ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- Creators: Papandreou-Suppappola, Antonia
- Creators: Shrivastava, Aviral
- Creators: Mobasher, Barzin
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
Manufacture of building materials requires significant energy, and as demand for these materials continues to increase, the energy requirement will as well. Offsetting this energy use will require increased focus on sustainable building materials. Further, the energy used in building, particularly in heating and air conditioning, accounts for 40 percent of a buildings energy use. Increasing the efficiency of building materials will reduce energy usage over the life time of the building. Current methods for maintaining the interior environment can be highly inefficient depending on the building materials selected. Materials such as concrete have low thermal efficiency and have a low heat capacity meaning it provides little insulation. Use of phase change materials (PCM) provides the opportunity to increase environmental efficiency of buildings by using the inherent latent heat storage as well as the increased heat capacity. Incorporating PCM into concrete via lightweight aggregates (LWA) by direct addition is seen as a viable option for increasing the thermal storage capabilities of concrete, thereby increasing building energy efficiency. As PCM change phase from solid to liquid, heat is absorbed from the surroundings, decreasing the demand on the air conditioning systems on a hot day or vice versa on a cold day. Further these materials provide an additional insulating capacity above the value of plain concrete. When the temperature drops outside the PCM turns back into a solid and releases the energy stored from the day. PCM is a hydrophobic material and causes reductions in compressive strength when incorporated directly into concrete, as shown in previous studies. A proposed method for mitigating this detrimental effect, while still incorporating PCM into concrete is to encapsulate the PCM in aggregate. This technique would, in theory, allow for the use of phase change materials directly in concrete, increasing the thermal efficiency of buildings, while negating the negative effect on compressive strength of the material.
ContributorsSharma, Breeann (Author) / Neithalath, Narayanan (Thesis advisor) / Mobasher, Barzin (Committee member) / Rajan, Subramaniam D. (Committee member) / Arizona State University (Publisher)
Created2013
Description
The alkali activation of aluminosilicate materials as binder systems derived from industrial byproducts have been extensively studied due to the advantages they offer in terms enhanced material properties, while increasing sustainability by the reuse of industrial waste and byproducts and reducing the adverse impacts of OPC production. Fly ash and ground granulated blast furnace slag are commonly used for their content of soluble silica and aluminate species that can undergo dissolution, polymerization with the alkali, condensation on particle surfaces and solidification. The following topics are the focus of this thesis: (i) the use of microwave assisted thermal processing, in addition to heat-curing as a means of alkali activation and (ii) the relative effects of alkali cations (K or Na) in the activator (powder activators) on the mechanical properties and chemical structure of these systems. Unsuitable curing conditions instigate carbonation, which in turn lowers the pH of the system causing significant reductions in the rate of fly ash activation and mechanical strength development. This study explores the effects of sealing the samples during the curing process, which effectively traps the free water in the system, and allows for increased aluminosilicate activation. The use of microwave-curing in lieu of thermal-curing is also studied in order to reduce energy consumption and for its ability to provide fast volumetric heating. Potassium-based powder activators dry blended into the slag binder system is shown to be effective in obtaining very high compressive strengths under moist curing conditions (greater than 70 MPa), whereas sodium-based powder activation is much weaker (around 25 MPa). Compressive strength decreases when fly ash is introduced into the system. Isothermal calorimetry is used to evaluate the early hydration process, and to understand the reaction kinetics of the alkali powder activated systems. A qualitative evidence of the alkali-hydroxide concentration of the paste pore solution through the use of electrical conductivity measurements is also presented, with the results indicating the ion concentration of alkali is more prevalent in the pore solution of potassium-based systems. The use of advanced spectroscopic and thermal analysis techniques to distinguish the influence of studied parameters is also discussed.
ContributorsChowdhury, Ussala (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramanium D. (Committee member) / Mobasher, Barzin (Committee member) / Arizona State University (Publisher)
Created2013
Description
Properties of random porous material such as pervious concrete are strongly dependant on its pore structure features. This research deals with the development of an understanding of the relationship between the material structure and the mechanical and functional properties of pervious concretes. The fracture response of pervious concrete specimens proportioned for different porosities, as a function of the pore structure features and fiber volume fraction, is studied. Stereological and morphological methods are used to extract the relevant pore structure features of pervious concretes from planar images. A two-parameter fracture model is used to obtain the fracture toughness (KIC) and critical crack tip opening displacement (CTODc) from load-crack mouth opening displacement (CMOD) data of notched beams under three-point bending. The experimental results show that KIC is primarily dependent on the porosity of pervious concretes. For a similar porosity, an increase in pore size results in a reduction in KIC. At similar pore sizes, the effect of fibers on the post-peak response is more prominent in mixtures with a higher porosity, as shown by the residual load capacity, stress-crack extension relationships, and GR curves. These effects are explained using the mean free spacing of pores and pore-to-pore tortuosity in these systems. A sensitivity analysis is employed to quantify the influence of material design parameters on KIC. This research has also focused on studying the relationship between permeability and tortuosity as it pertains to porosity and pore size of pervious concretes. Various ideal geometric shapes were also constructed that had varying pore sizes and porosities. The pervious concretes also had differing pore sizes and porosities. The permeabilities were determined using three different methods; Stokes solver, Lattice Boltzmann method and the Katz-Thompson equation. These values were then compared to the tortuosity values determined using a Matlab code that uses a pore connectivity algorithm. The tortuosity was also determined from the inverse of the conductivity determined from a numerical analysis that was necessary for using the Katz-Thompson equation. These tortuosity values were then compared to the permeabilities. The pervious concretes and ideal geometric shapes showed consistent similarities betbetween their tortuosities and permeabilities.
ContributorsRehder, Benjamin (Author) / Neithalath, Narayanana (Thesis advisor) / Mobasher, Barzin (Committee member) / Rajan, Subramaniam D. (Committee member) / Arizona State University (Publisher)
Created2013
Description
In recent years we have witnessed a shift towards multi-processor system-on-chips (MPSoCs) to address the demands of embedded devices (such as cell phones, GPS devices, luxury car features, etc.). Highly optimized MPSoCs are well-suited to tackle the complex application demands desired by the end user customer. These MPSoCs incorporate a constellation of heterogeneous processing elements (PEs) (general purpose PEs and application-specific integrated circuits (ASICS)). A typical MPSoC will be composed of a application processor, such as an ARM Coretex-A9 with cache coherent memory hierarchy, and several application sub-systems. Each of these sub-systems are composed of highly optimized instruction processors, graphics/DSP processors, and custom hardware accelerators. Typically, these sub-systems utilize scratchpad memories (SPM) rather than support cache coherency. The overall architecture is an integration of the various sub-systems through a high bandwidth system-level interconnect (such as a Network-on-Chip (NoC)). The shift to MPSoCs has been fueled by three major factors: demand for high performance, the use of component libraries, and short design turn around time. As customers continue to desire more and more complex applications on their embedded devices the performance demand for these devices continues to increase. Designers have turned to using MPSoCs to address this demand. By using pre-made IP libraries designers can quickly piece together a MPSoC that will meet the application demands of the end user with minimal time spent designing new hardware. Additionally, the use of MPSoCs allows designers to generate new devices very quickly and thus reducing the time to market. In this work, a complete MPSoC synthesis design flow is presented. We first present a technique \cite{leary1_intro} to address the synthesis of the interconnect architecture (particularly Network-on-Chip (NoC)). We then address the synthesis of the memory architecture of a MPSoC sub-system \cite{leary2_intro}. Lastly, we present a co-synthesis technique to generate the functional and memory architectures simultaneously. The validity and quality of each synthesis technique is demonstrated through extensive experimentation.
ContributorsLeary, Glenn (Author) / Chatha, Karamvir S (Thesis advisor) / Vrudhula, Sarma (Committee member) / Shrivastava, Aviral (Committee member) / Beraha, Rudy (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
Buildings consume a large portion of the world's energy, but with the integration of phase change materials (PCMs) in building elements this energy cost can be greatly reduced. The addition of PCMs into building elements, however, becomes a challenge to model and analyze how the material actually affects the energy flow and temperatures in the system. This research work presents a comprehensive computer program used to model and analyze PCM embedded wall systems. The use of the finite element method (FEM) provides the tool to analyze the energy flow of these systems. Finite element analysis (FEA) can model the transient analysis of a typical climate cycle along with nonlinear problems, which the addition of PCM causes. The use of phase change materials is also a costly material expense. The initial expense of using PCMs can be compensated by the reduction in energy costs it can provide. Optimization is the tool used to determine the optimal point between adding PCM into a wall and the amount of energy savings that layer will provide. The integration of these two tools into a computer program allows for models to be efficiently created, analyzed and optimized. The program was then used to understand the benefits between two different wall models, a wall with a single layer of PCM or a wall with two different PCM layers. The effect of the PCMs on the inside wall temperature along with the energy flow across the wall are computed. The numerical results show that a multi-layer PCM wall was more energy efficient and cost effective than the single PCM layer wall. A structural analysis was then performed on the optimized designs using ABAQUS v. 6.10 to ensure the structural integrity of the wall was not affected by adding PCM layer(s).
ContributorsStockwell, Amie (Author) / Rajan, Subramaniam D. (Thesis advisor) / Neithalath, Narayanan (Thesis advisor) / Mobasher, Barzin (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
This research examines the current challenges of using Lamb wave interrogation methods to localize fatigue crack damage in a complex metallic structural component subjected to unknown temperatures. The goal of this work is to improve damage localization results for a structural component interrogated at an unknown temperature, by developing a probabilistic and reference-free framework for estimating Lamb wave velocities and the damage location. The methodology for damage localization at unknown temperatures includes the following key elements: i) a model that can describe the change in Lamb wave velocities with temperature; ii) the extension of an advanced time-frequency based signal processing technique for enhanced time-of-flight feature extraction from a dispersive signal; iii) the development of a Bayesian damage localization framework incorporating data association and sensor fusion. The technique requires no additional transducers to be installed on a structure, and allows for the estimation of both the temperature and the wave velocity in the component. Additionally, the framework of the algorithm allows it to function completely in an unsupervised manner by probabilistically accounting for all measurement origin uncertainty. The novel algorithm was experimentally validated using an aluminum lug joint with a growing fatigue crack. The lug joint was interrogated using piezoelectric transducers at multiple fatigue crack lengths, and at temperatures between 20°C and 80°C. The results showed that the algorithm could accurately predict the temperature and wave speed of the lug joint. The localization results for the fatigue damage were found to correlate well with the true locations at long crack lengths, but loss of accuracy was observed in localizing small cracks due to time-of-flight measurement errors. To validate the algorithm across a wider range of temperatures the electromechanically coupled LISA/SIM model was used to simulate the effects of temperatures. The numerical results showed that this approach would be capable of experimentally estimating the temperature and velocity in the lug joint for temperatures from -60°C to 150°C. The velocity estimation algorithm was found to significantly increase the accuracy of localization at temperatures above 120°C when error due to incorrect velocity selection begins to outweigh the error due to time-of-flight measurements.
ContributorsHensberry, Kevin (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2013
Description
Woven fabric composite materials are widely used in the construction of aircraft engine fan containment systems, mostly due to their high strength to weight ratios and ease of implementation. The development of a predictive model for fan blade containment would provide great benefit to engine manufactures in shortened development cycle time, less risk in certification and fewer dollars lost to redesign/recertification cycles. A mechanistic user-defined material model subroutine has been developed at Arizona State University (ASU) that captures the behavioral response of these fabrics, namely Kevlar® 49, under ballistic loading. Previously developed finite element models used to validate the consistency of this material model neglected the effects of the physical constraints imposed on the test setup during ballistic testing performed at NASA Glenn Research Center (NASA GRC). Part of this research was to explore the effects of these boundary conditions on the results of the numerical simulations. These effects were found to be negligible in most instances. Other material models for woven fabrics are available in the LS-DYNA finite element code. One of these models, MAT234: MAT_VISCOELASTIC_LOOSE_FABRIC (Ivanov & Tabiei, 2004) was studied and implemented in the finite element simulations of ballistic testing associated with the FAA ASU research. The results from these models are compared to results obtained from the ASU UMAT as part of this research. The results indicate an underestimation in the energy absorption characteristics of the Kevlar 49 fabric containment systems. More investigation needs to be performed in the implementation of MAT234 for Kevlar 49 fabric. Static penetrator testing of Kevlar® 49 fabric was performed at ASU in conjunction with this research. These experiments are designed to mimic the type of loading experienced during fan blade out events. The resulting experimental strains were measured using a non-contact optical strain measurement system (ARAMIS).
ContributorsFein, Jonathan (Author) / Rajan, Subramaniam D. (Thesis advisor) / Mobasher, Barzin (Committee member) / Jiang, Hanqing (Committee member) / Arizona State University (Publisher)
Created2012