Engineering Commons^™

Design And Simulation Of A Micro-Coiled Digitally-Controlled Variable Inductor With A Monolithically Integrated Mems Switch, A. Sharaf, Sh M. Eladl, A. Nasr, M. Serry

Faculty Journal Articles

This work introduces the design, analysis, simulation, and a standard MEMS fabrication process for a three-dimensional micro-coil with a magnetic core and a digital switch configuration using a completely integrated, fully MEMS-compatible process to achieve a digitally controlled inductance. The proposed design can also be utilized as a micro-transformer. The proposed design consists of five identical 3D coils and their corresponding MEMS switches. These coils are digitally controlled to achieve a variable inductor ranging from one-fifth of the coil inductance up to five times the coil inductance. A standard five-layer Polymumps process is proposed to fabricate the micro-coils and the …

Hierarchy Of Exchange-Correlation Functionals In Computing Lattice Thermal Conductivities Of Rocksalt And Zinc-Blende Semiconductors, Jiacheng Wei, Zhonghao Xia, Yi Xia, Jiangang He

Mechanical and Materials Engineering Faculty Publications and Presentations

Lattice thermal conductivity (𝜅L) is a crucial characteristic of crystalline solids with significant implications for thermal management, energy conversion, and thermal barrier coating. The advancement of computational tools based on density functional theory (DFT) has enabled the effective utilization of phonon quasiparticle-based approaches to unravel the underlying physics of various crystalline systems. While the higher order of anharmonicity is commonly used for explaining extraordinary heat transfer behaviors in crystals, the impact of exchange-correlation (XC) functionals in DFT on describing anharmonicity has been largely overlooked. The XC functional is essential for determining the accuracy of DFT in describing interactions among electrons/ions …

Performance And Enhanced Efficiency Induced By Cold Plasma On Sapo-34 Membranes For Co2 And Ch4 Mixtures, Fnu Gorky, Vashanti Storr, Grace Jones, Apolo Nambo, Jacek B. Jasinski, Maria L. Carreon

Chemical Engineering Faculty Publications and Presentations

In this study, we investigate the influence of cold-plasma-induced enhanced performance and efficiency of SAPO-34 membranes in the separation of CO₂ and CH₄ mixtures. Placing the herein presented research in a broader context, we aim to address the question of whether cold plasma can significantly impact the membrane performance. We subjected SAPO-34 membranes to plasma mild disturbances and analyzed their performance in separating CO₂ and CH₄. Our findings reveal a notable enhancement in membrane efficiency and sustained performance when exposed to cold plasma. The pulsed plasma separation displayed improved structural integrity, and the experimental results …

Meteorological Data Mining And Synthesis For Supplementing On-Site Data For Regulatory Compliance, Ben Sonpon, Shoaib Usman, Joseph Smith, Sarah Kovaleski, Jason Wibbenmeyer

Nuclear Engineering and Radiation Science Faculty Research & Creative Works

Many regulatory requirements add significant delay in the licensing of new nuclear power stations. One area of particular interest is the environmental impact of potential atmospheric release. the purpose of this research is to demonstrate effectiveness of meteorological data mining and synthesis from offsite locations to reduce need for onsite data, hence allowing rapid licensing. the automated procedures tested for data mining and extraction of meteorological data from multiple offsite sources and the data analytic tool developed for data fusion are presented here. Three important meteorological parameters from regulatory compliance are considered for this analysis: wind velocity, wind direction, and …

Exploiting Physical Side-Channel Information For Offensive And Defensive Ends, Sisheng Liang

All Dissertations

Side-channel information consists of side effects of computation that range from microarchitectural to physical phenomena. Empirical studies have demonstrated the practical exploitability of these side effects in real-world systems for malicious attacks and effective defenses. In this dissertation, we discover, analyze, and exploit certain physical side-channel information for end-to-end attacks and defense across three studies.

In the first study, we demonstrate a new DNN model extraction attack named Clairvoyance that exploits certain far-field electromagnetic signals emitted from a GPU to steal DNN models several meters away from the victim machine, even with some physical obstacles in between. Using Clairvoyance, an …

Frameworks For The Techno-Economic Assessment Of Membrane-Based Bioprocessing Platforms, Juan Jose Romero Conde

All Dissertations

This dissertation describes developing and implementing computational frameworks for simulating and optimizing purification processes in the biopharmaceutical industry. The framework performs techno-economic analyses to establish value propositions for new process alternatives, especially membrane technologies. Initially, the focus is developing a framework capable of simulating monoclonal antibody (mAb) capture using membrane and resin media in multi-column chromatography (MCC) platforms for continuous manufacturing. Subsequently, the impact of capture MCC is compared against other intensification strategies in established mAb manufacturing facilities. Finally, the framework application expands to simulate the purification of adeno-associated virus (AAV) vectors for gene therapy.

Chapter 2 details the framework …

Improving Expressive Capacity Of Deep Neural Networks, Clayton Harper

Computer Science and Engineering Theses and Dissertations

Deep learning has had remarkable success in a variety of fields. However, architectures often rely on hyperparameter searches and heuristics for improved model performance. Performing hyperparameter searches is an arduous task--often time-consuming and potentially expensive to run on accelerated hardware. As a result, practitioners often rely on heuristics which may lead to sub-optimal results. In the context of deep learning, hyperparameters are set by the user prior to the training process and remain fixed. Deep learning uses gradient descent to learn complex feature representations from data, limiting human intervention. While the weights of the architecture can learn directly through data …

Introduction To Neuroengineering, Mercedes Terry, Abigail Tubbs, Blair Dupre, Brandon Fugger, Enrique Alvarez Vazquez

Open Educational Resources

"Introduction to Neuroengineering" serves as a comprehensive guide to the fast evolving field at the intersection of neuroscience, engineering, and technology. This book covers foundational concepts like neuron structure, action potentials, and neural modeling, moving through advanced topics like brain-computer interfaces (BCIs), neurostimulation, and imaging technologies (EEG, MEG, fMRI). Each chapter combines theory with practical exercises and lab examples, accessible through a GitHub repository to enhance hands-on learning. Aimed at students, educators, and professionals, this resource offers a foundation and inspiration for innovation in neuroengineering, promoting ongoing exploration in this impactful field.

Gps-Denied Navigation Using Location Estimation And Texel Image Correction, Nikolas I. Jensen

All Graduate Theses and Dissertations, Fall 2023 to Present

In recent years, the use of small drones, also categorized as small Unmanned Aerial Vehicles (sUAV), has surged. They are used for tasks like surveying land, collecting data from a distance, and performing maneuvers for military operations. These drones are popular because they are affordable, small, easy to use, and can navigate well in complex areas. These factors make them a cheap and quick option for tasks like surveying and surveillance when compared to traditional methods.

This thesis introduces a system that uses algorithms to figure out where the drone is. Typically, this relies on sensors and GPS, but GPS …

Federated Learning In Wireless Networks, Xiang Ma

All Graduate Theses and Dissertations, Fall 2023 to Present

Artificial intelligence (AI) is transitioning from a long development period into reality. Notable instances like AlphaGo, Tesla’s self-driving cars, and the recent innovation of ChatGPT stand as widely recognized exemplars of AI applications. These examples collectively enhance the quality of human life. An increasing number of AI applications are expected to integrate seamlessly into our daily lives, further enriching our experiences.

Although AI has demonstrated remarkable performance, it is accompanied by numerous challenges. At the forefront of AI’s advancement lies machine learning (ML), a cutting-edge technique that acquires knowledge by emulating the human brain’s cognitive processes. Like humans, ML requires …

Assessing Soil Properties & Suitability For Optimized Irrigation Development In Sudan, Northern Africa, Suhib O. Hamid

Department of Agricultural and Biological Systems Engineering: Masters Project Reports

In Chapter 1, soil properties essential for efficient irrigation are thoroughly assessed. Factors such as soil texture, infiltration rates, and nutrient content are analyzed to provide insights into the selection of modern irrigation systems. Utilizing data collected from 3,192 locations through GPS and laboratory analyses, alongside sophisticated ranking systems, the research determines the most suitable irrigation methods for specific soil series. Findings highlight the remarkable efficiency of drip irrigation across various soil types, contrasting with the consistently lower performance of surface irrigation. The chapter emphasizes the significance of considering soil variability, evapotranspiration, and investment factors in selecting irrigation methods for …

Developing A Comprehensive Genome-Scale Metabolic Model For The Arabidopsis Thaliana Root System, Lohani Esterhuizen

Department of Chemical and Biomolecular Engineering: Dissertations, Theses, and Student Research

Arabidopsis thaliana (Arabidopsis) is the most well-established model plant to date. Being the first plant to have its genome mapped, studies on Arabidopsis have provided insurmountable insights into the physiological and biochemical nature of plants. Methods that allow us to computationally study the metabolism of organisms include the use of genome-scale metabolic models (GEMs). Despite its popularity, no GEM currently maps the metabolic activity in the root system of Arabidopsis, which is the first organ to face and respond to stress conditions in the soil. This work aims to develop and implement a comprehensive GEM of the Arabidopsis root system …

Evaluation Of Unreinforced And Geosynthetic-Reinforced Subgrade Material Performance With Large-Scale Tracking Wheel Tests And Dynamic Cone Penetrometer Tests, Kenaz Owusu

Department of Civil and Environmental Engineering: Dissertations, Theses, and Student Research

Subgrade has a significant impact on the performance of a pavement as it serves as the foundation layer on which traffic loads are transferred. Many pavement failures can be attributed to weak subgrade strength. The integration of geosynthetics in road pavements has been identified as an effective solution for enhancing subgrade performance to withstand imposed traffic. This study evaluated the performance of unreinforced and geosynthetic-reinforced subgrades in Nebraska to quantify the contribution of geosynthetics to unpaved layer systems.

The strength and stiffness of different soil subgrades were assessed through unconfined compressive strength tests and repeated load triaxial (RLT) tests, which …

Smart Automatic Modal Hammer Predictor–Corrector Approach For Accurate Excitation Of Dynamical Systems, Mohammad Nasr

Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research

This research introduces an innovative solution that revolutionizes the study of linear and nonlinear dynamical systems—a smart automatic modal hammer. With its affordability and intelligent capabilities, this automatic modal hammer becomes an invaluable tool for research and industry, enabling repeatable strikes with precise force control. This system's significance becomes particularly evident when studying nonlinear systems, which heavily rely on the excitation level for their dynamics. By offering a cost-effective design this proposed system proves to be robust in accelerating research on nonlinear dynamics, providing researchers with an efficient and accessible means to delve deeper into these complex systems. The proposed …

Ultrasonic Nondestructive Quantification Of Case Depth In Railroad Bearing Components, Trevor K. Adelung

Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research

While in service, railroad bearings must withstand large loads to ensure smooth operation. These large loads can lead to failure due to rolling contact fatigue (RCF), for which small cracks initiate just beneath the surface of the components. As a preventative measure, bearings are manufactured with a hardened surface layer called the effective case depth (ECD). Current methods in industry use destructive hardness testing to quantify the ECD. While accurate, this method is costly and time consuming. Ultrasonic nondestructive testing would provide a more efficient and cost-effective method to determine ECD. In this thesis, diffuse ultrasonic shear backscatter was used …

Studies Of Exotic Electronic, Optical, And Electrochemical Properties In Nanoparticle Assembly And Graphene, Jay Min Lim

Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research

It is well known that exotic properties and phenomena emerge as structural dimensions shrink to nanoscales. We self-assembled one-dimensional chains of gold nanoparticles in solution and quantified the growth process by monitoring the redshift of localized surface plasmon resonance (LSPR). Curiously, the redshift stopped while the chains continued to rearrange as manifested by gradual reduction in the LSPR peak. Using electromagnetic simulations, we quantitatively explained the phenomena as a rapid “addition-polymerization” followed by a sharp transition to “condensation-process.” Next, recognizing the significant electric field enhancement in the interparticle gap, a photoluminescent-active Eu³⁺ ion was used to probe Surface-Enhanced Photoluminescence …

Enabling Sustainable Freight Forwarding Network Via Collaborative Games, Pang Jin Tan, Shih-Fen Cheng, Richard Chen

Research Collection School Of Computing and Information Systems

Freight forwarding plays a crucial role in facilitating global trade and logistics. However, as the freight forwarding market is extremely fragmented, freight forwarders often face the issue of not being able to fill the available shipping capacity. This recurrent issue motivates the creation of various freight forwarding networks that aim at exchanging capacities and demands so that the resource utilization of individual freight forwarders can be maximized. In this paper, we focus on how to design such a collaborative network based on collaborative game theory, with the Shapley value representing a fair scheme for profit sharing. Noting that the exact …

Classification Model For Discovering The Type Of Crop To Plant Using Ensemble Techniques, Uma Mahesh Addanki

Electronic Theses, Projects, and Dissertations

Farming plays a role in ensuring survival, especially with the growing need for increased agricultural output. It is vital for farmers to efficiently choose the crops to cultivate. By using crop recommendation systems farmers can make decisions on what crops to plant leading to yields and improved resource management. The success of crop production depends on maintaining the balance of soil nutrients and favorable weather conditions. In this research project, we created a crop recommendation system utilizing learning methods to predict the appropriate crops based on essential soil nutrients and weather patterns. We worked with a dataset sourced from Kaggle, …

Task Management Application, Dhaval Chaturbhai Hirpara

Electronic Theses, Projects, and Dissertations

The Task Management Application is a web-based platform designed to facilitate efficient task and project management, similar to other Project Management Tools like Jira, Trello, ClickUp, Wrike, Zoho Projects, and Asana. The application features three distinct roles: Administrator, Project Manager, and Employee, each with specific functionalities and permissions to streamline workflow.

Administrator: This role encompasses comprehensive project oversight, including adding, viewing, and managing project managers, supervising ongoing projects, and viewing employee details.

Project Manager: Project Managers can manage employees, assign tasks, and oversee project progress effortlessly.

Employee: Employees have dedicated functionalities to view and manage tasks assigned …

Crime Data Prediction Based On Geographical Location Using Machine Learning, Sai Bharath Yarlagadda

Electronic Theses, Projects, and Dissertations

This project employs machine learning methods like K Nearest Neighbors (KNN), Random Forest, Logistic Regression, and Decision Tree algorithms to monitor crime data based on location and pinpoint areas with risks. The project implements and tunes the four models to improve the precision of predicting crime levels. These models collaborate to offer a trustworthy evaluation of crime patterns. K Nearest Neighbors (KNN) categorizes locations by examining the proximity of data points considering coordinates and other factors to identify trends linked to increased crime data. Logistic Regression gauges the likelihood of crime incidents by studying the connection, between factors (like location …

Service Connect, Namrata Bomble

Electronic Theses, Projects, and Dissertations

ServiceConnect is an innovative web-based marketplace platform designed to revolutionize how local services are accessed and managed in the US. By connecting service providers and customers directly, ServiceConnect provides a simple, secure, user-friendly platform for a range of services such as home repairs, tutoring, pet care and more. Featuring convenient booking tools that increase efficiency while simultaneously building trust among both parties involved. ServiceConnect stands out with its comprehensive service range, user-friendly interface, secure payment processing and rigorous verification process for service providers. Leveraging advanced technologies like ReactJS on the frontend, Node.js & Express on the backend and MongoDB for …

Assessing Even Flat-Fan Nozzles For Spot Spray Herbicide Applications, Thiago H. Vitti

Department of Agronomy and Horticulture: Dissertations, Theses, and Student Research

Herbicide efficiency in row-crop agriculture can be improved using precision technologies for controlling weeds and minimize agronomic, economic, and environmental impacts. Site-specific weed management technologies, such as spot-sprayers, allow herbicides to be sprayed only where weeds are present in the field. Understanding application parameters and their influence on weed control for site-specific spot-spray applications is essential. This research involved laboratory and greenhouse studies to investigate the coverage and coefficient of variation (CV) of even flat-fan spray nozzles under different spot-spray application scenarios, as well as the effect of weed size and application method on the control of various weed species …

Understanding And Tuning Magnetism In Van Der Waals-Type Metal Thiophosphates, Rabindra Basnet, Jin Hu

Physics Faculty Publications and Presentations

Over the past two decades, significant progress in two-dimensional (2D) materials has invigorated research in condensed matter and material physics in low dimensions. While traditionally studied in three-dimensional systems, magnetism has now been extended to the 2D realm. Recent breakthroughs in 2D magnetism have attracted substantial interest from the scientific community, owing to the stable magnetic order achievable in atomically thin layers of the van der Waals (vdW)-type layered magnetic materials. These advances offer an exciting platform for investigating related phenomena in low dimensions and hold promise for spintronic applications. Consequently, vdW magnetic materials with tunable magnetism have attracted significant …

Fault Detection And Location In Renewable-Fed Distribution Systems Using Low Voltage Sensors, Bushra Farhat

All Theses

The penetration of Distributed Energy Resources (DERs) into the Electric Power System (EPS) is increasing. These (DERs) are mostly inverter-based resources (IBRs) that are integrated into (EPS) at the distribution level. Therefore, it has become necessary to leverage the integration of (IBRs) for both commercial and residential distribution systems through the deployment of Microgrid (MG). However, existing distribution systems that use fuses for sensing and isolating faults are not equipped to handle protection of microgrids where fault currents are fed from multiple sources and can flow downstream or upstream. Bulk of the protection schemes proposed in literature are heavily dependent …

Understanding The Challenges To Robotic-Assisted Surgery Adoption From The Perspectives Of The Human-Robot Interaction, Built Environment, And Training, Patrick A. Fuller

All Theses

Introduction: Robotic-assisted surgery (RAS) is a form of minimally invasive surgery that is increasing in both its adoption and development due to many perceived advantages such as tremor reduction and motion scaling. However, RAS is still relatively new and there are a variety of novel barriers and challenges to the adoption of these platforms. Objectives: This study aims to understand how the integration of RAS platforms impacts the interactions and outcomes of interactions between surgical team members to explore the barriers from three aspects critical to facilitating Robotic-assisted-surgery (RAS) adoption: the human-robotic interaction, built environment, and RAS training. Future …

Exploring Healthcare Chatbot Information Presentation: Applying Hierarchical Bayesian Regression And Inductive Thematic Analysis In A Mixed Methods Study, Samuel Nelson Koscelny

All Theses

High blood pressure, also known as hypertension, significantly increases the risk of heart disease and stroke, which are leading causes of death in the United States. While contributing to over 691,000 deaths in 2021 alone in the United States (U.S.), it also imposes immense economic burden on the healthcare system, costing approximately $131 billion annually. One way to address this issue is for increased self-care behaviors and medication adherence, both of which require sufficient health literacy. Despite the importance of health literacy, 90% of U.S. adults struggle with health-related subjects. Overcoming the issues associated with health literacy requires addressing the …

Understanding The Microstructure And Tribological Performance Of Cocrfeni-Based High Entropy Alloys, Ali Azarmi

All Theses

Due to their higher wear resistance compared to conventional alloys, high entropy alloys (HEAs) are now being considered as candidates for parts undergoing sliding contact during their lifetimes. While the engineering field has built some knowledge related to the performance of selected high entropy alloys, such as the CoCrFeNi alloy, more research is needed to determine if (how) expansions from four to five principal alloying elements alter the performance compared to the initial alloy. In this study, we started this research effort by investigating the relative performance of CoCrFeNiMn and CoCrFeNiTi with respect to CoCrFeNi. The two primary alloying elements …

Local Charge Distortion Due To Cr In Ni-Based Concentrated Alloys, Jacob Fischer

All Theses

Due to the presence of multiple elements consisting of a range of atomic radii, local lattice distortion (LLD) is commonly observed in concentrated (and high entropy) alloys. However, since these elements also have diverse electronegativities, recent works show that atoms can have a range of atomic charges. In this work, using density functional theory (DFT), we investigate electronic charge distribution in face centered cubic (FCC) Ni-based alloys and find significant charge-density distortion in HEAs. Specifically, Cr atoms have large charge density distortion that results in a wide range of bond lengths, atomic charges, and electronic density of states in Cr-containing …

Trust: The Inertia Taxonomy Of Trust And Learning Outcomes Of Trust Building Exercises, Ann B. Lyons

All Theses

This study investigates United States construction professionals’ view of trust in the workplace. In particular the focus is on building trust through trust building exercises. In order to accomplish this, a literature review was conducted focusing on trust in the worldwide construction industry. This was followed by interviews with US construction professionals who focus on team building. These interviews informed a survey sent to a wide variety of US construction professionals. The survey had two sections of content questions, 1. view of trust indicators and 2. perception of trust building exercises. Finally, experts in the field of construction team building …

Hyperspectral Image Classification Of Bacteria Using A Deep Convolutional Neural Network, Bruce S. Vogelsberg Jr

All Theses

Hyperspectral imaging is a non-invasive imaging method capable of collecting both spatial and spectral information. However, because of the large volume of data collected, much of it is redundant or not useful for classification. Deep learning is a subset of machine learning that uses artificial neurons in a multilayered structure to learn representations from data. One of the main advantages of deep learning is the powerful feature extraction capabilities, which allow the model to learn both high- and low-level features. Convolutional neural networks are a type of deep learning model that have alternating convolutional and pooling layers capable of extracting …