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 160
Filtering by
- Creators: Xue, Guoliang
- Creators: Kambhampati, Subbarao
Description
The purpose of this project was to commission, perform, and discuss a new work for an instrument pairing not often utilized, oboe and percussion. The composer, Alyssa Morris, was selected in June 2009. Her work, titled Forecast, was completed in October of 2009 and premiered in February of 2010, as part of a program showcasing music for oboe and percussion. Included in this document is a detailed biography of the composer, a description of the four movements of Forecast, performance notes for each movement, a diagram for stage set-up, the full score, the program from the premiere performance with biographies of all the performers involved, and both a live recording and MIDI sound file. The performance notes discuss issues that arose during preparation for the premiere and should help avoid potential pitfalls. TrevCo Music, publisher of the work, graciously allowed inclusion of the full score. This score is solely for use in this document; please visit the publisher's website for purchasing information. The commission and documentation of this composition are intended to add to the repertoire for oboe in an unusual instrument pairing and to encourage further exploration of such combinations.
ContributorsCreamer, Caryn (Author) / Schuring, Martin (Thesis advisor) / Hill, Gary (Committee member) / Holbrook, Amy (Committee member) / Micklich, Albie (Committee member) / Spring, Robert (Committee member) / Arizona State University (Publisher)
Created2011
Description
During the twentieth-century, the dual influence of nationalism and modernism in the eclectic music from Latin America promoted an idiosyncratic style which naturally combined traditional themes, popular genres and secular music. The saxophone, commonly used as a popular instrument, started to develop a prominent role in Latin American classical music beginning in 1970. The lack of exposure and distribution of the Latin American repertoire has created a general perception that composers are not interested in the instrument, and that Latin American repertoire for classical saxophone is minimal. However, there are more than 1100 works originally written for saxophone in the region, and the amount continues to grow. This Modern Latin American Repertoire for Classical Saxophone: Recording Project and Performance Guide document establishes and exhibits seven works by seven representative Latin American composers.The recording includes works by Carlos Gonzalo Guzman (Colombia), Ricardo Tacuchian (Brazil), Roque Cordero (Panama), Luis Naón (Argentina), Andrés Alén-Rodriguez (Cuba), Alejandro César Morales (Mexico) and Jose-Luis Maúrtua (Peru), featuring a range of works for solo alto saxophone to alto saxophone with piano, alto saxophone with vibraphone, and tenor saxophone with electronic tape; thus forming an important selection of Latin American repertoire. Complete recorded performances of all seven pieces are supplemented by biographical, historical, and performance practice suggestions. The result is a written and audio guide to some of the most important pieces composed for classical saxophone in Latin America, with an emphasis on fostering interest in, and research into, composers who have contributed in the development and creation of the instrument in Latin America.
ContributorsOcampo Cardona, Javier Andrés (Author) / McAllister, Timothy (Thesis advisor) / Spring, Robert (Committee member) / Hill, Gary (Committee member) / Pilafian, Sam (Committee member) / Rogers, Rodney (Committee member) / Gardner, Joshua (Committee member) / Arizona State University (Publisher)
Created2011
Description
Finger motion and hand posture of six professional clarinetists (defined by entrance into or completion of a doctorate of musical arts degree in clarinet performance) were recorded using a pair of CyberGloves® in Arizona State University's Center for Cognitive Ubiquitous Computing Laboratory. Performance tasks included performing a slurred three-octave chromatic scale in sixteenth notes, at sixty quarter-note beats per minute, three times, with a metronome and a short pause between repetitions, and forming three pedagogical hand postures. Following the CyberGloves® tasks, each subject completed a questionnaire about equipment, playing history, practice routines, health practices, and hand usage during computer and sports activities. CyberGlove® data were analyzed to find average hand/finger postures and differences for each pitch across subjects, subject variance in the performance task and differences in ascending and descending postures of the chromatic scale. The data were also analyzed to describe generalized finger posture characteristics based on hand size, whether right hand thumb position affects finger flexion, and whether professional clarinetists use similar finger/hand postures when performing on clarinet, holding a tennis ball, allowing hands to hang freely by the sides, or form a "C" shape. The findings of this study suggest an individual approach based on hand size is necessary for teaching clarinet hand posture.
ContributorsHarger, Stefanie (Author) / Spring, Robert (Thesis advisor) / Hill, Gary (Committee member) / Koonce, Frank (Committee member) / Norton, Kay (Committee member) / Stauffer, Sandy (Committee member) / Arizona State University (Publisher)
Created2011
Description
Owen Middleton (b. 1941) enjoys an established and growing reputation as a composer of classical guitar music, but his works for piano are comparatively little known. The close investigation offered here of Middleton's works for piano reveals the same impressive craftsmanship, compelling character, and innovative spirit found in his works for guitar. Indeed, the only significant thing Middleton's piano music currently lacks is the well-deserved attention of professional players and a wider audience. Middleton's piano music needs to be heard, not just discussed, so one of this document's purposes is to provide a recorded sample of his piano works. While the overall repertoire for solo piano is vast, and new works become established in that repertoire with increasing difficulty, Middleton's piano works have a significant potential to find their way into the concert hall as well as the private teaching studio. His solo piano music is highly effective, well suited to the instrument, and, perhaps most importantly, fresh sounding and truly original. His pedagogical works are of equal value. Middleton's piano music offers something for everyone: there one finds daring virtuosity, effusions of passion, intellectual force, colorful imagery, poetry, humor, and even a degree of idiomatic innovation. This study aims to reveal key aspects of the composer's musical style, especially his style of piano writing, and to provide pianists with helpful analytical, technical, and interpretive insights. These descriptions of the music are supported with recorded examples, selected from the works for solo piano written between 1962 and 1993: Sonata for Piano, Childhood Scenes, Katie's Collection, and Toccata for Piano. The complete scores of the recorded works are included in the appendix. A chapter briefly describing the piano pieces since 1993 concludes the study and invites the reader to further investigations of this unique and important body of work.
ContributorsMoreau, Barton Andrew (Author) / Hamilton, Robert (Thesis advisor) / Holbrook, Amy (Committee member) / Campbell, Andrew (Committee member) / Spring, Robert (Committee member) / Gardner, Joshua (Committee member) / Arizona State University (Publisher)
Created2011
Description
Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of focus. In supervised learning like regression, the data consists of many features and only a subset of the features may be responsible for the result. Also, the features might require special structural requirements, which introduces additional complexity for feature selection. The sparse learning package, provides a set of algorithms for learning a sparse set of the most relevant features for both regression and classification problems. Structural dependencies among features which introduce additional requirements are also provided as part of the package. The features may be grouped together, and there may exist hierarchies and over- lapping groups among these, and there may be requirements for selecting the most relevant groups among them. In spite of getting sparse solutions, the solutions are not guaranteed to be robust. For the selection to be robust, there are certain techniques which provide theoretical justification of why certain features are selected. The stability selection, is a method for feature selection which allows the use of existing sparse learning methods to select the stable set of features for a given training sample. This is done by assigning probabilities for the features: by sub-sampling the training data and using a specific sparse learning technique to learn the relevant features, and repeating this a large number of times, and counting the probability as the number of times a feature is selected. Cross-validation which is used to determine the best parameter value over a range of values, further allows to select the best parameter value. This is done by selecting the parameter value which gives the maximum accuracy score. With such a combination of algorithms, with good convergence guarantees, stable feature selection properties and the inclusion of various structural dependencies among features, the sparse learning package will be a powerful tool for machine learning research. Modular structure, C implementation, ATLAS integration for fast linear algebraic subroutines, make it one of the best tool for a large sparse setting. The varied collection of algorithms, support for group sparsity, batch algorithms, are a few of the notable functionality of the SLEP package, and these features can be used in a variety of fields to infer relevant elements. The Alzheimer Disease(AD) is a neurodegenerative disease, which gradually leads to dementia. The SLEP package is used for feature selection for getting the most relevant biomarkers from the available AD dataset, and the results show that, indeed, only a subset of the features are required to gain valuable insights.
ContributorsThulasiram, Ramesh (Author) / Ye, Jieping (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
As the information available to lay users through autonomous data sources continues to increase, mediators become important to ensure that the wealth of information available is tapped effectively. A key challenge that these information mediators need to handle is the varying levels of incompleteness in the underlying databases in terms of missing attribute values. Existing approaches such as Query Processing over Incomplete Autonomous Databases (QPIAD) aim to mine and use Approximate Functional Dependencies (AFDs) to predict and retrieve relevant incomplete tuples. These approaches make independence assumptions about missing values--which critically hobbles their performance when there are tuples containing missing values for multiple correlated attributes. In this thesis, I present a principled probabilis- tic alternative that views an incomplete tuple as defining a distribution over the complete tuples that it stands for. I learn this distribution in terms of Bayes networks. My approach involves min- ing/"learning" Bayes networks from a sample of the database, and using it do both imputation (predict a missing value) and query rewriting (retrieve relevant results with incompleteness on the query-constrained attributes, when the data sources are autonomous). I present empirical studies to demonstrate that (i) at higher levels of incompleteness, when multiple attribute values are missing, Bayes networks do provide a significantly higher classification accuracy and (ii) the relevant possible answers retrieved by the queries reformulated using Bayes networks provide higher precision and recall than AFDs while keeping query processing costs manageable.
ContributorsRaghunathan, Rohit (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Lee, Joohyung (Committee member) / Arizona State University (Publisher)
Created2011
Description
Source selection is one of the foremost challenges for searching deep-web. For a user query, source selection involves selecting a subset of deep-web sources expected to provide relevant answers to the user query. Existing source selection models employ query-similarity based local measures for assessing source quality. These local measures are necessary but not sufficient as they are agnostic to source trustworthiness and result importance, which, given the autonomous and uncurated nature of deep-web, have become indispensible for searching deep-web. SourceRank provides a global measure for assessing source quality based on source trustworthiness and result importance. SourceRank's effectiveness has been evaluated in single-topic deep-web environments. The goal of the thesis is to extend sourcerank to a multi-topic deep-web environment. Topic-sensitive sourcerank is introduced as an effective way of extending sourcerank to a deep-web environment containing a set of representative topics. In topic-sensitive sourcerank, multiple sourcerank vectors are created, each biased towards a representative topic. At query time, using the topic of query keywords, a query-topic sensitive, composite sourcerank vector is computed as a linear combination of these pre-computed biased sourcerank vectors. Extensive experiments on more than a thousand sources in multiple domains show 18-85% improvements in result quality over Google Product Search and other existing methods.
ContributorsJha, Manishkumar (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2011
Description
As clarinet compositions created by Taiwanese composers have not been widely discussed and published in academia, this research paper examines three unaccompanied clarinet works by three Taiwanese composers: Ballade by Hsiao-Wen Tseng, Chin Thoughts III by Ling-Huei Tsai, and Pointe de Flame by Chia-Lin Pan, all commissioned by the author in 2007. This research also includes a compact disc with recordings of these works, aiming to document the creativity of Taiwanese composers. This research paper examines these three commissioned works by analyzing their overall musical styles, notations, formal structures, rhythmical and melodic materials, unconventional clarinet techniques as well as the influence of Chinese musical elements. The analysis reveals the distinctive characteristic of each piece. Moreover, the author provides composers' insights and performance guides to help interested readers practice these pieces. To further understand how the composers create these pieces by drawing upon different life experiences, the paper also includes information about their backgrounds, program notes, lists of compositions, and music examples for reference. The author found that collaborating with these composers helped to establish a closer composer-performer relationship in interpreting the music. It is hoped that this compact disc recording will help make Taiwanese composers' clarinet works more accessible to a wider audience. Moreover, this research paper hopes to generate more interest in performing and appreciating music composed by Taiwanese composers.
ContributorsChuang, Yenting (Author) / Spring, Robert (Thesis advisor) / Schuring, Martin (Committee member) / Campbell, Andrew (Committee member) / Jiang, Danwen (Committee member) / Hackbarth, Glenn (Committee member) / Arizona State University (Publisher)
Created2011
Description
TaxiWorld is a Matlab simulation of a city with a fleet of taxis which operate within it, with the goal of transporting passengers to their destinations. The size of the city, as well as the number of available taxis and the frequency and general locations of fare appearances can all be set on a scenario-by-scenario basis. The taxis must attempt to service the fares as quickly as possible, by picking each one up and carrying it to its drop-off location. The TaxiWorld scenario is formally modeled using both Decentralized Partially-Observable Markov Decision Processes (Dec-POMDPs) and Multi-agent Markov Decision Processes (MMDPs). The purpose of developing formal models is to learn how to build and use formal Markov models, such as can be given to planners to solve for optimal policies in problem domains. However, finding optimal solutions for Dec-POMDPs is NEXP-Complete, so an empirical algorithm was also developed as an improvement to the method already in use on the simulator, and the methods were compared in identical scenarios to determine which is more effective. The empirical method is of course not optimal - rather, it attempts to simply account for some of the most important factors to achieve an acceptable level of effectiveness while still retaining a reasonable level of computational complexity for online solving.
ContributorsWhite, Christopher (Author) / Kambhampati, Subbarao (Thesis advisor) / Gupta, Sandeep (Committee member) / Varsamopoulos, Georgios (Committee member) / Arizona State University (Publisher)
Created2011
Description
Multi-task learning (MTL) aims to improve the generalization performance (of the resulting classifiers) by learning multiple related tasks simultaneously. Specifically, MTL exploits the intrinsic task relatedness, based on which the informative domain knowledge from each task can be shared across multiple tasks and thus facilitate the individual task learning. It is particularly desirable to share the domain knowledge (among the tasks) when there are a number of related tasks but only limited training data is available for each task. Modeling the relationship of multiple tasks is critical to the generalization performance of the MTL algorithms. In this dissertation, I propose a series of MTL approaches which assume that multiple tasks are intrinsically related via a shared low-dimensional feature space. The proposed MTL approaches are developed to deal with different scenarios and settings; they are respectively formulated as mathematical optimization problems of minimizing the empirical loss regularized by different structures. For all proposed MTL formulations, I develop the associated optimization algorithms to find their globally optimal solution efficiently. I also conduct theoretical analysis for certain MTL approaches by deriving the globally optimal solution recovery condition and the performance bound. To demonstrate the practical performance, I apply the proposed MTL approaches on different real-world applications: (1) Automated annotation of the Drosophila gene expression pattern images; (2) Categorization of the Yahoo web pages. Our experimental results demonstrate the efficiency and effectiveness of the proposed algorithms.
ContributorsChen, Jianhui (Author) / Ye, Jieping (Thesis advisor) / Kumar, Sudhir (Committee member) / Liu, Huan (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2011