Digital Commons Network^™

Hydrologic Response Of Meadow Restoration Following The Removal Of Encroached Conifers, Oriana Ramirez

Master's Theses

Meadows are important within forest ecosystems because they provide diverse species habitats, facilitate water cycling, help with sediment capture, aid in carbon sequestration, and create natural fire breaks in forested regions. However, fire suppression, poor grazing practices, and climate change have accelerated the encroachment of conifers into historical meadow habitat. This has led to an extensive loss of meadow habitat within the Sierra Nevada and Cascade Mountain Ranges. Therefore, the purpose of this study is to quantify changes in percent soil moisture and groundwater levels following the removal of encroached lodgepole pine (Pinus contorta) in a historic meadow habitat …

Emerging Diseases, Abiotic Disorders, And Macrophomina Root Rot Management Of California Strawberry, Cooper Calvin

Master's Theses

Strawberry is an economically important crop in California, with an estimated value of $2.68 billion in 2023. In California strawberry production, mitigation of low plant health and yield often focuses on major soilborne pathogens, while the contribution of minor pathogens and abiotic disorders to production shortcomings are often overlooked. The objectives of the first project in this thesis are to determine the pathogenicity of multiple minor pathogens and quantify other biotic and abiotic factors that can reduce plant health such as viruses and soil salinity. Two pathogens of the black root rot complex, Pythium spp. and Rhizoctonia spp. as well …

Artificial Intelligence For Mitigation Against Array Perturbations In Direction Of Arrival Estimation, Mathew Shaham

Master's Theses

Direction of Arrival (DOA) estimation with digital arrays under unknown Gaussian distributed element location perturbation has detrimental effects to the performance of traditional DOA estimation techniques. This work proposes an artificial intelligence (AI) approach as a solution to this problem. A Deep Convolutional Neural Network (DCNN) is proposed and experimentation into network parameters, classification networks, and how the DCNN is applied to the DOA problem are studied. It is shown that this AI based approach is successful in estimating the DOA with perturbed arrays where traditional approaches fail.

Design And Assembly Of A Variable Buoyancy System For An Autonomous Underwater Vehicle, Evan Brown

Master's Theses

Autonomous underwater vehicles (AUVs) play a vital role in the surveillance and mapping of our oceans, often requiring extensive travel in extreme environments. These data-gathering missions benefit from extended durations, making the minimization of power consumption a key consideration in AUV design. One effective strategy to reduce energy use in AUVs is to implement a variable buoyancy system (VBS) for depth control. The purpose of this work is to design, develop, test, and model a variable buoyancy system to enhance the efficiency and capabilities of a high-speed, long-range AUV.

After evaluating various VBS designs, a piston-cylinder buoyancy system was selected …

Non-Linear Modeling Of Hysteresis In Piezoelectric Actuated Cantilever Beam Using The Bouc-Wen Model, Andrew Donald Maas

Master's Theses

Piezoelectric actuators frequently exhibit a time-dependent behavioral phenomenon known as hysteresis, resulting in a lag in the deformation of the actuator compared to linear models. The presence of hysteresis complicates control systems involving piezoelectric actuators. However, traditional modeling methods for piezoelectric actuated smart structures often treat the piezoelectric patches as linear actuators without considering hysteresis, leading to suboptimal controller performance.

This thesis aims to establish a comprehensive model by integrating the Euler-Bernoulli beam bending model with the hysteresis dynamics induced by two opposing piezoelectric patches attached to a beam. A model expansion method is employed to transform the partial differential …

Providing Cadence Feedback In Real-Time To Guide Cardiovascular Workouts, Levi O. Rash

Master's Theses

Cardiovascular workouts offer numerous health benefits, yet beginners often find it challenging to initiate them. Existing wearable technologies, although providing valuable feedback such as heart rate zones, often disrupt the workout flow and distract users due to the need for interaction with the wearable display. In response, we propose an alternative feedback mechanism: cadence, measured in steps per minute. This feedback mechanism uses multiplicative control to produce the correct cadence for the user’s target heart rate (HR). To model the HR and cadence relationship, a first-order system was used. The prototype implementation of this system was completed in Arduino, using …

Design And Analysis Of A New Buck-Boost Converter With A Low-Side Switch, Abdullah Awidah

Master's Theses

This work explores the design, simulation, construction, and analysis of a novel Non-isolated DC-DC Buck-Boost converter which has the advantage of incorporating a low-side switch compared to the traditional buck-boost which requires a high-side switch. This allows the use of a low-side driver which further simplifies the design and operation of the converter. The proposed Buck-Boost converter was constructed to provide -24 V output from an input range of 12V-18V with 15V nominal input at 10W maximum output power utilizing 500kHz switching frequency. Findings from simulations and hardware tests verify that the converter effectively provides the desired -24 V output …

The Impact Of Video Assistant Referee (Var) On The English Premier League, Jack Kenyon Brown

Master's Theses

The aim of this study is to examine how the introduction of the Video Assisted Referee (VAR) system influenced the English Premier League (EPL). Since its implementation in the English Premier League in 2019, VAR has been a constant source of debate and controversy. Many studies have been done on the immediate impact of VAR on other elite professional soccer leagues, but the scope of results is very limited and due to be updated. The data for the ensuing analysis consists of 3800 matches played in the English Premier League during the five seasons before (14/15, 15/16, 16/17, 17/18, and …

Anomaly Detection In Heterogeneous Iot Systems: Leveraging Symbolic Encoding Of Performance Metrics For Anomaly Classification, Maanav Patel

Master's Theses

Anomaly detection in Internet of Things (IoT) systems has become an increasingly popular field of research as the number of IoT devices proliferate year over year. Recent research often relies on machine learning algorithms to classify sensor readings directly. However, this approach leads to solutions being non-portable and unable to be applied to varying IoT platform infrastructure, as they are trained with sensor data specific to one configuration. Moreover, sensors generate varying amounts of non-standard data which complicates model training and limits generalization. This research focuses on addressing these problems in three ways a) the creation of an IoT Testbed …

Instant Hdr-Nerf: Fast Learning Of High Dynamic Range View Synthesis With Unknown Exposure Settings, Nam Nguyen

Master's Theses

We propose Instant High Dynamic Range Neural Radiance Fields (Instant HDR-NeRF), a method of learning high dynamic range (HDR) view synthesis from a set of low dynamic range (LDR) views with unknown and varying exposure and white balance in as little as minutes. Our method can render novel HDR views without ground-truth supervision, and novel LDR views in different exposure settings, including those that match the ground-truth LDR views. The key to our method is to model the physical process of the camera with two implicit MLPs: a radiance field and a monotonically increasing tone-mapper. Built upon Instant Neural Graphics …

Recursive Marix Game Analysis: Optimal, Simplified, And Human Strategies In Brave Rats, William A. Medwid

Master's Theses

Brave Rats is a short game with simple rules, yet establishing a comprehensive strategy is very challenging without extensive computation. After explaining the rules, this paper begins by calculating the optimal strategy by recursively solving each turn’s Minimax strategy. It then provides summary statistics about the complex, branching Minimax solution. Next, we examine six other strategy models and evaluate their performance against each other. These models’ flaws highlight the key elements that contribute to the effectiveness of the Minimax strategy and offer insight into simpler strategies that human players could mimic. Finally, we analyze 123 games of human data collected …

A Federation Of Sentries: Secure And Efficient Trusted Hardware Element Communication, Blake A. Ward

Master's Theses

Previous work introduced TrustGuard, a design for a containment architecture that allows only the result of the correct execution of approved software to be outputted. A containment architecture prevents results from malicious hardware or software from being communicated externally. At the core of TrustGuard is a trusted, pluggable device that sits on the path between an untrusted processor and the outside world. This device, called the Sentry, is responsible for validating the correctness of all communication before it leaves the system. This thesis seeks to leverage the correctness guarantees that the Sentry provides to enable efficient secure communication between two …

Product Design For Repairability: Identifying Failure Modes With Topic Modeling And Designing For Electronic Waste Reuse, Claire J. Franz

Master's Theses

Design for repairability is imperative to making products that last long enough to justify the resources they consume and the pollution they generate. While design for repairability has been gaining steady momentum, especially with recent advances in Right to Repair legislation, there is still work to be done. There are gaps in both the tools available for repair-conscious designers and the products coming onto store shelves. This thesis work aims to help set sails in the right direction on both fronts.

This research explores the use of topic modeling (a natural language processing technique) to extract repairability design insights from …

Deciphering The Contribution Of Microglia To Neurodegeneration In Friedreich's Ataxia, Sydney N. Gillette

Master's Theses

Friedreich's ataxia (FRDA) is the most prevalent inherited ataxia, affecting one in every 50,000 individuals in the United States. This hereditary condition is caused by an abnormal GAA trinucleotide repeat expansion within the first intron of the frataxin gene resulting in decreased levels of the frataxin protein (FXN). Insufficient cellular frataxin levels results in iron accumulation, increased reactive oxygen species production and mitochondrial dysfunction. Tissues most heavily impacted are those most dependent on oxidative phosphorylation as an energy source and include the nervous system and muscle tissue. This is evident in the clinical phenotype which includes muscle weakness, ataxia, neurodegeneration …

Shuffled Faster Than Nyquist Signaling For Spectrally Efficient And Secure Wireless Communication, John Gharib

Master's Theses

This thesis investigates the implementation and performance of Shuffled Faster than Nyquist (SFTN) signaling, a communication method that enhances spectral efficiency and provides physical layer security (PLS) in wireless communications. In Faster than Nyquist signaling, the Nyquist inter-symbol interference (ISI) criterion is exceeded, thereby increasing spectral efficiency. By varying the transmission rate of symbols above the Nyquist rate, SFTN signaling is able to obfuscate the timing of transmitted symbols with ISI. The work in this thesis evaluates the performance of SFTN in Additive White Gaussian Noise (AWGN) channels and the MATLAB 802.11ax fading channels. Results show that while SFTN signaling …

Going Rogue: Existence, Spectral Stability, And Bifurcations Of Rogue Waves In Integrable And Non-Integrable Lattice Models, Madison L. Lytle

Master's Theses

The study of large nonlinear waves that seem to “appear out of nowhere and disappear without a trace”, known as rogue or freak waves, began largely in response to observations of catastrophic ocean waves. However, the study of rogue waves has since been expanded to a wider collection of physical scenarios, including discrete systems, such as those that appear in optics, as opposed to the continuous system of water waves. Waves in these discrete settings can be modeled as solutions to lattice wave equations.

The nonlinear Schrodinger equation (NLSE) is one of the most ubiquitous continuous wave models for physical …

Morp: Monocular Orientation Regression Pipeline, Jacob Gunderson

Master's Theses

Orientation estimation of objects plays a pivotal role in robotics, self-driving cars, and augmented reality. Beyond mere position, accurately determining the orientation of objects is essential for constructing precise models of the physical world. While 2D object detection has made significant strides, the field of orientation estimation still faces several challenges. Our research addresses these hurdles by proposing an efficient pipeline which facilitates rapid creation of labeled training data and enables direct regression of object orientation from a single image. We start by creating a digital twin of a physical object using an iPhone, followed by generating synthetic images using …

Liquefaction Case Histories From Oceano, California During The 2003 San Simeon Earthquake, Hayden Brake

Master's Theses

On December 22nd, 2003, the Mw=6.5 San Simeon earthquake occurred 12 kilometers east of San Simeon, California, causing damage to buildings, roads, and other infrastructure throughout the central coast. The community of Oceano, 80 kilometers southeast of the epicenter, experienced damage to foundations, roads, and utilities due to liquefaction and lateral spreading. The unique geologic environment in Oceano caused a local amplification of ground motions, liquefaction, and lateral spreading. This study entailed developing ten liquefaction case histories from Oceano during the 2003 San Simeon earthquake. Four of the ten case histories are liquefaction cases and six are non-liquefaction cases, with …

Point Cloud Initialization Techniques For Gaussian Splatting, Daniel L. Levin

Master's Theses

Over the past year, 3D Gaussian Splatting has become a widespread research topic of interest. It has proven itself against other popular Novel View Synthesis algorithms
like Neural Radiance Fields (NeRF) by replacing the approximation function with
a modified point cloud. This modified point cloud allows for the rendering of novel
views in real time. While this gives Gaussian Splatting an advantage over algorithms
like NeRF, it shares the same issue of requiring prior camera intrinsics such as a Point
Cloud and Camera Poses. Most Gaussian Splatting pipelines use Colmap Sparse, a
Structure-from-Motion (SfM) technology that efficiently registers the necessary …

Hydrological Analysis Of Post-Fire Responses Within The Little Creek Watershed Of Swanton Pacific Ranch, Alexander Jeffrey Wiens

Master's Theses

Climate change and a growing wildland-urban interface are associated with an increase in the number and severity of wildfires. While severe wildfires do cause a costly and dangerous impact on the environment and the public, their after-effects comprise important subjects of study. In post-fire hydrologic studies, the reduced infiltration and plant cover that results from wildfires can commonly be associated with increased peakflows and erosion. Phenomena such as the flooding and debris flows in Montecito after the Thomas Fire in late 2017 can have important implications on infrastructure and human lives. Quantifying post-fire peak flows and flooding volumes has therefore …

A Hardware-In-The-Loop Star Tracker Test Bed, Ashley Haraguchi

Master's Theses

As the use of small satellites for advanced space missions continues to grow, the importance of low mass and cost three-axis attitude stabilization systems increases as well, with these systems requiring high accuracy attitude knowledge. Star trackers provide the most accurate attitude knowledge of any type of attitude sensor, but the high cost, size, and weight of commercial star trackers can be prohibitive to small satellite missions. Many simple star trackers have been developed using commercial off-the-shelf camera sensors and processing hardware, but the challenge remains in testing and characterizing these devices. A common solution is night sky tests, in …

Unraveling The History Of Deforestation In The Amazon Rainforest With Statistical Modeling, Ryan Destefano

Master's Theses

The Amazon rainforest, a vital ecosystem of immense biodiversity and global climate significance, faces the ongoing threat of deforestation driven by agricultural expansion. This thesis employs remote sensing techniques, focusing on the Enhanced Vegetation Index (EVI) derived from Landsat satellite imagery, to track land cover dynamics within the Amazon. The study examines historical land cover changes in current plantations in Peru and Brazil, regions where the exact timing of deforestation is uncertain. By analyzing EVI measurements dating back to 1984, inflection points indicative of deforestation events preceding plantation establishment are identified. Statistical modeling techniques, including spline fitting to analyze time …

Scalable Hybrid Neuromorphic Accelerator & Hybrid Neural Networks, Joshua Nardone

Master's Theses

With machine learning workloads currently at very large scales, models are distributed across large compute systems. On distributed systems, the performance of these models are limited by the bandwidth limitations of chip-to-chip communication. To relieve this bottleneck, spiking neural networks (SNNs) can be utilized to reduce inter-chip communication traffic utilizing inherit network sparsity. However, in comparison to traditional artificial neural networks (ANNs), SNNs can have significant degradation in performance with increased network scale and complexity.

This research proposes a hybrid neural network accelerator that uses the best of both spiking and non-spiking layers by allocating a majority of resources to …

Development Of A Deep Learning Algorithm Using Electromyography (Emg) And Acceleration To Monitor Upper Extremity Behavior With Application To Individuals Post-Stroke, Nathan Dodd

Master's Theses

Stroke is a chronic illness which often impairs survivors for extended periods of time,

leaving the individual limited in motor function. The ability to perform daily activities

(ADL) is closely linked to motor recovery following a stroke. The objective of

this work is to employ surface electromyography (sEMG) gathered through a novel,

wearable armband sensor to monitor and quantify ADL performance. The first contribution

of this work seeks to develop a relationship between sEMG and and grip

aperture, a metric tied to the success of post-stroke individuals’ functional independence.

The second contribution of this work aims to develop a deep …

A Hybrid Computer To Solve The Selective Harmonic Minimization Problem In Single-Phase Inverters, Dillon Nguyen

Master's Theses

Power electronics possess forms of non-ideality due to the nonlinearity present in physical switches. These switches are driven with pulse width modulation (PWM), which properly down-converts or up-converts based on converter topology. When these switches turn on or off, they introduce the main proponent of efficiency loss: harmonics. This becomes a nonlinear optimization problem with many converters. The solution proposed in this thesis is an analog/digital hybrid computer that utilizes the speed of analog computation and the accuracy of digital computing together. A Plexim RT Box provides the programmability needed for algorithms, while the analog circuit provides low-power, high-speed computation …

A Study On Privacy Over Security And Privacy Enhancing Networks, Everett Lee Conway

Master's Theses

With rapid developments in communication technologies and awareness of security and privacy risks online, Security and Privacy Enhancing Networks (SPENs) have become increasingly popular. Especially during the COVID-19 pandemic, workplaces encouraged employees to take additional security measures, such as VPNs. In this work, we conduct a comprehensive study on website fingerprinting attacks. A comprehensive system model and threat model based on two types of SPENs (Virtual Private Networks and Tor Networks) are presented. Moreover, we demonstrate a website fingerprinting attack by ethically collecting website fetch data and analyzing the collected data using five different machine learning classification models including k …

Optimal False Data Injection (Fdi) In Simulated Cooperative Adaptive Cruise Control (Cacc) Systems, Lovro Dukic

Master's Theses

In the rapidly advancing field of autonomous vehicles, ensuring the security and reliability of self-driving systems is crucial. Autonomous vehicle systems, such as cooperative adaptive cruise control (CACC), must undergo significant research and testing before their integration into commercial intelligent transportation systems. CACC considers multiple vehicles in close proximity as a single entity, or platoon, with each vehicle equipped with a controller that uses sensor-based measurements and vehicle-to-vehicle (V2V) communication to control inter-vehicle spacing. While this system offers numerous potential benefits for traffic safety and efficiency, it is also susceptible to False Data Injection (FDI) attacks, which can cause the …

Kgscore-Open: Leveraging Knowledge Graph Semantics For Open-Qa Evaluation, Nicholas Hausman

Master's Theses

Evaluating active Question Answering (QA) systems, as users ask questions outside of the original testing data, has proven to be difficult, due to the difficulty of gauging answer quality without ground truth responses. We propose KGScore-Open, a configurable system capable of scoring questions and answers in Open Domain Question Answering (Open-QA) without ground truth answers present by leveraging DBPedia, a Knowledge Graph (KG) derived from Wikipedia, to score question-answer pairs. The system maps entities from questions and answers to DBPedia nodes, constructs a Knowledge Graph based on these entities, and calculates a relatedness score. Our system is validated on multiple …

Contrastive Filtering And Dual-Objective Supervised Learning For Novel Class Discovery In Document-Level Relation Extraction, Nicholas Hansen

Master's Theses

Relation extraction (RE) is a task within natural language processing focused on the classification of relationships between entities in a given text. Primary applications of RE can be seen in various contexts such as knowledge graph construction and question answering systems. Traditional approaches to RE tend towards the prediction of relationships between exactly two entity mentions in small text snippets. However, with the introduction of datasets such as DocRED, research in this niche has progressed into examining RE at the document-level. Document-level relation extraction (DocRE) disrupts conventional approaches as it inherently introduces the possibility of multiple mentions of each unique …

2-Tertbutylfuran At 550 And 700 K: A Multiplexed Photoionization Mass Spectrometric Investigation And Determination Of Cross Sections Of A Carbon- Bromine Bond And Various Brominated Organic Compounds, Ameyali Tapia

Master's Theses

This thesis presents the combustion study of 2-tertbutylfuran (2-TBF) in reaction with atomic oxygen (O(3P)). Data was collected at the Advanced Light Source (ALS) of the Lawrence Berkeley National Lab, using tunable vacuum ultraviolet radiation coupled with a multiplexed mass spectrometer to acquire the data. These data were taken at low pressures of 7 and 8 torr at temperatures of 550 K and 700 K, respectively. Primary products of the system were identified and characterized, while reaction pathways of the products are currently being studied to ensure thermodynamic feasibility. Branching fractions of the system were also calculated, to view the …