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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
Transorbital surgery has gained recent notoriety due to its incorporation into endoscopic skull base surgery. The body of published literature on the field is cadaveric and observation. The pre-clinical studies are focused on the use of the endoscope only. Furthermore the methodology utilised in the published literature is inconsistent and does not embody the optimal principles of scientific experimentation. This body of work evaluates a minimally invasive novel surgical corridor - the transorbital approach - its validity in neurosurgical practice, as well as both qualitatively and quantitatively assessing available technological advances in a robust experimental fashion. While the endoscope is an established means of visualisation used in clinical transorbital surgery, the microscope has never been assessed with respect to the transorbital approach. This question is investigated here and the anatomical and surgical benefits and limitations of microscopic visualisation demonstrated. The comparative studies provide increased knowledge on specifics pertinent to neurosurgeons and other skull base specialists when planning pre-operatively, such as pathology location, involved anatomical structures, instrument maneuvrability and the advantages and disadvantages of the distinct visualisation technologies. This is all with the intention of selecting the most suitable surgical approach and technology, specific to the patient, pathology and anatomy, so as to perform the best surgical procedure. The research findings illustrated in this body of work are diverse, reproducible and applicable. The transorbital surgical corridor has substantive potential for access to the anterior cranial fossa and specific surgical target structures. The neuroquantitative metrics investigated confirm the utility and benefits specific to the respective visualisation technologies i.e. the endoscope and microscope. The most appropriate setting wherein the approach should be used is also discussed. The transorbital corridor has impressive potential, can utilise all available technological advances, promotes multi-disciplinary co-operation and learning amongst clinicians and ultimately, is a means of improving operative patient care.
ContributorsHoulihan, Lena Mary (Author) / Preul, Mark C. (Thesis advisor) / Vernon, Brent (Thesis advisor) / O' Sullivan, Michael G.J. (Committee member) / Lawton, Michael T. (Committee member) / Santarelli, Griffin (Committee member) / Smith, Brian (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
The application of silicon thin films in solar cells has evolved from their use in amorphous silicon solar cells to their use as passivating and carrier-selective contacts in crystalline silicon solar cells. Their use as carrier-selective contacts has enabled crystalline silicon solar cell efficiencies above 26%, just 3% shy of the theoretical efficiency limit. The two cell architectures that have exceeded 26% are the silicon heterojunction and tunnel oxide passivating contact cell. These two cell architectures use two different forms of silicon thin films. In the case of the silicon heterojunction, the crystalline wafer is sandwiched between layers of intrinsic amorphous silicon, which acts as the passivation layer, and doped amorphous silicon, which acts as the carrier-selective layer. On the other hand, the tunnel oxide passivating contact cell uses a thin silicon oxide passivation layer and a doped polycrystalline silicon layer as the carrier-selective layer. Both cell structures have their distinct advantages and disadvantages when it comes to production. The processing of the silicon heterojunction relies on a low thermal budget and leads to high open-circuit voltages, but the cost of high-vacuum processing equipment presents a major hurdle for industrial scale production while the tunnel oxide passivating contact can be easily integrated into current industrial lines, yet it requires a higher thermal budgets and does not produce as high of an open-circuit voltage as the silicon heterojunction. This work focuses on using both forms of silicon thin films applied as passivating and carrier-selective contacts to crystalline silicon thin films.First, a thorough analysis of the series resistivity in silicon heterojunction solar cells is conducted. In particular, variations in the thickness and doping of the individual
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contact layers are performed to reveal their effect on the contact resistivity and in turn the total series resistivity of the cell. Second, a tunnel oxide passivated contact is created using a novel deposition method for the silicon oxide layer. A 21% efficient proof-of-concept device is presented demonstrating the potential of this deposition method. Finally, recommendations to further improve the efficiency of these cells is presented.
ContributorsWeigand, William (Author) / Holman, Zachary (Thesis advisor) / Yu, Zhengshan (Committee member) / Bertoni, Mariana (Committee member) / Tongay, Sefaattin (Committee member) / Arizona State University (Publisher)
Created2023
Description
Metal-Oxide-Semiconductor (MOS) is essential to modern VLSI devices. In the past decades, a wealth of literature has been created to understand the impact of the radiation-induced charges on the devices, i.e., the creation of electron-hole pairs in the oxide layer which is the most sensitive part of MOS structure to the radiation effect. In this work, both MOS and MNOS devices were fabricated at ASU NanoFab to study the total ionizing dose effect using capacitance-voltage (C-V) electrical characterization by observing the direction and amounts of the shift in C-V curves and electron holography observation to directly image the charge buildup at the irradiated oxide film of the oxide-only MOS device.C-V measurements revealed the C-V curves shifted to the left after irradiation (with a positive bias applied) because of the net positive charges trapped at the oxide layer for the oxide-only sample. On the other hand, for nitride/oxide samples with positive biased during irradiation, the C-V curve shifted to the right due to the net negative charges trapped at the oxide layer. It was also observed that the C-V curve has less shift in voltage for MNOS than MOS devices after irradiation due to the less charge buildup after irradiation.
Off-axis electron holography was performed to map the charge distribution across the MOSCAP sample. Compared with both pre-and post-irradiated samples, a larger potential drop at the Si/SiO2 was noticed in post-irradiation samples, which indicates the presence of greater amounts of positive charges that buildup the Si/SiO2 interface after the TID exposure.
TCAD modeling was used to extract the density of charges accumulated near the SiO2/Si and SiO2/ Metal interface by matching the simulation results to the potential data from holography. The increase of near-interface positive charges in post-irradiated samples is consistent with the C-V results.
ContributorsChang, Ching Tao (Author) / Barnaby, Hugh (Thesis advisor) / Holbert, Keith (Committee member) / Tongay, Sefaattin (Committee member) / Arizona State University (Publisher)
Created2023
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
Description
Little is known about how cognitive and brain aging patterns differ in older adults with autism spectrum disorder (ASD). However, recent evidence suggests that individuals with ASD may be at greater risk of pathological aging conditions than their neurotypical (NT) counterparts. A growing body of research indicates that older adults with ASD may experience accelerated cognitive decline and neurodegeneration as they age, although studies are limited by their cross-sectional design in a population with strong age-cohort effects. Studying aging in ASD and identifying biomarkers to predict atypical aging is important because the population of older individuals with ASD is growing. Understanding the unique challenges faced as autistic adults age is necessary to develop treatments to improve quality of life and preserve independence. In this study, a longitudinal design was used to characterize cognitive and brain aging trajectories in ASD as a function of autistic trait severity. Principal components analysis (PCA) was used to derive a cognitive metric that best explains performance variability on tasks measuring memory ability and executive function. The slope of the integrated persistent feature (SIP) was used to quantify functional connectivity; the SIP is a novel, threshold-free graph theory metric which summarizes the speed of information diffusion in the brain. Longitudinal mixed models were using to predict cognitive and brain aging trajectories (measured via the SIP) as a function of autistic trait severity, sex, and their interaction. The sensitivity of the SIP was also compared with traditional graph theory metrics. It was hypothesized that older adults with ASD would experience accelerated cognitive and brain aging and furthermore, age-related changes in brain network topology would predict age-related changes in cognitive performance.
For both cognitive and brain aging, autistic traits and sex interacted to predict trajectories, such that older men with high autistic traits were most at risk for poorer outcomes. In men with autism, variability in SIP scores across time points trended toward predicting cognitive aging trajectories. Findings also suggested that autistic traits are more sensitive to differences in brain aging than diagnostic group and that the SIP is more sensitive to brain aging trajectories than other graph theory metrics. However, further research is required to determine how physiological biomarkers such as the SIP are associated with cognitive outcomes.
ContributorsSullivan, Georgia (Author) / Braden, Blair (Thesis advisor) / Kodibagkar, Vikram (Thesis advisor) / Schaefer, Sydney (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Vanadium-dioxide-based devices show great switchability in their optical properties due to its dramatic thermochromic phase transition from insulator to metal, but generally have concerns due to its relatively high transition temperature at 68 °C. Doping the vanadium dioxide with tungsten has been shown to reduce its transition temperature at the cost lower optical property differences between its insulating and metallic phases. A recipe is developed through parametric experimentation to fabricate tungsten-doped vanadium dioxide consisting of a novel dual target co-sputtering deposition, a furnace oxidation process, and a post-oxidation annealing process. The transmittance spectra of the resulting films are measured via Fourier-transform infrared spectroscopy at different temperatures to confirm the lowered transition temperature and analyze their thermal-optical hysteresis behavior through the transition temperature range. Afterwards, the optical properties of undoped sputtered vanadium films are modeled and effective medium theory is used to explain the effect of tungsten dopants on the observed transmittance decrease of doped vanadium dioxide. The optical modeling is used to predict the performance of tungsten-doped vanadium dioxide devices, in particular a Fabry-Perot infrared emitter and a nanophotonic infrared transmission filter. Both devices show great promise in their optical properties despite a slight performance decrease from the tungsten doping. These results serve to illustrate the excellent performance of the co-sputtered tungsten-doped vanadium dioxide films.
ContributorsChao, Jeremy (Author) / Wang, Liping (Thesis advisor) / Wang, Robert (Committee member) / Tongay, Sefaattin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Rare-earth tritellurides (RTe3) are two-dimensional materials with unique quantum properties, ideal for investigating quantum phenomena and applications in supercapacitors, spintronics, and twistronics. This dissertation examines the electronic, magnetic, and phononic properties of the RTe3 family, exploring how these can be controlled using chemical pressure, cationic alloying, and external pressure.The impact of chemical pressure on RTe3 phononic properties was investigated through noninvasive micro-Raman spectroscopy, demonstrating the potential of optical measurements for determining charge density wave (CDW) transition temperatures. Cationic alloying studies showed seamless tuning of CDW transition temperatures by modifying lattice constants and revealed complex magnetism in alloyed RTe3 with multiple magnetic transitions.
A comprehensive external pressure study examined the influence of spacing between RTe3 layers on phononic and CDW properties across the RTe3 family. Comparisons between different RTe3 materials showed LaTe3, with the largest thermodynamic equilibrium interlayer spacing (smallest chemical pressure), has the most stable CDW phases at high pressures. Conversely, CDW phases in late RTe3 systems with larger internal chemical pressures were more easily suppressed by applied pressure.
The dissertation also investigated Schottky barrier realignment at RTe3/semiconductor interfaces induced by CDW transitions, revealing changes in Schottky barrier height and ideality factor around the CDW transition temperature. This indicates that chemical potential changes of RTe3 below the CDW transition temperature influence Schottky junction properties, enabling CDW state probing through interface property measurements.
A detailed experimental and theoretical analysis of the oxidation process of RTe3 compounds was performed, which revealed faster degradation in late RTe3 systems. Electronic property changes, like CDW transition temperature and chemical potential, are observed as degradation progresses. Quantum mechanical simulations suggested that degradation primarily results from strong oxidizing reactions with O2 molecules, while humidity (H2O) plays a negligible role unless Te vacancies exist.
Lastly, the dissertation establishes a large-area thin film deposition at relatively low temperatures using a soft sputtering technique. While focused on MoTe2 deposition, this technique may also apply to RTe3 thin film deposition.
Overall, this dissertation expands the understanding of the fundamental properties of RTe3 materials and lays the groundwork for potential device applications.
ContributorsYumigeta, Kentaro (Author) / Tongay, Sefaattin (Thesis advisor) / Ponce, Fernando (Committee member) / Drucker, Jeffery (Committee member) / Erten, Onur (Committee member) / Arizona State University (Publisher)
Created2023
Description
A description of numerical and analytical work pertaining to models that describe the growth and progression of glioblastoma multiforme (GBM), an aggressive form of primary brain cancer. Two reaction-diffusion models are used: the Fisher-Kolmogorov-Petrovsky-Piskunov equation and a 2-population model that divides the tumor into actively proliferating and quiescent (or necrotic) cells. The numerical portion of this work (chapter 2) focuses on simulating GBM expansion in patients undergoing treatment for recurrence of tumor following initial surgery. The models are simulated on 3-dimensional brain geometries derived from magnetic resonance imaging (MRI) scans provided by the Barrow Neurological Institute. The study consists of 17 clinical time intervals across 10 patients that have been followed in detail, each of whom shows significant progression of tumor over a period of 1 to 3 months on sequential follow up scans. A Taguchi sampling design is implemented to estimate the variability of the predicted tumors to using 144 different choices of model parameters. In 9 cases, model parameters can be identified such that the simulated tumor contains at least 40 percent of the volume of the observed tumor. In the analytical portion of the paper (chapters 3 and 4), a positively invariant region for our 2-population model is identified. Then, a rigorous derivation of the critical patch size associated with the model is performed. The critical patch (KISS) size is the minimum habitat size needed for a population to survive in a region. Habitats larger than the critical patch size allow a population to persist, while smaller habitats lead to extinction. The critical patch size of the 2-population model is consistent with that of the Fisher-Kolmogorov-Petrovsky-Piskunov equation, one of the first reaction-diffusion models proposed for GBM. The critical patch size may indicate that GBM tumors have a minimum size depending on the location in the brain. A theoretical relationship between the size of a GBM tumor at steady-state and its maximum cell density is also derived, which has potential applications for patient-specific parameter estimation based on magnetic resonance imaging data.
ContributorsHarris, Duane C. (Author) / Kuang, Yang (Thesis advisor) / Kostelich, Eric J. (Thesis advisor) / Preul, Mark C. (Committee member) / Crook, Sharon (Committee member) / Gardner, Carl (Committee member) / Arizona State University (Publisher)
Created2023