Engineering Commons^™

Modelling V-Band Atmospheric Loss With Deep Learning, Ralph Lyndon Gesner

Electrical and Computer Engineering ETDs

Modelling atmospheric loss is fundamental to implementation of future mm-wave terrestrial and earth space communication links. Atmospheric losses have traditionally been estimated by calculating the contributions of individual weather phenomena through in-situ meteorological measurements and statistically derived models for estimating localized atmospheric water content. Collecting data for all points along the propagation path and calculating the physical effects of gases, clouds, and precipitation individually is both time consuming and impractical for large-scale use cases.

In response to these present limitations, this work proposes the use of novel deep learning models for estimating atmospheric losses from tabular meteorological sensor data and …

Source Detection And Automatic Modulation Classification For Modern Antenna Array Processing, Jayakrishnan Vijayamohanan

Electrical and Computer Engineering ETDs

Source detection and automatic modulation classification are two of the most important steps in any array processing task. In this research a novel deep learning model referred to as RadioNet, is proposed that is focused on solving both these problems by reformulating them as a multi-label classification problem. Traditional approaches to both these topics face challenges in scenarios with noise, interference, fewer number of snapshots, and high number of sources. The limitations of the conventional models are investigated and overcome by the proposed solution. RadioNet is also compared with other existing state-of-the-art machine learning based solutions. The introduced model is …

Evaluation Of Electrode Configurations For (0,2) Piezoelectric Ultrasound Transducers, Abdul Qayoum Popal

Mechanical Engineering ETDs

Piezoelectric micro ultrasound transducers (PMUT array) are pivotal in MEMS applications. They typically operate in the fundamental (0,1) acoustic mode and are not significantly researched for higher modes (0,2) and (0,3). Similarly, vibrating mesh atomizers (VMAs) demand the second (0,2) mode and third mode (0,3) for effective aerosol generation, highlighting the need for optimized electrode configurations—a relatively unexplored area. This study presents the first comprehensive analysis of how electrode designs influence PMUT performance in the (0,2) mode for VMA applications.

Utilizing COMSOL multiphysics finite element modeling (FEM) with residual stress considerations alongside experimental validation, we demonstrate that optimized electrode configurations …

Targeting Myeloid Cell Iron Metabolism In Salmonella-Induced Colitis, Mariella Arcos Padilla

Biomedical Engineering ETDs

Salmonellosis is a severe infection caused by Salmonella enterica serovar Typhimurium, leading to significant global morbidity and mortality.

Previous studies have shown that mice lacking the iron storage protein ferritin heavy chain (FTH1) in myeloid cells exhibit worsened Salmonella infection. Nuclear receptor co-activator 4 (NCOA4) directs FTH1 autophagic degradation to release iron from storage during conditions of low iron. However, the role of myeloid NCOA4 in regulating bacterial infections remains unclear.

Here, we found that myeloid NCOA4 deficiency augments spleen iron levels and increases cellular iron accumulation, oxidative stress, and ferroptosis in bone marrow-derived macrophages (BMDM) cells. This deficiency also …

The State Of The City's Bridges, Leo G. Miranda

Capstones

An analysis by NYCity News Service of 2024 federal bridge inspection found that 118 segments of city bridges are in poor condition, around 8% of all bridges citywide. Among these are segments of major highways and bridges like the Brooklyn Queens Expressway, FDR Drive, Harlem River Drive, Grand Central Parkway and even a part of the iconic Brooklyn Bridge. On a single stretch of highway, where the Grand Central Parkway and the Whitestone Expressway meet in Queens, there are seven parts of highway or ramps in poor condition.

Bridge segments can be any raised roadway that passes over another parkway, …

Theory And Algorithms To Learn, Propagate, And Exploit Uncertainty For Stochastic Optimal Control Of Dynamical Systems, Vignesh Sivaramakrishnan

Electrical and Computer Engineering ETDs

Non-Gaussian uncertainty frequently arises in learning and control problems involving stochastic dynamical systems, particularly in autonomous vehicles, UAVs, satellites, and robotics. In this dissertation, we propose a new framework that leverages characteristic functions that provides a frequency-domain representation of random variables. The dissertation is structured into three key areas. First, we address model-based stochastic optimal control for linear systems with non-Gaussian noise, demonstrating that characteristic functions can be used to enforce chance constraints and control systems toward desired distributions. Second, we explore data-driven stochastic control, utilizing empirical characteristic functions to handle systems with unknown disturbances. In addition, we derive several …

Simulation Studies Of Mvdc Power Cable For All Electric Aircrafts, Aban Samimi Motlagh

Electrical and Computer Engineering ETDs

MVCD power cable has a vital task in future commercial electric aircraft, which is power delivery. The temperature of the cable during the cable’s operation under low pressure is higher than the atmospheric pressure, which increases the cable losses. In this paper, the temperature and electric field across the insulator of the 5 kV DC Southwire cable is simulated up to 10 hours of cable operation. Furthermore, the temperature of the cable is shown with and without the presence of the chamber box. The other issue of the MVDC power cable is the cable joints and terminations. The electric field …

Application Of Digital Rock Physics And Machine Learning To Improve The Understanding Of Fluid Flow Behavior Through Porous Media, Md Irfan Khan

Mechanical Engineering Theses

Accurately estimating reservoir rock properties and understanding capillary trapping mechanisms are crucial for fluid storage and flow modeling in porous media, particularly for applications such as carbon dioxide sequestration (CCS) and underground hydrogen storage (UHS). This thesis presents a comprehensive workflow that combines machine learning techniques and pore-network modeling approach to predict petrophysical properties and assess the impact of microscopic pore structures on capillary trapping. In the first part, a convolutional neural network (CNN) framework is used to predict rock properties—such as porosity, throat area, and pore surface area—from micro-computed tomography (micro-CT) X-ray images. It has been observed that incorporating …

Energy Efficiency Optimization In Integrated Sensing And Communication Networks, Arianna M. Santamaria Penafiel

Electrical and Computer Engineering ETDs

In the emerging landscape of Integrated Sensing and Communication (ISAC) networks, achieving energy efficiency while concurrently performing sensing and communication tasks remains challenging. This paper introduces a new framework, a novel solution that empowers User Equipment (UEs) to make informed decisions regarding their transmission power allocation, optimizing the energy efficiency of sensing, communication, and data reporting to the gNB (gNodeB) functions. Initially, a novel ISAC network paradigm is proposed, where the gNB employs rewards, such as monetary incentives, to motivate UEs to engage in sensing, data collection, and reporting within its coverage area based on the principles of Contract Theory. …

Rational Design Of Nanoparticles For Overcoming Barriers Of Nucleic Acid Delivery: Applications In Cancer Therapeutics, Angelea M. Maestas-Olguin

Chemical and Biological Engineering ETDs

The use of exogenous nucleic acid technologies to modulate aberrant protein expression resulting from genetic mutations is a promising therapeutic approach for the treatment of diseases such as cancer. The promise of nucleic-based therapeutics is dependent on the development of platforms that effectively protect nucleic acids from nuclease degradation and deliver the nucleic acids to specific intracellular locations (nucleus or cytosol) of target cells. The application of nanotechnology for development of nucleic acid-based nanomedicines is poised to revolutionize the clinical landscape of cancer. In this dissertation, I demonstrated the rational design, synthesis and evaluation of a lipid coated mesoporous silica …

Modeling The Performance And Resource Requirements For Gamma-Ray Telescope Signal Processing, Shijing Liang

McKelvey School of Engineering Graduate Student Theses & Dissertations

This thesis investigates the buffering requirements of the data pipeline for the Advanced Particle-astrophysics Telescope gamma-ray telescope. Given the importance and stochastic arrivals of astronomical signals, it is crucial to ensure that each gamma-ray signal is fully buffered to prevent data loss. To achieve this, FIFOs are inserted into the data processing pipeline to prevent bottlenecks during data packet processing. Buffers play a critical role in regulating data flow, preventing data loss, and ensuring efficient data processing. They act as temporary storage areas, absorbing data surges and releasing it steadily, thus maintaining the pipeline’s optimal performance. In general, buffer integration …

Decision Making And Performance Optimization In Distributed Cyber Physical Networks, Md Sahabul Hossain

Electrical and Computer Engineering ETDs

Cyber-Physical Systems (CPSs) integrate computation, networking, and physical processes, while Cyber-Physical Networks (CPNs) enable communication and coordination among components. With advances in IoT and smart devices, CPSs are increasingly vital for applications such as smart grids, industrial automation, and autonomous vehicles. This dissertation explores decision making and performance optimization in Distributed Cyber-Physical Networks (DCPNs), focusing on efficiency, reliability, decentralization, and security. Using network economic theories (e.g., game theory, contract theory) and machine learning (e.g., Reinforcement Learning, Federated Learning), it addresses key problems: 1) a cost-effective positioning system with Reconfigurable Intelligent Surfaces (RISs) and RL, 2) energy-efficient task offloading in Multi-access …

Development Of Sensing Capabilities For Agricultural Robotics, Carter Noh

Theses and Dissertations

Agricultural robotics is a rapidly growing industry with the possibility to revolutionize food production. Agricultural robots often require custom sensing solutions to perform specific tasks. Developing the appropriate sensing capabilities for an application is therefore an important step in the automation process. In this thesis I present two case studies in developing sensing capabilities for agricultural robotics applications. First, I introduce a sensor that can provide simultaneous measurements of force and position. The sensor was developed to enable high-throughput measurement of maize stalk stiffness on a robot platform. It uses multiple sets of strain gauges on a cantilever beam to …

Extracting Speech Content From High-Amplitude Background Machine Noise, Curtis L. Garner

Theses and Dissertations

The research presented in this dissertation aims to address the need for improved communication between heavy machinery operators and external personnel, aiming to improve safety and efficiency by overcoming the communication barriers posed by equipment noise and cab insulation. The primary purpose of this research was to develop a hands-free communication system that allows the operator to hear speech from outside the cab without requiring any personal communication devices like radios or headsets. Key objectives included effective speech extraction from ambient noise and accurate estimation of the location of the speaker. Operating without active user control, the system emphasizes clarity …

Development Of Robotic Sampling Method For Rees In Gps-Denied And Hazardous Environments, Alan Arthur

Graduate Theses & Non-Theses

In recent times, the demand for rare earth elements (REEs) has been fueled by industry shifts towards energy efficiency and climate neutrality, as well as the great potential for the use of REEs in all sectors of our daily lives. Currently, China accounts for over 75% of global REE production (Dal, 2024). Various REE producers are gearing up to produce significant amounts of REEs to solve the issue of global supply and break China’s monopoly. A spike in REE prices of nearly tenfold in 2011 provided the impetus for accelerating over 400 REE exploration projects around the world (Barakos, 2017). …

Critical Mineral Recovery From Metals-Laden Mine Water, Matthew Ingersoll

Graduate Theses & Non-Theses

The importance of critical mineral possession has increasingly risen as the transition to clean energy becomes more viable. Critical minerals play many roles in national security, energy independence, environmental well-being, and economic growth. Given that many companies and certain countries have goals to halve greenhouse gas emissions by 2030, and reaching zero emissions by 2050, critical minerals have become a paramount resource and a focus in many research fields. Acid mine drainage and other mine wastewaters have been found to be viable sources of critical minerals with potential for recovery. This critical mineral recovery research focuses on zinc and manganese …

Surface Chemistry And Adsorption Mechanism Of Orfom® D8 For Cu Depression In Cu-Mo Flotation, Patrick Jeff Mensah

Graduate Theses & Non-Theses

In traditional chalcopyrite-molybdenite (Cu-Mo) flotation separation systems, inorganic depressants such as NaSH and NaCN are used to selectively depress chalcopyrite making it more hydrophilic so molybdenite can be floated and recovered. NaSH and NaCN are highly toxic, have high explosion potential, strong odor, and readily oxidize in air resulting in a high consumption rates. To control oxidation, nitrogen gas, and Nokes reagent are both necessary, making the process even more expensive. In addressing these challenges, attention is fast shifting to disodium carboxymethyl trithiocarbonate (Orfom® D8), a novel organic depressant designed to replace conventional inorganic depressants because it does not easily …

Discrete Particle Dynamics Models: Computational Aspects And Applications, Yuxuan Wu

Doctoral Dissertations and Master's Theses

Particle dynamics considers discrete particles as Newtonian point masses in diverse domains such as, pedestrian particles in pedestrian movement modeling and molecules in material science modeling. These dynamic modeling approaches share a commonality in computational approach where a series of ordinary differential equations are solved for the particle’s motion and equilibrium under active interactions and boundary conditions. In general, particle dynamics approaches are computationally intensive and address problems that require a vast parameter design space. In this dissertation, a computational paradigm is developed for large-scale particle dynamics modeling and demonstrated by three process design applications, (a) infection risk modeling for …

Lignin Cationization For The Removal Of Phosphates And Nitrates From Effluents Of Wastewater Treatment Plants, Fannyuy V. Kewir

LSU Master's Theses

The removal of phosphates and nitrates from wastewater treatment plant (WWTP) effluents is important for preventing pollution of receiving waters. In this study, we chemically modified alkaline lignin (aLN) with quaternary ammonium groups to obtain biodegradable cationic lignin (cLN). We characterized the cLN and tested its efficacy for removing phosphates and nitrates in a lab setting and on field-collected WWTP samples. Adsorption isotherm and kinetic studies were performed in aqueous media, and the effects of several variables (contact time, pH, initial concentration, and adsorbent dose) were investigated. The Langmuir isotherm described phosphate and nitrate adsorption well, with R2 values of …

Applications Of Phosphorus Geochemistry To Utah Lake, Jacob Barry Taggart

Theses and Dissertations

This compilation of studies provides an examination of phosphorus dynamics in the suspended solids, water column, and lakebed sediments of Utah Lake, a large, shallow, and phosphorus-rich water body. The first study introduces a novel phosphorus microfractionation (P-MF) method designed for small samples of suspended solids and sediments. This method reveals that suspended solids in Utah Lake can have a significantly higher phosphorus content across most fractions compared to lakebed sediments, highlighting the importance of considering suspended solids in nutrient management strategies. The second study analyzes historical phosphorus mass and concentrations, demonstrating that water column phosphorus levels have remained stable …

Numerical Simulations On Rayleigh Taylor Instability For Co2 Sequestration In Saline Aquifers, Bhargavi Vandadu

Mechanical Engineering Theses

The density-driven convection appears promise for CO₂ storage, precise prediction on a reservoir size is problematic due to limited data availability. Changes in permeability impact flow patterns and the effectiveness of mixing processes. Permeability differences influence the sorts of density-driven mixing phenomena such as fingering and stable mixing. Improving CO₂ storage models is challenging due to the unpredictable nature of these processes. The study looks at the flow phenomena in which carbon dioxide with a lower viscosity displaces on heavier fluid water. Causes instability by producing finger-like structures at the gas-liquid interface. This study uses the species transport technique to …

Training Machine Learning Algorithms For The Transient Startup Of A Long-Life Modular Microreactor, Ahmad N. Shaheen

Nuclear Engineering ETDs

There is an increasing interest in developing and deploying small modular nuclear reactors (SMRs) and micro modular reactors (MMRs). They could be operated with minimal on-site personnel or remotely with a high degree of autonomy. This can be enabled through the use and application of Artificial Intelligence (AL) and Machine Learning (ML) algorithms and methods. The objective of the present work is to train machine learning algorithms for remote control mimicking the transient startup of the Very-Small, Long-LIfe, Modular (VSLLIM) microreactor. This walk-away safe microreactor design and a fully integrated transient model have been developed at the University of New …

Utilizing Pupper, A Social Robot Dog, To Increase Happiness And Improve Mood In Pediatric Patients Of A Cardiac Step-Down Unit, Angela Feng Wu

Master's Projects and Capstones

Objective The usage of social robots in pediatrics is an emerging field of study. Preliminary research shows that they are effective at improving the psychosocial well-being of pediatric patients. This quality improvement project focuses on Pupper, a newly developed quadruped social robot dog, and its ability in improving mood and happiness in pediatric patients of a cardiac step-down unit. Aim The aim of this project is to increase average mood scores of pediatric cardiac patients aged 3-25 years by 50% from their baseline of 3.75 to 5.63 on a six-point scale within a one-month time frame. Methods Before intervention and …

Crystal Plasticity Modeling To Capture Microstructural Variations In Cold-Sprayed Materials, Aulora Gail Williams

Theses and Dissertations

The high-velocity impact of powder particles in cold-spray additively manufactured (CSAM) parts creates intersplat boundaries with regions of high dislocation densities and sub-grain structures. Upon microstructure and mechanical characterization, CSAM Aluminum 6061 showed non-uniformity, with spatial variation in the microstructure and mechanical properties affecting the overall response of the additively manufactured parts. Post-processing treatments are conducted in as-printed samples to improve particle bonding, relieve residual stresses, and improve mechanical properties. In this work, we attempt to implement the effects of grain size and distribution of smaller grains along the intersplat boundaries using the grain size distribution function and powder size …

Enhanced Intellectual Property Protection Mechanisms Towards Collaborative Data Sharing In Metal-Based Additive Manufacturing, Durant Hayes Fullington

Theses and Dissertations

This dissertation aims to develop effective methodologies towards enhanced intellectual property protections for data sharing frameworks in metal-based additive manufacturing (AM). Currently, many small-to-medium sized manufacturers face data availability challenges due to the prohibitive high cost to collect, process, and analyze large amounts of process-related data for AM. Because these manufactures rely heavily on small-scale data, it can be difficult for them to effectively train complex machine learning (ML) algorithms, which are commonly used for AM process monitoring. One popular solution is to develop collaborative data sharing frameworks, where multiple independent AM users can share their data to increase the …

Experimental And Numerical Investigation Of Phase Change Material Melting Cycle In Microgravity, Ethan Michael Schuetzle

Theses and Dissertations

Efficient thermal management of spacecraft systems is crucial to overcome the extreme thermal conditions in space. Phase change materials (PCMs) offer significant advantages in thermal energy storage (TES) systems due to their high latent heat of fusion, the amount of energy stored or released during phase transitions. However, PCMs face limitations due to low thermal conductivity, especially in microgravity where heat transfer is primarily through conduction. This research addresses this issue by incorporating a 3D-printed aluminum lattice structure to enhance heat transfer to PCMs. Experimental data from sub-orbital flight tests and ground-based experiments, coupled with numerical simulations, are used to …

Assessing Plate Load Testing And Modulus Of Subgrade Reaction Determination For Pavements, Jami Lynn Rushing

Theses and Dissertations

This thesis aims to: 1) determine the effect of plate size on the modulus of subgrade reaction (k), 2) investigate the differences between the ASTM D1196-21 and CRD-C 655-95 plate load test methods, and 3) develop new effective k curves. 150 plate load tests were executed on three subgrade materials under varying base course thicknesses. Plate load testing was designed to evaluate various plate sizes and testing standards (e.g., the military standard – CRD-C 655-96 and the ASTM standard – ASTM D1196-21). By performing plate load tests on varying base course thicknesses, newly developed effective k curves were developed from …

The Plastics Crisis: Responsible Recycling In Civil Engineering And Infrastructure, Jason Howell Stewart

Theses and Dissertations

A global plastics crisis exists with around 400 million metric tons (MMT) of plastics production and 350 MMT of plastics waste generated annually around the globe. Plastics are durable and essentially non-biodegradable, so they accumulate in both terrestrial and marine environments as pollution and within landfills without breaking down over time. Plastics are ubiquitous in 21st-century society and culture, comprising essential elements in most industries including packaging, transportation, electrical/electronics, and building/construction. The role of civil engineering and infrastructure in addressing the plastics crisis was specifically explored. Responsible plastics recycling yields products with improved engineering properties, performance characteristics, marketability, and/or manufacturing …

A Case Against Exclusive Reliance On Volumetric Mixture Characterization For Future Asphalt Pavements And The Advancement Of A Viable Alternative, Jonathan Randy Easterling

Theses and Dissertations

Volumetric properties have been the cornerstone of asphalt mixture design and control dating back to the early 20th century. However, social and economic drivers have caused the asphalt industry to make changes in materials and processes used to produce mixtures that have stretched the limits of volumetric centric methodologies. The primary objectives of this dissertation were to present an extensive case on how volumetrics is limited in their ability to characterize asphalt mixtures and to advance the body of knowledge of a viable alternative. State agency specifications were examined and found that Departments of Transportation (DOTs) were using asphalt volumetric …

Predicting Vegetation Override Force For Off-Road Autonomy, Marc Nicholas Moore

Theses and Dissertations

Vegetation override is an important aspect of off-road ground vehicle mobility. An autonomous ground vehicle’s (AGV) perception system must distinguish between vegetation that can be easily driven through from vegetation that cannot. Predicting the resistance of vegetation could allow path- planning systems to make this distinction. However, despite its importance, direct measurement of vegetation resistance is rare, as most studies use indirect proprioceptive data, such as inertial measurements, as proxies for override force. Notably, there is a lack of empirical data on the override resistance of small stems (< 2.5 cm) and clusters of vegetation on medium-sized (approx. 1000kg) vehicles. To address this gap, a comprehensive dataset of override measurements was collected for clumps of small vegetation relevant to intermediate-sized AGVs navigating off-road terrain. This dataset includes over 70 recordings using the Robot Operating System (ROS) during controlled driving experiments through small trees, grasses, and bushes. The collected data includes light detection and ranging (LiDAR) scans, imagery, force measurements from integrated load cells, and simultaneous localization and mapping (SLAM) information. A key contribution of this research is the development and calibration of a custom push bar system equipped with load cells to directly measure override forces. These measurements are compared to empirical models previously developed by the U.S. Army Corps of Engineers for larger single-stem vegetation. A preprocessing pipeline was developed to automatically extract and label LiDAR and camera data according to these force measurements. This self-labeled dataset was then used to train machine learning models that predict override resistance of vegetation from LiDAR and camera scans alone. This research characterizes the relationship between override forces and the observable features of vegetation as measured by LiDAR and camera sensors. Deep learning models were developed and trained to predict override forces based on different input modalities and features derived from point clouds and images. The performance of these models was compared across various input features to investigate how deep learning can create a generalizable and accurate force prediction system.