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.
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- Creators: Alford, Terry
- Creators: Holbert, Keith E.
- Creators: Ye, Jieping
Description
Emission of CO2 into the atmosphere has become an increasingly concerning issue as we progress into the 21st century Flue gas from coal-burning power plants accounts for 40% of all carbon dioxide emissions. The key to successful separation and sequestration is to separate CO2 directly from flue gas (10-15% CO2, 70% N2), which can range from a few hundred to as high as 1000°C. Conventional microporous membranes (carbons/silicas/zeolites) are capable of separating CO2 from N2 at low temperatures, but cannot achieve separation above 200°C. To overcome the limitations of microporous membranes, a novel ceramic-carbonate dual-phase membrane for high temperature CO2 separation was proposed. The membrane was synthesized from porous La0.6Sr0.4Co0.8Fe0.2O3-d (LSCF) supports and infiltrated with molten carbonate (Li2CO3/Na2CO3/K2CO3). The CO2 permeation mechanism involves a reaction between CO2 (gas phase) and O= (solid phase) to form CO3=, which is then transported through the molten carbonate (liquid phase) to achieve separation. The effects of membrane thickness, temperature and CO2 partial pressure were studied. Decreasing thickness from 3.0 to 0.375 mm led to higher fluxes at 900°C, ranging from 0.186 to 0.322 mL.min-1.cm-2 respectively. CO2 flux increased with temperature from 700 to 900°C. Activation energy for permeation was similar to that for oxygen ion conduction in LSCF. For partial pressures above 0.05 atm, the membrane exhibited a nearly constant flux. From these observations, it was determined that oxygen ion conductivity limits CO2 permeation and that the equilibrium oxygen vacancy concentration in LSCF is dependent on the partial pressure of CO2 in the gas phase. Finally, the dual-phase membrane was used as a membrane reactor. Separation at high temperatures can produce warm, highly concentrated streams of CO2 that could be used as a chemical feedstock for the synthesis of syngas (H2 + CO). Towards this, three different membrane reactor configurations were examined: 1) blank system, 2) LSCF catalyst and 3) 10% Ni/y-alumina catalyst. Performance increased in the order of blank system < LSCF catalyst < Ni/y-alumina catalyst. Favorable conditions for syngas production were high temperature (850°C), low sweep gas flow rate (10 mL.min-1) and high methane concentration (50%) using the Ni/y-alumina catalyst.
ContributorsAnderson, Matthew Brandon (Author) / Lin, Jerry (Thesis advisor) / Alford, Terry (Committee member) / Rege, Kaushal (Committee member) / Anderson, James (Committee member) / Rivera, Daniel (Committee member) / Arizona State University (Publisher)
Created2011
Description
Reliable extraction of human pose features that are invariant to view angle and body shape changes is critical for advancing human movement analysis. In this dissertation, the multifactor analysis techniques, including the multilinear analysis and the multifactor Gaussian process methods, have been exploited to extract such invariant pose features from video data by decomposing various key contributing factors, such as pose, view angle, and body shape, in the generation of the image observations. Experimental results have shown that the resulting pose features extracted using the proposed methods exhibit excellent invariance properties to changes in view angles and body shapes. Furthermore, using the proposed invariant multifactor pose features, a suite of simple while effective algorithms have been developed to solve the movement recognition and pose estimation problems. Using these proposed algorithms, excellent human movement analysis results have been obtained, and most of them are superior to those obtained from state-of-the-art algorithms on the same testing datasets. Moreover, a number of key movement analysis challenges, including robust online gesture spotting and multi-camera gesture recognition, have also been addressed in this research. To this end, an online gesture spotting framework has been developed to automatically detect and learn non-gesture movement patterns to improve gesture localization and recognition from continuous data streams using a hidden Markov network. In addition, the optimal data fusion scheme has been investigated for multicamera gesture recognition, and the decision-level camera fusion scheme using the product rule has been found to be optimal for gesture recognition using multiple uncalibrated cameras. Furthermore, the challenge of optimal camera selection in multi-camera gesture recognition has also been tackled. A measure to quantify the complementary strength across cameras has been proposed. Experimental results obtained from a real-life gesture recognition dataset have shown that the optimal camera combinations identified according to the proposed complementary measure always lead to the best gesture recognition results.
ContributorsPeng, Bo (Author) / Qian, Gang (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011
Description
With the introduction of compressed sensing and sparse representation,many image processing and computer vision problems have been looked at in a new way. Recent trends indicate that many challenging computer vision and image processing problems are being solved using compressive sensing and sparse representation algorithms. This thesis assays some applications of compressive sensing and sparse representation with regards to image enhancement, restoration and classication. The first application deals with image Super-Resolution through compressive sensing based sparse representation. A novel framework is developed for understanding and analyzing some of the implications of compressive sensing in reconstruction and recovery of an image through raw-sampled and trained dictionaries. Properties of the projection operator and the dictionary are examined and the corresponding results presented. In the second application a novel technique for representing image classes uniquely in a high-dimensional space for image classification is presented. In this method, design and implementation strategy of the image classification system through unique affine sparse codes is presented, which leads to state of the art results. This further leads to analysis of some of the properties attributed to these unique sparse codes. In addition to obtaining these codes, a strong classier is designed and implemented to boost the results obtained. Evaluation with publicly available datasets shows that the proposed method outperforms other state of the art results in image classication. The final part of the thesis deals with image denoising with a novel approach towards obtaining high quality denoised image patches using only a single image. A new technique is proposed to obtain highly correlated image patches through sparse representation, which are then subjected to matrix completion to obtain high quality image patches. Experiments suggest that there may exist a structure within a noisy image which can be exploited for denoising through a low-rank constraint.
ContributorsKulkarni, Naveen (Author) / Li, Baoxin (Thesis advisor) / Ye, Jieping (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. To validate these approaches in a disease-specific context, we built a schizophreniaspecific network based on the inferred associations and performed a comprehensive prioritization of human genes with respect to the disease. These results are expected to be validated empirically, but computational validation using known targets are very positive.
ContributorsLee, Jang (Author) / Gonzalez, Graciela (Thesis advisor) / Ye, Jieping (Committee member) / Davulcu, Hasan (Committee member) / Gallitano-Mendel, Amelia (Committee member) / Arizona State University (Publisher)
Created2011
Description
Radiation-induced gain degradation in bipolar devices is considered to be the primary threat to linear bipolar circuits operating in the space environment. The damage is primarily caused by charged particles trapped in the Earth's magnetosphere, the solar wind, and cosmic rays. This constant radiation exposure leads to early end-of-life expectancies for many electronic parts. Exposure to ionizing radiation increases the density of oxide and interfacial defects in bipolar oxides leading to an increase in base current in bipolar junction transistors. Radiation-induced excess base current is the primary cause of current gain degradation. Analysis of base current response can enable the measurement of defects generated by radiation exposure. In addition to radiation, the space environment is also characterized by extreme temperature fluctuations. Temperature, like radiation, also has a very strong impact on base current. Thus, a technique for separating the effects of radiation from thermal effects is necessary in order to accurately measure radiation-induced damage in space. This thesis focuses on the extraction of radiation damage in lateral PNP bipolar junction transistors and the space environment. It also describes the measurement techniques used and provides a quantitative analysis methodology for separating radiation and thermal effects on the bipolar base current.
ContributorsCampola, Michael J (Author) / Barnaby, Hugh J (Thesis advisor) / Holbert, Keith E. (Committee member) / Vasileska, Dragica (Committee member) / Arizona State University (Publisher)
Created2011
Description
The increased use of commercial complementary metal-oxide-semiconductor (CMOS) technologies in harsh radiation environments has resulted in a new approach to radiation effects mitigation. This approach utilizes simulation to support the design of integrated circuits (ICs) to meet targeted tolerance specifications. Modeling the deleterious impact of ionizing radiation on ICs fabricated in advanced CMOS technologies requires understanding and analyzing the basic mechanisms that result in buildup of radiation-induced defects in specific sensitive regions. Extensive experimental studies have demonstrated that the sensitive regions are shallow trench isolation (STI) oxides. Nevertheless, very little work has been done to model the physical mechanisms that result in the buildup of radiation-induced defects and the radiation response of devices fabricated in these technologies. A comprehensive study of the physical mechanisms contributing to the buildup of radiation-induced oxide trapped charges and the generation of interface traps in advanced CMOS devices is presented in this dissertation. The basic mechanisms contributing to the buildup of radiation-induced defects are explored using a physical model that utilizes kinetic equations that captures total ionizing dose (TID) and dose rate effects in silicon dioxide (SiO2). These mechanisms are formulated into analytical models that calculate oxide trapped charge density (Not) and interface trap density (Nit) in sensitive regions of deep-submicron devices. Experiments performed on field-oxide-field-effect-transistors (FOXFETs) and metal-oxide-semiconductor (MOS) capacitors permit investigating TID effects and provide a comparison for the radiation response of advanced CMOS devices. When used in conjunction with closed-form expressions for surface potential, the analytical models enable an accurate description of radiation-induced degradation of transistor electrical characteristics. In this dissertation, the incorporation of TID effects in advanced CMOS devices into surface potential based compact models is also presented. The incorporation of TID effects into surface potential based compact models is accomplished through modifications of the corresponding surface potential equations (SPE), allowing the inclusion of radiation-induced defects (i.e., Not and Nit) into the calculations of surface potential. Verification of the compact modeling approach is achieved via comparison with experimental data obtained from FOXFETs fabricated in a 90 nm low-standby power commercial bulk CMOS technology and numerical simulations of fully-depleted (FD) silicon-on-insulator (SOI) n-channel transistors.
ContributorsSanchez Esqueda, Ivan (Author) / Barnaby, Hugh J (Committee member) / Schroder, Dieter (Thesis advisor) / Schroder, Dieter K. (Committee member) / Holbert, Keith E. (Committee member) / Gildenblat, Gennady (Committee member) / Arizona State University (Publisher)
Created2011
Description
Bridging semantic gap is one of the fundamental problems in multimedia computing and pattern recognition. The challenge of associating low-level signal with their high-level semantic interpretation is mainly due to the fact that semantics are often conveyed implicitly in a context, relying on interactions among multiple levels of concepts or low-level data entities. Also, additional domain knowledge may often be indispensable for uncovering the underlying semantics, but in most cases such domain knowledge is not readily available from the acquired media streams. Thus, making use of various types of contextual information and leveraging corresponding domain knowledge are vital for effectively associating high-level semantics with low-level signals with higher accuracies in multimedia computing problems. In this work, novel computational methods are explored and developed for incorporating contextual information/domain knowledge in different forms for multimedia computing and pattern recognition problems. Specifically, a novel Bayesian approach with statistical-sampling-based inference is proposed for incorporating a special type of domain knowledge, spatial prior for the underlying shapes; cross-modality correlations via Kernel Canonical Correlation Analysis is explored and the learnt space is then used for associating multimedia contents in different forms; model contextual information as a graph is leveraged for regulating interactions among high-level semantic concepts (e.g., category labels), low-level input signal (e.g., spatial/temporal structure). Four real-world applications, including visual-to-tactile face conversion, photo tag recommendation, wild web video classification and unconstrained consumer video summarization, are selected to demonstrate the effectiveness of the approaches. These applications range from classic research challenges to emerging tasks in multimedia computing. Results from experiments on large-scale real-world data with comparisons to other state-of-the-art methods and subjective evaluations with end users confirmed that the developed approaches exhibit salient advantages, suggesting that they are promising for leveraging contextual information/domain knowledge for a wide range of multimedia computing and pattern recognition problems.
ContributorsWang, Zhesheng (Author) / Li, Baoxin (Thesis advisor) / Sundaram, Hari (Committee member) / Qian, Gang (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2011
Description
Hydropower generation is one of the clean renewable energies which has received great attention in the power industry. Hydropower has been the leading source of renewable energy. It provides more than 86% of all electricity generated by renewable sources worldwide. Generally, the life span of a hydropower plant is considered as 30 to 50 years. Power plants over 30 years old usually conduct a feasibility study of rehabilitation on their entire facilities including infrastructure. By age 35, the forced outage rate increases by 10 percentage points compared to the previous year. Much longer outages occur in power plants older than 20 years. Consequently, the forced outage rate increases exponentially due to these longer outages. Although these long forced outages are not frequent, their impact is immense. If reasonable timing of rehabilitation is missed, an abrupt long-term outage could occur and additional unnecessary repairs and inefficiencies would follow. On the contrary, too early replacement might cause the waste of revenue. The hydropower plants of Korea Water Resources Corporation (hereafter K-water) are utilized for this study. Twenty-four K-water generators comprise the population for quantifying the reliability of each equipment. A facility in a hydropower plant is a repairable system because most failures can be fixed without replacing the entire facility. The fault data of each power plant are collected, within which only forced outage faults are considered as raw data for reliability analyses. The mean cumulative repair functions (MCF) of each facility are determined with the failure data tables, using Nelson's graph method. The power law model, a popular model for a repairable system, can also be obtained to represent representative equipment and system availability. The criterion-based analysis of HydroAmp is used to provide more accurate reliability of each power plant. Two case studies are presented to enhance the understanding of the availability of each power plant and represent economic evaluations for modernization. Also, equipment in a hydropower plant is categorized into two groups based on their reliability for determining modernization timing and their suitable replacement periods are obtained using simulation.
ContributorsKwon, Ogeuk (Author) / Holbert, Keith E. (Thesis advisor) / Heydt, Gerald T (Committee member) / Pan, Rong (Committee member) / Arizona State University (Publisher)
Created2011
Description
Thin films of ever reducing thickness are used in a plethora of applications and their performance is highly dependent on their microstructure. Computer simulations could then play a vital role in predicting the microstructure of thin films as a function of processing conditions. FACET is one such software tool designed by our research group to model polycrystalline thin film growth, including texture evolution and grain growth of polycrystalline films in 2D. Several modifications to the original FACET code were done to enhance its usability and accuracy. Simulations of sputtered silver thin films are presented here with FACET 2.0 with qualitative and semi-quantitative comparisons with previously published experimental results. Comparisons of grain size, texture and film thickness between simulations and experiments are presented which describe growth modes due to various deposition factors like flux angle and substrate temperature. These simulations provide reasonable agreement with the experimental data over a diverse range of process parameters. Preliminary experiments in depositions of Silver films are also attempted with varying substrates and thickness in order to generate complementary experimental and simulation studies of microstructure evolution. Overall, based on the comparisons, FACET provides interesting insights into thin film growth processes, and the effects of various deposition conditions on thin film structure and microstructure. Lastly, simple molecular dynamics simulations of deposition on bi-crystals are attempted for gaining insight into texture based grain competition during film growth. These simulations predict texture based grain coarsening mechanisms like twinning and grain boundary migration that have been commonly reported in FCC films.
ContributorsRairkar, Asit (Author) / Adams, James B (Thesis advisor) / Krause, Stephen (Committee member) / Alford, Terry (Committee member) / Arizona State University (Publisher)
Created2011
Description
Multi-label learning, which deals with data associated with multiple labels simultaneously, is ubiquitous in real-world applications. To overcome the curse of dimensionality in multi-label learning, in this thesis I study multi-label dimensionality reduction, which extracts a small number of features by removing the irrelevant, redundant, and noisy information while considering the correlation among different labels in multi-label learning. Specifically, I propose Hypergraph Spectral Learning (HSL) to perform dimensionality reduction for multi-label data by exploiting correlations among different labels using a hypergraph. The regularization effect on the classical dimensionality reduction algorithm known as Canonical Correlation Analysis (CCA) is elucidated in this thesis. The relationship between CCA and Orthonormalized Partial Least Squares (OPLS) is also investigated. To perform dimensionality reduction efficiently for large-scale problems, two efficient implementations are proposed for a class of dimensionality reduction algorithms, including canonical correlation analysis, orthonormalized partial least squares, linear discriminant analysis, and hypergraph spectral learning. The first approach is a direct least squares approach which allows the use of different regularization penalties, but is applicable under a certain assumption; the second one is a two-stage approach which can be applied in the regularization setting without any assumption. Furthermore, an online implementation for the same class of dimensionality reduction algorithms is proposed when the data comes sequentially. A Matlab toolbox for multi-label dimensionality reduction has been developed and released. The proposed algorithms have been applied successfully in the Drosophila gene expression pattern image annotation. The experimental results on some benchmark data sets in multi-label learning also demonstrate the effectiveness and efficiency of the proposed algorithms.
ContributorsSun, Liang (Author) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Liu, Huan (Committee member) / Mittelmann, Hans D. (Committee member) / Arizona State University (Publisher)
Created2011