Engineering Commons^™

Gate Driver Design In High-Temperature Cmos Process For Heterogeneous Integration Inside Sic Power Module, Khandoker Asif Faruque

Graduate Theses and Dissertations

The shift toward electrification in transportation, including electric and hybrid vehicles, presents challenges for power electronic converters. Silicon Carbide (SiC) power devices are promising due to their high efficiency, temperature tolerance, and reduced losses compared to traditional silicon devices. However, their use is hindered by issues such as parasitic capacitances and gate inductances, which can lead to voltage spikes and stress on the device gate oxide, hence impacting converter reliability. Increasing gate resistance can help manage these issues, but it reduces switching speed and increases losses. Snubber circuits can mitigate switching stress but add extra components, reducing efficiency. Active gate …

Neural Network Architecture Search Enabled Wide-Deep Learning (Nas-Wd) Integrated Hyperspectral Imaging Understanding For Woody Breast In Poultry Processing, Chaitanya Kumar Reddy Pallerla

Graduate Theses and Dissertations

The development and implementation of a Wide & Deep (WD) learning model tailored for classification and regression tasks utilizing spectral data provides a robust solution to evaluate woody breast (WB) conditions in poultry fillets. This process begins with thorough data preprocessing, which includes loading spectral and classification datasets, imputing missing values with medians, and splitting the data into training and testing sets to ensure rigorous model evaluation. The WD model architecture integrates wide linear models and deep neural networks to harness the strengths of both approaches. The wide component excels at memorizing sparse feature interactions, while the deep component captures …

Interaction-Sensitive Tree-Based Statistical Models, Xiaotong Sun

Graduate Theses and Dissertations

This dissertation introduces a tree-based framework to improve the interpretability and modeling of interaction effects among variables, essential in fields like biostatistics, healthcare, science and engineering. Traditional regression methods often fail to clearly capture complex interactions, while tree-based approaches, despite their interpretability, face performance limitations and overfitting concerns. Our proposed interaction-sensitive tree-based method, designed for seamless integration, combines various statistical techniques tailored to different data types, leveraging ensemble learning methods to enhance accuracy and mitigate overfitting. We present methods for regression, survival analysis, and classification, validated with case studies and benchmarked against traditional models using metrics like BIC and R-squared. …

A Cmos Ldo Voltage Regulator For Low Power Applications, Nicolaus Vail

Graduate Theses and Dissertations

This thesis presents the design, simulation, layout, and testing of a low dropout linear voltage regulator (LDO) in a 180 nm CMOS process. The LDO is intended for use in low-power, battery-operated applications, and it has an adjustable output for a variety of different implementations. It can supply up to 100 mA of current, which is enough to power many small electronic circuits. It has a dropout voltage of 150 mV, and it consumes less than 200 μA of current during operation, which is on par with many commercial regulators. The line regulation is also comparable to commercially available LDOs, …

Wind Tunnel Investigation Of The Along-Wind Dispersion In Finite-Duration Neutrally Buoyant Gas Releases, Daniel Williams

Graduate Theses and Dissertations

This thesis investigates the along-wind dispersion of hazardous gas releases within a turbulent boundary layer using controlled experiments in an ultra-low-speed wind tunnel, recognizing the limitations of numerical modeling and field tests. This study utilizes a neutrally buoyant gas mixture and dual Flame Ionization Detectors (FIDs) to capture representative, repeatable data on gas cloud behavior over finite durations. Ensemble averaging of 65 trials across varying release durations and downwind distances provided detailed insights into gas dispersion and the distinct time phases within finite-duration releases. Analysis revealed that along-wind dispersion coefficients depend on turbulence and vertical wind shear, with normalized coefficients …

Non-Contact Acoustic Emission Detection Of Rail Defects Using Air-Coupled Sensors, Lei Jia

UNLV Theses, Dissertations, Professional Papers, and Capstones

Rail defects, whether internal or external, present significant safety risks. Acoustic Emission (AE) technology has emerged as a promising technique for monitoring damage progression and detecting these rail defects. This project addresses this critical concern by testing air-coupled optical microphones for non-contact AE detection. The goal of this research is to investigate AE signal characteristics using both rail-mounted and vehicle-mounted methods, establishing the understanding of AE signals in relation to defects and their effectiveness in identifying them.

This research focused on investigating air-conduct sensors in the lab-controlled pencil lead break (PLB) test. The objective was to evaluate the propagation characteristics …

Isolation And Upgrading Of Lignin From Agricultural Sources Using Phase Equilibria, Bronson Lynn

All Dissertations

In the emerging bioeconomy, agricultural residues from our nationwide crop harvests are positioned to be a cornerstone of renewable and sustainable fuels, chemicals, and materials. However, to be economically viable, the basic constituents of this plant matter must be separated and funneled to the appropriate application to maximize value and overall usability.

This work focuses on the component that has, to this point, been largely left behind: lignin. Although it is the most abundant aromatic biopolymer on the planet, making up 15-40% of grasses, hardwoods, and softwoods, the processing involved to isolate usable lignins is too complex and expensive to …

Collision Hazard Prevention And Notification For Construction Worker Safety Using Audio Surveillance, Kehinde Elelu

All Dissertations

The construction industry faces significant safety challenges, with collision hazards ranking as the second highest cause of annual fatalities and injuries in the United States, as reported by the Occupational Safety and Health Administration (OSHA). Current collision detection methods predominantly rely on proximity technologies, which necessitate costly and complex installations on each construction equipment piece. Furthermore, auditory situational awareness declines among workers due to hearing loss and intricate construction noises, which further heightens collision risks. This research introduces an innovative, low-cost, audio-based collision prevention technology aimed at enhancing auditory situational awareness for construction workers exposed to high noise levels. The …

A Multiple Mixed-Method Analysis On Empathy In First Year Engineering Education, Libby Flanagan

All Dissertations

This multiple mixed method analysis focuses on exploring how teaching empathy to first year engineering students interacts with their engineering identity and their personal definition of the role of an engineer. The participants received a four-lesson series on empathy and engineering in the Fall of 2023. They completed a pre- and post-survey as well as wrote five written reflections over the course of this semester. Analysis of the survey results found that composite empathy and engineering identity scores increased significantly over the course of this semester and that the increase in female composite engineering identity scores was significantly larger than …

Neural Operator And Physics-Informed Deep Learning Approaches For Inverse Design Of Composites And Manufacturing Processes, Minglei Lu

All Dissertations

In this dissertation, artificial intelligence (AI) models are designed and used to accelerate inverse design of composites and manufacturing processes. The critical bottlenecks in machine learning (ML) including data availability, data quality, model generalization and adaptation, interpretability, physical consistency, and the ’black box’ nature of models for the inverse design are addressed. And the proposed AI models are tested under different engineering scenarios. Firstly, a fast deep neural operator (DNO) structure was developed to significantly reduce training time. This model was tested in the context of additive manufacturing, a transformative industrial technology that allows for the creation of materials with …

Advancing Visual Geometric Perception: Camera-Based Depth, Reconstruction, And Active Vision, Ziyue Feng

All Dissertations

The advancement of autonomous driving technology and intelligent robotic applications has emerged as a focal point in the realm of autonomy. One of the driving forces behind this trend is the profound understanding of the environment, and at the core of this endeavor lies the three-dimensional geometric perception. This dissertation embarks on a comprehensive exploration of this domain, emphasizing the advances of depth prediction, 3D scene reconstruction, and active vision to enhance geometric perception and scene understanding capabilities in autonomous driving, embodied AI, and robotics. In the domain of depth prediction, this research addresses the challenges of accurately inferring three-dimensional …

Space Operations Education Research: Studying Undergraduate Experiences As Predictors Of Early Career Performance For Military Space Operations Officers, Keith Balts

All Dissertations

Undergraduate students who go on to operate space systems, e.g., satellites, launch systems, and ground-based systems, come from a diverse set of demographics and undergraduate experiences. The United States Space Force (USSF), and United States Air Force (USAF) before them, like many professions, have a demonstrated interest in who enters their ranks, but have had limited research available to them to inform their recruiting, selection, training, and early career development policies and processes.

This Profession-Based Education Research study, a term introduced here in contrast to Discipline-Based Education Research, focused on the military space operations profession. This study analyzed newly commissioned …

Low Carbon, High Cooling Potential Alcohol Fuels In A High Compression Ratio Spark Ignition Engine, John Gandolfo

All Dissertations

Even though most of the effort towards implementing low-carbon alternative fuels has been directed towards heavy-duty vehicles that are difficult to electrify, the high-autoignition resistance of these fuels make them challenging to combust in compression ignition engines. However, several fuel candidates, such as ethanol and methanol, are ideal fuels for spark ignition engines. With the demand for electric vehicles slowing down and increasing recognition of the merits of hybrid powertrains, there is a need to maximize the performance of these fuels for spark ignition engines, which are likely to remain the combustion strategy of choice for hybrids due to their …

Investigating The Role Of Cd44 In Her2+ Breast Cancer Cells During Cancer Cell Redirection, Holly Caroline Jordan Campbell

All Dissertations

Despite a rise in survival rates of breast cancer patients due to advances in modern medicine, breast cancer is the most common type of cancer in women and is the second leading cause of cancer death in the United States. Early detection and a more personalized treatment of breast cancer has slowly improved patient outcomes. Nevertheless, there are impediments to better patient outcomes due to the complexity of the disease physiology. Therefore, there is a demand for a comprehensive understanding of cancer development as well as a need to understand tumor progression for enhanced therapies for patients.

Previous in vivo …

Characterizing Locust Anemotaxis: The Impact Of Sensory Perturbations On Flight Behavior, Yi-Xiao Huang

McKelvey School of Engineering Graduate Student Theses & Dissertations

This thesis investigates the effects of sensory perturbations on locust flight behavior, with the goal of better understanding how locusts integrate multiple sensory inputs to maintain flight control. Through a series of experiments, locusts were subjected to visual impairment, turbulent wind conditions, mechanical impairments of their wings and antennae, and pheromonal interference. These perturbations allowed for the exploration of the contributions each sensory pathway makes to flight stability and navigation in a controlled environment.

Results show that while visual impairment does not significantly impact average flight speed, it increases variability in flight stability, suggesting that visual cues are important for …

Human-Aware Motion Planning For Aerial Robots, Beichen Zhou

McKelvey School of Engineering Graduate Student Theses & Dissertations

This project shows a combination for drones autonomous navigation in dynamic environments. The algorithm combine Social GAN SGAN for human trajectory prediction with Rapidly-exploring Random Tree Star RRT* for path planning. The objective is to efficiently and safe navigate in the area with human. Drones would avoid moving human and maintaining optimal flight path. During training SGAN model, we use both public datasets and dataset collected in the lab, which improve its adaptability in the lab. This experiment was tested through simulations and real-word experiment. SGAN provided a good prediction of human trajectories, which help drones to adjust their path. …

Work-Stealing Scheduler For Parallel Cache-Adaptive Algorithms, Chuqi Jiang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Modern computing systems with hierarchical memory structures, such as multiple cache levels, main memory, and external storage, pose significant challenges in optimizing memory usage for algorithm efficiency. Traditional models like the Disk Access Model (DAM) and advancements such as cache-oblivious algorithms have focused on minimizing memory transfers without requiring explicit knowledge of memory hierarchy parameters. However, these approaches assume fixed memory sizes and exclusive cache access, limiting their applicability in real-world, shared-memory environments where memory allocations fluctuate. To address these limitations, cache-adaptive algorithms were developed to dynamically adjust to changing memory profiles, enabling near-optimal performance even in multi-process systems. While …

A Low-Cost Microvascular Phantom For Photoacoustic Imaging Using Loofah, Jinhua Xu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Existing photoacoustic phantoms are inadequate for mimicking the complex microvascular structures found in human tissue due to their inability to replicate varying sizes and distri- butions of vasculature. This limitation underscores the need for a new material capable of replicating intricate microvascular networks. In this thesis, we introduce loofah as a novel natural phantom material with complex fiber networks ranging from 50 to 400 μm, enabling the fabrication of phantoms with controlled optical properties comparable to those of human microvasculature.

By incorporating a controllable chromophore into the loofah material, we adjusted its ab- sorption properties to match desired specifications. The …

Accelerating Microbial-Induced Calcite Precipitation Treatment Protocols Using Calcium-Based Stabilizers Targeting Pavement Construction, Grant Goertzen

Boise State University Theses and Dissertations

The research, development, and implementation of sustainable and resilient infrastructure is a critical mission for all aspects of modern Civil Engineering. The engineering and construction sector accounts for 37% of all global greenhouse gas emissions, comprising the largest proportion of any industry (Kafu-Quvane et al. 2024). Typically comprising 100% of base and subbase layers, 90% of asphalt pavement, and 80% of concrete pavement, it takes approximately 38,000 tons of aggregate to construct a single-lane mile of interstate highway (Feuling 2024). This presents significant environmental challenges due to the negative impacts of quarrying and aggregate transportation such as habitat destruction, biodiversity …

Hydraulic Boundary Conditions Determine Biofilm Growth Patterns And Permeability In Groundwater, Jason Tyler Mick

Boise State University Theses and Dissertations

Biofilms are naturally occurring consortia of bacteria that colonize porous media such as streambed sediments, soils, and aquifers. Biofilm growth leads to clogging (i.e., bioclogging), which reduces the porosity and permeability of the porous medium and directly influences how nutrients and contaminants are transported and transformed in groundwater. To improve our understanding of how bioclogging regulates nutrient and contaminant fluxes, we must better characterize how flow conditions influence the spatial and temporal progression of biofilm growth within natural porous media geometries, as well as understand how this progression alters fluid flow.

In this study, I conducted microfluidic experiments to test …

Stability Of Nuclear Thermocouples, Scott Riley

Boise State University Theses and Dissertations

The mission of the Department of Energy, Office of Nuclear Energy (DOE-NE) is to advance nuclear power as a resource capable of meeting the nation’s energy, environmental and national security needs by resolving technical, cost, safety, proliferation resistance, and security barriers through research and development. In the pursuit of safer and more economic energy production from existing nuclear reactors and future generation IV reactors, new cladding, fuel, and structural materials are being developed. However, data on the performance of these materials in accident scenarios and relevant generation IV reactor conditions is limited. Currently, thermocouples are the most commonly used temperature …

Analysis Of Learning Mechanisms In Spiking Neural Networks With R(T) Elements And Memristive Synapses, Farhana Afrin

Boise State University Theses and Dissertations

As Moore's law ends, the conventional von Neumann computer architecture with binary-coded data representation has reached its bottleneck because of having separate computing and memory modules. In this architecture, continuous power is required due to sequential processing, making it challenging to improve efficiency further. The human brain can be regarded as the most energy-efficient computer architecture. In the brain, data is represented as small voltage pulses as spikes. That is why the neural units are called spiking neural networks (SNN). In SNNs, energy is required only when there is a spike, making it more energy efficient than the von-Neumann computer …

Multi-Scale Modeling Of Dna-Templated Aggregates For Excitonic Applications, German Barcenas Moncada

Boise State University Theses and Dissertations

Quantum entanglement occupies a central role in the application of quantum mechanics towards emerging technologies of quantum information systems. Meanwhile, quantum entanglement is believed to be observed at room temperature in photosynthetic complexes in plants and algae through the energy transfer of dye molecules. The goal of this project is to engineer dye properties and their arrangement when dyes and aggregates are covalently attached to DNA so that the emergent property of exciton delocalization is as coherent, predictable, detectable and controllable. The creation of such a platform would enable the continued study of near room-temperature quantum entanglement and the more …

Learning To Accelerate Globally Optimal Solutions: Applications In The Ac Optimal Power Flow Problem, Muhammet Fatih Cengil

Graduate Theses and Dissertations

The Alternating Current Optimal Power Flow (AC-OPF) problem is a fundamental optimization challenge critical to ensuring the economical and reliable operation of power grids. While fast heuristic methods provide upper-bound solutions, assessing their quality requires lower bounds obtained from relaxations of the AC-OPF problem. This dissertation focuses on finding globally optimal solutions to the AC-OPF problem by enhancing the effectiveness and efficiency of Quadratic Convex (QC) relaxations. Leveraging machine learning techniques, we aim to achieve tighter relaxations faster and improve computational performance, enabling practical scalability for real-time applications.

In Chapter 2, we propose a machine learning-based method to accelerate the …

Synthesis, Characterization And Testing Of Graphene-Based Materials For Proton-Exchange Membrane Fuel Cell Catalysts, Hassan Shirzadi Jahromi

Dissertations

The requirement for low cost and efficient catalysts for fuel cells motivated the proposed research on Nitrogen-doped reduced graphene oxide (N-rGO) integrated with transition metal zeolitic imidazolate frameworks (ZIFs). Synthesis, characterization and performance evaluation of the newly synthesized nitrogen-doped graphene oxide-based catalysts for Proton Exchange Membrane Fuel Cells (PEMFCs) constitutes the main objective of this thesis.

The synthesis of N-rGO combined with cobalt, nickel, or iron ZIFs was carried out using wet chemical methods followed by one step pyrolysis at 900°C, resulting in catalysts with highly porous structures and uniform metal nanoparticle distribution. These were characterized for structure, microstructure, composition …

Faulty Perception Correction Of Autonomous Vehicles In Real-Life Driving Scenarios, Mark Omwansa

Dissertations

Driving is one of the most popular modes of transportation in the world. The United States Department of Transportation’s (USDOT) Federal Highway Administration (FHWA) reported 2.8 trillion vehicle-miles traveled (VMT) in 2020, and the National Highway Traffic Association (NHTSA) recorded 3.2 trillion VMT in 2019. Also recorded in the NHTSA report were 39,096 fatalities and 2.7 million injuries due to traffic accidents, costing the economy an estimated $242 billion. Most of these recorded accidents can be attributed to human error or misjudgment. It is for this reason that governments and the automotive industry are looking at autonomous vehicle (AV) technologies …

Model Reference Adaptive Control For Mobile Manipulators And Beyond, Srivatsan Srinivasan

All Dissertations

In recent years, robotics has expanded into various sectors, including manufacturing, transportation, and household services, making the integration of autonomy a critical area of research. This shift aims to ensure safety and enhance the utility of autonomous systems. Traditionally, robotic applications focused separately on mobility, like automated guided vehicles, and manipulation, such as serial-chain arms in manufacturing. Today, however, we see a merging of these capabilities in the growing field of mobile manipulator robots that combine movement with purposeful interactive functionalities.

A typical mobile manipulator is a robotic arm mounted on a wheeled base. This thesis focuses on advancing control …

Regulating Cox Conversion Through Catalyst Design And Reaction Energy Input, Ewa Chukwu

All Dissertations

Carbon dioxide (CO₂) emissions, primarily from fossil fuel use in energy and chemical production, are the leading driver of global warming. The chemical industry is the third-largest source of these emissions, so reducing its carbon footprint—and ultimately achieving “CO₂ emissions-free” chemical manufacturing—is critical to combating global warming. Thermal catalysis is the workhorse of industrial chemical manufacturing, generating CO₂ emissions from two main sources: the actual chemical transformations of fossil feedstocks and burning of fossil fuels to provide the process heat. Therefore, developing and implementing technologies to mitigate the carbon emissions associated with these emissions sources is essential for decarbonizing the …

Deformation Mechanism In Gold Nanoparticles Under Compressive Loading: Insights From Atomistic Modelling And Unsupervised Machine Learning, Tanuj Gupta

All Dissertations

Gold nanoparticles (AuNPs) offer exciting possibilities due to their inertness, malleability, and tunable structures, making them valuable for applications ranging from nanomedicine to electronics. Their optical, mechanical, and other properties can be tailored by modifying shape and structure, underscoring the importance of understanding their deformation behaviour at the nanoscale. This study used classical molecular dynamics simulations with LAMMPS to investigate the deformation mechanisms of gold nanospheres (AuNS) under uniaxial compression. Employing the embedded atom method (EAM) potential to model atomic interactions, AuNS with 20 nm in diameter were compressed along the z-direction using planar indenters moving at a constant …

The Clot Thickens: Investigation Of The Mechanism Behind Hypercoagulation In Covid-19 Patients, Toni Warnick

All Dissertations

Since its emergence in 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been associated with cardiovascular complications, which correlate with illness severity and increased mortality. It was initially thought that this hypercoagulable state was induced by endothelial dysfunction or cytokine storm because of SARS-CoV-2 infection. Although platelets have been reported to express the SARS-CoV-2 primary target receptor, angiotensin-converting enzyme 2 (ACE2), this remains controversial due to conflicting findings that suggest minimal to no ACE2 expression on platelets. This ambiguity has led to exploring alternative entry pathways, notably via the virus’s RGD (arginine-glycine-aspartic acid) sequence within its spike protein receptor-binding …