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Description
Cameras have become commonplace with wide-ranging applications of phone photography, computer vision, and medical imaging. With a growing need to reduce size and costs while maintaining image quality, the need to look past traditional style of cameras is becoming more apparent. Several non-traditional cameras have shown to be promising options for size-constraint applications, and while they may offer several advantages, they also usually are limited by image quality degradation due to optical or a need to reconstruct a captured image. In this thesis, we take a look at three of these non-traditional cameras: a pinhole camera, a diffusion-mask lensless camera, and an under-display camera (UDC).
For each of these cases, I present a feasible image restoration pipeline to correct for their particular limitations. For the pinhole camera, I present an early pipeline to allow for practical pinhole photography by reducing noise levels caused by low-light imaging, enhancing exposure levels, and sharpening the blur caused by the pinhole. For lensless cameras, we explore a neural network architecture that performs joint image reconstruction and point spread function (PSF) estimation to robustly recover images captured with multiple PSFs from different cameras. Using adversarial learning, this approach achieves improved reconstruction results that do not require explicit knowledge of the PSF at test-time and shows an added improvement in the reconstruction model’s ability to generalize to variations in the camera’s PSF. This allows lensless cameras to be utilized in a wider range of applications that require multiple cameras without the need to explicitly train a separate model for each new camera. For UDCs, we utilize a multi-stage approach to correct for low light transmission, blur, and haze. This pipeline uses a PyNET deep neural network architecture to perform a majority of the restoration, while additionally using a traditional optimization approach which is then fused in a learned manner in the second stage to improve high-frequency features. I show results from this novel fusion approach that is on-par with the state of the art.
For each of these cases, I present a feasible image restoration pipeline to correct for their particular limitations. For the pinhole camera, I present an early pipeline to allow for practical pinhole photography by reducing noise levels caused by low-light imaging, enhancing exposure levels, and sharpening the blur caused by the pinhole. For lensless cameras, we explore a neural network architecture that performs joint image reconstruction and point spread function (PSF) estimation to robustly recover images captured with multiple PSFs from different cameras. Using adversarial learning, this approach achieves improved reconstruction results that do not require explicit knowledge of the PSF at test-time and shows an added improvement in the reconstruction model’s ability to generalize to variations in the camera’s PSF. This allows lensless cameras to be utilized in a wider range of applications that require multiple cameras without the need to explicitly train a separate model for each new camera. For UDCs, we utilize a multi-stage approach to correct for low light transmission, blur, and haze. This pipeline uses a PyNET deep neural network architecture to perform a majority of the restoration, while additionally using a traditional optimization approach which is then fused in a learned manner in the second stage to improve high-frequency features. I show results from this novel fusion approach that is on-par with the state of the art.
ContributorsRego, Joshua D (Author) / Jayasuriya, Suren (Thesis advisor) / Blain Christen, Jennifer (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2020
Description
A complex social system, whether artificial or natural, can possess its macroscopic properties as a collective, which may change in real time as a result of local behavioral interactions among a number of agents in it. If a reliable indicator is available to abstract the macrolevel states, decision makers could use it to take a proactive action, whenever needed, in order for the entire system to avoid unacceptable states or con-verge to desired ones. In realistic scenarios, however, there can be many challenges in learning a model of dynamic global states from interactions of agents, such as 1) high complexity of the system itself, 2) absence of holistic perception, 3) variability of group size, 4) biased observations on state space, and 5) identification of salient behavioral cues. In this dissertation, I introduce useful applications of macrostate estimation in complex multi-agent systems and explore effective deep learning frameworks to ad-dress the inherited challenges. First of all, Remote Teammate Localization (ReTLo)is developed in multi-robot teams, in which an individual robot can use its local interactions with a nearby robot as an information channel to estimate the holistic view of the group. Within the problem, I will show (a) learning a model of a modular team can generalize to all others to gain the global awareness of the team of variable sizes, and (b) active interactions are necessary to diversify training data and speed up the overall learning process. The complexity of the next focal system escalates to a colony of over 50 individual ants undergoing 18-day social stabilization since a chaotic event. I will utilize this natural platform to demonstrate, in contrast to (b), (c)monotonic samples only from “before chaos” can be sufficient to model the panicked society, and (d) the model can also be used to discover salient behaviors to precisely predict macrostates.
ContributorsChoi, Taeyeong (Author) / Pavlic, Theodore (Thesis advisor) / Richa, Andrea (Committee member) / Ben Amor, Heni (Committee member) / Yang, Yezhou (Committee member) / Liebig, Juergen (Committee member) / Arizona State University (Publisher)
Created2020
Description
Lateral programmable metallization cells (PMC) utilize the properties of electrodeposits grown over a solid electrolyte channel. Such devices have an active anode and an inert cathode separated by a long electrodeposit channel in a coplanar arrangement. The ability to transport large amount of metallic mass across the channel makes these devices attractive for various More-Than-Moore applications. Existing literature lacks a comprehensive study of electrodeposit growth kinetics in lateral PMCs. Moreover, the morphology of electrodeposit growth in larger, planar devices is also not understood. Despite the variety of applications, lateral PMCs are not embraced by the semiconductor industry due to incompatible materials and high operating voltages needed for such devices. In this work, a numerical model based on the basic processes in PMCs – cation drift and redox reactions – is proposed, and the effect of various materials parameters on the electrodeposit growth kinetics is reported. The morphology of the electrodeposit growth and kinetics of the electrodeposition process are also studied in devices based on Ag-Ge30Se70 materials system. It was observed that the electrodeposition process mainly consists of two regimes of growth – cation drift limited regime and mixed regime. The electrodeposition starts in cation drift limited regime at low electric fields and transitions into mixed regime as the field increases. The onset of mixed regime can be controlled by applied voltage which also affects the morphology of electrodeposit growth. The numerical model was then used to successfully predict the device kinetics and onset of mixed regime. The problem of materials incompatibility with semiconductor manufacturing was solved by proposing a novel device structure. A bilayer structure using semiconductor foundry friendly materials was suggested as a candidate for solid electrolyte. The bilayer structure consists of a low resistivity oxide shunt layer on top of a high resistivity ion carrying oxide layer. Devices using Cu2O as the low resistivity shunt on top of Cu doped WO3 oxide were fabricated. The bilayer devices provided orders of magnitude improvement in device performance in the context of operating voltage and switching time. Electrical and materials characterization revealed the structure of bilayers and the mechanism of electrodeposition in these devices.
ContributorsChamele, Ninad (Author) / Kozicki, Michael (Thesis advisor) / Barnaby, Hugh (Committee member) / Newman, Nathan (Committee member) / Gonzalez-Velo, Yago (Committee member) / Arizona State University (Publisher)
Created2020
Description
The passivity of metals is a phenomenon of vast importance as it prevents many materials in important applications from rapid deterioration by corrosion. Alloying with a sufficient quantity of passivating elements (Cr, Al, Si), typically in the range of 10% - 20%, is commonly employed to improve the corrosion resistance of elemental metals. However, the compositional criteria for enhanced corrosion resistance have been a long-standing unanswered question for alloys design. With the emerging interest in multi-principal element alloy design, a percolation model is developed herein for the initial stage of passive film formation, termed primary passivation. The successful validation of the assumptions and predictions of the model in three corrosion-resistant binary alloys, Fe-Cr, Ni-Cr, and Cu-Rh supports that the model which can be used to provide a quantitative design strategy for designing corrosion-resistant alloys. To date, this is the only model that can provide such criteria for alloy design.The model relates alloy passivation to site percolation of the passivating elements in the alloy matrix. In the initial passivation stage, Fe (Ni in Ni-Cr or Cu in Cu-Rh) is selectively dissolved, destroying the passive network built up by Cr (or Rh) oxides and undercutting isolated incipient Cr (Rh) oxide nuclei. The only way to prevent undercutting and form a stable protective passive film is if the concentration of Cr (Rh) is high enough to realize site percolation within the thickness of the passive film or the dissolution depth. This 2D-3D percolation cross-over transition explains the compositional dependent passivation of these alloys. The theoretical description of the transition and its assumptions is examined via experiments and kinetic Monte Carlo simulations. The initial passivation scenario of the dissolution selectivity is validated by the inductively coupled plasma mass spectrum (ICP-MS). The electronic effect not considered in the kinetic Monte Carlo simulations is addressed by density functional theory (DFT). Additionally, the impact of the atomic configuration parameter on alloy passivation is experimentally measured, which turns out to agree well with the model predictions developed using Monte Carlo renormalization group (MC-RNG) methods.
ContributorsXie, Yusi (Author) / Sieradzki, Karl KS (Thesis advisor) / Chan, Candace CC (Committee member) / Wang, Qing QHW (Committee member) / Buttry, Daniel DB (Committee member) / Arizona State University (Publisher)
Created2020
Description
Technological advances have allowed for the assimilation of a variety of data, driving a shift away from the use of simpler and constrained patterns to more complex and diverse patterns in retrieval and analysis of such data. This shift has inundated the conventional techniques and has stressed the need for intelligent mechanisms that can model the complex patterns in the data. Deep neural networks have shown some success at capturing complex patterns, including the so-called attentioned networks, have significant shortcomings in distinguishing what is important in data from what is noise. This dissertation observes that the traditional neural networks primarily rely solely on gradient-based learning to model deep features maps while ignoring the key insight in the data that can be leveraged as complementary information to help learn an accurate model. In particular, this dissertation shows that the localized multi-scale features (captured implicitly or explicitly) can be leveraged to help improve model performance as these features capture salient informative points in the data.
This dissertation focuses on “working with the data, not just on data”, i.e. leveraging feature saliency through pre-training, in-training, and post-training analysis of the data. In particular, non-neural localized multi-scale feature extraction, in images and time series, are relatively cheap to obtain and can provide a rough overview of the patterns in the data. Furthermore, localized features coupled with deep features can help learn a high performing network. A pre-training analysis of sizes, complexities, and distribution of these localized features can help intelligently allocate a user-provided kernel budget in the network as a single-shot hyper-parameter search. Additionally, these localized features can be used as a secondary input modality to the network for cross-attention. Retraining pre-trained networks can be a costly process, yet, a post-training analysis of model inferences can allow for learning the importance of individual network parameters to the model inferences thus facilitating a retraining-free network sparsification with minimal impact on the model performance. Furthermore, effective in-training analysis of the intermediate features in the network help learn the importance of individual intermediate features (neural attention) and this analysis can be achieved through simulating local-extrema detection or learning features simultaneously and understanding their co-occurrences. In summary, this dissertation argues and establishes that, if appropriately leveraged, localized features and their feature saliency can help learn high-accurate, yet cheaper networks.
This dissertation focuses on “working with the data, not just on data”, i.e. leveraging feature saliency through pre-training, in-training, and post-training analysis of the data. In particular, non-neural localized multi-scale feature extraction, in images and time series, are relatively cheap to obtain and can provide a rough overview of the patterns in the data. Furthermore, localized features coupled with deep features can help learn a high performing network. A pre-training analysis of sizes, complexities, and distribution of these localized features can help intelligently allocate a user-provided kernel budget in the network as a single-shot hyper-parameter search. Additionally, these localized features can be used as a secondary input modality to the network for cross-attention. Retraining pre-trained networks can be a costly process, yet, a post-training analysis of model inferences can allow for learning the importance of individual network parameters to the model inferences thus facilitating a retraining-free network sparsification with minimal impact on the model performance. Furthermore, effective in-training analysis of the intermediate features in the network help learn the importance of individual intermediate features (neural attention) and this analysis can be achieved through simulating local-extrema detection or learning features simultaneously and understanding their co-occurrences. In summary, this dissertation argues and establishes that, if appropriately leveraged, localized features and their feature saliency can help learn high-accurate, yet cheaper networks.
ContributorsGarg, Yash (Author) / Candan, K. Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Sapino, Maria Luisa (Committee member) / Arizona State University (Publisher)
Created2020
Description
While significant qualitative, user study-focused research has been done on augmented reality, relatively few studies have been conducted on multiple, co-located synchronously collaborating users in augmented reality. Recognizing the need for more collaborative user studies in augmented reality and the value such studies present, a user study is conducted of collaborative decision-making in augmented reality to investigate the following research question: “Does presenting data visualizations in augmented reality influence the collaborative decision-making behaviors of a team?” This user study evaluates how viewing data visualizations with augmented reality headsets impacts collaboration in small teams compared to viewing together on a single 2D desktop monitor as a baseline. Teams of two participants performed closed and open-ended evaluation tasks to collaboratively analyze data visualized in both augmented reality and on a desktop monitor. Multiple means of collecting and analyzing data were employed to develop a well-rounded context for results and conclusions, including software logging of participant interactions, qualitative analysis of video recordings of participant sessions, and pre- and post-study participant questionnaires. The results indicate that augmented reality doesn’t significantly change the quantity of team member communication but does impact the means and strategies participants use to collaborate.
ContributorsKintscher, Michael (Author) / Bryan, Chris (Thesis advisor) / Amresh, Ashish (Thesis advisor) / Hansford, Dianne (Committee member) / Johnson, Erik (Committee member) / Arizona State University (Publisher)
Created2020
Description
The origins of carrier mobility (μe) were thoroughly investigated in hydrogenated indium oxide (IO:H) and zinc-tin oxide (ZTO) transparent conducting oxide (TCO) thin films. A carrier transport model was developed for IO:H which studied the effects of ionized impurity scattering, polar optical phonon scattering, and grain boundary scattering. Ionized impurity scattering dominated at temperatures below ~240 K. A reduction in scattering charge Z from +2 to +1 as atomic %H increased from ~3 atomic %H to ~5 atomic %H allowed μe to attain >100 cm^2/Vs at ~5 atomic %H.
In highly hydrogenated IO:H, ne significantly decreased as temperature increased from 5 K to 140 K. To probe this unusual behavior, samples were illuminated, then ne, surface work function (WF), and spatially resolved microscopic current mapping were measured and tracked. Large increases in ne and corresponding decreases in WF were observed---these both exhibited slow reversions toward pre-illumination values over 6-12 days. A hydrogen-related defect was proposed as source of the photoexcitation, while a lattice defect diffusion mechanism causes the extended decay. Both arise from an under-coordination of the In.
An enhancement of μe was observed with increasing amorphous fraction in IO:H. An increase in population of corner- and edge-sharing polyhedra consisting of metal cations and oxygen anions is thought to be the origin. This indicates some measure of medium-range order in the amorphous structure, and gives rise to a general principle dictating μe in TCOs---even amorphous TCOs. Testing this principle resulted in observing an enhancement of μe up to 35 cm^2/Vs in amorphous ZTO (a-ZTO), one of the highest reported a-ZTO μe values (at ne > 10^19 cm^-3) to date. These results highlight the role of local distortions and cation coordination in determining the microscopic origins of carrier generation and transport. In addition, the strong likelihood of under-coordination of one cation species leading to high carrier concentrations is proposed. This diverges from the historical indictment of oxygen vacancies controlling carrier population in crystalline oxides, which by definition cannot occur in amorphous systems, and provides a framework to discuss key structural descriptors in these disordered phase materials.
In highly hydrogenated IO:H, ne significantly decreased as temperature increased from 5 K to 140 K. To probe this unusual behavior, samples were illuminated, then ne, surface work function (WF), and spatially resolved microscopic current mapping were measured and tracked. Large increases in ne and corresponding decreases in WF were observed---these both exhibited slow reversions toward pre-illumination values over 6-12 days. A hydrogen-related defect was proposed as source of the photoexcitation, while a lattice defect diffusion mechanism causes the extended decay. Both arise from an under-coordination of the In.
An enhancement of μe was observed with increasing amorphous fraction in IO:H. An increase in population of corner- and edge-sharing polyhedra consisting of metal cations and oxygen anions is thought to be the origin. This indicates some measure of medium-range order in the amorphous structure, and gives rise to a general principle dictating μe in TCOs---even amorphous TCOs. Testing this principle resulted in observing an enhancement of μe up to 35 cm^2/Vs in amorphous ZTO (a-ZTO), one of the highest reported a-ZTO μe values (at ne > 10^19 cm^-3) to date. These results highlight the role of local distortions and cation coordination in determining the microscopic origins of carrier generation and transport. In addition, the strong likelihood of under-coordination of one cation species leading to high carrier concentrations is proposed. This diverges from the historical indictment of oxygen vacancies controlling carrier population in crystalline oxides, which by definition cannot occur in amorphous systems, and provides a framework to discuss key structural descriptors in these disordered phase materials.
ContributorsHusein, Sebastian S.T. (Author) / Bertoni, Mariana I. (Thesis advisor) / Stuckelberger, Michael (Committee member) / Holman, Zachary C. (Committee member) / Crozier, Peter (Committee member) / Arizona State University (Publisher)
Created2020
Description
The burden of adaptation has been a major limiting factor in the adoption rates of new wearable assistive technologies. This burden has created a necessity for the exploration and combination of two key concepts in the development of upcoming wearables: anticipation and invisibility. The combination of these two topics has created the field of Anticipatory and Invisible Interfaces (AII)
In this dissertation, a novel framework is introduced for the development of anticipatory devices that augment the proprioceptive system in individuals with neurodegenerative disorders in a seamless way that scaffolds off of existing cognitive feedback models. The framework suggests three main categories of consideration in the development of devices which are anticipatory and invisible:
• Idiosyncratic Design: How do can a design encapsulate the unique characteristics of the individual in the design of assistive aids?
• Adaptation to Intrapersonal Variations: As individuals progress through the various stages of a disability
eurological disorder, how can the technology adapt thresholds for feedback over time to address these shifts in ability?
• Context Aware Invisibility: How can the mechanisms of interaction be modified in order to reduce cognitive load?
The concepts proposed in this framework can be generalized to a broad range of domains; however, there are two primary applications for this work: rehabilitation and assistive aids. In preliminary studies, the framework is applied in the areas of Parkinsonian freezing of gait anticipation and the anticipation of body non-compliance during rehabilitative exercise.
In this dissertation, a novel framework is introduced for the development of anticipatory devices that augment the proprioceptive system in individuals with neurodegenerative disorders in a seamless way that scaffolds off of existing cognitive feedback models. The framework suggests three main categories of consideration in the development of devices which are anticipatory and invisible:
• Idiosyncratic Design: How do can a design encapsulate the unique characteristics of the individual in the design of assistive aids?
• Adaptation to Intrapersonal Variations: As individuals progress through the various stages of a disability
eurological disorder, how can the technology adapt thresholds for feedback over time to address these shifts in ability?
• Context Aware Invisibility: How can the mechanisms of interaction be modified in order to reduce cognitive load?
The concepts proposed in this framework can be generalized to a broad range of domains; however, there are two primary applications for this work: rehabilitation and assistive aids. In preliminary studies, the framework is applied in the areas of Parkinsonian freezing of gait anticipation and the anticipation of body non-compliance during rehabilitative exercise.
ContributorsTadayon, Arash (Author) / Panchanathan, Sethuraman (Thesis advisor) / McDaniel, Troy (Committee member) / Krishnamurthi, Narayanan (Committee member) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2020
Description
Bio-modification of asphalt binder brings significant benefits in terms of increasing sustainable and environmental practices, stabilizing prices, and decreasing costs. However, bio-modified asphalt binders have shown varying performance regarding susceptibility to moisture damage; some bio-oil modifiers significantly increase asphalt binder's susceptibility to moisture damage. This variability in performance is largely due to the large number of bio-masses available for use as sources of bio-oil, as well as the type of processing procedure followed in converting the bio-mass into a bio-oil for modifying asphalt binder. Therefore, there is a need for a method of properly evaluating the potential impact of a bio-oil modifier for asphalt binder on the overall performance of asphalt pavement, in order to properly distinguish whether a particular bio-oil modifier increases or decreases the moisture susceptibility of asphalt binder. Therefore, the goal of this study is a multi-scale investigation of bio-oils with known chemical compositions to determine if there is a correlation between a fundamental property of a bio-oil and the resulting increase or decrease in moisture susceptibility of a binder when it is modified with the bio-oil. For instance, it was found that polarizability of asphalt constituents can be a promising indicator of moisture susceptibility of bitumen. This study will also evaluate the linkage of the fundamental property to newly developed binder-level test methods. It was found that moisture-induced shear thinning of bitumen containing glass beads can differentiate moisture susceptible bitumen samples. Based on the knowledge determined, alternative methods of reducing the moisture susceptibility of asphalt pavement will also be evaluated. It was shown that accumulation of acidic compounds at the interface of bitumen and aggregate could promote moisture damage. It was further found that detracting acidic compounds from the interface could be done by either of neutralizing active site of stone aggregate to reduce affinity for acids or by arresting acidic compounds using active mineral filler. The study results showed there is a strong relation between composition of bitumen and its susceptibility to moisture. This in turn emphasize the importance of integrating knowledge of surface chemistry and bitumen composition into the pavement design and evaluation.
ContributorsOldham, Daniel Joshua (Author) / Fini, Elham F (Thesis advisor) / Kaloush, Kamil (Committee member) / Deng, Shuguang (Committee member) / Mallick, Rajib B (Committee member) / Louie, Stacey M (Committee member) / Parast, Mahour M (Committee member) / Arizona State University (Publisher)
Created2020
Description
Information Retrieval (IR) is the task of obtaining pieces of data (such as documents or snippets of text) that are relevant to a particular query or need from a large repository of information. IR is a valuable component of several downstream Natural Language Processing (NLP) tasks, such as Question Answering. Practically, IR is at the heart of many widely-used technologies like search engines.
While probabilistic ranking functions, such as the Okapi BM25 function, have been utilized in IR systems since the 1970's, modern neural approaches pose certain advantages compared to their classical counterparts. In particular, the release of BERT (Bidirectional Encoder Representations from Transformers) has had a significant impact in the NLP community by demonstrating how the use of a Masked Language Model (MLM) trained on a considerable corpus of data can improve a variety of downstream NLP tasks, including sentence classification and passage re-ranking.
IR Systems are also important in the biomedical and clinical domains. Given the continuously-increasing amount of scientific literature across biomedical domain, the ability find answers to specific clinical queries from a repository of millions of articles is a matter of practical value to medics, doctors, and other medical professionals. Moreover, there are domain-specific challenges present in the biomedical domain, including handling clinical jargon and evaluating the similarity or relatedness of various medical symptoms when determining the relevance between a query and a sentence.
This work presents contributions to several aspects of the Biomedical Semantic Information Retrieval domain. First, it introduces Multi-Perspective Sentence Relevance, a novel methodology of utilizing BERT-based models for contextual IR. The system is evaluated using the BioASQ Biomedical IR Challenge. Finally, practical contributions in the form of a live IR system for medics and a proposed challenge on the Living Systematic Review clinical task are provided.
While probabilistic ranking functions, such as the Okapi BM25 function, have been utilized in IR systems since the 1970's, modern neural approaches pose certain advantages compared to their classical counterparts. In particular, the release of BERT (Bidirectional Encoder Representations from Transformers) has had a significant impact in the NLP community by demonstrating how the use of a Masked Language Model (MLM) trained on a considerable corpus of data can improve a variety of downstream NLP tasks, including sentence classification and passage re-ranking.
IR Systems are also important in the biomedical and clinical domains. Given the continuously-increasing amount of scientific literature across biomedical domain, the ability find answers to specific clinical queries from a repository of millions of articles is a matter of practical value to medics, doctors, and other medical professionals. Moreover, there are domain-specific challenges present in the biomedical domain, including handling clinical jargon and evaluating the similarity or relatedness of various medical symptoms when determining the relevance between a query and a sentence.
This work presents contributions to several aspects of the Biomedical Semantic Information Retrieval domain. First, it introduces Multi-Perspective Sentence Relevance, a novel methodology of utilizing BERT-based models for contextual IR. The system is evaluated using the BioASQ Biomedical IR Challenge. Finally, practical contributions in the form of a live IR system for medics and a proposed challenge on the Living Systematic Review clinical task are provided.
ContributorsRawal, Samarth (Author) / Baral, Chitta (Thesis advisor) / Devarakonda, Murthy (Committee member) / Anwar, Saadat (Committee member) / Arizona State University (Publisher)
Created2020