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- Genre: Doctoral Dissertation
- Creators: Rogers, Rodney
- Creators: Bakkaloglu, Bertan
Description
Several contemporary clarinet works use Chinese folk music elements from different regions in new compositions to entice listener's and performer's appreciation of Chinese culture. However, to date, limited academic research on this topic exists. This research paper introduces six contemporary clarinet works by six Chinese composers: Qigang Chen's Morning Song, Yan Wang's Mu ma zhi ge (The Song of Grazing Horses), An-lun Huang's Capriccio for Clarinet and Strings Op. 41, Bijing Hu's The Sound of Pamir Clarinet Concerto, Mei-Mi Lan's Concerto for Clarinet and String Orchestra with Harp and Percussion, and Yu-Hui Chang's Three Fantasias for Solo Clarinet in B-flat. They are examined from different perspectives, including general structure, style, and rejuvenated folk music use. The focus of this research paper is to investigate the use of Chinese folk music in several works in collaboration with the composers. The author found that although contemporary composers use Chinese folk music differently in their works (i.e., some use melodies, others use harmony, while others use modes), each work celebrates the music and culture of the folk music on which the pieces are based. It is the author's hope to stimulate people's interest in music using Chinese folk music elements, and bring these lesser known works into the common clarinet repertoire.
ContributorsFeng, Chiao-Ting (Author) / Spring, Robert (Thesis advisor) / Gardner, Joshua (Committee member) / Micklich, Albie (Committee member) / Rogers, Rodney (Committee member) / Schuring, Martin (Committee member) / Arizona State University (Publisher)
Created2013
Description
Distributed estimation uses many inexpensive sensors to compose an accurate estimate of a given parameter. It is frequently implemented using wireless sensor networks. There have been several studies on optimizing power allocation in wireless sensor networks used for distributed estimation, the vast majority of which assume linear radio-frequency amplifiers. Linear amplifiers are inherently inefficient, so in this dissertation nonlinear amplifiers are examined to gain efficiency while operating distributed sensor networks. This research presents a method to boost efficiency by operating the amplifiers in the nonlinear region of operation. Operating amplifiers nonlinearly presents new challenges. First, nonlinear amplifier characteristics change across manufacturing process variation, temperature, operating voltage, and aging. Secondly, the equations conventionally used for estimators and performance expectations in linear amplify-and-forward systems fail. To compensate for the first challenge, predistortion is utilized not to linearize amplifiers but rather to force them to fit a common nonlinear limiting amplifier model close to the inherent amplifier performance. This minimizes the power impact and the training requirements for predistortion. Second, new estimators are required that account for transmitter nonlinearity. This research derives analytically and confirms via simulation new estimators and performance expectation equations for use in nonlinear distributed estimation. An additional complication when operating nonlinear amplifiers in a wireless environment is the influence of varied and potentially unknown channel gains. The impact of these varied gains and both measurement and channel noise sources on estimation performance are analyzed in this paper. Techniques for minimizing the estimate variance are developed. It is shown that optimizing transmitter power allocation to minimize estimate variance for the most-compressed parameter measurement is equivalent to the problem for linear sensors. Finally, a method for operating distributed estimation in a multipath environment is presented that is capable of developing robust estimates for a wide range of Rician K-factors. This dissertation demonstrates that implementing distributed estimation using nonlinear sensors can boost system efficiency and is compatible with existing techniques from the literature for boosting efficiency at the system level via sensor power allocation. Nonlinear transmitters work best when channel gains are known and channel noise and receiver noise levels are low.
ContributorsSantucci, Robert (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioðlu, Cihan (Committee member) / Bakkaloglu, Bertan (Committee member) / Tsakalis, Kostas (Committee member) / Arizona State University (Publisher)
Created2013
Description
Bright Summer, a one-movement piece for orchestra, was composed in Arizona, and completed in February 2013. The piece is approximately twelve minutes long. The motivation for writing this piece was the death of my mother the year before, in 2012. The prevailing mood of this work is bright and pleasant, expressing my mother's cheerful personality when she was alive. It also portrays bright summer days which resemble my mother's spirit. Thus, soundscape plays an important role in this work. It depicts summer breeze, rustling sounds of leaves, and, to translate a Korean saying, "high blue skies." This soundscape opens the piece as well as closes it. In the middle section, the fast upbeat themes represent my mother's witty and optimistic personality. The piece also contains the presence of a hymn tune, The Love of God is Greater Far, which informs the motivic content and also functions as the climax of the piece. It was my mother's favorite hymn and we used to sing it together following her conversion to Christianity. The piece contains three main sections, which are held together by transitional material based on the soundscape and metric modulations. Unlike my earlier works, Bright Summer is tonal, with upper tertian harmonies prevailing throughout the piece. However, the opening and closing soundscapes do not have functional harmonies. For example, tertian chords appear and vanish silently, leaving behind some resonant sounds without any harmonic progression. Overall, the whole piece is reminiscent of my mother who lived a beautiful life.
ContributorsKim, JeeYeon (Composer) / DeMars, James (Thesis advisor) / Hackbarth, Glenn (Committee member) / Rogers, Rodney (Committee member) / Levy, Benjamin (Committee member) / Rockmaker, Jody (Committee member) / Arizona State University (Publisher)
Created2013
Description
Radio frequency (RF) transceivers require a disproportionately high effort in terms of test development time, test equipment cost, and test time. The relatively high test cost stems from two contributing factors. First, RF transceivers require the measurement of a diverse set of specifications, requiring multiple test set-ups and long test times, which complicates load-board design, debug, and diagnosis. Second, high frequency operation necessitates the use of expensive equipment, resulting in higher per second test time cost compared with mixed-signal or digital circuits. Moreover, in terms of the non-recurring engineering cost, the need to measure complex specfications complicates the test development process and necessitates a long learning process for test engineers. Test time is dominated by changing and settling time for each test set-up. Thus, single set-up test solutions are desirable. Loop-back configuration where the transmitter output is connected to the receiver input are used as the desirable test set- up for RF transceivers, since it eliminates the reliance on expensive instrumentation for RF signal analysis and enables measuring multiple parameters at once. In-phase and Quadrature (IQ) imbalance, non-linearity, DC offset and IQ time skews are some of the most detrimental imperfections in transceiver performance. Measurement of these parameters in the loop-back mode is challenging due to the coupling between the receiver (RX) and transmitter (TX) parameters. Loop-back based solutions are proposed in this work to resolve this issue. A calibration algorithm for a subset of the above mentioned impairments is also presented. Error Vector Magnitude (EVM) is a system-level parameter that is specified for most advanced communication standards. EVM measurement often takes extensive test development efforts, tester resources, and long test times. EVM is analytically related to system impairments, which are typically measured in a production test i environment. Thus, EVM test can be eliminated from the test list if the relations between EVM and system impairments are derived independent of the circuit implementation and manufacturing process. In this work, the focus is on the WLAN standard, and deriving the relations between EVM and three of the most detrimental impairments for QAM/OFDM based systems (IQ imbalance, non-linearity, and noise). Having low cost test techniques for measuring the RF transceivers imperfections and being able to analytically compute EVM from the measured parameters is a complete test solution for RF transceivers. These techniques along with the proposed calibration method can be used in improving the yield by widening the pass/fail boundaries for transceivers imperfections. For all of the proposed methods, simulation and hardware measurements prove that the proposed techniques provide accurate characterization of RF transceivers.
ContributorsNassery, Afsaneh (Author) / Ozev, Sule (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Kiaei, Sayfe (Committee member) / Kitchen, Jennifer (Committee member) / Arizona State University (Publisher)
Created2013
Description
Autonomous vehicle control systems utilize real-time kinematic Global Navigation Satellite Systems (GNSS) receivers to provide a position within two-centimeter of truth. GNSS receivers utilize the satellite signal time of arrival estimates to solve for position; and multipath corrupts the time of arrival estimates with a time-varying bias. Time of arrival estimates are based upon accurate direct sequence spread spectrum (DSSS) code and carrier phase tracking. Current multipath mitigating GNSS solutions include fixed radiation pattern antennas and windowed delay-lock loop code phase discriminators. A new multipath mitigating code tracking algorithm is introduced that utilizes a non-symmetric correlation kernel to reject multipath. Independent parameters provide a means to trade-off code tracking discriminant gain against multipath mitigation performance. The algorithm performance is characterized in terms of multipath phase error bias, phase error estimation variance, tracking range, tracking ambiguity and implementation complexity. The algorithm is suitable for modernized GNSS signals including Binary Phase Shift Keyed (BPSK) and a variety of Binary Offset Keyed (BOC) signals. The algorithm compensates for unbalanced code sequences to ensure a code tracking bias does not result from the use of asymmetric correlation kernels. The algorithm does not require explicit knowledge of the propagation channel model. Design recommendations for selecting the algorithm parameters to mitigate precorrelation filter distortion are also provided.
ContributorsMiller, Steven (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Tsakalis, Konstantinos (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2013
Description
ABSTRACT Musicians endure injuries at an alarming rate, largely due to the misuse of their bodies. Musicians move their bodies for a living and therefore should understand how to move them in a healthy way. This paper presents Body Mapping as an injury prevention technique specifically directed toward collaborative pianists. A body map is the self-representation in one's brain that includes information on the structure, function, and size of one's body; Body Mapping is the process of refining one's body map to produce coordinated movement. In addition to preventing injury, Body Mapping provides a means to achieve greater musical artistry through the training of movement, attention, and the senses. With the main function of collaborating with one or more musical partners, a collaborative pianist will have the opportunity to share the knowledge of Body Mapping with many fellow musicians. This study demonstrates the author's credentials as a Body Mapping instructor, the current status of the field of collaborative piano, and the recommendation for increased body awareness. Information on the nature and abundance of injuries and Body Mapping concepts are also analyzed. The study culminates in a course syllabus entitled An Introduction to Collaborative Piano and Body Mapping with the objective of imparting fundamental collaborative piano skills integrated with proper body use. The author hopes to inform educators of the benefits of prioritizing health among their students and to provide a Body Mapping foundation upon which their students can build technique.
ContributorsBindel, Jennifer (Author) / Campbell, Andrew (Thesis advisor) / Doan, Jerry (Committee member) / Rogers, Rodney (Committee member) / Ryan, Russell (Committee member) / Schuring, Martin (Committee member) / Arizona State University (Publisher)
Created2013
Description
There are a significant number of musical compositions for violin by composers who used folk songs and dances of various cultures in their music, including works by George Enescu, Béla Bartók and György Ligeti. Less known are pieces that draw on the plethora of melodies and rhythms from Turkey. The purpose of this paper is to help performers become more familiar with two such compositions: Fazil Say's Sonata for Violin and Piano and Cleopatra for Solo Violin. Fazil Say (b. 1970) is considered to be a significant, contemporary Turkish composer. Both of the works discussed in this document simulate traditional "Eastern" instruments, such as the kemenҫe, the baðlama, the kanun and the ud. Additionally, both pieces use themes from folk melodies of Turkey, Turkish dance rhythms and Arabian scales, all framed within traditional structural techniques, such as ostinato bass and the fughetta. Both the Sonata for Violin and Piano and Cleopatra are enormously expressive and musically interesting works, demanding virtuosity and a wide technical range. Although this document does not purport to be a full theoretical analysis, by providing biographical information, analytical descriptions, notes regarding interpretation, and suggestions to assist performers in overcoming technical obstacles, the writer hopes to inspire other violinists to consider learning and performing these works.
ContributorsKalantzi, Panagiota (Author) / Jiang, Danwen (Thesis advisor) / Hill, Gary (Committee member) / Rogers, Rodney (Committee member) / Rotaru, Catalin (Committee member) / Arizona State University (Publisher)
Created2013
Description
The purpose of this project is twofold: to contribute to the literature of chamber ensembles comprising mixed wind, string, and percussion instruments by producing arrangements of three piano rags by William Bolcom; and to highlight Bolcom's pivotal role in the ragtime revival of the 1960's and 1970's. Through his influence as a scholar, composer, and performer, Bolcom (b. 1938), one of the most prominent American composers of his generation, helped garner respect for ragtime as art music and as one of America's great popular music genres. Bolcom's 3 Ghost Rags were written in the tradition of classic piano rags, but with a compositional sensibility that is influenced by the fifty years that separate them from the close of the original ragtime era. The basis for the present orchestrations of 3 Ghost Rags is the collection of instrumental arrangements of piano rags published by Stark Publishing Co., entitled Standard High-Class Rags. More familiarly known as the "Red Back Book," this publication was representative of the exchange of repertoire between piano and ensembles and served as a repertory for the various ragtime revivals that occurred later in the twentieth century. In creating these orchestrations of Bolcom's piano rags, the author strove to provide another medium in which Bolcom's music could be performed, while orchestrating the music for an historically appropriate ensemble.
ContributorsMelley, Eric Charles (Author) / Hill, Gary W. (Thesis advisor) / Bailey, Wayne (Committee member) / Norton, Kay (Committee member) / Rogers, Rodney (Committee member) / Russell, Timothy (Committee member) / Arizona State University (Publisher)
Created2013
Description
Image resolution limits the extent to which zooming enhances clarity, restricts the size digital photographs can be printed at, and, in the context of medical images, can prevent a diagnosis. Interpolation is the supplementing of known data with estimated values based on a function or model involving some or all of the known samples. The selection of the contributing data points and the specifics of how they are used to define the interpolated values influences how effectively the interpolation algorithm is able to estimate the underlying, continuous signal. The main contributions of this dissertation are three fold: 1) Reframing edge-directed interpolation of a single image as an intensity-based registration problem. 2) Providing an analytical framework for intensity-based registration using control grid constraints. 3) Quantitative assessment of the new, single-image enlargement algorithm based on analytical intensity-based registration. In addition to single image resizing, the new methods and analytical approaches were extended to address a wide range of applications including volumetric (multi-slice) image interpolation, video deinterlacing, motion detection, and atmospheric distortion correction. Overall, the new approaches generate results that more accurately reflect the underlying signals than less computationally demanding approaches and with lower processing requirements and fewer restrictions than methods with comparable accuracy.
ContributorsZwart, Christine M. (Author) / Frakes, David H (Thesis advisor) / Karam, Lina (Committee member) / Kodibagkar, Vikram (Committee member) / Spanias, Andreas (Committee member) / Towe, Bruce (Committee member) / Arizona State University (Publisher)
Created2013
Description
Image understanding has been playing an increasingly crucial role in vision applications. Sparse models form an important component in image understanding, since the statistics of natural images reveal the presence of sparse structure. Sparse methods lead to parsimonious models, in addition to being efficient for large scale learning. In sparse modeling, data is represented as a sparse linear combination of atoms from a "dictionary" matrix. This dissertation focuses on understanding different aspects of sparse learning, thereby enhancing the use of sparse methods by incorporating tools from machine learning. With the growing need to adapt models for large scale data, it is important to design dictionaries that can model the entire data space and not just the samples considered. By exploiting the relation of dictionary learning to 1-D subspace clustering, a multilevel dictionary learning algorithm is developed, and it is shown to outperform conventional sparse models in compressed recovery, and image denoising. Theoretical aspects of learning such as algorithmic stability and generalization are considered, and ensemble learning is incorporated for effective large scale learning. In addition to building strategies for efficiently implementing 1-D subspace clustering, a discriminative clustering approach is designed to estimate the unknown mixing process in blind source separation. By exploiting the non-linear relation between the image descriptors, and allowing the use of multiple features, sparse methods can be made more effective in recognition problems. The idea of multiple kernel sparse representations is developed, and algorithms for learning dictionaries in the feature space are presented. Using object recognition experiments on standard datasets it is shown that the proposed approaches outperform other sparse coding-based recognition frameworks. Furthermore, a segmentation technique based on multiple kernel sparse representations is developed, and successfully applied for automated brain tumor identification. Using sparse codes to define the relation between data samples can lead to a more robust graph embedding for unsupervised clustering. By performing discriminative embedding using sparse coding-based graphs, an algorithm for measuring the glomerular number in kidney MRI images is developed. Finally, approaches to build dictionaries for local sparse coding of image descriptors are presented, and applied to object recognition and image retrieval.
ContributorsJayaraman Thiagarajan, Jayaraman (Author) / Spanias, Andreas (Thesis advisor) / Frakes, David (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013