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- All Subjects: Guitar music
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
ContributorsDaval, Charles (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-26
DescriptionThe purpose of this project is to explore the influence of folk music in guitar compositions by Manuel Ponce from 1923 to 1932. It focuses on his Tres canciones populares mexicanas and Tropico and Rumba.
ContributorsGarcia Santos, Arnoldo (Author) / Koonce, Frank (Thesis advisor) / Rogers, Rodney (Committee member) / Rotaru, Catalin (Committee member) / Arizona State University (Publisher)
Created2014
ContributorsKotronakis, Dimitris (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-01
Description
The purpose of this thesis project was to examine the trajectories of physical activity among newly-diagnosed Obstructive Sleep Apnea (OSA) patients within the UC+WS group of the SleepWell24 study across the first 60 days of CPAP use, alone and based on Apnea-Hypopnea Index (AHI), Body Mass Index (BMI), sex, and age. The study utilizes objective data from the SleepWell24 randomized controlled trial conducted by a collaborative research team at Arizona State University and Mayo Clinic Arizona and Rochester. Participants use wearable sensors to track activity behaviors, such as sleep, sedentary behavior, light-intensity physical activity (LPA), and moderate-vigorous physical activity (MVPA). The primary aim of the study was to examine the physical activity trajectories among newly-diagnosed OSA patients over the first 8 weeks of CPAP use, utilizing the physical activity data from wearable sensors. The secondary aim was to assess the trajectories of physical activity between categories of AHI, BMI, sex, and age. Multilevel modeling was used to account for clustering within participants considering between and within subject variations, and week was used as a level 1 predictor in the model for LPA, and MVPA, and total activity (sum of LPA and MVPA), while between subject factors of BMI, sex, age, and AHI were also included in the model. It was found that there were no statistically significant trajectories of LPA, MVPA or total activity over the first 8 weeks of CPAP use within the sample of 30 participants. However, a few notable differences in physical activity were seen between categories of age, sex, and BMI. Also, there was a significant interaction found between BMI and each week that influenced the trajectory of physical activity within obese patients, as compared to participants considered overweight or with a lower BMI. Ultimately, this study provides insight into patterns of physical activity seen in a clinical population of OSA patients over the initial period of CPAP use.
ContributorsDavis, Kiley Lynn (Author) / Buman, Matthew P. (Thesis director) / Petrov, Megan (Committee member) / Dean, W.P. Carey School of Business (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
ContributorsDavin, Colin (Performer) / ASU Library. Music Library (Publisher)
Created2018-10-05
ContributorsSanchez, Armand (Performer) / Nordstrom, Nathan (Performer) / Roubison, Ryan (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-13
ContributorsChan, Robbie (Performer) / McCarrel, Kyla (Performer) / Sadownik, Stephanie (Performer) / ASU Library. Music Library (Contributor)
Created2018-04-18
ContributorsMayo, Joshua (Contributor) / Betts, Sarah (Performer) / Cericola, Marco (Performer) / Gonzalez, Nikolas (Performer) / ASU Library. Music Library (Publisher)
Created2021-04-28