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 138
Description
The increasing popularity of Twitter renders improved trustworthiness and relevance assessment of tweets much more important for search. However, given the limitations on the size of tweets, it is hard to extract measures for ranking from the tweet's content alone. I propose a method of ranking tweets by generating a reputation score for each tweet that is based not just on content, but also additional information from the Twitter ecosystem that consists of users, tweets, and the web pages that tweets link to. This information is obtained by modeling the Twitter ecosystem as a three-layer graph. The reputation score is used to power two novel methods of ranking tweets by propagating the reputation over an agreement graph based on tweets' content similarity. Additionally, I show how the agreement graph helps counter tweet spam. An evaluation of my method on 16~million tweets from the TREC 2011 Microblog Dataset shows that it doubles the precision over baseline Twitter Search and achieves higher precision than current state of the art method. I present a detailed internal empirical evaluation of RAProp in comparison to several alternative approaches proposed by me, as well as external evaluation in comparison to the current state of the art method.
ContributorsRavikumar, Srijith (Author) / Kambhampati, Subbarao (Thesis advisor) / Davulcu, Hasan (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Automating aspects of biocuration through biomedical information extraction could significantly impact biomedical research by enabling greater biocuration throughput and improving the feasibility of a wider scope. An important step in biomedical information extraction systems is named entity recognition (NER), where mentions of entities such as proteins and diseases are located within natural-language text and their semantic type is determined. This step is critical for later tasks in an information extraction pipeline, including normalization and relationship extraction. BANNER is a benchmark biomedical NER system using linear-chain conditional random fields and the rich feature set approach. A case study with BANNER locating genes and proteins in biomedical literature is described. The first corpus for disease NER adequate for use as training data is introduced, and employed in a case study of disease NER. The first corpus locating adverse drug reactions (ADRs) in user posts to a health-related social website is also described, and a system to locate and identify ADRs in social media text is created and evaluated. The rich feature set approach to creating NER feature sets is argued to be subject to diminishing returns, implying that additional improvements may require more sophisticated methods for creating the feature set. This motivates the first application of multivariate feature selection with filters and false discovery rate analysis to biomedical NER, resulting in a feature set at least 3 orders of magnitude smaller than the set created by the rich feature set approach. Finally, two novel approaches to NER by modeling the semantics of token sequences are introduced. The first method focuses on the sequence content by using language models to determine whether a sequence resembles entries in a lexicon of entity names or text from an unlabeled corpus more closely. The second method models the distributional semantics of token sequences, determining the similarity between a potential mention and the token sequences from the training data by analyzing the contexts where each sequence appears in a large unlabeled corpus. The second method is shown to improve the performance of BANNER on multiple data sets.
ContributorsLeaman, James Robert (Author) / Gonzalez, Graciela (Thesis advisor) / Baral, Chitta (Thesis advisor) / Cohen, Kevin B (Committee member) / Liu, Huan (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
GaN high electron mobility transistors (HEMTs) based on the III-V nitride material system have been under extensive investigation because of their superb performance as high power RF devices. Two dimensional electron gas(2-DEG) with charge density ten times higher than that of GaAs-based HEMT and mobility much higher than Si enables a low on-resistance required for RF devices. Self-heating issues with GaN HEMT and lack of understanding of various phenomena are hindering their widespread commercial development. There is a need to understand device operation by developing a model which could be used to optimize electrical and thermal characteristics of GaN HEMT design for high power and high frequency operation. In this thesis work a physical simulation model of AlGaN/GaN HEMT is developed using commercially available software ATLAS from SILVACO Int. based on the energy balance/hydrodynamic carrier transport equations. The model is calibrated against experimental data. Transfer and output characteristics are the key focus in the analysis along with saturation drain current. The resultant IV curves showed a close correspondence with experimental results. Various combinations of electron mobility, velocity saturation, momentum and energy relaxation times and gate work functions were attempted to improve IV curve correlation. Thermal effects were also investigated to get a better understanding on the role of self-heating effects on the electrical characteristics of GaN HEMTs. The temperature profiles across the device were observed. Hot spots were found along the channel in the gate-drain spacing. These preliminary results indicate that the thermal effects do have an impact on the electrical device characteristics at large biases even though the amount of self-heating is underestimated with respect to thermal particle-based simulations that solve the energy balance equations for acoustic and optical phonons as well (thus take proper account of the formation of the hot-spot). The decrease in drain current is due to decrease in saturation carrier velocity. The necessity of including hydrodynamic/energy balance transport models for accurate simulations is demonstrated. Possible ways for improving model accuracy are discussed in conjunction with future research.
ContributorsChowdhury, Towhid (Author) / Vasileska, Dragica (Thesis advisor) / Goodnick, Stephen (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2013
Description
Distributed inference has applications in a wide range of fields such as source localization, target detection, environment monitoring, and healthcare. In this dissertation, distributed inference schemes which use bounded transmit power are considered. The performance of the proposed schemes are studied for a variety of inference problems. In the first part of the dissertation, a distributed detection scheme where the sensors transmit with constant modulus signals over a Gaussian multiple access channel is considered. The deflection coefficient of the proposed scheme is shown to depend on the characteristic function of the sensing noise, and the error exponent for the system is derived using large deviation theory. Optimization of the deflection coefficient and error exponent are considered with respect to a transmission phase parameter for a variety of sensing noise distributions including impulsive ones. The proposed scheme is also favorably compared with existing amplify-and-forward (AF) and detect-and-forward (DF) schemes. The effect of fading is shown to be detrimental to the detection performance and simulations are provided to corroborate the analytical results. The second part of the dissertation studies a distributed inference scheme which uses bounded transmission functions over a Gaussian multiple access channel. The conditions on the transmission functions under which consistent estimation and reliable detection are possible is characterized. For the distributed estimation problem, an estimation scheme that uses bounded transmission functions is proved to be strongly consistent provided that the variance of the noise samples are bounded and that the transmission function is one-to-one. The proposed estimation scheme is compared with the amplify and forward technique and its robustness to impulsive sensing noise distributions is highlighted. It is also shown that bounded transmissions suffer from inconsistent estimates if the sensing noise variance goes to infinity. For the distributed detection problem, similar results are obtained by studying the deflection coefficient. Simulations corroborate our analytical results. In the third part of this dissertation, the problem of estimating the average of samples distributed at the nodes of a sensor network is considered. A distributed average consensus algorithm in which every sensor transmits with bounded peak power is proposed. In the presence of communication noise, it is shown that the nodes reach consensus asymptotically to a finite random variable whose expectation is the desired sample average of the initial observations with a variance that depends on the step size of the algorithm and the variance of the communication noise. The asymptotic performance is characterized by deriving the asymptotic covariance matrix using results from stochastic approximation theory. It is shown that using bounded transmissions results in slower convergence compared to the linear consensus algorithm based on the Laplacian heuristic. Simulations corroborate our analytical findings. Finally, a robust distributed average consensus algorithm in which every sensor performs a nonlinear processing at the receiver is proposed. It is shown that non-linearity at the receiver nodes makes the algorithm robust to a wide range of channel noise distributions including the impulsive ones. It is shown that the nodes reach consensus asymptotically and similar results are obtained as in the case of transmit non-linearity. Simulations corroborate our analytical findings and highlight the robustness of the proposed algorithm.
ContributorsDasarathan, Sivaraman (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Reisslein, Martin (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2013
Description
This research emphasizes the use of low energy and low temperature post processing to improve the performance and lifetime of thin films and thin film transistors, by applying the fundamentals of interaction of materials with conductive heating and electromagnetic radiation. Single frequency microwave anneal is used to rapidly recrystallize the damage induced during ion implantation in Si substrates. Volumetric heating of the sample in the presence of the microwave field facilitates quick absorption of radiation to promote recrystallization at the amorphous-crystalline interface, apart from electrical activation of the dopants due to relocation to the substitutional sites. Structural and electrical characterization confirm recrystallization of heavily implanted Si within 40 seconds anneal time with minimum dopant diffusion compared to rapid thermal annealed samples. The use of microwave anneal to improve performance of multilayer thin film devices, e.g. thin film transistors (TFTs) requires extensive study of interaction of individual layers with electromagnetic radiation. This issue has been addressed by developing detail understanding of thin films and interfaces in TFTs by studying reliability and failure mechanisms upon extensive stress test. Electrical and ambient stresses such as illumination, thermal, and mechanical stresses are inflicted on the mixed oxide based thin film transistors, which are explored due to high mobilities of the mixed oxide (indium zinc oxide, indium gallium zinc oxide) channel layer material. Semiconductor parameter analyzer is employed to extract transfer characteristics, useful to derive mobility, subthreshold, and threshold voltage parameters of the transistors. Low temperature post processing anneals compatible with polymer substrates are performed in several ambients (oxygen, forming gas and vacuum) at 150 °C as a preliminary step. The analysis of the results pre and post low temperature anneals using device physics fundamentals assists in categorizing defects leading to failure/degradation as: oxygen vacancies, thermally activated defects within the bandgap, channel-dielectric interface defects, and acceptor-like or donor-like trap states. Microwave anneal has been confirmed to enhance the quality of thin films, however future work entails extending the use of electromagnetic radiation in controlled ambient to facilitate quick post fabrication anneal to improve the functionality and lifetime of these low temperature fabricated TFTs.
ContributorsVemuri, Rajitha (Author) / Alford, Terry L. (Thesis advisor) / Theodore, N David (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2013
Description
Carrier lifetime is one of the few parameters which can give information about the low defect densities in today's semiconductors. In principle there is no lower limit to the defect density determined by lifetime measurements. No other technique can easily detect defect densities as low as 10-9 - 10-10 cm-3 in a simple, contactless room temperature measurement. However in practice, recombination lifetime τr measurements such as photoconductance decay (PCD) and surface photovoltage (SPV) that are widely used for characterization of bulk wafers face serious limitations when applied to thin epitaxial layers, where the layer thickness is smaller than the minority carrier diffusion length Ln. Other methods such as microwave photoconductance decay (µ-PCD), photoluminescence (PL), and frequency-dependent SPV, where the generated excess carriers are confined to the epitaxial layer width by using short excitation wavelengths, require complicated configuration and extensive surface passivation processes that make them time-consuming and not suitable for process screening purposes. Generation lifetime τg, typically measured with pulsed MOS capacitors (MOS-C) as test structures, has been shown to be an eminently suitable technique for characterization of thin epitaxial layers. It is for these reasons that the IC community, largely concerned with unipolar MOS devices, uses lifetime measurements as a "process cleanliness monitor." However when dealing with ultraclean epitaxial wafers, the classic MOS-C technique measures an effective generation lifetime τg eff which is dominated by the surface generation and hence cannot be used for screening impurity densities. I have developed a modified pulsed MOS technique for measuring generation lifetime in ultraclean thin p/p+ epitaxial layers which can be used to detect metallic impurities with densities as low as 10-10 cm-3. The widely used classic version has been shown to be unable to effectively detect such low impurity densities due to the domination of surface generation; whereas, the modified version can be used suitably as a metallic impurity density monitoring tool for such cases.
ContributorsElhami Khorasani, Arash (Author) / Alford, Terry (Thesis advisor) / Goryll, Michael (Committee member) / Bertoni, Mariana (Committee member) / Arizona State University (Publisher)
Created2013
Description
Photodetectors in the 1.7 to 4.0 μm range are being commercially developed on InP substrates to meet the needs of longer wavelength applications such as thermal and medical sensing. Currently, these devices utilize high indium content metamorphic Ga1-xInxAs (x > 0.53) layers to extend the wavelength range beyond the 1.7 μm achievable using lattice matched GaInAs. The large lattice mismatch required to reach the extended wavelengths results in photodetector materials that contain a large number of misfit dislocations. The low quality of these materials results in a large nonradiative Shockley Read Hall generation/recombination rate that is manifested as an undesirable large thermal noise level in these photodetectors. This work focuses on utilizing the different band structure engineering methods to design more efficient devices on InP substrates. One prospective way to improve photodetector performance at the extended wavelengths is to utilize lattice matched GaInAs/GaAsSb structures that have a type-II band alignment, where the ground state transition energy of the superlattice is smaller than the bandgap of either constituent material. Over the extended wavelength range of 2 to 3 μm this superlattice structure has an optimal period thickness of 3.4 to 5.2 nm and a wavefunction overlap of 0.8 to 0.4, respectively. In using a type-II superlattice to extend the cutoff wavelength there is a tradeoff between the wavelength reached and the electron-hole wavefunction overlap realized, and hence absorption coefficient achieved. This tradeoff and the subsequent reduction in performance can be overcome by two methods: adding bismuth to this type-II material system; applying strain on both layers in the system to attain strain-balanced condition. These allow the valance band alignment and hence the wavefunction overlap to be tuned independently of the wavelength cutoff. Adding 3% bismuth to the GaInAs constituent material, the resulting lattice matched Ga0.516In0.484As0.970Bi0.030/GaAs0.511Sb0.489superlattice realizes a 50% larger absorption coefficient. While as, similar results can be achieved with strain-balanced condition with strain limited to 1.9% on either layer. The optimal design rules derived from the different possibilities make it feasible to extract superlattice period thickness with the best absorption coefficient for any cutoff wavelength in the range.
ContributorsSharma, Ankur R (Author) / Johnson, Shane (Thesis advisor) / Goryll, Michael (Committee member) / Roedel, Ronald (Committee member) / Arizona State University (Publisher)
Created2013
Description
Data mining is increasing in importance in solving a variety of industry problems. Our initiative involves the estimation of resource requirements by skill set for future projects by mining and analyzing actual resource consumption data from past projects in the semiconductor industry. To achieve this goal we face difficulties like data with relevant consumption information but stored in different format and insufficient data about project attributes to interpret consumption data. Our first goal is to clean the historical data and organize it into meaningful structures for analysis. Once the preprocessing on data is completed, different data mining techniques like clustering is applied to find projects which involve resources of similar skillsets and which involve similar complexities and size. This results in "resource utilization templates" for groups of related projects from a resource consumption perspective. Then project characteristics are identified which generate this diversity in headcounts and skillsets. These characteristics are not currently contained in the data base and are elicited from the managers of historical projects. This represents an opportunity to improve the usefulness of the data collection system for the future. The ultimate goal is to match the product technical features with the resource requirement for projects in the past as a model to forecast resource requirements by skill set for future projects. The forecasting model is developed using linear regression with cross validation of the training data as the past project execution are relatively few in number. Acceptable levels of forecast accuracy are achieved relative to human experts' results and the tool is applied to forecast some future projects' resource demand.
ContributorsBhattacharya, Indrani (Author) / Sen, Arunabha (Thesis advisor) / Kempf, Karl G. (Thesis advisor) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Contemporary online social platforms present individuals with social signals in the form of news feed on their peers' activities. On networks such as Facebook, Quora, network operator decides how that information is shown to an individual. Then the user, with her own interests and resource constraints selectively acts on a subset of items presented to her. The network operator again, shows that activity to a selection of peers, and thus creating a behavioral loop. That mechanism of interaction and information flow raises some very interesting questions such as: can network operator design social signals to promote a particular activity like sustainability, public health care awareness, or to promote a specific product? The focus of my thesis is to answer that question. In this thesis, I develop a framework to personalize social signals for users to guide their activities on an online platform. As the result, we gradually nudge the activity distribution on the platform from the initial distribution p to the target distribution q. My work is particularly applicable to guiding collaborations, guiding collective actions, and online advertising. In particular, I first propose a probabilistic model on how users behave and how information flows on the platform. The main part of this thesis after that discusses the Influence Individuals through Social Signals (IISS) framework. IISS consists of four main components: (1) Learner: it learns users' interests and characteristics from their historical activities using Bayesian model, (2) Calculator: it uses gradient descent method to compute the intermediate activity distributions, (3) Selector: it selects users who can be influenced to adopt or drop specific activities, (4) Designer: it personalizes social signals for each user. I evaluate the performance of IISS framework by simulation on several network topologies such as preferential attachment, small world, and random. I show that the framework gradually nudges users' activities to approach the target distribution. I use both simulation and mathematical method to analyse convergence properties such as how fast and how close we can approach the target distribution. When the number of activities is 3, I show that for about 45% of target distributions, we can achieve KL-divergence as low as 0.05. But for some other distributions KL-divergence can be as large as 0.5.
ContributorsLe, Tien D (Author) / Sundaram, Hari (Thesis advisor) / Davulcu, Hasan (Thesis advisor) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2014
Description
Most data cleaning systems aim to go from a given deterministic dirty database to another deterministic but clean database. Such an enterprise pre–supposes that it is in fact possible for the cleaning process to uniquely recover the clean versions of each dirty data tuple. This is not possible in many cases, where the most a cleaning system can do is to generate a (hopefully small) set of clean candidates for each dirty tuple. When the cleaning system is required to output a deterministic database, it is forced to pick one clean candidate (say the "most likely" candidate) per tuple. Such an approach can lead to loss of information. For example, consider a situation where there are three equally likely clean candidates of a dirty tuple. An appealing alternative that avoids such an information loss is to abandon the requirement that the output database be deterministic. In other words, even though the input (dirty) database is deterministic, I allow the reconstructed database to be probabilistic. Although such an approach does avoid the information loss, it also brings forth several challenges. For example, how many alternatives should be kept per tuple in the reconstructed database? Maintaining too many alternatives increases the size of the reconstructed database, and hence the query processing time. Second, while processing queries on the probabilistic database may well increase recall, how would they affect the precision of the query processing? In this thesis, I investigate these questions. My investigation is done in the context of a data cleaning system called BayesWipe that has the capability of producing multiple clean candidates per each dirty tuple, along with the probability that they are the correct cleaned version. I represent these alternatives as tuples in a tuple disjoint probabilistic database, and use the Mystiq system to process queries on it. This probabilistic reconstruction (called BayesWipe–PDB) is compared to a deterministic reconstruction (called BayesWipe–DET)—where the most likely clean candidate for each tuple is chosen, and the rest of the alternatives discarded.
ContributorsRihan, Preet Inder Singh (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013