ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Displaying 1 - 10 of 119
Description
Topological insulators with conducting surface states yet insulating bulk states have generated a lot of interest amongst the physics community due to their varied characteristics and possible applications. Doped topological insulators have presented newer physical states of matter where topological order co&ndashexists; with other physical properties (like magnetic order). The electronic states of these materials are very intriguing and pose problems and the possible solutions to understanding their unique behaviors. In this work, we use Electron Energy Loss Spectroscopy (EELS) – an analytical TEM tool to study both core&ndashlevel; and valence&ndashlevel; excitations in Bi2Se3 and Cu(doped)Bi2Se3 topological insulators. We use this technique to retrieve information on the valence, bonding nature, co-ordination and lattice site occupancy of the undoped and the doped systems. Using the reference materials Cu(I)Se and Cu(II)Se we try to compare and understand the nature of doping that copper assumes in the lattice. And lastly we utilize the state of the art monochromated Nion UltraSTEM 100 to study electronic/vibrational excitations at a record energy resolution from sub-nm regions in the sample.
ContributorsSubramanian, Ganesh (Author) / Spence, John (Thesis advisor) / Jiang, Nan (Committee member) / Chen, Tingyong (Committee member) / Chan, Candace (Committee member) / Arizona State University (Publisher)
Created2013
Description
Dealloying, the selective dissolution of an elemental component from an alloy, is an important corrosion mechanism and a technological significant means to fabricate nanoporous structures for a variety of applications. In noble metal alloys, dealloying proceeds above a composition dependent critical potential, and bi-continuous structure evolves "simultaneously" as a result of the interplay between percolation dissolution and surface diffusion. In contrast, dealloying in alloys that show considerable solid-state mass transport at ambient temperature is largely unexplored despite its relevance to nanoparticle catalysts and Li-ion anodes. In my dissertation, I discuss the behaviors of two alloy systems in order to elucidate the role of bulk lattice diffusion in dealloying. First, Mg-Cd alloys are chosen to show that when the dealloying is controlled by bulk diffusion, a new type of porosity - negative void dendrites will form, and the process mirrors electrodeposition. Then, Li-Sn alloys are studied with respect to the composition, particle size and dealloying rate effects on the morphology evolution. Under the right condition, dealloying of Li-Sn supported by percolation dissolution results in the same bi-continuous structure as nanoporous noble metals; whereas lattice diffusion through the otherwise "passivated" surface allows for dealloying with no porosity evolution. The interactions between bulk diffusion, surface diffusion and dissolution are revealed by chronopotentiometry and linear sweep voltammetry technics. The better understanding of dealloying from these experiments enables me to construct a brief review summarizing the electrochemistry and morphology aspects of dealloying as well as offering interpretations to new observations such as critical size effect and encased voids in nanoporous gold. At the end of the dissertation, I will describe a preliminary attempt to generalize the morphology evolution "rules of dealloying" to all solid-to-solid interfacial controlled phase transition process, demonstrating that bi-continuous morphologies can evolve regardless of the nature of parent phase.
ContributorsChen, Qing (Author) / Sieradzki, Karl (Thesis advisor) / Friesen, Cody (Committee member) / Buttry, Daniel (Committee member) / Chan, Candace (Committee member) / Arizona State University (Publisher)
Created2013
Description
Membrane-based gas separation is promising for efficient propylene/propane (C3H6/C3H8) separation with low energy consumption and minimum environment impact. Two microporous inorganic membrane candidates, MFI-type zeolite membrane and carbon molecular sieve membrane (CMS) have demonstrated excellent thermal and chemical stability. Application of these membranes into C3H6/C3H8 separation has not been well investigated. This dissertation presents fundamental studies on membrane synthesis, characterization and C3H6/C3H8 separation properties of MFI zeolite membrane and CMS membrane.
MFI zeolite membranes were synthesized on α-alumina supports by secondary growth method. Novel positron annihilation spectroscopy (PAS) techniques were used to non-destructively characterize the pore structure of these membranes. PAS reveals a bimodal pore structure consisting of intracrystalline zeolitic micropores of ~0.6 nm in diameter and irregular intercrystalline micropores of 1.4 to 1.8 nm in size for the membranes. The template-free synthesized membrane exhibited a high permeance but a low selectivity in C3H6/C3H8 mixture separation.
CMS membranes were synthesized by coating/pyrolysis method on mesoporous γ-alumina support. Such supports allow coating of thin, high-quality polymer films and subsequent CMS membranes with no infiltration into support pores. The CMS membranes show strong molecular sieving effect, offering a high C3H6/C3H8 mixture selectivity of ~30. Reduction in membrane thickness from 500 nm to 300 nm causes an increase in C3H8 permeance and He/N2 selectivity, but a decrease in the permeance of He, N2 and C3H6 and C3H6/C3H8 selectivity. This can be explained by the thickness dependent chain mobility of the polymer film resulting in final carbon membrane of reduced pore size with different effects on transport of gas of different sizes, including possible closure of C3H6-accessible micropores.
CMS membranes demonstrate excellent C3H6/C3H8 separation performance over a wide range of feed pressure, composition and operation temperature. No plasticization was observed at a feed pressure up to 100 psi. The permeation and separation is mainly controlled by diffusion instead of adsorption. CMS membrane experienced a decline in permeance, and an increase in selectivity over time under on-stream C3H6/C3H8 separation. This aging behavior is due to the reduction in effective pore size and porosity caused by oxygen chemisorption and physical aging of the membrane structure.
MFI zeolite membranes were synthesized on α-alumina supports by secondary growth method. Novel positron annihilation spectroscopy (PAS) techniques were used to non-destructively characterize the pore structure of these membranes. PAS reveals a bimodal pore structure consisting of intracrystalline zeolitic micropores of ~0.6 nm in diameter and irregular intercrystalline micropores of 1.4 to 1.8 nm in size for the membranes. The template-free synthesized membrane exhibited a high permeance but a low selectivity in C3H6/C3H8 mixture separation.
CMS membranes were synthesized by coating/pyrolysis method on mesoporous γ-alumina support. Such supports allow coating of thin, high-quality polymer films and subsequent CMS membranes with no infiltration into support pores. The CMS membranes show strong molecular sieving effect, offering a high C3H6/C3H8 mixture selectivity of ~30. Reduction in membrane thickness from 500 nm to 300 nm causes an increase in C3H8 permeance and He/N2 selectivity, but a decrease in the permeance of He, N2 and C3H6 and C3H6/C3H8 selectivity. This can be explained by the thickness dependent chain mobility of the polymer film resulting in final carbon membrane of reduced pore size with different effects on transport of gas of different sizes, including possible closure of C3H6-accessible micropores.
CMS membranes demonstrate excellent C3H6/C3H8 separation performance over a wide range of feed pressure, composition and operation temperature. No plasticization was observed at a feed pressure up to 100 psi. The permeation and separation is mainly controlled by diffusion instead of adsorption. CMS membrane experienced a decline in permeance, and an increase in selectivity over time under on-stream C3H6/C3H8 separation. This aging behavior is due to the reduction in effective pore size and porosity caused by oxygen chemisorption and physical aging of the membrane structure.
ContributorsMa, Xiaoli (Author) / Lin, Jerry (Thesis advisor) / Alford, Terry (Committee member) / Chan, Candace (Committee member) / Arizona State University (Publisher)
Created2015
Description
In a collaborative environment where multiple robots and human beings are expected
to collaborate to perform a task, it becomes essential for a robot to be aware of multiple
agents working in its work environment. A robot must also learn to adapt to
different agents in the workspace and conduct its interaction based on the presence
of these agents. A theoretical framework was introduced which performs interaction
learning from demonstrations in a two-agent work environment, and it is called
Interaction Primitives.
This document is an in-depth description of the new state of the art Python
Framework for Interaction Primitives between two agents in a single as well as multiple
task work environment and extension of the original framework in a work environment
with multiple agents doing a single task. The original theory of Interaction
Primitives has been extended to create a framework which will capture correlation
between more than two agents while performing a single task. The new state of the
art Python framework is an intuitive, generic, easy to install and easy to use python
library which can be applied to use the Interaction Primitives framework in a work
environment. This library was tested in simulated environments and controlled laboratory
environment. The results and benchmarks of this library are available in the
related sections of this document.
to collaborate to perform a task, it becomes essential for a robot to be aware of multiple
agents working in its work environment. A robot must also learn to adapt to
different agents in the workspace and conduct its interaction based on the presence
of these agents. A theoretical framework was introduced which performs interaction
learning from demonstrations in a two-agent work environment, and it is called
Interaction Primitives.
This document is an in-depth description of the new state of the art Python
Framework for Interaction Primitives between two agents in a single as well as multiple
task work environment and extension of the original framework in a work environment
with multiple agents doing a single task. The original theory of Interaction
Primitives has been extended to create a framework which will capture correlation
between more than two agents while performing a single task. The new state of the
art Python framework is an intuitive, generic, easy to install and easy to use python
library which can be applied to use the Interaction Primitives framework in a work
environment. This library was tested in simulated environments and controlled laboratory
environment. The results and benchmarks of this library are available in the
related sections of this document.
ContributorsKumar, Ashish, M.S (Author) / Amor, Hani Ben (Thesis advisor) / Zhang, Yu (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2017
Description
Computer Vision as a eld has gone through signicant changes in the last decade.
The eld has seen tremendous success in designing learning systems with hand-crafted
features and in using representation learning to extract better features. In this dissertation
some novel approaches to representation learning and task learning are studied.
Multiple-instance learning which is generalization of supervised learning, is one
example of task learning that is discussed. In particular, a novel non-parametric k-
NN-based multiple-instance learning is proposed, which is shown to outperform other
existing approaches. This solution is applied to a diabetic retinopathy pathology
detection problem eectively.
In cases of representation learning, generality of neural features are investigated
rst. This investigation leads to some critical understanding and results in feature
generality among datasets. The possibility of learning from a mentor network instead
of from labels is then investigated. Distillation of dark knowledge is used to eciently
mentor a small network from a pre-trained large mentor network. These studies help
in understanding representation learning with smaller and compressed networks.
The eld has seen tremendous success in designing learning systems with hand-crafted
features and in using representation learning to extract better features. In this dissertation
some novel approaches to representation learning and task learning are studied.
Multiple-instance learning which is generalization of supervised learning, is one
example of task learning that is discussed. In particular, a novel non-parametric k-
NN-based multiple-instance learning is proposed, which is shown to outperform other
existing approaches. This solution is applied to a diabetic retinopathy pathology
detection problem eectively.
In cases of representation learning, generality of neural features are investigated
rst. This investigation leads to some critical understanding and results in feature
generality among datasets. The possibility of learning from a mentor network instead
of from labels is then investigated. Distillation of dark knowledge is used to eciently
mentor a small network from a pre-trained large mentor network. These studies help
in understanding representation learning with smaller and compressed networks.
ContributorsVenkatesan, Ragav (Author) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Yang, Yezhou (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2017
Description
Photocatalytic water splitting has been proposed as a promising way of generating carbon-neutral fuels from sunlight and water. In one approach, water decomposition is enabled by the use of functionalized nano-particulate photocatalyst composites. The atomic structures of the photocatalysts dictate their electronic and photonic structures, which are controlled by synthesis methods and may alter under reaction conditions. Characterizing these structures, especially the ones associated with photocatalysts’ surfaces, is essential because they determine the efficiencies of various reaction steps involved in photocatalytic water splitting. Due to its superior spatial resolution, (scanning) transmission electron microscopy (STEM/TEM), which includes various imaging and spectroscopic techniques, is a suitable tool for probing materials’ local atomic, electronic and optical structures. In this work, techniques specific for the study of photocatalysts are developed using model systems.
Nano-level structure-reactivity relationships as well as deactivation mechanisms of Ni core-NiO shell co-catalysts loaded on Ta2O5 particles are studied using an aberration-corrected TEM. It is revealed that nanometer changes in the shell thickness lead to significant changes in the H2 production. Also, deactivation of this system is found to be related to a photo-driven process resulting in the loss of the Ni core.
In addition, a special form of monochromated electron energy-loss spectroscopy (EELS), the so-called aloof beam EELS, is used to probe surface electronic states as well as light-particle interactions from model oxide nanoparticles. Surface states associated with hydrate species are analyzed using spectral simulations based on a dielectric theory and a density of states model. Geometry-induced optical-frequency resonant modes are excited using fast electrons in catalytically relevant oxides. Combing the spectral features detected in experiments with classical electrodynamics simulations, the underlying physics involved in this excitation process and the various influencing factors of the modes are investigated.
Finally, an in situ light illumination system is developed for an aberration-corrected environmental TEM to enable direct observation of atomic structural transformations of model photocatalysts while they are exposed to near reaction conditions.
Nano-level structure-reactivity relationships as well as deactivation mechanisms of Ni core-NiO shell co-catalysts loaded on Ta2O5 particles are studied using an aberration-corrected TEM. It is revealed that nanometer changes in the shell thickness lead to significant changes in the H2 production. Also, deactivation of this system is found to be related to a photo-driven process resulting in the loss of the Ni core.
In addition, a special form of monochromated electron energy-loss spectroscopy (EELS), the so-called aloof beam EELS, is used to probe surface electronic states as well as light-particle interactions from model oxide nanoparticles. Surface states associated with hydrate species are analyzed using spectral simulations based on a dielectric theory and a density of states model. Geometry-induced optical-frequency resonant modes are excited using fast electrons in catalytically relevant oxides. Combing the spectral features detected in experiments with classical electrodynamics simulations, the underlying physics involved in this excitation process and the various influencing factors of the modes are investigated.
Finally, an in situ light illumination system is developed for an aberration-corrected environmental TEM to enable direct observation of atomic structural transformations of model photocatalysts while they are exposed to near reaction conditions.
ContributorsLiu, Qianlang (Author) / Crozier, Peter A. (Thesis advisor) / Chan, Candace (Committee member) / Buttry, Daniel (Committee member) / Liu, Jingyue (Committee member) / Nemanich, Robert (Committee member) / Arizona State University (Publisher)
Created2018
Description
With the rise of the Big Data Era, an exponential amount of network data is being generated at an unprecedented rate across a wide-range of high impact micro and macro areas of research---from protein interaction to social networks. The critical challenge is translating this large scale network data into actionable information.
A key task in the data translation is the analysis of network connectivity via marked nodes---the primary focus of our research. We have developed a framework for analyzing network connectivity via marked nodes in large scale graphs, utilizing novel algorithms in three interrelated areas: (1) analysis of a single seed node via it’s ego-centric network (AttriPart algorithm); (2) pathway identification between two seed nodes (K-Simple Shortest Paths Multithreaded and Search Reduced (KSSPR) algorithm); and (3) tree detection, defining the interaction between three or more seed nodes (Shortest Path MST algorithm).
In an effort to address both fundamental and applied research issues, we have developed the LocalForcasting algorithm to explore how network connectivity analysis can be applied to local community evolution and recommender systems. The goal is to apply the LocalForecasting algorithm to various domains---e.g., friend suggestions in social networks or future collaboration in co-authorship networks. This algorithm utilizes link prediction in combination with the AttriPart algorithm to predict future connections in local graph partitions.
Results show that our proposed AttriPart algorithm finds up to 1.6x denser local partitions, while running approximately 43x faster than traditional local partitioning techniques (PageRank-Nibble). In addition, our LocalForecasting algorithm demonstrates a significant improvement in the number of nodes and edges correctly predicted over baseline methods. Furthermore, results for the KSSPR algorithm demonstrate a speed-up of up to 2.5x the standard k-simple shortest paths algorithm.
A key task in the data translation is the analysis of network connectivity via marked nodes---the primary focus of our research. We have developed a framework for analyzing network connectivity via marked nodes in large scale graphs, utilizing novel algorithms in three interrelated areas: (1) analysis of a single seed node via it’s ego-centric network (AttriPart algorithm); (2) pathway identification between two seed nodes (K-Simple Shortest Paths Multithreaded and Search Reduced (KSSPR) algorithm); and (3) tree detection, defining the interaction between three or more seed nodes (Shortest Path MST algorithm).
In an effort to address both fundamental and applied research issues, we have developed the LocalForcasting algorithm to explore how network connectivity analysis can be applied to local community evolution and recommender systems. The goal is to apply the LocalForecasting algorithm to various domains---e.g., friend suggestions in social networks or future collaboration in co-authorship networks. This algorithm utilizes link prediction in combination with the AttriPart algorithm to predict future connections in local graph partitions.
Results show that our proposed AttriPart algorithm finds up to 1.6x denser local partitions, while running approximately 43x faster than traditional local partitioning techniques (PageRank-Nibble). In addition, our LocalForecasting algorithm demonstrates a significant improvement in the number of nodes and edges correctly predicted over baseline methods. Furthermore, results for the KSSPR algorithm demonstrate a speed-up of up to 2.5x the standard k-simple shortest paths algorithm.
ContributorsFreitas, Scott (Author) / Tong, Hanghang (Thesis advisor) / Maciejewski, Ross (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2018
Description
This dissertation will investigate two of the most promising high-capacity anode
materials for lithium-based batteries: silicon (Si) and metal lithium (Li). It will focus on
studying the mechanical behaviors of the two materials during charge and discharge and
understanding how these mechanical behaviors may affect their electrochemical
performance.
In the first part, amorphous Si anode will be studied. Despite many existing studies
on silicon (Si) anodes for lithium ion batteries (LIBs), many essential questions still exist
on compound formation, composition, and properties. Here it is shown that some
previously accepted findings do not truthfully reflect the actual lithiation mechanisms in
realistic battery configurations. Furthermore the correlation between structure and
mechanical properties in these materials has not been properly established. Here, a rigorous
and thorough study is performed to comprehensively understand the electrochemical
reaction mechanisms of amorphous-Si (a-Si) in a realistic LIB configuration. In-depth
microstructural characterization was performed and correlations were established between
Li-Si composition, volumetric expansion, and modulus/hardness. It is found that the
lithiation process of a-Si in a real battery setup is a single-phase reaction rather than the
accepted two-phase reaction obtained from in-situ TEM experiments. The findings in this
dissertation establish a reference to quantitatively explain many key metrics for lithiated a
Si as anodes in real LIBs, and can be used to rationally design a-Si based high-performance
LIBs guided by high-fidelity modeling and simulations.
In the second part, Li metal anode will be investigated. Problems related to dendrite
growth on lithium metal anodes such as capacity loss and short circuit present major
barriers to the next-generation high-energy-density batteries. The development of
successful mitigation strategies is impeded by the incomplete understanding of the Li
dendrite growth mechanisms. Here the enabling role of plating residual stress in dendrite
initiation through novel experiments of Li electrodeposition on soft substrates is confirmed,
and the observations is explained with a stress-driven dendrite growth model. Dendrite
growth is mitigated on such soft substrates through surface-wrinkling-induced stress
relaxation in deposited Li film. It is demonstrated that this new dendrite mitigation
mechanism can be utilized synergistically with other existing approaches in the form of
three-dimensional (3D) soft scaffolds for Li plating, which achieves superior coulombic
efficiency over conventional hard copper current collectors under large current density.
materials for lithium-based batteries: silicon (Si) and metal lithium (Li). It will focus on
studying the mechanical behaviors of the two materials during charge and discharge and
understanding how these mechanical behaviors may affect their electrochemical
performance.
In the first part, amorphous Si anode will be studied. Despite many existing studies
on silicon (Si) anodes for lithium ion batteries (LIBs), many essential questions still exist
on compound formation, composition, and properties. Here it is shown that some
previously accepted findings do not truthfully reflect the actual lithiation mechanisms in
realistic battery configurations. Furthermore the correlation between structure and
mechanical properties in these materials has not been properly established. Here, a rigorous
and thorough study is performed to comprehensively understand the electrochemical
reaction mechanisms of amorphous-Si (a-Si) in a realistic LIB configuration. In-depth
microstructural characterization was performed and correlations were established between
Li-Si composition, volumetric expansion, and modulus/hardness. It is found that the
lithiation process of a-Si in a real battery setup is a single-phase reaction rather than the
accepted two-phase reaction obtained from in-situ TEM experiments. The findings in this
dissertation establish a reference to quantitatively explain many key metrics for lithiated a
Si as anodes in real LIBs, and can be used to rationally design a-Si based high-performance
LIBs guided by high-fidelity modeling and simulations.
In the second part, Li metal anode will be investigated. Problems related to dendrite
growth on lithium metal anodes such as capacity loss and short circuit present major
barriers to the next-generation high-energy-density batteries. The development of
successful mitigation strategies is impeded by the incomplete understanding of the Li
dendrite growth mechanisms. Here the enabling role of plating residual stress in dendrite
initiation through novel experiments of Li electrodeposition on soft substrates is confirmed,
and the observations is explained with a stress-driven dendrite growth model. Dendrite
growth is mitigated on such soft substrates through surface-wrinkling-induced stress
relaxation in deposited Li film. It is demonstrated that this new dendrite mitigation
mechanism can be utilized synergistically with other existing approaches in the form of
three-dimensional (3D) soft scaffolds for Li plating, which achieves superior coulombic
efficiency over conventional hard copper current collectors under large current density.
ContributorsWang, Xu (Author) / Jiang, Hanqing (Thesis advisor) / Yu, Hongbin (Thesis advisor) / Chan, Candace (Committee member) / Wang, Liping (Committee member) / Qiong, Nian (Committee member) / Arizona State University (Publisher)
Created2018
Description
This work demonstrates a capable reverse pulse deposition methodology to influence gap fill behavior inside microvia along with a uniform deposit in the fine line patterned regions for substrate packaging applications. Interconnect circuitry in IC substrate packages comprises of stacked microvia that varies in depth from 20µm to 100µm with an aspect ratio of 0.5 to 1.5 and fine line patterns defined by photolithography. Photolithography defined pattern regions incorporate a wide variety of feature sizes including large circular pad structures with diameter of 20µm - 200µm, fine traces with varying widths of 3µm - 30µm and additional planar regions to define a IC substrate package. Electrodeposition of copper is performed to establish the desired circuit. Electrodeposition of copper in IC substrate applications holds certain unique challenges in that they require a low cost manufacturing process that enables a void-free gap fill inside the microvia along with uniform deposition of copper on exposed patterned regions. Deposition time scales to establish the desired metal thickness for such packages could range from several minutes to few hours. This work showcases a reverse pulse electrodeposition methodology that achieves void-free gap fill inside the microvia and uniform plating in FLS (Fine Lines and Spaces) regions with significantly higher deposition rates than traditional approaches. In order to achieve this capability, systematic experimental and simulation studies were performed. A strong correlation of independent parameters that govern the electrodeposition process such as bath temperature, reverse pulse plating parameters and the ratio of electrolyte concentrations is shown to the deposition kinetics and deposition uniformity in fine patterned regions and gap fill rate inside the microvia. Additionally, insight into the physics of via fill process is presented with secondary and tertiary current simulation efforts. Such efforts lead to show “smart” control of deposition rate at the top and bottom of via to avoid void formation. Finally, a parametric effect on grain size and the ensuing copper metallurgical characteristics of bulk copper is also shown to enable high reliability substrate packages for the IC packaging industry.
ContributorsGanesan, Kousik (Author) / Tasooji, Amaneh (Thesis advisor) / Manepalli, Rahul (Committee member) / Alford, Terry (Committee member) / Chan, Candace (Committee member) / Arizona State University (Publisher)
Created2018
Description
The performance of most of the visual computing tasks depends on the quality of the features extracted from the raw data. Insightful feature representation increases the performance of many learning algorithms by exposing the underlying explanatory factors of the output for the unobserved input. A good representation should also handle anomalies in the data such as missing samples and noisy input caused by the undesired, external factors of variation. It should also reduce the data redundancy. Over the years, many feature extraction processes have been invented to produce good representations of raw images and videos.
The feature extraction processes can be categorized into three groups. The first group contains processes that are hand-crafted for a specific task. Hand-engineering features requires the knowledge of domain experts and manual labor. However, the feature extraction process is interpretable and explainable. Next group contains the latent-feature extraction processes. While the original feature lies in a high-dimensional space, the relevant factors for a task often lie on a lower dimensional manifold. The latent-feature extraction employs hidden variables to expose the underlying data properties that cannot be directly measured from the input. Latent features seek a specific structure such as sparsity or low-rank into the derived representation through sophisticated optimization techniques. The last category is that of deep features. These are obtained by passing raw input data with minimal pre-processing through a deep network. Its parameters are computed by iteratively minimizing a task-based loss.
In this dissertation, I present four pieces of work where I create and learn suitable data representations. The first task employs hand-crafted features to perform clinically-relevant retrieval of diabetic retinopathy images. The second task uses latent features to perform content-adaptive image enhancement. The third task ranks a pair of images based on their aestheticism. The goal of the last task is to capture localized image artifacts in small datasets with patch-level labels. For both these tasks, I propose novel deep architectures and show significant improvement over the previous state-of-art approaches. A suitable combination of feature representations augmented with an appropriate learning approach can increase performance for most visual computing tasks.
The feature extraction processes can be categorized into three groups. The first group contains processes that are hand-crafted for a specific task. Hand-engineering features requires the knowledge of domain experts and manual labor. However, the feature extraction process is interpretable and explainable. Next group contains the latent-feature extraction processes. While the original feature lies in a high-dimensional space, the relevant factors for a task often lie on a lower dimensional manifold. The latent-feature extraction employs hidden variables to expose the underlying data properties that cannot be directly measured from the input. Latent features seek a specific structure such as sparsity or low-rank into the derived representation through sophisticated optimization techniques. The last category is that of deep features. These are obtained by passing raw input data with minimal pre-processing through a deep network. Its parameters are computed by iteratively minimizing a task-based loss.
In this dissertation, I present four pieces of work where I create and learn suitable data representations. The first task employs hand-crafted features to perform clinically-relevant retrieval of diabetic retinopathy images. The second task uses latent features to perform content-adaptive image enhancement. The third task ranks a pair of images based on their aestheticism. The goal of the last task is to capture localized image artifacts in small datasets with patch-level labels. For both these tasks, I propose novel deep architectures and show significant improvement over the previous state-of-art approaches. A suitable combination of feature representations augmented with an appropriate learning approach can increase performance for most visual computing tasks.
ContributorsChandakkar, Parag Shridhar (Author) / Li, Baoxin (Thesis advisor) / Yang, Yezhou (Committee member) / Turaga, Pavan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2017