Matching Items (388)
Description
The end of the nineteenth century was an exhilarating and revolutionary era for the flute. This period is the Second Golden Age of the flute, when players and teachers associated with the Paris Conservatory developed what would be considered the birth of the modern flute school. In addition, the founding in 1871 of the Société Nationale de Musique by Camille Saint-Saëns (1835-1921) and Romain Bussine (1830-1899) made possible the promotion of contemporary French composers. The founding of the Société des Instruments à Vent by Paul Taffanel (1844-1908) in 1879 also invigorated a new era of chamber music for wind instruments. Within this groundbreaking environment, Mélanie Hélène Bonis (pen name Mel Bonis) entered the Paris Conservatory in 1876, under the tutelage of César Franck (1822-1890). Many flutists are dismayed by the scarcity of repertoire for the instrument in the Romantic and post-Romantic traditions; they make up for this absence by borrowing the violin sonatas of Gabriel Fauré (1845-1924) and Franck. The flute and piano works of Mel Bonis help to fill this void with music composed originally for flute. Bonis was a prolific composer with over 300 works to her credit, but her works for flute and piano have not been researched or professionally recorded in the United States before the present study. Although virtually unknown today in the American flute community, Bonis's music received much acclaim from her contemporaries and deserves a prominent place in the flutist's repertoire. After a brief biographical introduction, this document examines Mel Bonis's musical style and describes in detail her six works for flute and piano while also offering performance suggestions.
ContributorsDaum, Jenna Elyse (Author) / Buck, Elizabeth (Thesis advisor) / Holbrook, Amy (Committee member) / Micklich, Albie (Committee member) / Schuring, Martin (Committee member) / Norton, Kay (Committee member) / Arizona State University (Publisher)
Created2013
ContributorsMatthews, Eyona (Performer) / Yoo, Katie Jihye (Performer) / Roubison, Ryan (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-25
ContributorsHoeckley, Stephanie (Performer) / Lee, Juhyun (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-24
Description
Time-series plots are used in many scientific and engineering applications. In this thesis, two new plug-ins for piecewise constant and event time-series are developed within the Eclipse BIRT (Business Intelligence and Reporting Tools) framework. These customizable plug-ins support superdense time, which is required for plotting the dynamics of Parallel DEVS models. These plug-ins are designed to receive time-based alphanumerical data sets from external computing sources, which can then be dynamically plotted. Static and dynamic time-series plotting are demonstrated in two settings. First, as standalone plug-ins, they can be used to create static plots, which can then be included in BIRT reports. Second, the plug-ins are integrated into the DEVS-Suite simulator where runtime simulated data generated from model components are dynamically plotted. Visual representation of data sets can simplify and improve model verification and simulation validation.
ContributorsSundaramoorthi, Savitha (Author) / Sarjoughian, Hessam S. (Thesis advisor) / Maciejewski, Ross (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2015
Description
Over the last two decades, Alternative Project Delivery Methods (APDM), such as Design-Build (DB), have become more popular in the construction industry, specifically in the U.S., and the competition for APDM projects has risen among construction companies. The Engineering News Record (ENR) magazine analyzes DB firms and publishes the list of the top 100 every year. According to ENR articles and many scientific papers, the implementation of DB method has grown drastically over the last decade, however, information about growth trends depending on firm size and segment is lacking. Also missing is knowledge the future market trends over the next five years. Furthermore, public agencies and DB firms may be worried that DB projects do not distribute wealth equally among DB firms. Using the top 100 firms deemed representative of the DB market, the author has divided the market into volumes based on rankings to analyze the total DB market revenue growth. A comparison between international and domestic revenues indicated that the top five DB firms have 64% more involvement in the international market compared to the domestic market. Furthermore, while the research shows increasing market share only for the top five firms, the author has found that (1) a large portion of their market share is due to a large growth in their international market, and (2) revenues for all volumes of the DB market have increased. Moreover, regression and time series analyses allow for the forecasting of the DB market growth, which the author anticipate to move from about $100B to about $150B in 2020.
ContributorsVashani, Hossein (Author) / El Asmar, Mounir (Thesis advisor) / Ernzen, James (Committee member) / Bearup, Wylie (Committee member) / Arizona State University (Publisher)
Created2014
Description
Time series analysis of dynamic networks is an important area of study that helps in predicting changes in networks. Changes in networks are used to analyze deviations in the network characteristics. This analysis helps in characterizing any network that has dynamic behavior. This area of study has applications in many domains such as communication networks, climate networks, social networks, transportation networks, and biological networks. The aim of this research is to analyze the structural characteristics of such dynamic networks. This thesis examines tools that help to analyze the structure of the networks and explores a technique for computation and analysis of a large climate dataset. The computations for analyzing the structural characteristics are done in a computing cluster and there is a linear speed up in computation time compared to a single-core computer. As an application, a large sea ice concentration anomaly dataset is analyzed. The large dataset is used to construct a correlation based graph. The results suggest that the climate data has the characteristics of a small-world graph.
ContributorsParamasivam, Kumaraguru (Author) / Colbourn, Charles J (Thesis advisor) / Sen, Arunabhas (Committee member) / Syrotiuk, Violet R. (Committee member) / Arizona State University (Publisher)
Created2011
Description
In the late 1960s, Granger published a seminal study on causality in time series, using linear interdependencies and information transfer. Recent developments in the field of information theory have introduced new methods to investigate the transfer of information in dynamical systems. Using concepts from Chaos and Markov theory, much of these methods have evolved to capture non-linear relations and information flow between coupled dynamical systems with applications to fields like biomedical signal processing. This thesis deals with the application of information theory to non-linear multivariate time series and develops measures of information flow to identify significant drivers and response (driven) components in networks of coupled sub-systems with variable coupling in strength and direction (uni- or bi-directional) for each connection. Transfer Entropy (TE) is used to quantify pairwise directional information. Four TE-based measures of information flow are proposed, namely TE Outflow (TEO), TE Inflow (TEI), TE Net flow (TEN), and Average TE flow (ATE). First, the reliability of the information flow measures on models, with and without noise, is evaluated. The driver and response sub-systems in these models are identified. Second, these measures are applied to electroencephalographic (EEG) data from two patients with focal epilepsy. The analysis showed dominant directions of information flow between brain sites and identified the epileptogenic focus as the system component typically with the highest value for the proposed measures (for example, ATE). Statistical tests between pre-seizure (preictal) and post-seizure (postictal) information flow also showed a breakage of the driving of the brain by the focus after seizure onset. The above findings shed light on the function of the epileptogenic focus and understanding of ictogenesis. It is expected that they will contribute to the diagnosis of epilepsy, for example by accurate identification of the epileptogenic focus from interictal periods, as well as the development of better seizure detection, prediction and control methods, for example by isolating pathologic areas of excessive information flow through electrical stimulation.
ContributorsPrasanna, Shashank (Author) / Jassemidis, Leonidas (Thesis advisor) / Tsakalis, Konstantinos (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2011
Description
Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning of the relevant patterns This dissertation proposes TS representations and methods for supervised TS analysis. The approaches combine new representations that handle translations and dilations of patterns with bag-of-features strategies and tree-based ensemble learning. This provides flexibility in handling time-warped patterns in a computationally efficient way. The ensemble learners provide a classification framework that can handle high-dimensional feature spaces, multiple classes and interaction between features. The proposed representations are useful for classification and interpretation of the TS data of varying complexity. The first contribution handles the problem of time warping with a feature-based approach. An interval selection and local feature extraction strategy is proposed to learn a bag-of-features representation. This is distinctly different from common similarity-based time warping. This allows for additional features (such as pattern location) to be easily integrated into the models. The learners have the capability to account for the temporal information through the recursive partitioning method. The second contribution focuses on the comprehensibility of the models. A new representation is integrated with local feature importance measures from tree-based ensembles, to diagnose and interpret time intervals that are important to the model. Multivariate time series (MTS) are especially challenging because the input consists of a collection of TS and both features within TS and interactions between TS can be important to models. Another contribution uses a different representation to produce computationally efficient strategies that learn a symbolic representation for MTS. Relationships between the multiple TS, nominal and missing values are handled with tree-based learners. Applications such as speech recognition, medical diagnosis and gesture recognition are used to illustrate the methods. Experimental results show that the TS representations and methods provide better results than competitive methods on a comprehensive collection of benchmark datasets. Moreover, the proposed approaches naturally provide solutions to similarity analysis, predictive pattern discovery and feature selection.
ContributorsBaydogan, Mustafa Gokce (Author) / Runger, George C. (Thesis advisor) / Atkinson, Robert (Committee member) / Gel, Esma (Committee member) / Pan, Rong (Committee member) / Arizona State University (Publisher)
Created2012
ContributorsMcClain, Katelyn (Performer) / Buringrud, Deanna (Contributor) / Lee, Juhyun (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-31
Description
The primary objective in time series analysis is forecasting. Raw data often exhibits nonstationary behavior: trends, seasonal cycles, and heteroskedasticity. After data is transformed to a weakly stationary process, autoregressive moving average (ARMA) models may capture the remaining temporal dynamics to improve forecasting. Estimation of ARMA can be performed through regressing current values on previous realizations and proxy innovations. The classic paradigm fails when dynamics are nonlinear; in this case, parametric, regime-switching specifications model changes in level, ARMA dynamics, and volatility, using a finite number of latent states. If the states can be identified using past endogenous or exogenous information, a threshold autoregressive (TAR) or logistic smooth transition autoregressive (LSTAR) model may simplify complex nonlinear associations to conditional weakly stationary processes. For ARMA, TAR, and STAR, order parameters quantify the extent past information is associated with the future. Unfortunately, even if model orders are known a priori, the possibility of over-fitting can lead to sub-optimal forecasting performance. By intentionally overestimating these orders, a linear representation of the full model is exploited and Bayesian regularization can be used to achieve sparsity. Global-local shrinkage priors for AR, MA, and exogenous coefficients are adopted to pull posterior means toward 0 without over-shrinking relevant effects. This dissertation introduces, evaluates, and compares Bayesian techniques that automatically perform model selection and coefficient estimation of ARMA, TAR, and STAR models. Multiple Monte Carlo experiments illustrate the accuracy of these methods in finding the "true" data generating process. Practical applications demonstrate their efficacy in forecasting.
ContributorsGiacomazzo, Mario (Author) / Kamarianakis, Yiannis (Thesis advisor) / Reiser, Mark R. (Committee member) / McCulloch, Robert (Committee member) / Hahn, Richard (Committee member) / Fricks, John (Committee member) / Arizona State University (Publisher)
Created2018