Physical Sciences and Mathematics Commons^™

Thermal, Electrical, And Spin Transport: Encompassing Low-Damping Ferromagnets And Antiferromagnetic/Ferromagnetic Heterostructures, Matthew Ryan Natale

Electronic Theses and Dissertations

Continuing technological advancements bring forth escalating challenges in global energy consumption and subsequent power dissipation, posing significant economic and environmental concerns. In response to these difficulties, the fields of thermoelectrics, spintronics, and spincaloritronics emerge as contemporary solutions, each presenting unique advantages. Thermoelectric devices, based on the Seebeck effect, other a passive, carbon-free energy generating solution from waste heat. Although current thermoelectric technology encounters hurdles in achieving optimal efficiencies without intricate designs or complex materials engineering, recently research into low-damping metallic ferromagnetic thin films have provided a new method to enhance spin wave lifetimes, thus contributing to thermoelectric voltage improvements. As …

Attribution Robustness Of Neural Networks, Sunanda Gamage

Electronic Thesis and Dissertation Repository

While deep neural networks have demonstrated excellent learning capabilities, explainability of model predictions remains a challenge due to their black box nature. Attributions or feature significance methods are tools for explaining model predictions, facilitating model debugging, human-machine collaborative decision making, and establishing trust and compliance in critical applications. Recent work has shown that attributions of neural networks can be distorted by imperceptible adversarial input perturbations, which makes attributions unreliable as an explainability method. This thesis addresses the research problem of attribution robustness of neural networks and introduces novel techniques that enable robust training at scale.

Firstly, a novel generic framework …

Mri Image Regression Cnn For Bone Marrow Lesion Volume Prediction, Kevin Yanagisawa

Theses and Dissertations

Bone marrow lesions (BMLs), occurs from fluid build up in the soft tissues inside your bone. This can be seen on magnetic resonance imaging (MRI) scans and is characterized by excess water signals in the bone marrow space. This disease is commonly caused by osteoarthritis (OA), a degenerative join disease where tissues within the joint breakdown over time [1]. These BMLs are an emerging target for OA, as they are commonly related to pain and worsening of the diseased area until surgical intervention is required [2]–[4]. In order to assess the BMLs, MRIs were utilized as input into a regression …

Modeling Thermosyphon And Heat Pipe Performance For Mold Cooling Applications, Dwaipayan Sarkar

Electronic Thesis and Dissertation Repository

Thermosyphons are enhanced heat transfer devices that can continuously transfer very large amounts of heat rapidly over long distances with small temperature differences. The high heat transfer rate is achieved through simultaneous boiling and condensation of the working fluid and the continuous heat transfer is achieved through recirculation of the working fluid in its liquid and vapor phase. A potentially important application of the thermosyphons has been towards reducing the cycle times of the mold cooling processes which would provide economic incentives to the automotive industry.

Different operational and geometrical parameters such as the input heating power, fill ratio (FR), …

Containerization Of Seafarers In The International Shipping Industry: Contemporary Seamanship, Maritime Social Infrastructures, And Mobility Politics Of Global Logistics, Liang Wu

Dissertations, Theses, and Capstone Projects

This dissertation discusses the mobility politics of container shipping and argues that technological development, political-economic order, and social infrastructure co-produce one another. Containerization, the use of standardized containers to carry cargo across modes of transportation that is said to have revolutionized and globalized international trade since the late 1950s, has served to expand and extend the power of international coalitions of states and corporations to control the movements of commodities (shipments) and labor (seafarers). The advent and development of containerization was driven by a sociotechnical imaginary and international social contract of seamless shipping and cargo flows. In practice, this liberal, …

Rational Design Of Peptide-Based Materials Informed By Multiscale Molecular Dynamics Simulations, Dhwanit Rahul Dave

Dissertations, Theses, and Capstone Projects

The challenge of establishing a sustainable and circular economy for materials in medicine and technology necessitates bioinspired design. Nature's intricate machinery, forged through evolution, relies on a finite set of biomolecular building blocks with through-bond and through-space interactions. Repurposing these molecular building blocks requires a seamless integration of computational modeling, design, and experimental validation. The tools and concepts developed in this thesis pioneer new directions in peptide-materials design, grounded in fundamental principles of physical chemistry. We present a synergistic approach that integrates experimental designs and computational methods, specifically molecular dynamics simulations, to gain in-depth molecular insights crucial for advancing the …

Molecular Understanding And Design Of Deep Eutectic Solvents And Proteins Using Computer Simulations And Machine Learning, Usman Lame Abbas

Theses and Dissertations--Chemical and Materials Engineering

Hydrophobic deep eutectic solvents (DESs) have emerged as excellent extractants. A major challenge is the lack of an efficient tool to discover DES candidates. Currently, the search relies heavily on the researchers’ intuition or a trial-and-error process, which leads to a low success rate or bypassing of promising candidates. DES performance depends on the heterogeneous hydrogen bond environment formed by multiple hydrogen bond donors and acceptors. Understanding this heterogeneous hydrogen bond environment can help develop principles for designing high performance DESs for extraction and other separation applications. This work investigates the structure and dynamics of hydrogen bonds in hydrophobic DESs …

Nonuniform Sampling-Based Breast Cancer Classification, Santiago Posso

Theses and Dissertations--Electrical and Computer Engineering

The emergence of deep learning models and their success in visual object recognition have fueled the medical imaging community's interest in integrating these algorithms to improve medical diagnosis. However, natural images, which have been the main focus of deep learning models and mammograms, exhibit fundamental differences. First, breast tissue abnormalities are often smaller than salient objects in natural images. Second, breast images have significantly higher resolutions but are generally heavily downsampled to fit these images to deep learning models. Models that handle high-resolution mammograms require many exams and complex architectures. Additionally, spatially resizing mammograms leads to losing discriminative details essential …

Data Driven And Machine Learning Based Modeling And Predictive Control Of Combustion At Reactivity Controlled Compression Ignition Engines, Behrouz Khoshbakht Irdmousa

Dissertations, Master's Theses and Master's Reports

Reactivity Controlled Compression Ignition (RCCI) engines operates has capacity to provide higher thermal efficiency, lower particular matter (PM), and lower oxides of nitrogen (NOx) emissions compared to conventional diesel combustion (CDC) operation. Achieving these benefits is difficult since real-time optimal control of RCCI engines is challenging during transient operation. To overcome these challenges, data-driven machine learning based control-oriented models are developed in this study. These models are developed based on Linear Parameter-Varying (LPV) modeling approach and input-output based Kernelized Canonical Correlation Analysis (KCCA) approach. The developed dynamic models are used to predict combustion timing (CA50), indicated mean effective pressure (IMEP), …

The Integration Of Neuromorphic Computing In Autonomous Robotic Systems, Md Abu Bakr Siddique

Dissertations, Master's Theses and Master's Reports

Deep Neural Networks (DNNs) have come a long way in many cognitive tasks by training on large, labeled datasets. However, this method has problems in places with limited data and energy, like when planetary robots are used or when edge computing is used [1]. In contrast to this data-heavy approach, animals demonstrate an innate ability to learn by communicating with their environment and forming associative memories among events and entities, a process known as associative learning [2-4]. For instance, rats in a T-maze learn to associate different stimuli with outcomes through exploration without needing labeled data [5]. This learning paradigm …

Implementing Unmanned Aerial Vehicles To Collect Human Gait Data At Distance And Altitude For Identification And Re-Identification, Donn E. Bartram

Graduate Theses, Dissertations, and Problem Reports

Gait patterns are a class of biometric information pertaining to the way a person moves and poses. Gait information is unique to each person and can be used to identify and reidentify people. Historically, this task has been achieved through the use of multiple ground-based imaging sensors. However, as Unmanned Aerial Vehicles (UAVs) advance, they present the opportunity to evolve the process of persons identification and re-identification. Collecting human gait data using UAVs at distances ranging from 20m to 500m and altitudes ranging from 0m to 120m is a challenging task. The current biometric data collection methods, primarily designed for …

The Precedence-Constrained Quadratic Knapsack Problem, Changkun Guan

Honors Theses

This thesis investigates the previously unstudied Precedence-Constrained Quadratic Knapsack Problem (PC-QKP), an NP-hard nonlinear combinatorial optimization problem. The PC-QKP is a variation of the traditional Knapsack Problem (KP) that introduces several additional complexities. By developing custom exact and approximate solution methods, and testing these on a wide range of carefully structured PC-QKP problem instances, we seek to identify and understand patterns that make some cases easier or harder to solve than others. The findings aim to help develop better strategies for solving this and similar problems in the future.

Computationally Modeling The Human-Structure Interaction Response Of An Occupied Cantilevered Structure, Brennan Smith

Honors Theses

There is a limited understanding of the impact that passive human occupants have on a dynamic structural system, referred to as Human-Structure Interaction (HSI). Cantilevers are naturally prone to excessive vibrations due to their long unsupported spans, and cantilevered structures such as those commonly found in the seating area of a stadium facility or concert hall are designed to support a high density of occupancy.

This study determined that HSI in cantilevered structures can be modeled using a simple two-degree-of-freedom system. The results of the model were validated by data that was collected on a small-scale laboratory structure intentionally designed …

Photoluminescence Of Beryllium-Related Defects In Gallium Nitride, Mykhailo Vorobiov, Mykhailo Vorobiov

Theses and Dissertations

This study explores the potential of beryllium (Be) as an alternative dopant to magnesium (Mg) for achieving higher hole concentrations in gallium nitride (GaN). Despite Mg prominence as an acceptor in optoelectronic and high-power devices, its deep acceptor level at 0.22 eV above the valence band limits its effectiveness. By examining Be, this research aims to pave the way to overcoming these limitations and extend the findings to aluminum nitride and aluminum gallium nitride (AlGaN) alloy. Key contributions of this work include. i)Identification of three Be-related luminescence bands in GaN through photoluminescence spectroscopy, improving the understanding needed for further material …

Developing Machine Learning And Time-Series Analysis Methods With Applications In Diverse Fields, Muhammed Aljifri

Theses and Dissertations

This dissertation introduces methodologies that combine machine learning models with time-series analysis to tackle data analysis challenges in varied fields. The first study enhances the traditional cumulative sum control charts with machine learning models to leverage their predictive power for better detection of process shifts, applying this advanced control chart to monitor hospital readmission rates. The second project develops multi-layer models for predicting chemical concentrations from ultraviolet-visible spectroscopy data, specifically addressing the challenge of analyzing chemicals with a wide range of concentrations. The third study presents a new method for detecting multiple changepoints in autocorrelated ordinal time series, using the …

Adaptable And Trustworthy Machine Learning For Human Activity Recognition From Bioelectric Signals, Morgan S. Stuart

Theses and Dissertations

Enabling machines to learn measures of human activity from bioelectric signals has many applications in human-machine interaction and healthcare. However, labeled activity recognition datasets are costly to collect and highly varied, which challenges machine learning techniques that rely on large datasets. Furthermore, activity recognition in practice needs to account for user trust - models are motivated to enable interpretability, usability, and information privacy. The objective of this dissertation is to improve adaptability and trustworthiness of machine learning models for human activity recognition from bioelectric signals. We improve adaptability by developing pretraining techniques that initialize models for later specialization to unseen …

Simulation Of Wave Propagation In Granular Particles Using A Discrete Element Model, Syed Tahmid Hussan

Electronic Theses and Dissertations

The understanding of Bender Element mechanism and utilization of Particle Flow Code (PFC) to simulate the seismic wave behavior is important to test the dynamic behavior of soil particles. Both discrete and finite element methods can be used to simulate wave behavior. However, Discrete Element Method (DEM) is mostly suitable, as the micro scaled soil particle cannot be fully considered as continuous specimen like a piece of rod or aluminum. Recently DEM has been widely used to study mechanical properties of soils at particle level considering the particles as balls. This study represents a comparative analysis of Voigt and Best …

Language Models For Rare Disease Information Extraction: Empirical Insights And Model Comparisons, Shashank Gupta

Theses and Dissertations--Computer Science

End-to-end relation extraction (E2ERE) is a crucial task in natural language processing (NLP) that involves identifying and classifying semantic relationships between entities in text. This thesis compares three paradigms for end-to-end relation extraction (E2ERE) in biomedicine, focusing on rare diseases with discontinuous and nested entities. We evaluate Named Entity Recognition (NER) to Relation Extraction (RE) pipelines, sequence-to-sequence models, and generative pre-trained transformer (GPT) models using the RareDis information extraction dataset. Our findings indicate that pipeline models are the most effective, followed closely by sequence-to-sequence models. GPT models, despite having eight times as many parameters, perform worse than sequence-to-sequence models and …

Organic Fouling Mitigation In Forward Osmosis Technology Through The Use Of Oscilatting Alternating Current Electric Fields, Logan Werner

Graduate College Dissertations and Theses

Forward osmosis (FO) is the term given to osmosis in water filtration applications. FO has many advantages to conventional membrane filtration processes. The lack of external pressure needed to force solvent through the membrane is dramatically decreased in FO, resulting in a lower cost of operation compared to reverse osmosis. Lower external pressures also result in decreased fouling on the membrane surface and improved permeate flux. Fouling is one of the foremost challenges within the membrane filtration industry and is one of the biggest contributors to operating costs. While FO results in less fouling than RO, fouling remains a major …

Cross-Layer Design Of Highly Scalable And Energy-Efficient Ai Accelerator Systems Using Photonic Integrated Circuits, Sairam Sri Vatsavai

Theses and Dissertations--Electrical and Computer Engineering

Artificial Intelligence (AI) has experienced remarkable success in recent years, solving complex computational problems across various domains, including computer vision, natural language processing, and pattern recognition. Much of this success can be attributed to the advancements in deep learning algorithms and models, particularly Artificial Neural Networks (ANNs). In recent times, deep ANNs have achieved unprecedented levels of accuracy, surpassing human capabilities in some cases. However, these deep ANN models come at a significant computational cost, with billions to trillions of parameters. Recent trends indicate that the number of parameters per ANN model will continue to grow exponentially in the foreseeable …

Development Of A Collaborative Research Platform For Efficient Data Management And Visualization Of Qubit Control, Devanshu Brahmbhatt

Computer Science and Engineering Theses

This thesis introduces QubiCSV, a pioneering open-source platform for quantum computing field. With an emphasis on collaborative research, QubiCSV addresses the critical need for specialized data management and visualization tools in qubit control. The platform is crafted to overcome the challenges posed by the high costs and complexities associated with quantum experimental setups. It emphasizes efficient utilization of resources through shared ideas, data, and implementation strategies. One of the primary obstacles in quantum computing research has been the ineffective management of extensive calibration data and the inability to visualize complex quantum experiment outcomes effectively. QubiCSV fills this gap by offering …

Autonomous Shuttle Car Docking To A Continuous Miner Using Rgb-Depth Imagery, Sky Rose

Theses and Dissertations--Mining Engineering

A great deal of research is currently being conducted in automating mining equipment to improve worker health and safety and increase mine productivity. Significant progress has been made in some applications, e.g., autonomous haul trucks for surface mining. However, little progress has been made in autonomous face haulage in underground room-and pillar coal mines. Accordingly, this thesis addresses automating the operation of a shuttle car, focusing on positioning the shuttle car under the continuous miner coal-discharge conveyor during cutting and loading operations. The approach uses a stereo depth camera as the sensor, and machine-learning algorithms are used to identify various …

Effective Drag Coefficient Prediction On Single-View 2d Images Of Snowflakes, Cameron Hudson

Graduate College Dissertations and Theses

The drag coefficient of snowflakes is an crucial particle descriptor that can quantify the relationships with the mass, shape, size, and fall speed of snowflake particles. Previous studies has relied on estimating and improving empirical correlations for the drag coefficient of particles, utilizing 3D images from the Multi-Angled Snowflake Camera Database (MASCDB) to estimate snowflake properties such as mass, geometry, shape classification, and rimming degree. However, predictions of the drag coefficient with single-view 2D images of snowflakes has proven to be a challenging problem, primarily due to the lack of data and time-consuming, expensive methods used to estimate snowflake shape …

Towards Algorithmic Justice: Human Centered Approaches To Artificial Intelligence Design To Support Fairness And Mitigate Bias In The Financial Services Sector, Jihyun Kim

CMC Senior Theses

Artificial Intelligence (AI) has positively transformed the Financial services sector but also introduced AI biases against protected groups, amplifying existing prejudices against marginalized communities. The financial decisions made by biased algorithms could cause life-changing ramifications in applications such as lending and credit scoring. Human Centered AI (HCAI) is an emerging concept where AI systems seek to augment, not replace human abilities while preserving human control to ensure transparency, equity and privacy. The evolving field of HCAI shares a common ground with and can be enhanced by the Human Centered Design principles in that they both put humans, the user, at …

Assessing Performance Optimization Strategies In Cloud-Native Environments Through Containerization And Orchestration Analysis, Daniel E. Ukene

Electronic Theses and Dissertations

This thesis comprises three distinct, yet interconnected studies addressing critical aspects of web infrastructure management. We begin by studying containerization via Docker and its impact on web server performance, focusing on Apache and Nginx hosted on virtualized environments. Through meticulous load testing and analysis, we provide insights into the comparative performance of these servers, adding users of this technology know which webservers to leverage when hosting their webservice along alongside the infrastructure to host it on. Next, we expand our focus to examine the performance of caching systems, namely Redis and Memcached, across traditional VMs and Docker containers. By comparing …

Railroad Condition Monitoring Using Distributed Acoustic Sensing And Deep Learning Techniques, Md Arifur Rahman

Electronic Theses and Dissertations

Proper condition monitoring has been a major issue among railroad administrations since it might cause catastrophic dilemmas that lead to fatalities or damage to the infrastructure. Although various aspects of train safety have been conducted by scholars, in-motion monitoring detection of defect occurrence, cause, and severity is still a big concern. Hence extensive studies are still required to enhance the accuracy of inspection methods for railroad condition monitoring (CM). Distributed acoustic sensing (DAS) has been recognized as a promising method because of its sensing capabilities over long distances and for massive structures. As DAS produces large datasets, algorithms for precise …

Volumetric Imaging Using The Pupil-Matched Remote Focusing Technique In Light-Sheet Microscopy, Sayed Hassan Dibaji Foroushani

Optical Science and Engineering ETDs

ABSTRACT

The dissertation explores innovative techniques in light sheet microscopy, a pivotal tool in biomedical imaging, to enhance its speed, resolution, and efficiency in capturing dynamic biological processes. Light sheet microscopy allows for quick 3D imaging of biological specimens ranging from cells to organs with high spatiotemporal resolution, large field-of-view, and minimal damage, making it vital for in vivo imaging.

The first project introduces a novel optical concept designed to optimize Axially Swept Light Sheet Microscopy (ASLM). This technique is crucial for imaging specimens ranging from live cells to chemically cleared organs due to its versatility across different immersion media. …

Solar-Powered Microgrids In Northern California: An Opportunity For Resilience, Marina Riddle

Master's Projects and Capstones

Planned and unplanned power outages have been increasing in frequency and duration, negatively impacting all public sectors, and threatening public safety. These outages are deadly to those who rely on medical devices. As climate change-fueled extreme weather events (wildfires, earthquakes, storms, etc.) also increase in frequency, our electrical grid must be prepared to bounce back. Microgrids provide necessary redundancy and reliability. Through a novel GIS suitability analysis, based on solar radiation, land use type, local energy demand, distance to transmission lines, distance to roads, and slope, optimal locations for solar-powered microgrids throughout Northern California were determined. The counties of Fresno, …

Advances In Cellulose Nanomaterial-Based Foams For Environmental Applications, Md Musfiqur Rahman

Electronic Theses and Dissertations

The use of metal-oxide nanoparticles adsorbents is limited to fixed-bed columns in industrial-scale water treatment applications. This limitation is commonly attributed to the tendency of nanoparticles to aggregate, the use of non-sustainable and inefficient polymeric resins as supporting materials, or a lack of adsorption capacity. Foams and aerogels derived from cellulose nanomaterials have unique characteristics, such as high porosity and low density, which enables their use in a variety of environmental applications, including water treatment. However, the overall use of cellulose nanomaterial-based foams in various environmental sectors is limited due to the high cost of production associated with time- and …

A Map-Algebra-Inspired Approach For Interacting With Wireless Sensor Networks, Cyber-Physical Systems Or Internet Of Things, David Almeida

Electronic Theses and Dissertations

The typical approach for consuming data from wireless sensor networks (WSN) and Internet of Things (IoT) has been to send data back to central servers for processing and analysis. This thesis develops an alternative strategy for processing and acting on data directly in the environment referred to as Active embedded Map Algebra (AeMA). Active refers to the near real time production of data, and embedded refers to the architecture of distributed embedded sensor nodes. Network macroprogramming, a style of programming adopted for wireless sensor networks and IoT, addresses the challenges of coordinating the behavior of multiple connected devices through a …