Matching Items (381)
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
Description
With the rapid development of mobile sensing technologies like GPS, RFID, sensors in smartphones, etc., capturing position data in the form of trajectories has become easy. Moving object trajectory analysis is a growing area of interest these days owing to its applications in various domains such as marketing, security, traffic monitoring and management, etc. To better understand movement behaviors from the raw mobility data, this doctoral work provides analytic models for analyzing trajectory data. As a first contribution, a model is developed to detect changes in trajectories with time. If the taxis moving in a city are viewed as sensors that provide real time information of the traffic in the city, a change in these trajectories with time can reveal that the road network has changed. To detect changes, trajectories are modeled with a Hidden Markov Model (HMM). A modified training algorithm, for parameter estimation in HMM, called m-BaumWelch, is used to develop likelihood estimates under assumed changes and used to detect changes in trajectory data with time. Data from vehicles are used to test the method for change detection. Secondly, sequential pattern mining is used to develop a model to detect changes in frequent patterns occurring in trajectory data. The aim is to answer two questions: Are the frequent patterns still frequent in the new data? If they are frequent, has the time interval distribution in the pattern changed? Two different approaches are considered for change detection, frequency-based approach and distribution-based approach. The methods are illustrated with vehicle trajectory data. Finally, a model is developed for clustering and outlier detection in semantic trajectories. A challenge with clustering semantic trajectories is that both numeric and categorical attributes are present. Another problem to be addressed while clustering is that trajectories can be of different lengths and also have missing values. A tree-based ensemble is used to address these problems. The approach is extended to outlier detection in semantic trajectories.
ContributorsKondaveeti, Anirudh (Author) / Runger, George C. (Thesis advisor) / Mirchandani, Pitu (Committee member) / Pan, Rong (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2012
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
While discrete emotions like joy, anger, disgust etc. are quite popular, continuous
emotion dimensions like arousal and valence are gaining popularity within the research
community due to an increase in the availability of datasets annotated with these
emotions. Unlike the discrete emotions, continuous emotions allow modeling of subtle
and complex affect dimensions but are difficult to predict.
Dimension reduction techniques form the core of emotion recognition systems and
help create a new feature space that is more helpful in predicting emotions. But these
techniques do not necessarily guarantee a better predictive capability as most of them
are unsupervised, especially in regression learning. In emotion recognition literature,
supervised dimension reduction techniques have not been explored much and in this
work a solution is provided through probabilistic topic models. Topic models provide
a strong probabilistic framework to embed new learning paradigms and modalities.
In this thesis, the graphical structure of Latent Dirichlet Allocation has been explored
and new models tuned to emotion recognition and change detection have been built.
In this work, it has been shown that the double mixture structure of topic models
helps 1) to visualize feature patterns, and 2) to project features onto a topic simplex
that is more predictive of human emotions, when compared to popular techniques
like PCA and KernelPCA. Traditionally, topic models have been used on quantized
features but in this work, a continuous topic model called the Dirichlet Gaussian
Mixture model has been proposed. Evaluation of DGMM has shown that while modeling
videos, performance of LDA models can be replicated even without quantizing
the features. Until now, topic models have not been explored in a supervised context
of video analysis and thus a Regularized supervised topic model (RSLDA) that
models video and audio features is introduced. RSLDA learning algorithm performs
both dimension reduction and regularized linear regression simultaneously, and has outperformed supervised dimension reduction techniques like SPCA and Correlation
based feature selection algorithms. In a first of its kind, two new topic models, Adaptive
temporal topic model (ATTM) and SLDA for change detection (SLDACD) have
been developed for predicting concept drift in time series data. These models do not
assume independence of consecutive frames and outperform traditional topic models
in detecting local and global changes respectively.
emotion dimensions like arousal and valence are gaining popularity within the research
community due to an increase in the availability of datasets annotated with these
emotions. Unlike the discrete emotions, continuous emotions allow modeling of subtle
and complex affect dimensions but are difficult to predict.
Dimension reduction techniques form the core of emotion recognition systems and
help create a new feature space that is more helpful in predicting emotions. But these
techniques do not necessarily guarantee a better predictive capability as most of them
are unsupervised, especially in regression learning. In emotion recognition literature,
supervised dimension reduction techniques have not been explored much and in this
work a solution is provided through probabilistic topic models. Topic models provide
a strong probabilistic framework to embed new learning paradigms and modalities.
In this thesis, the graphical structure of Latent Dirichlet Allocation has been explored
and new models tuned to emotion recognition and change detection have been built.
In this work, it has been shown that the double mixture structure of topic models
helps 1) to visualize feature patterns, and 2) to project features onto a topic simplex
that is more predictive of human emotions, when compared to popular techniques
like PCA and KernelPCA. Traditionally, topic models have been used on quantized
features but in this work, a continuous topic model called the Dirichlet Gaussian
Mixture model has been proposed. Evaluation of DGMM has shown that while modeling
videos, performance of LDA models can be replicated even without quantizing
the features. Until now, topic models have not been explored in a supervised context
of video analysis and thus a Regularized supervised topic model (RSLDA) that
models video and audio features is introduced. RSLDA learning algorithm performs
both dimension reduction and regularized linear regression simultaneously, and has outperformed supervised dimension reduction techniques like SPCA and Correlation
based feature selection algorithms. In a first of its kind, two new topic models, Adaptive
temporal topic model (ATTM) and SLDA for change detection (SLDACD) have
been developed for predicting concept drift in time series data. These models do not
assume independence of consecutive frames and outperform traditional topic models
in detecting local and global changes respectively.
ContributorsLade, Prasanth (Author) / Panchanathan, Sethuraman (Thesis advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Balasubramanian, Vineeth N (Committee member) / Arizona State University (Publisher)
Created2015
ContributorsMcClain, Katelyn (Performer) / Buringrud, Deanna (Contributor) / Lee, Juhyun (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-31
Description
Motion estimation is a core task in computer vision and many applications utilize optical flow methods as fundamental tools to analyze motion in images and videos. Optical flow is the apparent motion of objects in image sequences that results from relative motion between the objects and the imaging perspective. Today, optical flow fields are utilized to solve problems in various areas such as object detection and tracking, interpolation, visual odometry, etc. In this dissertation, three problems from different areas of computer vision and the solutions that make use of modified optical flow methods are explained.
The contributions of this dissertation are approaches and frameworks that introduce i) a new optical flow-based interpolation method to achieve minimally divergent velocimetry data, ii) a framework that improves the accuracy of change detection algorithms in synthetic aperture radar (SAR) images, and iii) a set of new methods to integrate Proton Magnetic Resonance Spectroscopy (1HMRSI) data into threedimensional (3D) neuronavigation systems for tumor biopsies.
In the first application an optical flow-based approach for the interpolation of minimally divergent velocimetry data is proposed. The velocimetry data of incompressible fluids contain signals that describe the flow velocity. The approach uses the additional flow velocity information to guide the interpolation process towards reduced divergence in the interpolated data.
In the second application a framework that mainly consists of optical flow methods and other image processing and computer vision techniques to improve object extraction from synthetic aperture radar images is proposed. The proposed framework is used for distinguishing between actual motion and detected motion due to misregistration in SAR image sets and it can lead to more accurate and meaningful change detection and improve object extraction from a SAR datasets.
In the third application a set of new methods that aim to improve upon the current state-of-the-art in neuronavigation through the use of detailed three-dimensional (3D) 1H-MRSI data are proposed. The result is a progressive form of online MRSI-guided neuronavigation that is demonstrated through phantom validation and clinical application.
The contributions of this dissertation are approaches and frameworks that introduce i) a new optical flow-based interpolation method to achieve minimally divergent velocimetry data, ii) a framework that improves the accuracy of change detection algorithms in synthetic aperture radar (SAR) images, and iii) a set of new methods to integrate Proton Magnetic Resonance Spectroscopy (1HMRSI) data into threedimensional (3D) neuronavigation systems for tumor biopsies.
In the first application an optical flow-based approach for the interpolation of minimally divergent velocimetry data is proposed. The velocimetry data of incompressible fluids contain signals that describe the flow velocity. The approach uses the additional flow velocity information to guide the interpolation process towards reduced divergence in the interpolated data.
In the second application a framework that mainly consists of optical flow methods and other image processing and computer vision techniques to improve object extraction from synthetic aperture radar images is proposed. The proposed framework is used for distinguishing between actual motion and detected motion due to misregistration in SAR image sets and it can lead to more accurate and meaningful change detection and improve object extraction from a SAR datasets.
In the third application a set of new methods that aim to improve upon the current state-of-the-art in neuronavigation through the use of detailed three-dimensional (3D) 1H-MRSI data are proposed. The result is a progressive form of online MRSI-guided neuronavigation that is demonstrated through phantom validation and clinical application.
ContributorsKanberoglu, Berkay (Author) / Frakes, David (Thesis advisor) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2018
ContributorsHur, Jiyoun (Performer) / Lee, Juhyun (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-01
ContributorsZaleski, Kimberly (Contributor) / Kazarian, Trevor (Performer) / Ryan, Russell (Performer) / IN2ATIVE (Performer) / ASU Library. Music Library (Publisher)
Created2018-09-28
ContributorsDelaney, Erin (Performer) / Novak, Gail (Pianist) (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-18