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- Genre: Doctoral Dissertation
- Creators: Bakkaloglu, Bertan
- Creators: Papandreou-Suppappola, Antonia
- Creators: Campbell, Andrew
- Member of: Theses and Dissertations
Description
This document is intended to show the various kinds of stylistically appropriate melodic and rhythmic ornamentation that can be used in the improvisation of the Sarabandes by J.S. Bach. Traditional editions of Bach's and other Baroque-era keyboard works have reflected evolving historical trends. The historical performance movement and other attempts to "clean up" pre-1950s romanticized performances have greatly limited the freedom and experimentation that was the original intention of these dances. Prior to this study, few ornamented editions of these works have been published. Although traditional practices do not necessarily encourage classical improvisation in performance I argue that manipulation of the melodic and rhythmic layers over the established harmonic progressions will not only provide diversity within the individual dance movements, but also further engage the ears of the performer and listener which encourages further creative exploration. I will focus this study on the ornamentation of all six Sarabandes from J.S. Bach's French Suites and show how various types of melodic and rhythmic variation can provide aurally pleasing alternatives to the composed score without disrupting the harmonic fluency. The author intends this document to be used as a pedagogical tool and the fully ornamented Sarabandes from J.S. Bach's French Suites are included with this document.
ContributorsOakley, Ashley (Author) / Meir, Baruch (Thesis advisor) / Campbell, Andrew (Committee member) / Norton, Kay (Committee member) / Pagano, Caio (Committee member) / Ryan, Russell (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
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
Distributed inference has applications in a wide range of fields such as source localization, target detection, environment monitoring, and healthcare. In this dissertation, distributed inference schemes which use bounded transmit power are considered. The performance of the proposed schemes are studied for a variety of inference problems. In the first part of the dissertation, a distributed detection scheme where the sensors transmit with constant modulus signals over a Gaussian multiple access channel is considered. The deflection coefficient of the proposed scheme is shown to depend on the characteristic function of the sensing noise, and the error exponent for the system is derived using large deviation theory. Optimization of the deflection coefficient and error exponent are considered with respect to a transmission phase parameter for a variety of sensing noise distributions including impulsive ones. The proposed scheme is also favorably compared with existing amplify-and-forward (AF) and detect-and-forward (DF) schemes. The effect of fading is shown to be detrimental to the detection performance and simulations are provided to corroborate the analytical results. The second part of the dissertation studies a distributed inference scheme which uses bounded transmission functions over a Gaussian multiple access channel. The conditions on the transmission functions under which consistent estimation and reliable detection are possible is characterized. For the distributed estimation problem, an estimation scheme that uses bounded transmission functions is proved to be strongly consistent provided that the variance of the noise samples are bounded and that the transmission function is one-to-one. The proposed estimation scheme is compared with the amplify and forward technique and its robustness to impulsive sensing noise distributions is highlighted. It is also shown that bounded transmissions suffer from inconsistent estimates if the sensing noise variance goes to infinity. For the distributed detection problem, similar results are obtained by studying the deflection coefficient. Simulations corroborate our analytical results. In the third part of this dissertation, the problem of estimating the average of samples distributed at the nodes of a sensor network is considered. A distributed average consensus algorithm in which every sensor transmits with bounded peak power is proposed. In the presence of communication noise, it is shown that the nodes reach consensus asymptotically to a finite random variable whose expectation is the desired sample average of the initial observations with a variance that depends on the step size of the algorithm and the variance of the communication noise. The asymptotic performance is characterized by deriving the asymptotic covariance matrix using results from stochastic approximation theory. It is shown that using bounded transmissions results in slower convergence compared to the linear consensus algorithm based on the Laplacian heuristic. Simulations corroborate our analytical findings. Finally, a robust distributed average consensus algorithm in which every sensor performs a nonlinear processing at the receiver is proposed. It is shown that non-linearity at the receiver nodes makes the algorithm robust to a wide range of channel noise distributions including the impulsive ones. It is shown that the nodes reach consensus asymptotically and similar results are obtained as in the case of transmit non-linearity. Simulations corroborate our analytical findings and highlight the robustness of the proposed algorithm.
ContributorsDasarathan, Sivaraman (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Reisslein, Martin (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2013
Description
Electrical neural activity detection and tracking have many applications in medical research and brain computer interface technologies. In this thesis, we focus on the development of advanced signal processing algorithms to track neural activity and on the mapping of these algorithms onto hardware to enable real-time tracking. At the heart of these algorithms is particle filtering (PF), a sequential Monte Carlo technique used to estimate the unknown parameters of dynamic systems. First, we analyze the bottlenecks in existing PF algorithms, and we propose a new parallel PF (PPF) algorithm based on the independent Metropolis-Hastings (IMH) algorithm. We show that the proposed PPF-IMH algorithm improves the root mean-squared error (RMSE) estimation performance, and we demonstrate that a parallel implementation of the algorithm results in significant reduction in inter-processor communication. We apply our implementation on a Xilinx Virtex-5 field programmable gate array (FPGA) platform to demonstrate that, for a one-dimensional problem, the PPF-IMH architecture with four processing elements and 1,000 particles can process input samples at 170 kHz by using less than 5% FPGA resources. We also apply the proposed PPF-IMH to waveform-agile sensing to achieve real-time tracking of dynamic targets with high RMSE tracking performance. We next integrate the PPF-IMH algorithm to track the dynamic parameters in neural sensing when the number of neural dipole sources is known. We analyze the computational complexity of a PF based method and propose the use of multiple particle filtering (MPF) to reduce the complexity. We demonstrate the improved performance of MPF using numerical simulations with both synthetic and real data. We also propose an FPGA implementation of the MPF algorithm and show that the implementation supports real-time tracking. For the more realistic scenario of automatically estimating an unknown number of time-varying neural dipole sources, we propose a new approach based on the probability hypothesis density filtering (PHDF) algorithm. The PHDF is implemented using particle filtering (PF-PHDF), and it is applied in a closed-loop to first estimate the number of dipole sources and then their corresponding amplitude, location and orientation parameters. We demonstrate the improved tracking performance of the proposed PF-PHDF algorithm and map it onto a Xilinx Virtex-5 FPGA platform to show its real-time implementation potential. Finally, we propose the use of sensor scheduling and compressive sensing techniques to reduce the number of active sensors, and thus overall power consumption, of electroencephalography (EEG) systems. We propose an efficient sensor scheduling algorithm which adaptively configures EEG sensors at each measurement time interval to reduce the number of sensors needed for accurate tracking. We combine the sensor scheduling method with PF-PHDF and implement the system on an FPGA platform to achieve real-time tracking. We also investigate the sparsity of EEG signals and integrate compressive sensing with PF to estimate neural activity. Simulation results show that both sensor scheduling and compressive sensing based methods achieve comparable tracking performance with significantly reduced number of sensors.
ContributorsMiao, Lifeng (Author) / Chakrabarti, Chaitali (Thesis advisor) / Papandreou-Suppappola, Antonia (Thesis advisor) / Zhang, Junshan (Committee member) / Bliss, Daniel (Committee member) / Kovvali, Narayan (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
The purpose of the paper is to outline the process that was used to write a reduction for Henry Brant's Concerto for Alto Saxophone and Orchestra, to describe the improvements in saxophone playing since the premiere of the piece, and to demonstrate the necessity of having a reduction in the process of learning a concerto. The Concerto was inspired by internationally known saxophonist, Sigurd Rascher, who demonstrated for Brant the extent of his abilities on the saxophone. These abilities included use of four-octave range and two types of extended techniques: slap-tonguing and flutter-tonguing. Brant incorporated all three elements in his Concerto, and believed that only Rascher had the command over the saxophone needed to perform the piece. To prevent the possibility of an unsuccessful performance, Brant chose to make the piece unavailable to saxophonists by leaving the Concerto without a reduction. Subsequently, there were no performances of this piece between 1953 and 2001. In 2011, the two directors of Brant's Estate decided to allow for a reduction to be written for the piece so that it would become more widely available to saxophonists.
ContributorsAmes, Elizabeth (Pianist) (Author) / Ryan, Russell (Thesis advisor) / Levy, Benjamin (Committee member) / Hill, Gary (Committee member) / Campbell, Andrew (Committee member) / Arizona State University (Publisher)
Created2013
Discovering Puerto Rican art song: a research project on four art song works by Héctor Campos Parsi
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 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
Description
Artistic trends of the mid-nineteenth century demonstrate the popularity of incorporating Asian elements into various artistic media. This paper discusses why the stereotypical Asian female provided an attractive character for operatic librettists, composers and audiences. To support the discussion, six operas from 1885 to 2010 are examined, and the dramatic and musical portrayal of representative female characters is discussed. The familiar character of Cio-cio-san from Giocamo Puccini's Madama Butterfly (1904) provides a foundation to discuss these stereotypical Asian female characteristics, specifically one archetype, that of the naïve, yet sexually desirable female. Prior to Cio-cio-san, Sir W. S. Gilbert and Sir Arthur Sullivan's Yum-Yum from The Mikado (1885), Iris of Pietro Mascagni's Iris (1898) exemplify this archetype, as does Liù from Puccini's Turandot (1924). At the other extreme is the icy, cold and bloodthirsty archetype found in the title role of Puccini's Turandot and Katisha from The Mikado. Chiang Ch'ing (also known as Madame Mao) from John Adams's Nixon in China (1987), and Madame White Snake from Chinese-American composer Zhou Long's Madame White Snake (2010) feature leading characters that demonstrate elements of both of these archetypes, and this combination of the two archetypes yields more complex and richer characters. These two extremes of the female Asian stereotype and the evolution of these characteristics provide an interesting outlook on the incorporation of non-Western musical styles into these operas, and the understanding of a Western perception of foreign peoples, especially foreign females.
ContributorsLo, Wan-Yi (Author) / Campbell, Andrew (Thesis advisor) / Carpenter, Ellon (Committee member) / Kopta, Anne (Committee member) / Mills, Robert (Committee member) / Ryan, Russell (Committee member) / Arizona State University (Publisher)
Created2013
Description
The teaching of singing remained remarkably stable until, at the end of the twentieth century, advances in the understanding of voice science stimulated dramatic changes in approach to vocal pedagogy. Previously, the technology needed to accurately measure physiologic change within the larynx and breath-support musculature during the process of singing simply did not exist. Any prior application of scientific study to the voice was based primarily upon auditory evaluation, rather than objective data accumulation and assessment. After a centuries-long history, within a span of twenty years, vocal pedagogy evolved from an approach solely derived from subjective, auditory evidence to an application grounded in scientific data. By means of analysis of significant publications by Richard Miller, Robert Sataloff, and Ingo Titze, as well as articles from The Journal of Singing and The Journal of Voice, I establish a baseline of scientific knowledge and pedagogic practice ca. 1980. Analysis and comparison of a timeline of advancement in scientific insight and the discussion of science in pedagogical texts, 1980-2000, reveal the extent to which voice teachers have dramatically changed their method of instruction. I posit that voice pedagogy has undergone a fundamental change, from telling the student only what to do, via auditory demonstration and visual imagery, to validating with scientific data how and why students should change their vocal approach. The consequence of this dramatic pedagogic evolution has produced singers who comprehend more fully the science of their art.
ContributorsVelarde, Rachel (Author) / Doan, Jerry (Thesis advisor) / Campbell, Andrew (Committee member) / Solis, Theodore (Committee member) / Elgar Kopta, Anne (Committee member) / Britton, David (Committee member) / Arizona State University (Publisher)
Created2013