Matching Items (802)
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- Creators: Novak, Gail (Pianist)
ContributorsKierum, Caitlin (Contributor) / Novak, Gail (Pianist) (Performer) / Liang, Jack (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-11
Description
Detecting anatomical structures, such as the carina, the pulmonary trunk and the aortic arch, is an important step in designing a CAD system of detection Pulmonary Embolism. The presented CAD system gets rid of the high-level prior defined knowledge to become a system which can easily extend to detect other anatomic structures. The system is based on a machine learning algorithm --- AdaBoost and a general feature --- Haar. This study emphasizes on off-line and on-line AdaBoost learning. And in on-line AdaBoost, the thesis further deals with extremely imbalanced condition. The thesis first reviews several knowledge-based detection methods, which are relied on human being's understanding of the relationship between anatomic structures. Then the thesis introduces a classic off-line AdaBoost learning. The thesis applies different cascading scheme, namely multi-exit cascading scheme. The comparison between the two methods will be provided and discussed. Both of the off-line AdaBoost methods have problems in memory usage and time consuming. Off-line AdaBoost methods need to store all the training samples and the dataset need to be set before training. The dataset cannot be enlarged dynamically. Different training dataset requires retraining the whole process. The retraining is very time consuming and even not realistic. To deal with the shortcomings of off-line learning, the study exploited on-line AdaBoost learning approach. The thesis proposed a novel pool based on-line method with Kalman filters and histogram to better represent the distribution of the samples' weight. Analysis of the performance, the stability and the computational complexity will be provided in the thesis. Furthermore, the original on-line AdaBoost performs badly in imbalanced conditions, which occur frequently in medical image processing. In image dataset, positive samples are limited and negative samples are countless. A novel Self-Adaptive Asymmetric On-line Boosting method is presented. The method utilized a new asymmetric loss criterion with self-adaptability according to the ratio of exposed positive and negative samples and it has an advanced rule to update sample's importance weight taking account of both classification result and sample's label. Compared to traditional on-line AdaBoost Learning method, the new method can achieve far more accuracy in imbalanced conditions.
ContributorsWu, Hong (Author) / Liang, Jianming (Thesis advisor) / Farin, Gerald (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2011
ContributorsLougheed, Julia (Performer) / Novak, Gail (Pianist) (Performer) / Bayer, Elizabeth Kennedy (Performer) / Clifton-Armenta, Tyler (Performer) / Park, Julie (Performer) / Javier de Alba, Francisco (Performer) / Vientos Dulces (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-07
ContributorsCoffey, Brennan (Performer) / Novak, Gail (Pianist) (Performer) / ASU Library. Music Library (Publisher)
Created2021-04-26
ContributorsHolly, Sean (Performer) / Wright, Aaron (Performer) / Novak, Gail (Pianist) (Performer) / ASU Library. Music Library (Publisher)
Created2021-04-29
ContributorsBreeden, Katherine (Performer) / German, Lindsey (Performer) / Novak, Gail (Pianist) (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-13
Description
ABSTRACT Many musicians, both amateur and professional alike, are continuously seeking to expand and explore their performance literature and repertory. Introducing new works into the standard repertory is an exciting endeavor for any active musician. Establishing connections, commissioning new works, and collaborating on performances can all work together toward the acceptance and success of a composer's music within an instrument community. For the flute, one such composer is Daniel Dorff (b. 1956). Dorff, a Philadelphia-based composer, has written for symphony orchestra, clarinet, contrabassoon, and others; however, his award-winning works for flute and piccolo are earning him much recognition. He has written works for such illustrious flutists as Mimi Stillman, Walfrid Kujala, and Gary Schocker; his flute works have been recorded by Laurel Zucker, Pamela Youngblood and Lois Bliss Herbine; and his pieces have been performed and premiered at each of the National Flute Association Conventions from 2004 to 2009. Despite this success, little has been written about Dorff's life, compositional style, and contributions to the flute repertory. In order to further promote the flute works of Daniel Dorff, the primary focus of this study is the creation of a compact disc recording of Dorff's most prominent works for flute: April Whirlwind, 9 Walks Down 7th Avenue, both for flute and piano, and Nocturne Caprice for solo flute. In support of this recording, the study also provides biographical information regarding Daniel Dorff, discusses his compositional methods and ideology, and presents background information, description, and performance notes for each piece. Interviews with Daniel Dorff regarding biographical and compositional details serve as the primary source for this document. Suggestions for the performance of the three flute works were gathered through interviews with prominent flutists who have studied and performed Dorff's pieces. Additional performance suggestions for Nocturne Caprice were gathered through a coaching session between the author and the composer. This project is meant to promote the flute works of Daniel Dorff and to help establish their role in the standard flute repertory.
ContributorsRich, Angela Marie (Contributor) / Novak, Gail (Pianist) (Performer) / Buck, Elizabeth Y (Thesis advisor) / Hill, Gary W. (Committee member) / Holbrook, Amy (Committee member) / Schuring, Martin (Committee member) / Arizona State University (Publisher)
Created2010
ContributorsBroome-Robinson, Julia (Performer) / Novak, Gail (Pianist) (Performer) / Glick, Philip (Performer) / Lynch, Paul (Performer) / Ryall, Blake (Performer) / ASU Library. Music Library (Publisher)
Created2018-10-19
ContributorsCrimminger, Jordan (Performer) / Novak, Gail (Pianist) (Performer) / Hong, Dylan (Performer) / Larson, Ben (Performer) / Russell, Liam (Performer) / Raschko, Hannah (Performer) / ASU Library. Music Library (Publisher)
Created2017-10-22
ContributorsStrickland, Kiefer (Performer) / Novak, Gail (Pianist) (Performer) / McKinch, Riley (Performer) / Hoeckley, Stephanie (Performer) / Bates-Kennard, Sarah (Performer) / Moonitz, Olivia (Performer) / Lovelady, Alexis (Performer) / ASU Library. Music Library (Publisher)
Created2017-10-31