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- Genre: Academic theses
- Creators: Chakrabarti, Chaitali
Increasing the efficiency of Doppler processing and backend processing in medical ultrasound systems
Description
Ultrasound imaging is one of the major medical imaging modalities. It is cheap, non-invasive and has low power consumption. Doppler processing is an important part of many ultrasound imaging systems. It is used to provide blood velocity information and is built on top of B-mode systems. We investigate the performance of two velocity estimation schemes used in Doppler processing systems, namely, directional velocity estimation (DVE) and conventional velocity estimation (CVE). We find that DVE provides better estimation performance and is the only functioning method when the beam to flow angle is large. Unfortunately, DVE is computationally expensive and also requires divisions and square root operations that are hard to implement. We propose two approximation techniques to replace these computations. The simulation results on cyst images show that the proposed approximations do not affect the estimation performance. We also study backend processing which includes envelope detection, log compression and scan conversion. Three different envelope detection methods are compared. Among them, FIR based Hilbert Transform is considered the best choice when phase information is not needed, while quadrature demodulation is a better choice if phase information is necessary. Bilinear and Gaussian interpolation are considered for scan conversion. Through simulations of a cyst image, we show that bilinear interpolation provides comparable contrast-to-noise ratio (CNR) performance with Gaussian interpolation and has lower computational complexity. Thus, bilinear interpolation is chosen for our system.
ContributorsWei, Siyuan (Author) / Chakrabarti, Chaitali (Thesis advisor) / Frakes, David (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2013
Description
ABSTRACT Developing new non-traditional device models is gaining popularity as the silicon-based electrical device approaches its limitation when it scales down. Membrane systems, also called P systems, are a new class of biological computation model inspired by the way cells process chemical signals. Spiking Neural P systems (SNP systems), a certain kind of membrane systems, is inspired by the way the neurons in brain interact using electrical spikes. Compared to the traditional Boolean logic, SNP systems not only perform similar functions but also provide a more promising solution for reliable computation. Two basic neuron types, Low Pass (LP) neurons and High Pass (HP) neurons, are introduced. These two basic types of neurons are capable to build an arbitrary SNP neuron. This leads to the conclusion that these two basic neuron types are Turing complete since SNP systems has been proved Turing complete. These two basic types of neurons are further used as the elements to construct general-purpose arithmetic circuits, such as adder, subtractor and comparator. In this thesis, erroneous behaviors of neurons are discussed. Transmission error (spike loss) is proved to be equivalent to threshold error, which makes threshold error discussion more universal. To improve the reliability, a new structure called motif is proposed. Compared to Triple Modular Redundancy improvement, motif design presents its efficiency and effectiveness in both single neuron and arithmetic circuit analysis. DRAM-based CMOS circuits are used to implement the two basic types of neurons. Functionality of basic type neurons is proved using the SPICE simulations. The motif improved adder and the comparator, as compared to conventional Boolean logic design, are much more reliable with lower leakage, and smaller silicon area. This leads to the conclusion that SNP system could provide a more promising solution for reliable computation than the conventional Boolean logic.
ContributorsAn, Pei (Author) / Cao, Yu (Thesis advisor) / Barnaby, Hugh (Committee member) / Chakrabarti, Chaitali (Committee member) / Arizona State University (Publisher)
Created2013
Description
In addition to his many other works, Russian-American composer Leo Ornstein (1893-2002) contributed a substantial body of literature for cello and piano, including Sonata No. 1 (1915-1916), Sonata No. 2 (circa 1920), Composition No. 1 (date unknown), Two Pieces (date unknown), and Six Preludes (1930-1931). His cello music is an eclectic mix of twentieth-century Neoromantic and atonal styles. This study includes a recording of the complete works for cello and piano by Leo Ornstein and a description of the music that details the formal procedures and how the cello and piano relate to one another. The discussion offers extensive musical examples in support of the descriptions. The recording was completed at the Banff Centre for the Arts in Alberta, Canada (October 2009), with R. Nicolas Alvarez, cello, in collaboration with pianist Keith Kirchoff. Andre Shrimski produced and edited the recording.
ContributorsAlvarez, Rodolfo Nicolas (Author) / Landschoot, Thomas (Thesis advisor) / Rotaru, Catalin (Committee member) / Jiang, Danwen (Committee member) / Holbrook, Amy (Committee member) / Arizona State University (Publisher)
Created2013
Description
With increasing transistor volume and reducing feature size, it has become a major design constraint to reduce power consumption also. This has given rise to aggressive architectural changes for on-chip power management and rapid development to energy efficient hardware accelerators. Accordingly, the objective of this research work is to facilitate software developers to leverage these hardware techniques and improve energy efficiency of the system. To achieve this, I propose two solutions for Linux kernel: Optimal use of these architectural enhancements to achieve greater energy efficiency requires accurate modeling of processor power consumption. Though there are many models available in literature to model processor power consumption, there is a lack of such models to capture power consumption at the task-level. Task-level energy models are a requirement for an operating system (OS) to perform real-time power management as OS time multiplexes tasks to enable sharing of hardware resources. I propose a detailed design methodology for constructing an architecture agnostic task-level power model and incorporating it into a modern operating system to build an online task-level power profiler. The profiler is implemented inside the latest Linux kernel and validated for Intel Sandy Bridge processor. It has a negligible overhead of less than 1\% hardware resource consumption. The profiler power prediction was demonstrated for various application benchmarks from SPEC to PARSEC with less than 4\% error. I also demonstrate the importance of the proposed profiler for emerging architectural techniques through use case scenarios, which include heterogeneous computing and fine grained per-core DVFS. Along with architectural enhancement in general purpose processors to improve energy efficiency, hardware accelerators like Coarse Grain reconfigurable architecture (CGRA) are gaining popularity. Unlike vector processors, which rely on data parallelism, CGRA can provide greater flexibility and compiler level control making it more suitable for present SoC environment. To provide streamline development environment for CGRA, I propose a flexible framework in Linux to do design space exploration for CGRA. With accurate and flexible hardware models, fine grained integration with accurate architectural simulator, and Linux memory management and DMA support, a user can carry out limitless experiments on CGRA in full system environment.
ContributorsDesai, Digant Pareshkumar (Author) / Vrudhula, Sarma (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Wu, Carole-Jean (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
Multicore processors have proliferated in nearly all forms of computing, from servers, desktop, to smartphones. The primary reason for this large adoption of multicore processors is due to its ability to overcome the power-wall by providing higher performance at a lower power consumption rate. With multi-cores, there is increased need for dynamic energy management (DEM), much more than for single-core processors, as DEM for multi-cores is no more a mechanism just to ensure that a processor is kept under specified temperature limits, but also a set of techniques that manage various processor controls like dynamic voltage and frequency scaling (DVFS), task migration, fan speed, etc. to achieve a stated objective. The objectives span a wide range from maximizing throughput, minimizing power consumption, reducing peak temperature, maximizing energy efficiency, maximizing processor reliability, and so on, along with much more wider constraints of temperature, power, timing, and reliability constraints. Thus DEM can be very complex and challenging to achieve. Since often times many DEMs operate together on a single processor, there is a need to unify various DEM techniques. This dissertation address such a need. In this work, a framework for DEM is proposed that provides a unifying processor model that includes processor power, thermal, timing, and reliability models, supports various DEM control mechanisms, many different objective functions along with equally diverse constraint specifications. Using the framework, a range of novel solutions is derived for instances of DEM problems, that include maximizing processor performance, energy efficiency, or minimizing power consumption, peak temperature under constraints of maximum temperature, memory reliability and task deadlines. Finally, a robust closed-loop controller to implement the above solutions on a real processor platform with a very low operational overhead is proposed. Along with the controller design, a model identification methodology for obtaining the required power and thermal models for the controller is also discussed. The controller is architecture independent and hence easily portable across many platforms. The controller has been successfully deployed on Intel Sandy Bridge processor and the use of the controller has increased the energy efficiency of the processor by over 30%
ContributorsHanumaiah, Vinay (Author) / Vrudhula, Sarma (Thesis advisor) / Chatha, Karamvir (Committee member) / Chakrabarti, Chaitali (Committee member) / Rodriguez, Armando (Committee member) / Askin, Ronald (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
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
Description
Arnold Schoenberg's 1908-09 song cycle, Das Buch der hängenden Gärten [The Book of the Hanging Gardens], opus 15, represents one of his most decisive early steps into the realm of musical modernism. In the midst of personal and artistic crises, Schoenberg set texts by Stefan George in a style he called "pantonality," and described his composition as radically new. Though stylistically progressive, however, Schoenberg's musical achievement had certain ideologically conservative roots: the composer numbered among turn-of-the-century Viennese artists and thinkers whose opposition to the conventional and the popular--in favor of artistic autonomy and creativity--concealed a reactionary misogyny. A critical reading of Hanging Gardens through the lens of gender reveals that Schoenberg, like many of his contemporaries, incorporated strong frauenfeindlich [anti-women] elements into his work, through his modernist account of artistic creativity, his choice of texts, and his musical settings. Although elements of Hanging Gardens' atonal music suggest that Schoenberg valued gendered-feminine principles in his compositional style, a closer analysis of the work's musical language shows an intact masculinist hegemony. Through his deployment of uncanny tonal reminiscences, underlying tonal gestures, and closed forms in Hanging Gardens, Schoenberg ensures that the feminine-associated "excesses" of atonality remain under masculine control. This study draws upon the critical musicology of Susan McClary while arguing that Schoenberg's music is socially contingent, affected by the gender biases of his social and literary milieux. It addresses likely influences on Schoenberg's worldview including the philosophy of Otto Weininger, Freudian psychoanalysis, and a complex web of personal relationships. Finally, this analysis highlights the relevance of Schoenberg's world and its constructions of gender to modern performance practice, and argues that performers must consider interrelated historical, textual, and musical factors when interpreting Hanging Gardens in new contexts.
ContributorsGinger, Kerry Anne (Author) / FitzPatrick, Carole (Thesis advisor) / Dreyfoos, Dale (Committee member) / Mook, Richard (Committee member) / Norton, Kay (Committee member) / Ryan, Russell (Committee member) / Arizona State University (Publisher)
Created2012
Description
ABSTRACT This document introduces singers and voice teachers to Dr. Alfred A. Tomatis's listening training method with a particular emphasis on its relevance to singers. After presenting an overview of Tomatis's work in the field of audio-psycho-phonology (circa 1947 through the 1990s) and specific ways that aspects of his theory are relevant to singers' performance skills, this project investigates the impact of listening training on singers by examining published research. The studies described in this document have investigated the impact of listening training on elements of the singer's skill set, including but not limited to measures of vocal quality such as intonation, vocal control, intensity, and sonority, as well as language pronunciation and general musicianship. Anecdotal evidence, presented by performers and their observers, is also considered. The evidence generated by research studies and anecdotal reports strongly favors Tomatis-based listening training as a valid way to improve singers' performance abilities.
ContributorsHurley, Susan Lynn (Author) / Doan, Jerry (Thesis advisor) / Dreyfoos, Dale (Committee member) / Kopta, Anne (Committee member) / Norton, Kay (Committee member) / Thompson, Billie M (Committee member) / Arizona State University (Publisher)
Created2012