Matching Items (134)
Description
The prevalence and unique properties of airborne nanoparticles have raised concerns regarding their potential adverse health effects. Despite their significance, the understanding of nanoparticle generation, transport, and exposure remains incomplete. This study first aimed to assess nanoparticle exposure in indoor workplace environments, in the semiconductor manufacturing industry. On-site observations during tool preventive maintenance revealed a significant release of particles smaller than 30 nm, which subsequent instrumental analysis confirmed as predominantly composed of transition metals. Although the measured mass concentration levels did not exceed current federal limits, it prompted concerns regarding how well filter-based air sampling methods would capture the particles for exposure assessment and how well common personal protective equipment would protect from exposure. To address these concerns, this study evaluated the capture efficiency of filters and masks. When challenged by aerosolized engineered nanomaterials, common filters used in industrial hygiene sampling exhibited capture efficiencies of over 60%. Filtering Facepiece Respirators, such as the N95 mask, exhibited a capture efficiency of over 98%. In contrast, simple surgical masks showed a capture efficiency of approximately 70%. The experiments showed that face velocity and ambient humidity influence capture performance and mostly identified the critical role of mask and particle surface charge in capturing nanoparticles. Masks with higher surface potential exhibited higher capture efficiency towards nanoparticles. Eliminating their surface charge resulted in a significantly diminished capture efficiency, up to 43%. Finally, this study characterized outdoor nanoparticle concentrations in the Phoenix metropolitan area, revealing typical concentrations on the order of 10^4 #/cm3 consistent with other urban environments. During the North American monsoon season, in dust storms, with elevated number concentrations of large particles, particularly in the size range of 1-10 μm, the number concentration of nanoparticles in the size range of 30-100 nm was substantially lower by approximately 55%. These findings provide valuable insights for future assessments of nanoparticle exposure risks and filter capture mechanisms associated with airborne nanoparticles.
ContributorsZhang, Zhaobo (Author) / Herckes, Pierre (Thesis advisor) / Westerhoff, Paul (Committee member) / Shock, Everett (Committee member) / Fraser, Matthew (Committee member) / Arizona State University (Publisher)
Created2023
Description
Communicating with computers through thought has been a remarkable achievement in recent years. This was made possible by the use of Electroencephalography (EEG). Brain-computer interface (BCI) relies heavily on Electroencephalography (EEG) signals for communication between humans and computers. With the advent ofdeep learning, many studies recently applied these techniques to EEG data to perform various tasks like emotion recognition, motor imagery classification, sleep analysis, and many more.
Despite the rise of interest in EEG signal classification, very few studies have explored the MindBigData dataset, which collects EEG signals recorded at the stimulus of seeing a digit and thinking about it. This dataset takes us closer to realizing the idea of mind-reading or communication via thought. Thus classifying these signals into the respective digit that the user thinks about is a challenging task. This serves as a motivation to study this dataset and apply existing deep learning techniques to study it.
Given the recent success of transformer architecture in different domains like Computer Vision and Natural language processing, this thesis studies transformer architecture for EEG signal classification. Also, it explores other deep learning
techniques for the same. As a result, the proposed classification pipeline achieves comparable performance with the existing methods.
ContributorsMuglikar, Omkar Dushyant (Author) / Wang, Yalin (Thesis advisor) / Liang, Jianming (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2021
Description
Statistical Shape Modeling is widely used to study the morphometrics of deformable objects in computer vision and biomedical studies. There are mainly two viewpoints to understand the shapes. On one hand, the outer surface of the shape can be taken as a two-dimensional embedding in space. On the other hand, the outer surface along with its enclosed internal volume can be taken as a three-dimensional embedding of interests. Most studies focus on the surface-based perspective by leveraging the intrinsic features on the tangent plane. But a two-dimensional model may fail to fully represent the realistic properties of shapes with both intrinsic and extrinsic properties. In this thesis, severalStochastic Partial Differential Equations (SPDEs) are thoroughly investigated and several methods are originated from these SPDEs to try to solve the problem of both two-dimensional and three-dimensional shape analyses. The unique physical meanings of these SPDEs inspired the findings of features, shape descriptors, metrics, and kernels in this series of works. Initially, the data generation of high-dimensional shapes, here, the tetrahedral meshes, is introduced. The cerebral cortex is taken as the study target and an automatic pipeline of generating the gray matter tetrahedral mesh is introduced. Then, a discretized Laplace-Beltrami operator (LBO) and a Hamiltonian operator (HO) in tetrahedral domain with Finite Element Method (FEM) are derived. Two high-dimensional shape descriptors are defined based on the solution of the heat equation and Schrödinger’s equation. Considering the fact that high-dimensional shape models usually contain massive redundancies, and the demands on effective landmarks in many applications, a Gaussian process landmarking on tetrahedral meshes is further studied. A SIWKS-based metric space is used to define a geometry-aware Gaussian process. The study of the periodic potential diffusion process further inspired the idea of a new kernel call the geometry-aware convolutional kernel. A series of Bayesian learning methods are then introduced to tackle the problem of shape retrieval and classification. Experiments of every single item are demonstrated. From the popular SPDE such as the heat equation and Schrödinger’s equation to the general potential diffusion equation and the specific periodic potential diffusion equation, it clearly shows that classical SPDEs play an important role in discovering new features, metrics, shape descriptors and kernels. I hope this thesis could be an example of using interdisciplinary knowledge to solve problems.
ContributorsFan, Yonghui (Author) / Wang, Yalin (Thesis advisor) / Lepore, Natasha (Committee member) / Turaga, Pavan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
Description
Graph matching is a fundamental but notoriously difficult problem due to its NP-hard nature, and serves as a cornerstone for a series of applications in machine learning and computer vision, such as image matching, dynamic routing, drug design, to name a few. Although there has been massive previous investigation on high-performance graph matching solvers, it still remains a challenging task to tackle the matching problem under real-world scenarios with severe graph uncertainty (e.g., noise, outlier, misleading or ambiguous link).In this dissertation, a main focus is to investigate the essence and propose solutions to graph matching with higher reliability under such uncertainty. To this end, the proposed research was conducted taking into account three perspectives related to reliable graph matching: modeling, optimization and learning. For modeling, graph matching is extended from typical quadratic assignment problem to a more generic mathematical model by introducing a specific family of separable function, achieving higher capacity and reliability. In terms of optimization, a novel high gradient-efficient determinant-based regularization technique is proposed in this research, showing high robustness against outliers. Then learning paradigm for graph matching under intrinsic combinatorial characteristics is explored. First, a study is conducted on the way of filling the gap between discrete problem and its continuous approximation under a deep learning framework. Then this dissertation continues to investigate the necessity of more reliable latent topology of graphs for matching, and propose an effective and flexible framework to obtain it. Coherent findings in this dissertation include theoretical study and several novel algorithms, with rich experiments demonstrating the effectiveness.
ContributorsYu, Tianshu (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Yang, Yezhou (Committee member) / Yang, Yingzhen (Committee member) / Arizona State University (Publisher)
Created2021
Description
Graph matching is a fundamental but notoriously difficult problem due to its NP-hard nature, and serves as a cornerstone for a series of applications in machine learning and computer vision, such as image matching, dynamic routing, drug design, to name a few. Although there has been massive previous investigation on high-performance graph matching solvers, it still remains a challenging task to tackle the matching problem under real-world scenarios with severe graph uncertainty (e.g., noise, outlier, misleading or ambiguous link).In this dissertation, a main focus is to investigate the essence and propose solutions to graph matching with higher reliability under such uncertainty. To this end, the proposed research was conducted taking into account three perspectives related to reliable graph matching: modeling, optimization and learning. For modeling, graph matching is extended from typical quadratic assignment problem to a more generic mathematical model by introducing a specific family of separable function, achieving higher capacity and reliability. In terms of optimization, a novel high gradient-efficient determinant-based regularization technique is proposed in this research, showing high robustness against outliers. Then learning paradigm for graph matching under intrinsic combinatorial characteristics is explored. First, a study is conducted on the way of filling the gap between discrete problem and its continuous approximation under a deep learning framework. Then this dissertation continues to investigate the necessity of more reliable latent topology of graphs for matching, and propose an effective and flexible framework to obtain it. Coherent findings in this dissertation include theoretical study and several novel algorithms, with rich experiments demonstrating the effectiveness.
ContributorsYu, Tianshu (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Yang, Yezhou (Committee member) / Yang, Yingzhen (Committee member) / Arizona State University (Publisher)
Created2021
Description
Alzheimer's disease (AD) is a neurodegenerative disease that damages the cognitive abilities of a patient. It is critical to diagnose AD early to begin treatment as soon as possible which can be done through biomarkers. One such biomarker is the beta-amyloid (Aβ) peptide which can be quantified using the centiloid (CL) scale. For identifying the Aβ biomarker, A deep learning model that can model AD progression by predicting the CL value for brain magnetic resonance images (MRIs) is proposed. Brain MRI images can be obtained through the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets, however a single model cannot perform well on both datasets at once. Thus, A regularization-based continuous learning framework to perform domain adaptation on the previous model is also proposed which captures the latent information about the relationship between Aβ and AD progression within both datasets.
ContributorsTrinh, Matthew Brian (Author) / Wang, Yalin (Thesis advisor) / Liang, Jianming (Committee member) / Su, Yi (Committee member) / Arizona State University (Publisher)
Created2022
Description
Lithium (Li) is a trace element in kerogen, but the content and isotopic distribution (δ7Li) in kerogen has not previously been quantified. Furthermore, kerogen has been overlooked as a potential source of Li to sedimentary porefluids and buried sediments. Thus, knowing the content and isotopic composition of Li derived from kerogen may have implications for research focused on the Li-isotopes of buried sediments (e.g., evaluating paleoclimate variations using marine carbonates).The objective of this work is to better understand the role of kerogen in the Li geochemical cycle. The research approach consisted of 1) developing reference materials and methodologies to measure the Li-contents and δ7Li of kerogen in-situ by Secondary Ion Mass Spectrometry, 2) surveying the Li-contents and δ7Li of kerogen bearing rocks from different depositional and diagenetic environments and 3) quantifying the Li-content and δ7Li variations in kerogen empirically in a field study and 4) experimentally through hydrous pyrolysis.
A survey of δ7Li of coals from depositional basins across the USA showed that thermally immature coals have light δ7Li values (–20 to – 10‰) compared to typical terrestrial materials (> –10‰) and the δ7Li of coal increases with burial temperature suggesting that 6Li is preferentially released from kerogen to porefluids during hydrocarbon generation. A field study was conducted on two Cretaceous coal seams in Colorado (USA) intruded by dikes (mafic and felsic) creating a temperature gradient from the intrusives into the country rock. Results showed that δ7Li values of the unmetamorphosed vitrinite macerals were up to 37‰ lighter than vitrinite macerals and coke within the contact metamorphosed coal.
To understand the significance of Li derived from kerogen during burial diagenesis, hydrous pyrolysis experiments of three coals were conducted. Results showed that Li is released from kerogen during hydrocarbon generation and could increase sedimentary porefluid Li-contents up to ~100 mg/L. The δ7Li of coals becomes heavier with increased temperature except where authigenic silicates may compete for the released Li. These results indicate that kerogen is a significant source of isotopically light Li to diagenetic fluids and is an important contributor to the global geochemical cycle.
ContributorsTeichert, Zebadiah (Author) / Williams, Lynda B. (Thesis advisor) / Bose, Maitrayee (Thesis advisor) / Hervig, Richard (Committee member) / Semken, Steven (Committee member) / Shock, Everett (Committee member) / Arizona State University (Publisher)
Created2022
Description
The Greater Obsidian Pool Area just south of the Mud Volcano area in Yellowstone National Park is an active and ever-changing hot spring region. Situated next to a lake in a meadow between several hills of glacial deposits, north of the Elephant Back rhyolite flow, a diverse group of hot springs has been developing. This study examines the geologic and geomorphic context of the hot springs, finding evidence for a previously undiscovered hydrothermal explosion crater and examining the deposits around the region that contribute to properties of the groundwater table. Hot spring geochemical measurements (Cl- and SO4-2) taken over the course of 20 years are used to determine fluid sourcing of the springs. The distribution of Cl-, an indicator of water-rock interaction, in the hot springs leads to the theory of a fissure delivering hydrothermal fluid in a line across the hot spring zone, with meteoric water from incoming groundwater diluting hot springs moving further from the fissure. A possible second dry fissure delivering mostly gas is also a possible explanation for some elevated sulfate concentrations in certain springs. The combination of geology, geomorphology, and geochemistry reveals how the surface and subsurface operate to generate different hot spring compositions.
ContributorsAlexander, Erin (Author) / Shock, Everett (Thesis director) / Whipple, Kelin (Committee member) / Barrett, The Honors College (Contributor) / School of Earth and Space Exploration (Contributor) / School of Molecular Sciences (Contributor)
Created2022-05
Description
The Population Receptive Field (pRF) model is widely used to predict the location (retinotopy) and size of receptive fields on the visual space. Doing so allows for the creation of a mapping from locations in the visual field to the associated groups of neurons in the cortical region (within the visual cortex of the brain). However, using the pRF model is very time consuming. Past research has focused on the creation of Convolutional Neural Networks (CNN) to mimic the pRF model in a fraction of the time, and they have worked well under highly controlled conditions. However, these models have not been thoroughly tested on real human data. This thesis focused on adapting one of these CNNs to accurately predict the retinotopy of a real human subject using a dataset from the Human Connectome Project. The results show promise towards creating a fully functioning CNN, but they also expose new challenges that must be overcome before the model could be used to predict the retinotopy of new human subjects.
ContributorsBurgard, Braeden (Author) / Wang, Yalin (Thesis director) / Ta, Duyan (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05
Description
The purpose of the overall project is to create a simulated environment similar to Google map and traffic but simplified for education purposes. Students can choose different traffic patterns and program a car to navigate through the traffic dynamically based on the changing traffic. The environment used in the project is ASU VIPLE (Visual IoT/Robotics Programming Language Environment). It is a visual programming environment for Computer Science education. VIPLE supports a number of devices and platforms, including a traffic simulator developed using Unity game engine. This thesis focuses on creating realistic traffic data for the traffic simulator and implementing dynamic routing algorithm in VIPLE. The traffic data is generated from the recorded real traffic data published at Arizona Maricopa County website. Based on the generated traffic data, VIPLE programs are developed to implement the traffic simulation based on dynamic changing traffic data.
ContributorsZhang, Zhemin (Author) / Chen, Yinong (Thesis advisor) / Wang, Yalin (Thesis advisor) / De Luca, Gennaro (Committee member) / Arizona State University (Publisher)
Created2022