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ContributorsWasbotten, Leia (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-30
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Description
Libby Larsen is one of the most performed and acclaimed composers today. She is a spirited, compelling, and sensitive composer whose music enhances the poetry of America's most prominent authors. Notable among her works are song cycles for soprano based on the poetry of female writers, among them novelist and

Libby Larsen is one of the most performed and acclaimed composers today. She is a spirited, compelling, and sensitive composer whose music enhances the poetry of America's most prominent authors. Notable among her works are song cycles for soprano based on the poetry of female writers, among them novelist and poet Willa Cather (1873-1947). Larsen has produced two song cycles on works from Cather's substantial output of fiction: one based on Cather's short story, "Eric Hermannson's Soul," titled Margaret Songs: Three Songs from Willa Cather (1996); and later, My Antonia (2000), based on Cather's novel of the same title. In Margaret Songs, Cather's poetry and short stories--specifically the character of Margaret Elliot--combine with Larsen's unique compositional style to create a surprising collaboration. This study explores how Larsen in these songs delves into the emotional and psychological depths of Margaret's character, not fully formed by Cather. It is only through Larsen's music and Cather's poetry that Margaret's journey through self-discovery and love become fully realized. This song cycle is a glimpse through the eyes of two prominent female artists on the societal pressures placed upon Margaret's character, many of which still resonate with women in today's culture. This study examines the work Margaret Songs by discussing Willa Cather, her musical influences, and the conditions surrounding the writing of "Eric Hermannson's Soul." It looks also into Cather's influence on Libby Larsen and the commission leading to Margaret Songs. Finally, a description of the musical, dramatic, and textual content of the songs completes this interpretation of the interactions of Willa Cather, Libby Larsen, and the character of Margaret Elliot.
ContributorsMcLain, Christi Marie (Author) / FitzPatrick, Carole (Thesis advisor) / Dreyfoos, Dale (Committee member) / Holbrook, Amy (Committee member) / Ryan, Russell (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Puerto Rico has produced many important composers who have contributed to the musical culture of the nation during the last 200 years. However, a considerable amount of their music has proven to be difficult to access and may contain numerous errors. This research project intends to contribute to the accessibility

Puerto Rico has produced many important composers who have contributed to the musical culture of the nation during the last 200 years. However, a considerable amount of their music has proven to be difficult to access and may contain numerous errors. This research project intends to contribute to the accessibility of such music and to encourage similar studies of Puerto Rican music. This study focuses on the music of Héctor Campos Parsi (1922-1998), one of the most prominent composers of the 20th century in Puerto Rico. After an overview of the historical background of music on the island and the biography of the composer, four works from his art song repertoire are given for detailed examination. A product of this study is the first corrected edition of his cycles Canciones de Cielo y Agua, Tres Poemas de Corretjer, Los Paréntesis, and the song Majestad Negra. These compositions date from 1947 to 1959, and reflect both the European and nationalistic writing styles of the composer during this time. Data for these corrections have been obtained from the composer's manuscripts, published and unpublished editions, and published recordings. The corrected scores are ready for publication and a compact disc of this repertoire, performed by soprano Melliangee Pérez and the author, has been recorded to bring to life these revisions. Despite the best intentions of the author, the various copyright issues have yet to be resolved. It is hoped that this document will provide the foundation for a resolution and that these important works will be available for public performance and study in the near future.
ContributorsRodríguez Morales, Luis F., 1980- (Author) / Campbell, Andrew (Thesis advisor) / Buck, Elizabeth (Committee member) / Holbrook, Amy (Committee member) / Kopta, Anne (Committee member) / Ryan, Russell (Committee member) / Arizona State University (Publisher)
Created2013
ContributorsYi, Joyce (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-22
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Description
With the increasing user demand for low latency, elastic provisioning of computing resources coupled with ubiquitous and on-demand access to real-time data, cloud computing has emerged as a popular computing paradigm to meet growing user demands.

With the increasing user demand for low latency, elastic provisioning of computing resources coupled with ubiquitous and on-demand access to real-time data, cloud computing has emerged as a popular computing paradigm to meet growing user demands. However, with the introduction and rising use of wear- able technology and evolving uses of smart-phones, the concept of Internet of Things (IoT) has become a prevailing notion in the currently growing technology industry. Cisco Inc. has projected a data creation of approximately 403 Zetabytes (ZB) by 2018. The combination of bringing benign devices and connecting them to the web has resulted in exploding service and data aggregation requirements, thus requiring a new and innovative computing platform. This platform should have the capability to provide robust real-time data analytics and resource provisioning to clients, such as IoT users, on-demand. Such a computation model would need to function at the edge-of-the-network, forming a bridge between the large cloud data centers and the distributed connected devices.

This research expands on the notion of bringing computational power to the edge- of-the-network, and then integrating it with the cloud computing paradigm whilst providing services to diverse IoT-based applications. This expansion is achieved through the establishment of a new computing model that serves as a platform for IoT-based devices to communicate with services in real-time. We name this paradigm as Gateway-Oriented Reconfigurable Ecosystem (GORE) computing. Finally, this thesis proposes and discusses the development of a policy management framework for accommodating our proposed computational paradigm. The policy framework is designed to serve both the hosted applications and the GORE paradigm by enabling them to function more efficiently. The goal of the framework is to ensure uninterrupted communication and service delivery between users and their applications.
ContributorsDsouza, Clinton (Author) / Ahn, Gail-Joon (Thesis advisor) / Doupe, Adam (Committee member) / Dasgupta, Partha (Committee member) / Arizona State University (Publisher)
Created2015
ContributorsCummiskey, Hannah (Performer) / Kim, Olga (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-23
ContributorsGoglia, Adrienne (Performer)
Created2018-03-02
ContributorsEvans, Emily (Performer) / Sherrill, Amanda (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-02
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Description
Rapid growth of internet and connected devices ranging from cloud systems to internet of things have raised critical concerns for securing these systems. In the recent past, security attacks on different kinds of devices have evolved in terms of complexity and diversity. One of the challenges is establishing secure communication

Rapid growth of internet and connected devices ranging from cloud systems to internet of things have raised critical concerns for securing these systems. In the recent past, security attacks on different kinds of devices have evolved in terms of complexity and diversity. One of the challenges is establishing secure communication in the network among various devices and systems. Despite being protected with authentication and encryption, the network still needs to be protected against cyber-attacks. For this, the network traffic has to be closely monitored and should detect anomalies and intrusions. Intrusion detection can be categorized as a network traffic classification problem in machine learning. Existing network traffic classification methods require a lot of training and data preprocessing, and this problem is more serious if the dataset size is huge. In addition, the machine learning and deep learning methods that have been used so far were trained on datasets that contain obsolete attacks. In this thesis, these problems are addressed by using ensemble methods applied on an up to date network attacks dataset. Ensemble methods use multiple learning algorithms to get better classification accuracy that could be obtained when the corresponding learning algorithm is applied alone. This dataset for network traffic classification has recent attack scenarios and contains over fifteen attacks. This approach shows that ensemble methods can be used to classify network traffic and detect intrusions with less training times of the model, and lesser pre-processing without feature selection. In addition, this thesis also shows that only with less than ten percent of the total features of input dataset will lead to similar accuracy that is achieved on whole dataset. This can heavily reduce the training times and classification duration in real-time scenarios.
ContributorsPonneganti, Ramu (Author) / Yau, Stephen (Thesis advisor) / Richa, Andrea (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2019
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Description
As integrated technologies are scaling down, there is an increasing trend in the

process,voltage and temperature (PVT) variations of highly integrated RF systems.

Accounting for these variations during the design phase requires tremendous amount

of time for prediction of RF performance and optimizing it accordingly. Thus, there

is an increasing gap between the need

As integrated technologies are scaling down, there is an increasing trend in the

process,voltage and temperature (PVT) variations of highly integrated RF systems.

Accounting for these variations during the design phase requires tremendous amount

of time for prediction of RF performance and optimizing it accordingly. Thus, there

is an increasing gap between the need to relax the RF performance requirements at

the design phase for rapid development and the need to provide high performance

and low cost RF circuits that function with PVT variations. No matter how care-

fully designed, RF integrated circuits (ICs) manufactured with advanced technology

nodes necessitate lengthy post-production calibration and test cycles with expensive

RF test instruments. Hence design-for-test (DFT) is proposed for low-cost and fast

measurement of performance parameters during both post-production and in-eld op-

eration. For example, built-in self-test (BIST) is a DFT solution for low-cost on-chip

measurement of RF performance parameters. In this dissertation, three aspects of

automated test and calibration, including DFT mathematical model, BIST hardware

and built-in calibration are covered for RF front-end blocks.

First, the theoretical foundation of a post-production test of RF integrated phased

array antennas is proposed by developing the mathematical model to measure gain

and phase mismatches between antenna elements without any electrical contact. The

proposed technique is fast, cost-efficient and uses near-field measurement of radiated

power from antennas hence, it requires single test setup, it has easy implementation

and it is short in time which makes it viable for industrialized high volume integrated

IC production test.

Second, a BIST model intended for the characterization of I/Q offset, gain and

phase mismatch of IQ transmitters without relying on external equipment is intro-

duced. The proposed BIST method is based on on-chip amplitude measurement as

in prior works however,here the variations in the BIST circuit do not affect the target

parameter estimation accuracy since measurements are designed to be relative. The

BIST circuit is implemented in 130nm technology and can be used for post-production

and in-field calibration.

Third, a programmable low noise amplifier (LNA) is proposed which is adaptable

to different application scenarios depending on the specification requirements. Its

performance is optimized with regards to required specifications e.g. distance, power

consumption, BER, data rate, etc.The statistical modeling is used to capture the

correlations among measured performance parameters and calibration modes for fast

adaptation. Machine learning technique is used to capture these non-linear correlations and build the probability distribution of a target parameter based on measurement results of the correlated parameters. The proposed concept is demonstrated by

embedding built-in tuning knobs in LNA design in 130nm technology. The tuning

knobs are carefully designed to provide independent combinations of important per-

formance parameters such as gain and linearity. Minimum number of switches are

used to provide the desired tuning range without a need for an external analog input.
ContributorsShafiee, Maryam (Author) / Ozev, Sule (Thesis advisor) / Diaz, Rodolfo (Committee member) / Ogras, Umit Y. (Committee member) / Bakkaloglu, Bertan (Committee member) / Arizona State University (Publisher)
Created2018