Matching Items (428)
Description
The Concerto for Oboe and String by Ralph Vaughan Williams is often described as a pastoral work without any consideration for what makes that an accurate description. This paper outlines the connections to English folk song that create what are considered the pastoral qualities in the work. Vaughan Williams' relationship with English folk song, as collector and arranger, is well-documented, as is his advocacy for their use in compositions. By the time he wrote the Oboe Concerto at the end of his career, folk song elements had completely infused his compositional style. The Oboe Concerto shares many stylistic traits with English folk song. These stylistic elements: mode, melodic structure, form, and rhythm and meter are first analyzed in terms of English folk song, then how these features are utilized in the Oboe Concerto. Another connection to English folk song is in the manner of accompanying the Concerto. Vaughan Williams had firm opinions on how to accompany folk songs and wrote many sample accompaniments, which bear a marked resemblance to the accompaniment for the Oboe Concerto. The same is true for the accompaniment he wrote for a specifically folk song-inspired work, the Six Studies in English Folk Song for Violoncello and Pianoforte. Specific examples from both works are compared to the Concerto accompaniment. Finally, several motives and melodic figures found in folk songs included in the Penguin Book of English Folk Songs, which was edited by Vaughan Williams, are also found in the Oboe Concerto. An understanding of the use of English folk song elements and specific quotes in the Oboe Concerto, as well as the folk song-style treatment in accompaniment provide concrete evidence of the pastoral quality prevalent in many works of Vaughan Williams. Not only can this support a well-informed and more rewarding performance of the Oboe Concerto, but the same analysis can be applied to many of his other works as well, in addition to the works of a generation of English composers whose style he influenced.
ContributorsKupitz, Emily Anne (Author) / Schuring, Martin (Thesis advisor) / Micklich, Albie (Committee member) / Oldani, Robert (Committee member) / Arizona State University (Publisher)
Created2013
Description
Despite the wealth of folk music traditions in Portugal and the importance of the clarinet in the music of bandas filarmonicas, it is uncommon to find works featuring the clarinet using Portuguese folk music elements. In the interest of expanding this type of repertoire, three new works were commissioned from three different composers. The resulting works are Seres Imaginarios 3 by Luis Cardoso; Delirio Barroco by Tiago Derrica; and Memória by Pedro Faria Gomes. In an effort to submit these new works for inclusion into mainstream performance literature, the author has recorded these works on compact disc. This document includes interview transcripts with each composer, providing first-person discussion of each composition, as well as detailed biographical information on each composer. To provide context, the author has included a brief discussion on Portuguese folk music, and in particular, the role that the clarinet plays in Portuguese folk music culture.
ContributorsFerreira, Wesley (Contributor) / Spring, Robert S (Thesis advisor) / Bailey, Wayne (Committee member) / Gardner, Joshua (Committee member) / Hill, Gary (Committee member) / Schuring, Martin (Committee member) / Solis, Theodore (Committee member) / Arizona State University (Publisher)
Created2013
Description
Nino Rota was a prolific composer of twentieth-century film and concert music, including the Concerto for bassoon and orchestra in b-flat major. Composing over 150 film scores for directors such as Federico Fellini, Francis Ford Coppola, Henry Cass, King Vidor and Franco Zeffirelli, Rota received distinguished acclaim from several film institutions, professional film reviewers and film music experts for his contributions to the art form. Rota also composed a great deal of diverse repertoire for the concert stage (ballet, opera, incidental music, concerti, symphonies, as well as several chamber works). The purpose of this analysis is to emphasize the expressive charm and accessibility of his concerto in the bassoon repertoire. The matter of this analysis of the Concerto for bassoon and orchestra concentrates on a single concerto from his concert repertoire completed in 1977, two years before Rota's death. The discussion includes a brief introduction to Nino Rota and his accomplishments as a musician and film composer, and a detailed outline of the motivic and structural events of contained in each movement of the concerto. The shape of the work is analyzed both in detailed discussion and by the use of charts, including reduced score figures of excerpts of the piece, which illustrate significant thematic events and relationships. The analysis reveals how Rota uses lyrical thematic material in a consistently, and he develops the music by creating melodic sequences and varied repetitions of thematic material. He is comfortable writing several forms, as indicated by the first movement, Toccata - a sonata-type form; the second movement, Recitativo, opening with a cadenza and followed by a theme and brief development; and the third movement, a theme (Andantino) and set of six variations. Rota's writing also includes contrapuntal techniques such as imitation, inversion, retrograde and augmentation, all creating expressive interest during thematic development. It is clear from the discussion that Rota is an accomplished, well-studied and lyrical composer. This analysis will inform the bassoonist and conductor, and aid in developing a fondness for the Concerto for bassoon and orchestra and perhaps other concert works.
ContributorsKluesener, Joseph (Author) / Micklich, Albie (Thesis advisor) / Hill, Gary (Committee member) / Levy, Benjamin (Committee member) / Russell, Timothy (Committee member) / Schuring, Martin (Committee member) / Arizona State University (Publisher)
Created2012
Description
The life and pedagogy of Saburo Sumi (1902-1984) has had a major influence on the violin world, particularly in Japan. Born of humble origins and lacking any formal musical training until his adulthood, Sumi nevertheless rose to become one of the most important violin pedagogues of Japan. His non-traditional musical background had a profound effect on the teacher he became and contributed to his tremendous success as a pedagogue. Since most of the existing information on Sumi is written in Japanese, this study is designed to acquaint the Western reader with this amazing pedagogue. The information for this study was gathered through books, articles, and documents related to his life as well as the writer's personal experiences with the Sumi family.
ContributorsHayashi, Junko (Author) / McLin, Katherine (Thesis advisor) / Hill, Gary (Committee member) / Holbrook, Amy (Committee member) / Arizona State University (Publisher)
Created2012
Description
American music of late-nineteenth and early twentieth centuries represents some of the first mature achievements in classical music written by American composers.John Knowles Paine (1839-1906), Arthur Foote (1853-1937), George Whitefield Chadwick (1854-1931), Horatio Parker (1868-1919), and Amy Beach (1867-1944) from the Second New England School were among the most prominent musical figures in America during this time period. These composers shared similar compositional characteristics, perhaps due to the profound influences of German Romantic tradition, either through their direct study with musicians in Germany or with professional German-trained musicians in America.They were active in Boston, affiliated with important music organizations, and had publications through A. P. Schmidt, the most important music publisher of that time. Piano chamber music of the Second New England School is a small but important portion of their diverse repertoire. It is generally considered the first successful body of such repertoire by American composers. Even though most of these works were premiered to great acclaim during the composers' lifetimes, many of them no longer have place in current recital programs and very few are available to the public in published or recorded form. The purpose of this study is to reintroduce this important and worthwhile literature to today's audience. For the purpose of this study the repertoire will be limited to music that involves at least three performers, one of whom must be a pianist. The repertoire must be originally composed for a piano chamber group and must have been published or performed at least once during the composer's lifetime. While Edward MacDowell (1860-1908) is generally considered a member of the Second New England School, he surprisingly did not write any piano chamber music, and therefore has no works in this study. This research project will provide general background information about each composer and their piano chamber music, and a closer examination of one particularly representative work or movement, including performance guidelines from the collaborative pianist's point of view. The author's hope is to awaken greater curiosity about this rich repertoire and to increase its presence on the concert stage.
ContributorsHsu, Juiling (Author) / Campbell, Andrew (Thesis advisor) / Micklich, Albert (Committee member) / Holbrook, Amy (Committee member) / Ryan, Russell (Committee member) / Arizona State University (Publisher)
Created2012
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
Although high performance, light-weight composites are increasingly being used in applications ranging from aircraft, rotorcraft, weapon systems and ground vehicles, the assurance of structural reliability remains a critical issue. In composites, damage is absorbed through various fracture processes, including fiber failure, matrix cracking and delamination. An important element in achieving reliable composite systems is a strong capability of assessing and inspecting physical damage of critical structural components. Installation of a robust Structural Health Monitoring (SHM) system would be very valuable in detecting the onset of composite failure. A number of major issues still require serious attention in connection with the research and development aspects of sensor-integrated reliable SHM systems for composite structures. In particular, the sensitivity of currently available sensor systems does not allow detection of micro level damage; this limits the capability of data driven SHM systems. As a fundamental layer in SHM, modeling can provide in-depth information on material and structural behavior for sensing and detection, as well as data for learning algorithms. This dissertation focusses on the development of a multiscale analysis framework, which is used to detect various forms of damage in complex composite structures. A generalized method of cells based micromechanics analysis, as implemented in NASA's MAC/GMC code, is used for the micro-level analysis. First, a baseline study of MAC/GMC is performed to determine the governing failure theories that best capture the damage progression. The deficiencies associated with various layups and loading conditions are addressed. In most micromechanics analysis, a representative unit cell (RUC) with a common fiber packing arrangement is used. The effect of variation in this arrangement within the RUC has been studied and results indicate this variation influences the macro-scale effective material properties and failure stresses. The developed model has been used to simulate impact damage in a composite beam and an airfoil structure. The model data was verified through active interrogation using piezoelectric sensors. The multiscale model was further extended to develop a coupled damage and wave attenuation model, which was used to study different damage states such as fiber-matrix debonding in composite structures with surface bonded piezoelectric sensors.
ContributorsMoncada, Albert (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Rajadas, John (Committee member) / Yekani Fard, Masoud (Committee member) / Arizona State University (Publisher)
Created2012
Description
A signal with time-varying frequency content can often be expressed more clearly using a time-frequency representation (TFR), which maps the signal into a two-dimensional function of time and frequency, similar to musical notation. The thesis reviews one of the most commonly used TFRs, the Wigner distribution (WD), and discusses its application in Fourier optics: it is shown that the WD is analogous to the spectral dispersion that results from a diffraction grating, and time and frequency are similarly analogous to a one dimensional spatial coordinate and wavenumber. The grating is compared with a simple polychromator, which is a bank of optical filters. Another well-known TFR is the short time Fourier transform (STFT). Its discrete version can be shown to be equivalent to a filter bank, an array of bandpass filters that enable localized processing of the analysis signals in different sub-bands. This work proposes a signal-adaptive method of generating TFRs. In order to minimize distortion in analyzing a signal, the method modifies the filter bank to consist of non-overlapping rectangular bandpass filters generated using the Butterworth filter design process. The information contained in the resulting TFR can be used to reconstruct the signal, and perfect reconstruction techniques involving quadrature mirror filter banks are compared with a simple Fourier synthesis sum. The optimal filter parameters of the rectangular filters are selected adaptively by minimizing the mean-squared error (MSE) from a pseudo-reconstructed version of the analysis signal. The reconstruction MSE is proposed as an error metric for characterizing TFRs; a practical measure of the error requires normalization and cross correlation with the analysis signal. Simulations were performed to demonstrate the the effectiveness of the new adaptive TFR and its relation to swept-tuned spectrum analyzers.
ContributorsWeber, Peter C. (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Kovvali, Narayan (Committee member) / Arizona State University (Publisher)
Created2012
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
Immunosignaturing is a medical test for assessing the health status of a patient by applying microarrays of random sequence peptides to determine the patient's immune fingerprint by associating antibodies from a biological sample to immune responses. The immunosignature measurements can potentially provide pre-symptomatic diagnosis for infectious diseases or detection of biological threats. Currently, traditional bioinformatics tools, such as data mining classification algorithms, are used to process the large amount of peptide microarray data. However, these methods generally require training data and do not adapt to changing immune conditions or additional patient information. This work proposes advanced processing techniques to improve the classification and identification of single and multiple underlying immune response states embedded in immunosignatures, making it possible to detect both known and previously unknown diseases or biothreat agents. Novel adaptive learning methodologies for un- supervised and semi-supervised clustering integrated with immunosignature feature extraction approaches are proposed. The techniques are based on extracting novel stochastic features from microarray binding intensities and use Dirichlet process Gaussian mixture models to adaptively cluster the immunosignatures in the feature space. This learning-while-clustering approach allows continuous discovery of antibody activity by adaptively detecting new disease states, with limited a priori disease or patient information. A beta process factor analysis model to determine underlying patient immune responses is also proposed to further improve the adaptive clustering performance by formatting new relationships between patients and antibody activity. In order to extend the clustering methods for diagnosing multiple states in a patient, the adaptive hierarchical Dirichlet process is integrated with modified beta process factor analysis latent feature modeling to identify relationships between patients and infectious agents. The use of Bayesian nonparametric adaptive learning techniques allows for further clustering if additional patient data is received. Significant improvements in feature identification and immune response clustering are demonstrated using samples from patients with different diseases.
ContributorsMalin, Anna (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Bliss, Daniel (Committee member) / Chakrabarti, Chaitali (Committee member) / Kovvali, Narayan (Committee member) / Lacroix, Zoé (Committee member) / Arizona State University (Publisher)
Created2013