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: Ye, Jieping
- Creators: Mobasher, Barzin
Description
The main objective of this study is to numerically investigate: (i) the ionic transport, especially chloride ion penetration into cementitious materials under imposed electric fields, and (ii) moisture transport through cracked concretes as a function of the crack geometry. Numerical methods were implemented to simulate the ionic transport process, based on coupling the Nernst-Planck equation and Poisson's equation to account for transport dominated by electromigration. This mathematical model was also modified to account for the chloride binding mechanism (physical and chemical trapping of chlorides by the cement hydrates) and the concentration dependence of the diffusion coefficient of each ion in the transport process. To validate the numerical model, experimental data from a companion work was used in this study. The non-steady state migration test, which is one of the common accelerated chloride ion transport test, is numerically simulated. The simulation provides a linear relationship between ionic concentration and ionic flux, which indicates that the diffusion part is negligible under a strong external voltage environment. The numerical models along with adjustments for the concentration-dependent diffusion coefficients, a pore structure factor (from electrical measurements) and chloride binding considerations are found to be successful in predicting the chloride penetration depth into plain and modified concretes under imposed electrical potentials. Moisture transport through cracked concrete was examined in the second part of this thesis. To better understand the crack's influence on the permeability, modified Louis' equation was chosen to relate the permeability with crack characteristics. 3D concrete crack models were developed using a MATLAB program with distinct crack tortuosities, roughnesses and sizes. As a comparison, Navier-Stokes equation and the Lattice Boltzmann method were also applied on the 3D model of the cracked concrete to evaluate their permeability. The methodology developed here is expected to be useful in understanding the influence of cracking on moisture transport, and when properly coupled with an ionic transport model that will be further developed, helps comprehensively understand the coupling effects of moisture and ionic transport on deterioration in concrete structures.
ContributorsYang, Pu (Author) / Neithalath, Narayanan (Thesis advisor) / Dharmarajan, Subramaniam (Committee member) / Mobasher, Barzin (Committee member) / Arizona State University (Publisher)
Created2014
Description
Multidimensional data have various representations. Thanks to their simplicity in modeling multidimensional data and the availability of various mathematical tools (such as tensor decompositions) that support multi-aspect analysis of such data, tensors are increasingly being used in many application domains including scientific data management, sensor data management, and social network data analysis. Relational model, on the other hand, enables semantic manipulation of data using relational operators, such as projection, selection, Cartesian-product, and set operators. For many multidimensional data applications, tensor operations as well as relational operations need to be supported throughout the data life cycle. In this thesis, we introduce a tensor-based relational data model (TRM), which enables both tensor- based data analysis and relational manipulations of multidimensional data, and define tensor-relational operations on this model. Then we introduce a tensor-relational data management system, so called, TensorDB. TensorDB is based on TRM, which brings together relational algebraic operations (for data manipulation and integration) and tensor algebraic operations (for data analysis). We develop optimization strategies for tensor-relational operations in both in-memory and in-database TensorDB. The goal of the TRM and TensorDB is to serve as a single environment that supports the entire life cycle of data; that is, data can be manipulated, integrated, processed, and analyzed.
ContributorsKim, Mijung (Author) / Candan, K. Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Sundaram, Hari (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2014
Description
The widespread adoption of mobile devices gives rise to new opportunities and challenges for authentication mechanisms. Many traditional authentication mechanisms become unsuitable for smart devices. For example, while password is widely used on computers as user identity authentication, inputting password on small smartphone screen is error-prone and not convenient. In the meantime, there are emerging demands for new types of authentication. Proximity authentication is an example, which is not needed for computers but quite necessary for smart devices. These challenges motivate me to study and develop novel authentication mechanisms specific for smart devices.
In this dissertation, I am interested in the special authentication demands of smart devices and about to satisfy the demands. First, I study how the features of smart devices affect user identity authentications. For identity authentication domain, I aim to design a continuous, forge-resistant authentication mechanism that does not interrupt user-device interactions. I propose a mechanism that authenticates user identity based on the user's finger movement patterns. Next, I study a smart-device-specific authentication, proximity authentication, which authenticates whether two devices are in close proximity. For prox- imity authentication domain, I aim to design a user-friendly authentication mechanism that can defend against relay attacks. In addition, I restrict the authenticated distance to the scale of near field, i.e., a few centimeters. My first design utilizes a user's coherent two-finger movement on smart device screen to restrict the distance. To achieve a fully-automated system, I explore acoustic communications and propose a novel near field authentication system.
In this dissertation, I am interested in the special authentication demands of smart devices and about to satisfy the demands. First, I study how the features of smart devices affect user identity authentications. For identity authentication domain, I aim to design a continuous, forge-resistant authentication mechanism that does not interrupt user-device interactions. I propose a mechanism that authenticates user identity based on the user's finger movement patterns. Next, I study a smart-device-specific authentication, proximity authentication, which authenticates whether two devices are in close proximity. For prox- imity authentication domain, I aim to design a user-friendly authentication mechanism that can defend against relay attacks. In addition, I restrict the authenticated distance to the scale of near field, i.e., a few centimeters. My first design utilizes a user's coherent two-finger movement on smart device screen to restrict the distance. To achieve a fully-automated system, I explore acoustic communications and propose a novel near field authentication system.
ContributorsLi, Lingjun (Author) / Xue, Guoliang (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Ye, Jieping (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2014
Description
In many fields one needs to build predictive models for a set of related machine learning tasks, such as information retrieval, computer vision and biomedical informatics. Traditionally these tasks are treated independently and the inference is done separately for each task, which ignores important connections among the tasks. Multi-task learning aims at simultaneously building models for all tasks in order to improve the generalization performance, leveraging inherent relatedness of these tasks. In this thesis, I firstly propose a clustered multi-task learning (CMTL) formulation, which simultaneously learns task models and performs task clustering. I provide theoretical analysis to establish the equivalence between the CMTL formulation and the alternating structure optimization, which learns a shared low-dimensional hypothesis space for different tasks. Then I present two real-world biomedical informatics applications which can benefit from multi-task learning. In the first application, I study the disease progression problem and present multi-task learning formulations for disease progression. In the formulations, the prediction at each point is a regression task and multiple tasks at different time points are learned simultaneously, leveraging the temporal smoothness among the tasks. The proposed formulations have been tested extensively on predicting the progression of the Alzheimer's disease, and experimental results demonstrate the effectiveness of the proposed models. In the second application, I present a novel data-driven framework for densifying the electronic medical records (EMR) to overcome the sparsity problem in predictive modeling using EMR. The densification of each patient is a learning task, and the proposed algorithm simultaneously densify all patients. As such, the densification of one patient leverages useful information from other patients.
ContributorsZhou, Jiayu (Author) / Ye, Jieping (Thesis advisor) / Mittelmann, Hans (Committee member) / Li, Baoxin (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2014
Description
The main objective of this study is to investigate drying properties and plastic shrinkage cracking resistance of fresh cement-based pastes reinforced with fibers and textiles. Naturally occurring mineral wollastonite has been studied independently as well as in combination with AR-glass textile. A series of blended mixes with Portland cement and wollastonite nano-fibers were developed and tested under low vacuum conditions to simulate severe evaporation conditions and expedite the drying process causing plastic shrinkage cracks. Cumulative moisture loss, evaporation rates, and diffusivity were analyzed by means of a 2-stage diffusion simulation approach, developed previously in Arizona State University. Effect of fiber-matrix interaction on the transport properties of the composite were evaluated using the existing approach. Morphology of the cracked surface was investigated by the means of image analysis wherein length, width, area and density of the cracks were computed to help characterize the contribution of fiber and textile in the cracking phenomenon. Additionally, correlation between cumulative moisture loss and crack propagation was attempted. The testing procedures and associated analytical methods were applied to evaluate effectiveness of four wollastonite fiber sizes and also a hybrid reinforcement system with alkali-resistant glass (ARG) textile in improving shrinkage cracking related parameters. Furthermore, the experimental and analytical approach was extended to magnified version of the existing shrinkage testing set-up to study the size effect of these composites when subjected to matching drying conditions. Different restraining mechanisms were used to study the simulation of the cracking phenomena on a larger specimen. Paste and mortar formulations were developed to investigate size effect on shrinkage resistance of cementitious composites.
ContributorsKachala, Robert (Author) / Mobasher, Barzin (Thesis advisor) / Dharmarajan, Subramaniam (Committee member) / Neithalath, Narayanan (Committee member) / Arizona State University (Publisher)
Created2014
Description
Many learning models have been proposed for various tasks in visual computing. Popular examples include hidden Markov models and support vector machines. Recently, sparse-representation-based learning methods have attracted a lot of attention in the computer vision field, largely because of their impressive performance in many applications. In the literature, many of such sparse learning methods focus on designing or application of some learning techniques for certain feature space without much explicit consideration on possible interaction between the underlying semantics of the visual data and the employed learning technique. Rich semantic information in most visual data, if properly incorporated into algorithm design, should help achieving improved performance while delivering intuitive interpretation of the algorithmic outcomes. My study addresses the problem of how to explicitly consider the semantic information of the visual data in the sparse learning algorithms. In this work, we identify four problems which are of great importance and broad interest to the community. Specifically, a novel approach is proposed to incorporate label information to learn a dictionary which is not only reconstructive but also discriminative; considering the formation process of face images, a novel image decomposition approach for an ensemble of correlated images is proposed, where a subspace is built from the decomposition and applied to face recognition; based on the observation that, the foreground (or salient) objects are sparse in input domain and the background is sparse in frequency domain, a novel and efficient spatio-temporal saliency detection algorithm is proposed to identify the salient regions in video; and a novel hidden Markov model learning approach is proposed by utilizing a sparse set of pairwise comparisons among the data, which is easier to obtain and more meaningful, consistent than tradition labels, in many scenarios, e.g., evaluating motion skills in surgical simulations. In those four problems, different types of semantic information are modeled and incorporated in designing sparse learning algorithms for the corresponding visual computing tasks. Several real world applications are selected to demonstrate the effectiveness of the proposed methods, including, face recognition, spatio-temporal saliency detection, abnormality detection, spatio-temporal interest point detection, motion analysis and emotion recognition. In those applications, data of different modalities are involved, ranging from audio signal, image to video. Experiments on large scale real world data with comparisons to state-of-art methods confirm the proposed approaches deliver salient advantages, showing adding those semantic information dramatically improve the performances of the general sparse learning methods.
ContributorsZhang, Qiang (Author) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Wang, Yalin (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2014
Description
Concrete is the most widely used infrastructure material worldwide. Production of portland cement, the main binding component in concrete, has been shown to require significant energy and account for approximately 5-7% of global carbon dioxide production. The expected continued increased use of concrete over the coming decades indicates this is an ideal time to implement sustainable binder technologies. The current work aims to explore enhanced sustainability concretes, primarily in the context of limestone and flow. Aspects such as hydration kinetics, hydration product formation and pore structure add to the understanding of the strength development and potential durability characteristics of these binder systems. Two main strategies for enhancing this sustainability are explored in this work: (i) the use of high volume limestone in combination with other alternative cementitious materials to decrease the portland cement quantity in concrete and (ii) the use of geopolymers as the binder phase in concrete. The first phase of the work investigates the use of fine limestone as cement replacement from the perspective of hydration, strength development, and pore structure. The nature of the potential synergistic benefit of limestone and alumina will be explored. The second phase will focus on the rheological characterization of these materials in the fresh state, as well as a more general investigation of the rheological characterization of suspensions. The results of this work indicate several key ideas. (i) There is a potential synergistic benefit for strength, hydration, and pore structure by using alumina and in portland limestone cements, (ii) the limestone in these systems is shown to react to some extent, and fine limestone is shown to accelerate hydration, (iii) rheological characteristics of cementitious suspensions are complex, and strongly dependent on several key parameters including: the solid loading, interparticle forces, surface area of the particles present, particle size distribution of the particles, and rheological nature of the media in which the particles are suspended, and (iv) stress plateau method is proposed for the determination of rheological properties of concentrated suspensions, as it more accurately predicts apparent yield stress and is shown to correlate well with other viscoelastic properties of the suspensions.
ContributorsVance, Kirk (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Mobasher, Barzin (Committee member) / Chawla, Nikhilesh (Committee member) / Marzke, Robert (Committee member) / Arizona State University (Publisher)
Created2014
Description
The main objective of this study is to investigate the behaviour and applications of strain hardening cement composites (SHCC). Application of SHCC for use in slabs of common configurations was studied and design procedures are prepared by employing yield line theory and integrating it with simplified tri-linear model developed in Arizona State University by Dr. Barzin Mobasher and Dr. Chote Soranakom. Intrinsic material property of moment-curvature response for SHCC was used to derive the relationship between applied load and deflection in a two-step process involving the limit state analysis and kinematically admissible displacements. For application of SHCC in structures such as shear walls, tensile and shear properties are necessary for design. Lot of research has already been done to study the tensile properties and therefore shear property study was undertaken to prepare a design guide. Shear response of textile reinforced concrete was investigated based on picture frame shear test method. The effects of orientation, volume of cement paste per layer, planar cross-section and volume fraction of textiles were investigated. Pultrusion was used for the production of textile reinforced concrete. It is an automated set-up with low equipment cost which provides uniform production and smooth final surface of the TRC. A 3-D optical non-contacting deformation measurement technique of digital image correlation (DIC) was used to conduct the image analysis on the shear samples by means of tracking the displacement field through comparison between the reference image and deformed images. DIC successfully obtained full-field strain distribution, displacement and strain versus time responses, demonstrated the bonding mechanism from perspective of strain field, and gave a relation between shear angle and shear strain.
ContributorsAswani, Karan (Author) / Mobasher, Barzin (Thesis advisor) / Dharmarajan, Subramaniam (Committee member) / Neithalath, Narayanan (Committee member) / Arizona State University (Publisher)
Created2014
Description
Social networking services have emerged as an important platform for large-scale information sharing and communication. With the growing popularity of social media, spamming has become rampant in the platforms. Complex network interactions and evolving content present great challenges for social spammer detection. Different from some existing well-studied platforms, distinct characteristics of newly emerged social media data present new challenges for social spammer detection. First, texts in social media are short and potentially linked with each other via user connections. Second, it is observed that abundant contextual information may play an important role in distinguishing social spammers and normal users. Third, not only the content information but also the social connections in social media evolve very fast. Fourth, it is easy to amass vast quantities of unlabeled data in social media, but would be costly to obtain labels, which are essential for many supervised algorithms. To tackle those challenges raise in social media data, I focused on developing effective and efficient machine learning algorithms for social spammer detection.
I provide a novel and systematic study of social spammer detection in the dissertation. By analyzing the properties of social network and content information, I propose a unified framework for social spammer detection by collectively using the two types of information in social media. Motivated by psychological findings in physical world, I investigate whether sentiment analysis can help spammer detection in online social media. In particular, I conduct an exploratory study to analyze the sentiment differences between spammers and normal users; and present a novel method to incorporate sentiment information into social spammer detection framework. Given the rapidly evolving nature, I propose a novel framework to efficiently reflect the effect of newly emerging social spammers. To tackle the problem of lack of labeling data in social media, I study how to incorporate network information into text content modeling, and design strategies to select the most representative and informative instances from social media for labeling. Motivated by publicly available label information from other media platforms, I propose to make use of knowledge learned from cross-media to help spammer detection on social media.
I provide a novel and systematic study of social spammer detection in the dissertation. By analyzing the properties of social network and content information, I propose a unified framework for social spammer detection by collectively using the two types of information in social media. Motivated by psychological findings in physical world, I investigate whether sentiment analysis can help spammer detection in online social media. In particular, I conduct an exploratory study to analyze the sentiment differences between spammers and normal users; and present a novel method to incorporate sentiment information into social spammer detection framework. Given the rapidly evolving nature, I propose a novel framework to efficiently reflect the effect of newly emerging social spammers. To tackle the problem of lack of labeling data in social media, I study how to incorporate network information into text content modeling, and design strategies to select the most representative and informative instances from social media for labeling. Motivated by publicly available label information from other media platforms, I propose to make use of knowledge learned from cross-media to help spammer detection on social media.
ContributorsHu, Xia, Ph.D (Author) / Liu, Huan (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Ye, Jieping (Committee member) / Faloutsos, Christos (Committee member) / Arizona State University (Publisher)
Created2015
Description
A myriad of social media services are emerging in recent years that allow people to communicate and express themselves conveniently and easily. The pervasive use of social media generates massive data at an unprecedented rate. It becomes increasingly difficult for online users to find relevant information or, in other words, exacerbates the information overload problem. Meanwhile, users in social media can be both passive content consumers and active content producers, causing the quality of user-generated content can vary dramatically from excellence to abuse or spam, which results in a problem of information credibility. Trust, providing evidence about with whom users can trust to share information and from whom users can accept information without additional verification, plays a crucial role in helping online users collect relevant and reliable information. It has been proven to be an effective way to mitigate information overload and credibility problems and has attracted increasing attention.
As the conceptual counterpart of trust, distrust could be as important as trust and its value has been widely recognized by social sciences in the physical world. However, little attention is paid on distrust in social media. Social media differs from the physical world - (1) its data is passively observed, large-scale, incomplete, noisy and embedded with rich heterogeneous sources; and (2) distrust is generally unavailable in social media. These unique properties of social media present novel challenges for computing distrust in social media: (1) passively observed social media data does not provide necessary information social scientists use to understand distrust, how can I understand distrust in social media? (2) distrust is usually invisible in social media, how can I make invisible distrust visible by leveraging unique properties of social media data? and (3) little is known about distrust and its role in social media applications, how can distrust help make difference in social media applications?
The chief objective of this dissertation is to figure out solutions to these challenges via innovative research and novel methods. In particular, computational tasks are designed to {\it understand distrust}, a innovative task, i.e., {\it predicting distrust} is proposed with novel frameworks to make invisible distrust visible, and principled approaches are develop to {\it apply distrust} in social media applications. Since distrust is a special type of negative links, I demonstrate the generalization of properties and algorithms of distrust to negative links, i.e., {\it generalizing findings of distrust}, which greatly expands the boundaries of research of distrust and largely broadens its applications in social media.
As the conceptual counterpart of trust, distrust could be as important as trust and its value has been widely recognized by social sciences in the physical world. However, little attention is paid on distrust in social media. Social media differs from the physical world - (1) its data is passively observed, large-scale, incomplete, noisy and embedded with rich heterogeneous sources; and (2) distrust is generally unavailable in social media. These unique properties of social media present novel challenges for computing distrust in social media: (1) passively observed social media data does not provide necessary information social scientists use to understand distrust, how can I understand distrust in social media? (2) distrust is usually invisible in social media, how can I make invisible distrust visible by leveraging unique properties of social media data? and (3) little is known about distrust and its role in social media applications, how can distrust help make difference in social media applications?
The chief objective of this dissertation is to figure out solutions to these challenges via innovative research and novel methods. In particular, computational tasks are designed to {\it understand distrust}, a innovative task, i.e., {\it predicting distrust} is proposed with novel frameworks to make invisible distrust visible, and principled approaches are develop to {\it apply distrust} in social media applications. Since distrust is a special type of negative links, I demonstrate the generalization of properties and algorithms of distrust to negative links, i.e., {\it generalizing findings of distrust}, which greatly expands the boundaries of research of distrust and largely broadens its applications in social media.
ContributorsTang, Jiliang (Author) / Liu, Huan (Thesis advisor) / Xue, Guoliang (Committee member) / Ye, Jieping (Committee member) / Aggarwal, Charu (Committee member) / Arizona State University (Publisher)
Created2015