Lauded for their ability to capture population heterogeneity, nonlinear dynamics, and emergent behaviors, disease ABMs are also computationally expensive and often require detailed inputs that describe each agent at the individual-level, known as synthetic population data. Current approaches for synthetic population data generation generally fall into one of two categories: sampling or simulation. These methods are both feasible only under restricted conditions and suffer from challenges surrounding data availability and computing power. This thesis proposes compartmental disaggregation, an intermediate method for …

Predicting Which Genes Will Respond To Transcription Factor Perturbation, Eric L. Jia

McKelvey School of Engineering Graduate Student Theses & Dissertations

A fundamental issue in mapping regulatory networks between transcription factors and their target genes is the poor overlap between the set of genes bound by a given transcription factor (TF), and the set of genes that are differentially expressed after knocking out or overexpressing the same TF. We began with the hypothesis that to predict whether a gene will respond to perturbation of a TF, it is important to not only consider whether that TF is bound at the gene’s promoter, but also whether other TFs bind at the same promoter.

In this work, we propose a novel modeling procedure …

Interference Management For Next-Generation Dynamic Spectrum Sharing, Jie Wang

McKelvey School of Engineering Graduate Student Theses & Dissertations

The exponential growth of wireless devices and bandwidth-intensive applications has intensified the demand for efficient spectrum utilization, exposing the limitations of traditional static spectrum allocation schemes. Next-generation spectrum sharing has emerged as a transformative solution to enhance spectrum efficiency by enabling multiple wireless systems to coexist opportunistically in the same frequency band and geographic area. However, such dynamism introduces significant challenges, particularly in interference management, which is critical to enabling reliable coexistence, ensuring access priority, and building mutual trust across diverse applications. This thesis advances interference management by developing advanced techniques for interference prediction, monitoring, and control in dynamic spectrum …

Aerosol Technology For Carbon Capture And Utilization, Onochie Chinedum Okonkwo

McKelvey School of Engineering Graduate Student Theses & Dissertations

The CO2 concentration in the atmosphere has continued to increase over the past century and now poses an increasing potentially catastrophic effect to human life and the environment via global warming and the concomitant effect of climate change. Anthropogenic carbon dioxide emissions due to fossil fuel combustion for increasing energy demand is the main driver for the increased carbon dioxide in the atmosphere. A multifaceted approach including carbon dioxide capture and utilization is required to mitigate anthropogenic carbon dioxide emissions. Aerosol science and technology, an enabler for clean combustion technologies and nanomaterial synthesis, can contribute significantly to the advancement of …

Josephson Junctions: Fabrication And Applications For The Axion Dark Matter Experiment, Jonah M. Sachs

Senior Honors Papers / Undergraduate Theses

The observation of axions could revolutionize the world of physics. Through microwave frequency cavity readout of the photons associated with these axions, the ADMX project at WashU utilizes multiple different forms of the Josephson junction (JJ), a superconductive circuit element. The physics behind the JJ are essential to understanding its operation for resonant cavity readout in addition to parametric amplification. Parametric amplifiers produced using JJs can approach the signal-to-noise ratio set by quantum mechanics, and prove essential for the amplification chain used by the ADMX experiment for axionic detection. The limits of these amplifiers are set by the noise tuning …

Electrochemical Hydrogen Pump – Methods To Combat Co Poisoning, Neha Namburi

ENGR 310: Technical Writing Final Project

As global demand for hydrogen grows, both as a clean energy carrier and an industrial fuel, electrochemical hydrogen pumps (EHPs) are gaining attention for their ability to separate and compress hydrogen efficiently. EHPs work by selectively drawing hydrogen from low-pressure hydrogen gas mixtures and electrochemically converting it into high-purity, high-pressure hydrogen. EHPs are particularly useful for purifying hydrogen from industrial byproducts like syngas, an industrial mixture containing hydrogen and carbon monoxide (CO). However, CO binds to the platinum catalyst used in EHPs and reduces its effectiveness – a problem known as catalyst poisoning.

This literature review explores a range of …

Painting Rich Six-Dimensional Pictures Using Polarized Fluorescence Microscopy, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

No abstract provided.

Dynamic Spectral And Systems-Theoretic Approaches For Inference And Control Of Heterogeneous Complex Networks, Bharat Kumar Singhal

McKelvey School of Engineering Graduate Student Theses & Dissertations

Networks of nonlinear systems are commonly employed to describe a diverse range of phenomena across physics, engineering, neuroscience, and biology. The undesirable behaviors of such systems, in the form of neurological disorders, power grid failures, or ecological collapses, have spurred significant interest in understanding their dynamic structures and developing effective control strategies. These systems are typically large-scale, consist of heterogeneous units, and are partially observable with limited measurement data, presenting theoretical and computational challenges for control design and connectivity inference. This thesis addresses these challenges by developing novel algorithms for pattern formation in populations of stable limit-cycle oscillators and connectivity …

Dynamic Spectral And Systems-Theoretic Approaches For Inference And Control Of Heterogeneous Complex Networks, Bharat Kumar Singhal

McKelvey School of Engineering Graduate Student Theses & Dissertations

Networks of nonlinear systems are commonly employed to describe a diverse range of phenomena across physics, engineering, neuroscience, and biology. The undesirable behaviors of such systems, in the form of neurological disorders, power grid failures, or ecological collapses, have spurred significant interest in understanding their dynamic structures and developing effective control strategies. These systems are typically large-scale, consist of heterogeneous units, and are partially observable with limited measurement data, presenting theoretical and computational challenges for control design and connectivity inference. This thesis addresses these challenges by developing novel algorithms for pattern formation in populations of stable limit-cycle oscillators and connectivity …

Investigating Catalytic Conversion Of Lignin To Valuable Products, Jialiang Zhang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Lignin is a complex, random, and heterogeneous biopolymer in lignocellulosic biomass. It has significant potential for conversion into valuable chemicals and fuels. However, its natural recalcitrant structure has made selective depolymerization challenging. This dissertation presents a detailed investigation into the mechanisms of lignin disassembly and structural evolution under various catalytic and solvent conditions. The research spans three key studies. In Chapter 2, operando magic-angle spinning (MAS) solid-state nuclear magnetic resonance (NMR) spectroscopy was employed to study reaction pathways and kinetics of lignin model polymers during catalytic hydrogenolysis. We used a Ni-Al2O3 catalyst in methanol under high temperature and pressure, and …

Investigating Catalytic Conversion Of Lignin To Valuable Products, Jialiang Zhang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Lignin is a complex, random, and heterogeneous biopolymer in lignocellulosic biomass. It has significant potential for conversion into valuable chemicals and fuels. However, its natural recalcitrant structure has made selective depolymerization challenging. This dissertation presents a detailed investigation into the mechanisms of lignin disassembly and structural evolution under various catalytic and solvent conditions. The research spans three key studies. In Chapter 2, operando magic-angle spinning (MAS) solid-state nuclear magnetic resonance (NMR) spectroscopy was employed to study reaction pathways and kinetics of lignin model polymers during catalytic hydrogenolysis. We used a Ni-Al2O3 catalyst in methanol under high temperature and pressure, and …

Pan-Cancer Analysis Of Rna Dysregulation, Somatic Mutations, And Matrix Stiffness Using Bioinformatics Approaches, Gongyu Tang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Cancer is a complex disease driven by genetic mutations, epigenetic modifications, and alterations in the tumor microenvironment. Understanding these intricate molecular mechanisms is crucial for advancing cancer diagnosis, prognosis, and treatment. RNA sequencing (RNA-seq) has emerged as a powerful tool to study cancer by providing comprehensive insights into gene expression, alternative splicing, and mutations at the transcriptome level. This dissertation leverages RNA-seq data to explore different dimensions of cancer biology through multi-omics integration and computational approaches. The research is divided into three main projects. First, I developed OncoDB, an interactive database to analyze gene expression, methylation patterns, and viral interactions …