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 91
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
Integrated photonics requires high gain optical materials in the telecom wavelength range for optical amplifiers and coherent light sources. Erbium (Er) containing materials are ideal candidates due to the 1.5 μm emission from Er3+ ions. However, the Er density in typical Er-doped materials is less than 1 x 1020 cm-3, thus limiting the maximum optical gain to a few dB/cm, too small to be useful for integrated photonics applications. Er compounds could potentially solve this problem since they contain much higher Er density. So far the existing Er compounds suffer from short lifetime and strong upconversion effects, mainly due to poor quality of crystals produced by various methods of thin film growth and deposition. This dissertation explores a new Er compound: erbium chloride silicate (ECS, Er3(SiO4)2Cl ) in the nanowire form, which facilitates the growth of high quality single crystals. Growth methods for such single crystal ECS nanowires have been established. Various structural and optical characterizations have been carried out. The high crystal quality of ECS material leads to a long lifetime of the first excited state of Er3+ ions up to 1 ms at Er density higher than 1022 cm-3. This Er lifetime-density product was found to be the largest among all Er containing materials. A unique integrating sphere method was developed to measure the absorption cross section of ECS nanowires from 440 to 1580 nm. Pump-probe experiments demonstrated a 644 dB/cm signal enhancement from a single ECS wire. It was estimated that such large signal enhancement can overcome the absorption to result in a net material gain, but not sufficient to compensate waveguide propagation loss. In order to suppress the upconversion process in ECS, Ytterbium (Yb) and Yttrium (Y) ions are introduced as substituent ions of Er in the ECS crystal structure to reduce Er density. While the addition of Yb ions only partially succeeded, erbium yttrium chloride silicate (EYCS) with controllable Er density was synthesized successfully. EYCS with 30 at. % Er was found to be the best. It shows the strongest PL emission at 1.5 μm, and thus can be potentially used as a high gain material.
ContributorsYin, Leijun (Author) / Ning, Cun-Zheng (Thesis advisor) / Chamberlin, Ralph (Committee member) / Yu, Hongbin (Committee member) / Menéndez, Jose (Committee member) / Ponce, Fernando (Committee member) / Arizona State University (Publisher)
Created2013
Description
This dissertation is focused on material property exploration and analysis using computational quantum mechanics methods. Theoretical calculations were performed on the recently discovered hexahydride materials A2SiH6 (A=Rb, K) to calculate the lattice dynamics of the systems in order to check for structural stability, verify the experimental Raman and infrared spectrospcopy results, and obtain the theoretical free energies of formation. The electronic structure of the systems was calculated and the bonding and ionic properties of the systems were analyzed. The novel hexahydrides were compared to the important hydrogen storage material KSiH3. This showed that the hypervalent nature of the SiH62- ions reduced the Si-H bonding strength considerably. These hydrogen rich compounds could have promising energy applications as they link to alternative hydrogen fuel technology. The carbide systems Li-C (A=Li,Ca,Mg) were studied using \emph{ab initio} and evolutionary algorithms at high pressures. At ambient pressure Li2C2 and CaC2 are known to contain C22- dumbbell anions and CaC2 is polymorphic. At elevated pressure both CaC2 and Li2C2 display polymorphism. At ambient pressure the Mg-C system contains several experimentally known phases, however, all known phases are shown to be metastable with respect to the pure elements Mg and C. First principle investigation of the configurational space of these compounds via evolutionary algorithms results in a variety of metastable and unique structures. The binary compounds ZnSb and ZnAs are II-V electron-poor semiconductors with interesting thermoelectric properties. They contain rhomboid rings composed of Zn2Sb2 (Zn2As2) with multi-centered covalent bonds which are in turn covalently bonded to other rings via two-centered, two-electron bonds. Ionicity was explored via Bader charge analysis and it appears that the low ionicity that these materials display is a necessary condition of their multicentered bonding. Both compounds were found to have narrow, indirect band gaps with multi-valley valence and conduction bands; which are important characteristics for high thermopower in thermoelectric materials. Future work is needed to analyze the lattice properties of the II-V CdSb-type systems, especially in order to find the origin of the extremely low thermal conductivity that these systems display.
ContributorsBenson, Daryn Eugene (Author) / Häussermann, Ulrich (Thesis advisor) / Shumway, John (Thesis advisor) / Chamberlin, Ralph (Committee member) / Sankey, Otto (Committee member) / Treacy, Mike (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
The theory of quantum electrodynamics predicts that beta decay of the neutron into a proton, electron, and anti-neutrino should be accompanied by a continuous spectrum of photons. A recent experiment, RDK I, reported the first detection of radiative decay photons from neutron beta decay with a branching ratio of (3.09 ± 0.32) × 10-3 in the energy range of 15 keV to 340 keV. This was achieved by prompt coincident detection of an electron and photon, in delayed coincidence with a proton. The photons were detected by using a single bar of bismuth germanate scintillating crystal coupled to an avalanche photodiode. This thesis deals with the follow-up experiment, RDK II, to measure the branching ratio at the level of approximately 1% and the energy spectrum at the level of a few percent. The most significant improvement of RDK II is the use of a photon detector with about an order of magnitude greater solid angle coverage than RDK I. In addition, the detectable energy range has been extended down to approximately 250 eV and up to the endpoint energy of 782 keV. This dissertation presents an overview of the apparatus, development of a new data analysis technique for radiative decay, and results for the ratio of electron-proton-photon coincident Repg to electron-proton coincident Rep events.
ContributorsO'Neill, Benjamin (Author) / Alarcon, Ricardo (Thesis advisor) / Drucker, Jeffery (Committee member) / Lebed, Richard (Committee member) / Comfort, Joseph (Committee member) / Chamberlin, Ralph (Committee member) / Arizona State University (Publisher)
Created2012
Description
In this dissertation, remote plasma interactions with the surfaces of low-k interlayer dielectric (ILD), Cu and Cu adhesion layers are investigated. The first part of the study focuses on the simultaneous plasma treatment of ILD and chemical mechanical polishing (CMP) Cu surfaces using N2/H2 plasma processes. H atoms and radicals in the plasma react with the carbon groups leading to carbon removal for the ILD films. Results indicate that an N2 plasma forms an amide-like layer on the surface which apparently leads to reduced carbon abstraction from an H2 plasma process. In addition, FTIR spectra indicate the formation of hydroxyl (Si-OH) groups following the plasma exposure. Increased temperature (380 °C) processing leads to a reduction of the hydroxyl group formation compared to ambient temperature processes, resulting in reduced changes of the dielectric constant. For CMP Cu surfaces, the carbonate contamination was removed by an H2 plasma process at elevated temperature while the C-C and C-H contamination was removed by an N2 plasma process at elevated temperature. The second part of this study examined oxide stability and cleaning of Ru surfaces as well as consequent Cu film thermal stability with the Ru layers. The ~2 monolayer native Ru oxide was reduced after H-plasma processing. The thermal stability or islanding of the Cu film on the Ru substrate was characterized by in-situ XPS. After plasma cleaning of the Ru adhesion layer, the deposited Cu exhibited full coverage. In contrast, for Cu deposition on the Ru native oxide substrate, Cu islanding was detected and was described in terms of grain boundary grooving and surface and interface energies. The thermal stability of 7 nm Ti, Pt and Ru ii interfacial adhesion layers between a Cu film (10 nm) and a Ta barrier layer (4 nm) have been investigated in the third part. The barrier properties and interfacial stability have been evaluated by Rutherford backscattering spectrometry (RBS). Atomic force microscopy (AFM) was used to measure the surfaces before and after annealing, and all the surfaces are relatively smooth excluding islanding or de-wetting phenomena as a cause of the instability. The RBS showed no discernible diffusion across the adhesion layer/Ta and Ta/Si interfaces which provides a stable underlying layer. For a Ti interfacial layer RBS indicates that during 400 °C annealing Ti interdiffuses through the Cu film and accumulates at the surface. For the Pt/Cu system Pt interdiffuion is detected which is less evident than Ti. Among the three adhesion layer candidates, Ru shows negligible diffusion into the Cu film indicating thermal stability at 400 °C.
ContributorsLiu, Xin (Author) / Nemanich, Robert (Thesis advisor) / Chamberlin, Ralph (Committee member) / Chen, Tingyong (Committee member) / Smith, David (Committee member) / Ponce, Fernando (Committee member) / Arizona State University (Publisher)
Created2012
Description
The heat transfer enhancements available from expanding the cross-section of a boiling microchannel are explored analytically and experimentally. Evaluation of the literature on critical heat flux in flow boiling and associated pressure drop behavior is presented with predictive critical heat flux (CHF) and pressure drop correlations. An optimum channel configuration allowing maximum CHF while reducing pressure drop is sought. A perturbation of the channel diameter is employed to examine CHF and pressure drop relationships from the literature with the aim of identifying those adequately general and suitable for use in a scenario with an expanding channel. Several CHF criteria are identified which predict an optimizable channel expansion, though many do not. Pressure drop relationships admit improvement with expansion, and no optimum presents itself. The relevant physical phenomena surrounding flow boiling pressure drop are considered, and a balance of dimensionless numbers is presented that may be of qualitative use. The design, fabrication, inspection, and experimental evaluation of four copper microchannel arrays of different channel expansion rates with R-134a refrigerant is presented. Optimum rates of expansion which maximize the critical heat flux are considered at multiple flow rates, and experimental results are presented demonstrating optima. The effect of expansion on the boiling number is considered, and experiments demonstrate that expansion produces a notable increase in the boiling number in the region explored, though no optima are observed. Significant decrease in the pressure drop across the evaporator is observed with the expanding channels, and no optima appear. Discussion of the significance of this finding is presented, along with possible avenues for future work.
ContributorsMiner, Mark (Author) / Phelan, Patrick E (Thesis advisor) / Baer, Steven (Committee member) / Chamberlin, Ralph (Committee member) / Chen, Kangping (Committee member) / Herrmann, Marcus (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
In this research, our goal was to fabricate Josephson junctions that can be stably processed at 300°C or higher. With the purpose of integrating Josephson junction fabrication with the current semiconductor circuit fabrication process, back-end process temperatures (>350 °C) will be a key for producing large scale junction circuits reliably, which requires the junctions to be more thermally stable than current Nb/Al-AlOx/Nb junctions. Based on thermodynamics, Hf was chosen to produce thermally stable Nb/Hf-HfOx/Nb superconductor tunnel Josephson junctions that can be grown or processed at elevated temperatures. Also elevated synthesis temperatures improve the structural and electrical properties of Nb electrode layers that could potentially improve junction device performance. The refractory nature of Hf, HfO2 and Nb allow for the formation of flat, abrupt and thermally-stable interfaces. But the current Al-based barrier will have problems when using with high-temperature grown and high-quality Nb. So our work is aimed at using Nb grown at elevated temperatures to fabricate thermally stable Josephson tunnel junctions. As a junction barrier metal, Hf was studied and compared with the traditional Al-barrier material. We have proved that Hf-HfOx is a good barrier candidate for high-temperature synthesized Josephson junction. Hf deposited at 500 °C on Nb forms flat and chemically abrupt interfaces. Nb/Hf-HfOx/Nb Josephson junctions were synthesized, fabricated and characterized with different oxidizing conditions. The results of materials characterization and junction electrical measurements are reported and analyzed. We have improved the annealing stability of Nb junctions and also used high-quality Nb grown at 500 °C as the bottom electrode successfully. Adding a buffer layer or multiple oxidation steps improves the annealing stability of Josephson junctions. We also have attempted to use the Atomic Layer Deposition (ALD) method for the growth of Hf oxide as the junction barrier and got tunneling results.
ContributorsHuang, Mengchu, 1987- (Author) / Newman, Nathan (Thesis advisor) / Rowell, John M. (Committee member) / Singh, Rakesh K. (Committee member) / Chamberlin, Ralph (Committee member) / Wang, Robert (Committee member) / Arizona State University (Publisher)
Created2013