Digital Commons Network^™

Neuromorphic Computing Applications In Robotics, Noah Zins

Dissertations, Master's Theses and Master's Reports

Deep learning achieves remarkable success through training using massively labeled datasets. However, the high demands on the datasets impede the feasibility of deep learning in edge computing scenarios and suffer from the data scarcity issue. Rather than relying on labeled data, animals learn by interacting with their surroundings and memorizing the relationships between events and objects. This learning paradigm is referred to as associative learning. The successful implementation of associative learning imitates self-learning schemes analogous to animals which resolve the challenges of deep learning. Current state-of-the-art implementations of associative memory are limited to simulations with small-scale and offline paradigms. Thus, …

Microscopic And Laboratory Scale Characterization Methods To Evaluate Biomass Deconstruction, Meenaa Chandrasekar

Dissertations, Master's Theses and Master's Reports

Renewable fuels from lignocellulosic biomass are an appealing option because they can seamlessly integrate into the existing fuel distribution infrastructure. Lignocellulosic biomass constitutes nonedible plant material obtained from plant cell walls. The natural recalcitrance of lignocellulosic biomass poses a challenge in accessing the cell wall carbohydrates during biochemical conversion. Despite various approaches, enzymatic hydrolysis of lignocellulosic biomass remains economically impractical due to incomplete knowledge about biomass recalcitrance and the influence of environmental factors on biomass quality.

The first goal of this dissertation was to construct a microfluidic imaging reactor to better understand the tissue-specific deconstruction of plant materials. Confocal laser …

Machine Learning Methods For Prediction Of Human Infectious Virus And Imputation Of Hla Alleles, Xiaoqing Gao

Dissertations, Master's Theses and Master's Reports

This dissertation contains three Chapters. The following is a concise description of each Chapters.

In Chapter 1, we introduced the Random Forest, a machine learning method, to foresee whether a virus is capable of infecting humans. The Covid pandemic informs us the importance of predicting the ability of a zoonotic virus that can infect humans from its genomic sequence. We used the -mer with and as features of a virus to predict if it can affect humans. We further employed the Boruta algorithm to select the important features, then fed those important features into the Random Forest method to train …

Remote Sensing Approach For Terramechanics Applications Utilizing Machine And Deep Learning, Jordan J. Ewing

Dissertations, Master's Theses and Master's Reports

Terrain traversability is critical for developing Go/No Go maps, significantly impacting a mission's success. To predict the mobility of a vehicle over a terrain, one must understand the soil characteristics. In situ measurements performed by soldiers in the field are the current method of collecting this information, which is time-consuming, are only point measurements, and can put soldiers in harm's way. Therefore, this study investigates using remote sensing as an alternative approach to characterize terrain properties.

This approach will explore the relationships between electromagnetic radiation and soil types with varying properties. Optical, thermal, and hyperspectral sensors will be used to …

Optimizing The Extrudability Of 6082 Aluminum By Varying The Magnesium And Silicon Concentration, Eli A. Harma

Dissertations, Master's Theses and Master's Reports

Alloy 6082 aluminum is used for high-volume manufacturing in the automotive industry due to its high strength, impact performance, and corrosion resistance. However, given these improved properties, the alloy has decreased formability compared to other 6xxx series alloys, especially in the extrusion process. Controlling the dynamic recovery and recrystallization properties by changing the additions of Mg and Si can improve the hot deformation properties. Five alloys of varying Mg and Si concentrations between 0.6 to 1.2wt% Mg and 0.7 to 1.3wt% Si were made with constant concentrations of Cr, Fe, and Mn and the same homogenization heat treatment. The proposed …

The Flight Attendants Of Academia: Liminality, Emotional Labor, And Feminization In Graduate Student Writing Program Administrators, Laura Vidal Chiesa

Dissertations, Master's Theses and Master's Reports

Writing Program Administration (WPA) is an interdisciplinary field that addresses the management and development of writing programs in educational institutions. WPA entails writing instruction pedagogy, curriculum design, assessment, and faculty development pertaining to the teaching of writing. Graduate students in Humanities and English-based programs typically fill this position which can offer a career trajectory. However, the position is often experienced as demanding, unrewarding and does not deliver on the career-enhancing experience it seems to promise. Historically, this is unsurprising given that the position has been a subordinated role occupied by women in composition. And yet, the troubles besetting contemporary graduate …

Study Of Nanocomposite Materials Using Molecular Dynamics, Prashik Sunil Gaikwad

Dissertations, Master's Theses and Master's Reports

There is an increase in demand for new lightweight structural materials in the aerospace industry for more efficient and affordable human space travel. Polymer matrix composites (PMCs) with reinforcement material as carbon nanotubes (CNTs) have shown exceptional increase in the mechanical properties. Flattened carbon nanotubes (flCNTs) are a primary component of many carbon nanotube (CNT) yarn and sheet materials, which are promising reinforcements for the next generation of ultra-strong composites for aerospace applications. These flCNT/polymer materials are subjected to extreme pressure and temperature during curing process. Therefore there is a need to investigate the evolution of properties during the curing …

Machine Learning And Deep Learning Approaches For Gene Regulatory Network Inference In Plant Species, Sai Teja Mummadi

Dissertations, Master's Theses and Master's Reports

The construction of gene regulatory networks (GRNs) is vital for understanding the regulation of metabolic pathways, biological processes, and complex traits during plant growth and responses to environmental cues and stresses. The increasing availability of public databases has facilitated the development of numerous methods for inferring gene regulatory relationships between transcription factors and their targets. However, there is limited research on supervised learning techniques that utilize available regulatory relationships of plant species in public databases.

This study investigates the potential of machine learning (ML), deep learning (DL), and hybrid approaches for constructing GRNs in plant species, specifically Arabidopsis thaliana, …

Explicit Rule Learning: A Cognitive Tutorial Method To Train Users Of Artificial Intelligence/Machine Learning Systems, Anne Linja

Dissertations, Master's Theses and Master's Reports

Today’s intelligent software systems, such as Artificial Intelligence/Machine Learning systems, are sophisticated, complicated, sometimes complex systems. In order to effectively interact with these systems, novice users need to have a certain level of understanding. An awareness of a system’s underlying principles, rationale, logic, and goals can enhance the synergistic human-machine interaction. It also benefits the user to know when they can trust the systems’ output, and to discern boundary conditions that might change the output. The purpose of this research is to empirically test the viability of a Cognitive Tutorial approach, called Explicit Rule Learning. Several approaches have been used …

Collagen V Promotes Fibroblast Contractility, And Adhesion Formation, And Stability, Shaina P. Royer-Weeden

Dissertations, Master's Theses and Master's Reports

Ehlers-Danlos syndrome, classical type, (cEDS) is a hereditary connective tissue disorder causing excessive elasticity and fragility of the connective tissue and problems with wound healing. Most cases of cEDS are caused by haploinsufficiency for collagen V. Collagen V regulates collagen fibril diameter. In cEDS fibroblast migration is impaired and integrin expression is altered.

The effects of collagen V on collagen gel ultrastructure and how it alters its mechanical properties were measured using scanning electron microscopy (SEM) and rheology respectively. Fibroblast contractility and adhesion dynamics were investigated to better understand the role of fibroblast disfunction in wound healing in cEDS. To …

White-Tailed Deer In A Winter-Wonderland: Long-Term Deer Yard Use And Methodological Considerations For Ungulate Fecal Dna Metabarcoding, Melanie A. Ottino

Dissertations, Master's Theses and Master's Reports

Northern white-tailed deer (Odocoileus virginianus) mitigate the increased energetic costs of severe winter conditions through obligate migration to densely congregated areas with abundant conifer cover, a behavior referred to as yarding. “Deer yards” in the Western Upper Peninsula (UP) of Michigan are principally located within the reduced snowpack and increased ambient temperature microclimates of densely canopied eastern hemlock (Tsuga canadensis) stands, but specific drivers of long-term site fidelity and utilization are largely uncharacterized. As an important game species of high economic and cultural value and a keystone herbivore with critical impact of plant community composition and structure, identifying winter yarding …

Wavelet-Based Real-Time Harmonic Phasor Estimation In Complex Signals With Inter-Harmonics And Transient Events, M A Aziz Jahan

Dissertations, Master's Theses and Master's Reports

Addressing the evolving challenges in modern power systems induced by the integration of Inverter Based Resources (IBRs), such as solar inverters, wind turbine converters, electric vehicle charging stations etc is the main goal of this research. The study focuses on the critical task of harmonic phasor estimation in the presence of sub-harmonics, inter-harmonics, time-varying harmonics, DC offset, transient events, and other power quality disturbances. A novel wavelet-based harmonic phasor estimation method is proposed to enhance accuracy, particularly during transient events, ensuring a nuanced understanding of phase angle variations. The research emphasizes the versatility of wavelet analysis for both steady-state and …

Comprehensive Analysis Of Seismic Signals From Pacaya Volcano Using Deep Learning Event Detection, Jessica L. Devlieg

Dissertations, Master's Theses and Master's Reports

Pacaya volcano located 30 km SW of Guatemala City, Guatemala, has been erupting intermittently since 1961. Monitoring of seismicity is crucial to understanding current activity levels within Pacaya. Traditional methods of picking these small earthquakes in this noisy environment are imprecise. Pacaya produces many small events that can easily blend in with the background noise. A possible solution for this problem is a machine learning program to pick first arrivals for these earthquakes. We tested a deep learning algorithm (Mousavi et al., 2020) for fast and reliable seismic signal detection within a volcanic system. Data from multiple deployments were used, …

Design And Implementation Of A Graphql Mesh Gateway: Federating Api Endpoints Based On A Defined Data Model, Marcus D. Scese

Dissertations, Master's Theses and Master's Reports

This paper introduces the GraphQL Mesh federated API (Application Programming Interface) gateway project, a comprehensive initiative implemented using GraphQL Mesh to solve data related issues within the USW-DSS (Undersea Warfare - Decision Support System). The project contributes to the evolving discourse on the pivotal role of Data Fabrics and Data Meshes in dismantling the barriers imposed by digital data silos. The project is a collaboration between researchers at Michigan Technological University, and engineers at ARiA (Applied Research in Acoustics LLC). The aim of the project is to resolve difficulties in understanding a large collection of API endpoints. By navigating the …

Sequencing Antisense Transcriptome Of Populus Through Antisense/Sense Transcript Pair Enrichment, Emma V. Burke

Dissertations, Master's Theses and Master's Reports

Past transcriptome research on plants focused primarily on protein coding genes, and only recently researchers began looking into the non-protein coding regions that may play significant roles in gene regulation. Antisense RNA transcripts that are found naturally in the cell share complementary sequence with sense transcripts and have been shown to regulate expression of their sense counterparts. Since antisense RNA has been largely under-studied and difficult to sequence because of their low relative abundance, new methods are needed to target antisense RNA for efficient genome-wide profiling. To address this gap in methods to efficiently and cost effectively enrich antisense RNA …

Has Bollywood Lost The Plot? Analyzing The Influence Of Item Songs On Rape Culture In India, Donna Susan Mathew

Dissertations, Master's Theses and Master's Reports

An item song is a provocative musical song and dance sequence that appears in Bollywood films that have little to no connection to the film’s plot, performed by an item girl for the male gaze. Contemporary Bollywood item songs have become highly contested social and political texts in the discourse surrounding rape culture in India. This research explores the socio-cultural context of rape culture in India and examines what makes item songs a popular and acceptable form of entertainment in a patriarchal, conservative society where sexualized violence against women is a major cause for concern.

The study also addresses the …

Treating Contaminated Water: Predicting Reactivities Of Aqueous Organic Contaminants With Hydrated Electrons, And Development Of A Biogarden To Manage Greywater Discharge In Monteverde, Costa Rica, Rose C. Daily

Dissertations, Master's Theses and Master's Reports

Advanced reduction processes (ARPs) that generate highly reactive solvated electrons (e^-_aq) are a promising method for the destruction of conventional and emerging aqueous organic contaminants. While there is a large database of contaminant reactivity with e^-_aq in the literature, there is little information on the detailed elementary mechanisms for reduction of multifunctional group compounds and the impact of those functional groups on reactivity. As it is difficult to determine specific mechanisms through experiments and time consuming to measure reactivity, the development of computational approaches to elucidate mechanisms and predict reactivity is becoming increasingly important. In …

Development And Verification Of Automated Fixtures For Functional Testing Of Space Grade Printed Circuit Board Assemblies, Nicholas A. Wylie

Dissertations, Master's Theses and Master's Reports

Orbion Space Technology is a developer and manufacturer of electric propulsion systems for military and commercial spacecraft. Orbion’s products include a Power Processing Unit (PPU) which is utilized for power and control of the satellite propulsion system. These PPUs are complex electro-mechanical assemblies that include multiple Printed Circuit Board Assemblies (PCBA) and are built to IPC standards. To ensure smooth fabrication and to reduce the risk of complications from in-process rework of PCBAs, comprehensive electrical functional testing at the board-level is required before higher- level assembly. Electrical functional testing provides verification of quality, workmanship, and manufacturing defects of the PCBAs. …

Fabrication And Optical Properties Of Two-Dimensional Transition Metal Dichalcogenides, Manpreet Boora

Dissertations, Master's Theses and Master's Reports

Two-dimensional layered materials such as graphene and transition metal dichalcogenides have gained a lot of attention because of their distinctive chemical, optical, and electronic properties. Transition metal dichalcogenides can have a tunable bandgap in the range of 1 − 3 eV (visible), which enables their applications in ultrathin field effect transistors, photovoltaics, sensors, and optoelectronic devices. They exhibit an indirect-to-direct band gap transition when thinned down from bulk to a single layer. They show strong photoluminescence, electron–photon interaction, valley pseudospin, nonlinear optical response, and lack of dangling bonds. Many exotic phenomena appear when materials are thinned to nanoscale size because …

Nanomaterials For Cardiovascular Engineering, Roya Bagheri

Dissertations, Master's Theses and Master's Reports

Cardiovascular diseases and disorders (i.e., those related to heart and blood vessels) are the main reasons for mortality worldwide. Nanomaterials, with their unique morphologies and properties, have a great potential for advancing cardiovascular engineering to treat diseases and disorders. In this dissertation, several cardiovascular applications of conductive nanomaterials were investigated. First, a conductive nanomaterial was explored to fabricate biohybrid nanomaterial-cardiomyocyte (CM – heart muscle cell) systems. Using Carbon Nanotube (CNT) forest as a 3D porous and conductive scaffold was investigated. The influence of the CNT forest on the viability, attachment, and spreading of CMs and their genetic information was …

Memory Optimizations For High-Throughput Computer Systems, Zhiyuan Lu

Dissertations, Master's Theses and Master's Reports

The emergence of new non-volatile memory (NVM) technology and deep neural network (DNN) inferences bring challenges related to off-chip memory access. Ensuring crash consistency leads to additional memory operations and exposes memory update operations on the critical execution path. DNN inference execution on some accelerators suffers from intensive off-chip memory access. The focus of this dissertation is to tackle the issues related to off-chip memory in these high performance computing systems.

The logging operations, required by the crash consistency, impose a significant performance overhead due to the extra memory access. To mitigate the persistence time of log requests, we introduce …

An Investigation Of Microseismicity During The 2018 Kilauea Caldera Collapse, Isabella S. Metts

Dissertations, Master's Theses and Master's Reports

The 2018 Kīlauea volcano eruption and incremental caldera collapse was accompanied by more than 60,000 seismic events cataloged by the Hawaiian Volcano Observatory as well as 62 caldera collapse events that occurred roughly once per day. The majority of seismicity occurred on the eastern side of the caldera between daily collapses. However, the majority of caldera subsidence occurred to the west. To understand the collapse mechanics behind this variance in subsidence and seismicity across the caldera region, repetitive waveforms and source properties can be studied.

Repeating seismic events suggest a common source that is not moving or destroyed. At Kīlauea, …

Development And Testing Of A Low Mass Vibratory Lunar Compactor, Charles Carey

Dissertations, Master's Theses and Master's Reports

NASA and other agencies are working to return to the moon, with the Artemis Program [1]. As a part of this new effort, an emphasis is being placed on having a sustained presence, building lunar bases and other permanent structures. The development of such infrastructure will require the development of civil engineering structures, and site preparation becomes a necessity. The Planetary Surface Technology Development Laboratory is developing a low mass lunar compactor as part of an autonomous site preparation vehicle in partnership with Colorado School of Mines, funded by NASA’s 2021 Lunar Surface Technology Research grant. The low mass lunar …

A Search For Compact Object Dark Matter In The Universe Utilizing Gravitational Millilensing Of Gamma-Ray Bursts, Oindabi Mukherjee

Dissertations, Master's Theses and Master's Reports

In this dissertation, I explore the existence of Compact Object (CO) Dark Matter (DM), examining its predicted millilensing impact on the light curves of Gamma-Ray Bursts (GRBs). The hypothesis under investigation is that if CO DM exists, it will act as a gravitational lens, influencing light emanating from GRBs and creating a measurable gravitational echo. The detection of this echo is conditional on several factors, including the spatial alignment of the observer, lens, and source and the mass of the CO DM.

Recent studies have reported the potential detection of millilensing in several GRBs, including GRB 950830, GRB 081122A, GRB …

Multilevel Computational Investigation Into The Catalytic Mechanisms Of Matrix Metalloproteinase-1 And Fat Mass And Obesity-Associated Enzyme, Ann Varghese

Dissertations, Master's Theses and Master's Reports

Enzymes are biological macromolecules, typically proteins, that efficiently accelerate the rate of chemical reactions. Their remarkable catalytic power plays a vital role in essential processes across all kingdoms of life. Nowadays, computational chemistry methods provide valuable insights into enzymatic functions aiding our understanding of biological processes and providing advancements in fields such as biomedical sciences, biomimetic catalysis, drug design and biotechnology. This dissertation employs multilevel computational chemistry methods to investigate the structure-function relationships and the catalytic mechanisms of two metalloenzymes -Zn(II)-dependent matrix metalloproteinase-1 (MMP-1) and non-heme Fe(II)/2-oxoglutarate (2OG) dependent fat-mass and obesity-associated (FTO) enzyme. Chapter 2 explores the role of …

Supporting Team Innovation With Design Thinking Cognitive Strategies, Lisa M. Casper

Dissertations, Master's Theses and Master's Reports

Innovation is a 21st-century skill needed to design new systems, solve challenging problems, and develop novel solutions. Design Thinking (DT) is a tool used to support team innovation. In this experiment, 145 students (47 teams) used one of two DT methods during a semester-long project to come up with an innovative solution to one of the UNESCO 17 sustainable development goals. The key experimental manipulation was during the DT Ideate phase where teams brainstormed potential solutions. Teams either used a baseline DT Ideate strategy or an expanded one with additional prompts during a 10-minute period. Results indicated that teams using …

Using Artificial Intelligence To Understand Environmental Policy Innovation Labs' Contribution To The Sustainable Development Goals, Sidney M. Mechling

Dissertations, Master's Theses and Master's Reports

Policy innovation labs (PILs) serve as centers of experimental innovation pursued by governmental, private, and non-profit entities that employ design-thinking methodology, scientific experimentation, and user-centric approaches (Wellstead et al., 2021). The multi-disciplinary expertise of policy innovation labs in social and economic topics equips them to offer policy recommendations addressing public issues, including environmental concerns. When designing innovative solutions, it is customary for policy innovation labs to provide substantial information and reports detailing their progress and methods. However, the wealth of information available becomes a challenge to fully divest in comprehending a lab's contribution to solving global issues. This report addresses …

Investigating Collaborative Explainable Ai (Cxai)/Social Forum As An Explainable Ai (Xai) Method In Autonomous Driving (Ad), Tauseef Ibne Mamun

Dissertations, Master's Theses and Master's Reports

Explainable AI (XAI) systems primarily focus on algorithms, integrating additional information into AI decisions and classifications to enhance user or developer comprehension of the system's behavior. These systems often incorporate untested concepts of explainability, lacking grounding in the cognitive and educational psychology literature (S. T. Mueller et al., 2021). Consequently, their effectiveness may be limited, as they may address problems that real users don't encounter or provide information that users do not seek.

In contrast, an alternative approach called Collaborative XAI (CXAI), as proposed by S. Mueller et al (2021), emphasizes generating explanations without relying solely on algorithms. CXAI centers …

Benchmarking Model Predictive Control And Reinforcement Learning For Legged Robot Locomotion, Shivayogi Akki

Dissertations, Master's Theses and Master's Reports

This research delves into the realm of quadrupedal robotics, focusing on the comparative analysis of Model Predictive Control (MPC) and Reinforcement Learning (RL) as predominant control strategies. Through the comprehensive dataset compiled and the insights derived from this analysis, this research aims to serve as a valuable resource for the legged robotics community, guiding researchers and practitioners in the selection and implementation of control strategies. The ultimate goal is to contribute to the advancement of legged robot capabilities and facilitate their successful deployment in real-world applications.

In this study, we employ the Unitree Go1 quadrupedal robot as a testbed, subjecting …

Novel Bayesian Neural Networks And Uncertainty Quantification Of Computational Mechanics Models, Ponkrshnan Thiagarajan

Dissertations, Master's Theses and Master's Reports

Computational and data-driven models suffer from a wide range of uncertainties that impact the reliability of such models. Given the exponential proliferation of machine learning models in real-world systems, establishing a degree of confidence in their predictions becomes paramount. Reliability in predictions takes on utmost significance in domains such as autonomous driving, medical image analysis, etc., where human lives are involved, and inaccuracies in predictions could lead to disastrous outcomes. For these reasons, comprehending and quantifying uncertainties in computational and data-driven models is of utmost importance. A number of techniques have been developed to quantify uncertainties in machine learning models. …