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- Genre: Doctoral Dissertation
- Creators: Papandreou-Suppappola, Antonia
- Member of: Theses and Dissertations
Description
Three dimensional (3-D) ultrasound is safe, inexpensive, and has been shown to drastically improve system ease-of-use, diagnostic efficiency, and patient throughput. However, its high computational complexity and resulting high power consumption has precluded its use in hand-held applications.
In this dissertation, algorithm-architecture co-design techniques that aim to make hand-held 3-D ultrasound a reality are presented. First, image enhancement methods to improve signal-to-noise ratio (SNR) are proposed. These include virtual source firing techniques and a low overhead digital front-end architecture using orthogonal chirps and orthogonal Golay codes.
Second, algorithm-architecture co-design techniques to reduce the power consumption of 3-D SAU imaging systems is presented. These include (i) a subaperture multiplexing strategy and the corresponding apodization method to alleviate the signal bandwidth bottleneck, and (ii) a highly efficient iterative delay calculation method to eliminate complex operations such as multiplications, divisions and square-root in delay calculation during beamforming. These techniques were used to define Sonic Millip3De, a 3-D die stacked architecture for digital beamforming in SAU systems. Sonic Millip3De produces 3-D high resolution images at 2 frames per second with system power consumption of 15W in 45nm technology.
Third, a new beamforming method based on separable delay decomposition is proposed to reduce the computational complexity of the beamforming unit in an SAU system. The method is based on minimizing the root-mean-square error (RMSE) due to delay decomposition. It reduces the beamforming complexity of a SAU system by 19x while providing high image fidelity that is comparable to non-separable beamforming. The resulting modified Sonic Millip3De architecture supports a frame rate of 32 volumes per second while maintaining power consumption of 15W in 45nm technology.
Next a 3-D plane-wave imaging system that utilizes both separable beamforming and coherent compounding is presented. The resulting system has computational complexity comparable to that of a non-separable non-compounding baseline system while significantly improving contrast-to-noise ratio and SNR. The modified Sonic Millip3De architecture is now capable of generating high resolution images at 1000 volumes per second with 9-fire-angle compounding.
In this dissertation, algorithm-architecture co-design techniques that aim to make hand-held 3-D ultrasound a reality are presented. First, image enhancement methods to improve signal-to-noise ratio (SNR) are proposed. These include virtual source firing techniques and a low overhead digital front-end architecture using orthogonal chirps and orthogonal Golay codes.
Second, algorithm-architecture co-design techniques to reduce the power consumption of 3-D SAU imaging systems is presented. These include (i) a subaperture multiplexing strategy and the corresponding apodization method to alleviate the signal bandwidth bottleneck, and (ii) a highly efficient iterative delay calculation method to eliminate complex operations such as multiplications, divisions and square-root in delay calculation during beamforming. These techniques were used to define Sonic Millip3De, a 3-D die stacked architecture for digital beamforming in SAU systems. Sonic Millip3De produces 3-D high resolution images at 2 frames per second with system power consumption of 15W in 45nm technology.
Third, a new beamforming method based on separable delay decomposition is proposed to reduce the computational complexity of the beamforming unit in an SAU system. The method is based on minimizing the root-mean-square error (RMSE) due to delay decomposition. It reduces the beamforming complexity of a SAU system by 19x while providing high image fidelity that is comparable to non-separable beamforming. The resulting modified Sonic Millip3De architecture supports a frame rate of 32 volumes per second while maintaining power consumption of 15W in 45nm technology.
Next a 3-D plane-wave imaging system that utilizes both separable beamforming and coherent compounding is presented. The resulting system has computational complexity comparable to that of a non-separable non-compounding baseline system while significantly improving contrast-to-noise ratio and SNR. The modified Sonic Millip3De architecture is now capable of generating high resolution images at 1000 volumes per second with 9-fire-angle compounding.
ContributorsYang, Ming (Author) / Chakrabarti, Chaitali (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Karam, Lina (Committee member) / Frakes, David (Committee member) / Ogras, Umit Y. (Committee member) / Arizona State University (Publisher)
Created2015
Description
Analysis of social networks has the potential to provide insights into wide range of applications. As datasets continue to grow, a key challenge is the lack of a widely applicable algorithmic framework for detection of statistically anomalous networks and network properties. Unlike traditional signal processing, where models of truth or empirical verification and background data exist and are often well defined, these features are commonly lacking in social and other networks. Here, a novel algorithmic framework for statistical signal processing for graphs is presented. The framework is based on the analysis of spectral properties of the residuals matrix. The framework is applied to the detection of innovation patterns in publication networks, leveraging well-studied empirical knowledge from the history of science. Both the framework itself and the application constitute novel contributions, while advancing algorithmic and mathematical techniques for graph-based data and understanding of the patterns of emergence of novel scientific research. Results indicate the efficacy of the approach and highlight a number of fruitful future directions.
ContributorsBliss, Nadya Travinin (Author) / Laubichler, Manfred (Thesis advisor) / Castillo-Chavez, Carlos (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2015
Description
The healthcare system in this country is currently unacceptable. New technologies may contribute to reducing cost and improving outcomes. Early diagnosis and treatment represents the least risky option for addressing this issue. Such a technology needs to be inexpensive, highly sensitive, highly specific, and amenable to adoption in a clinic. This thesis explores an immunodiagnostic technology based on highly scalable, non-natural sequence peptide microarrays designed to profile the humoral immune response and address the healthcare problem. The primary aim of this thesis is to explore the ability of these arrays to map continuous (linear) epitopes. I discovered that using a technique termed subsequence analysis where epitopes could be decisively mapped to an eliciting protein with high success rate. This led to the discovery of novel linear epitopes from Plasmodium falciparum (Malaria) and Treponema palladium (Syphilis), as well as validation of previously discovered epitopes in Dengue and monoclonal antibodies. Next, I developed and tested a classification scheme based on Support Vector Machines for development of a Dengue Fever diagnostic, achieving higher sensitivity and specificity than current FDA approved techniques. The software underlying this method is available for download under the BSD license. Following this, I developed a kinetic model for immunosignatures and tested it against existing data driven by previously unexplained phenomena. This model provides a framework and informs ways to optimize the platform for maximum stability and efficiency. I also explored the role of sequence composition in explaining an immunosignature binding profile, determining a strong role for charged residues that seems to have some predictive ability for disease. Finally, I developed a database, software and indexing strategy based on Apache Lucene for searching motif patterns (regular expressions) in large biological databases. These projects as a whole have advanced knowledge of how to approach high throughput immunodiagnostics and provide an example of how technology can be fused with biology in order to affect scientific and health outcomes.
ContributorsRicher, Joshua Amos (Author) / Johnston, Stephen A. (Thesis advisor) / Woodbury, Neal (Committee member) / Stafford, Phillip (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2014
Description
Peptide microarrays have been used in molecular biology to profile immune responses and develop diagnostic tools. When the microarrays are printed with random peptide sequences, they can be used to identify antigen antibody binding patterns or immunosignatures. In this thesis, an advanced signal processing method is proposed to estimate epitope antigen subsequences as well as identify mimotope antigen subsequences that mimic the structure of epitopes from random-sequence peptide microarrays. The method first maps peptide sequences to linear expansions of highly-localized one-dimensional (1-D) time-varying signals and uses a time-frequency processing technique to detect recurring patterns in subsequences. This technique is matched to the aforementioned mapping scheme, and it allows for an inherent analysis on how substitutions in the subsequences can affect antibody binding strength. The performance of the proposed method is demonstrated by estimating epitopes and identifying potential mimotopes for eight monoclonal antibody samples.
The proposed mapping is generalized to express information on a protein's sequence location, structure and function onto a highly localized three-dimensional (3-D) Gaussian waveform. In particular, as analysis of protein homology has shown that incorporating different kinds of information into an alignment process can yield more robust alignment results, a pairwise protein structure alignment method is proposed based on a joint similarity measure of multiple mapped protein attributes. The 3-D mapping allocates protein properties into distinct regions in the time-frequency plane in order to simplify the alignment process by including all relevant information into a single, highly customizable waveform. Simulations demonstrate the improved performance of the joint alignment approach to infer relationships between proteins, and they provide information on mutations that cause changes to both the sequence and structure of a protein.
In addition to the biology-based signal processing methods, a statistical method is considered that uses a physics-based model to improve processing performance. In particular, an externally developed physics-based model for sea clutter is examined when detecting a low radar cross-section target in heavy sea clutter. This novel model includes a process that generates random dynamic sea clutter based on the governing physics of water gravity and capillary waves and a finite-difference time-domain electromagnetics simulation process based on Maxwell's equations propagating the radar signal. A subspace clutter suppression detector is applied to remove dominant clutter eigenmodes, and its improved performance over matched filtering is demonstrated using simulations.
The proposed mapping is generalized to express information on a protein's sequence location, structure and function onto a highly localized three-dimensional (3-D) Gaussian waveform. In particular, as analysis of protein homology has shown that incorporating different kinds of information into an alignment process can yield more robust alignment results, a pairwise protein structure alignment method is proposed based on a joint similarity measure of multiple mapped protein attributes. The 3-D mapping allocates protein properties into distinct regions in the time-frequency plane in order to simplify the alignment process by including all relevant information into a single, highly customizable waveform. Simulations demonstrate the improved performance of the joint alignment approach to infer relationships between proteins, and they provide information on mutations that cause changes to both the sequence and structure of a protein.
In addition to the biology-based signal processing methods, a statistical method is considered that uses a physics-based model to improve processing performance. In particular, an externally developed physics-based model for sea clutter is examined when detecting a low radar cross-section target in heavy sea clutter. This novel model includes a process that generates random dynamic sea clutter based on the governing physics of water gravity and capillary waves and a finite-difference time-domain electromagnetics simulation process based on Maxwell's equations propagating the radar signal. A subspace clutter suppression detector is applied to remove dominant clutter eigenmodes, and its improved performance over matched filtering is demonstrated using simulations.
ContributorsO'Donnell, Brian (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Bliss, Daniel (Committee member) / Johnston, Stephen A. (Committee member) / Kovvali, Narayan (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2014
Description
Underwater acoustic communications face significant challenges unprecedented in radio terrestrial communications including long multipath delay spreads, strong Doppler effects, and stringent bandwidth requirements. Recently, multi-carrier communications based on orthogonal frequency division multiplexing (OFDM) have seen significant growth in underwater acoustic (UWA) communications, thanks to their well well-known robustness against severely time-dispersive channels. However, the performance of OFDM systems over UWA channels significantly deteriorates due to severe intercarrier interference (ICI) resulting from rapid time variations of the channel. With the motivation of developing enabling techniques for OFDM over UWA channels, the major contributions of this thesis include (1) two effective frequencydomain equalizers that provide general means to counteract the ICI; (2) a family of multiple-resampling receiver designs dealing with distortions caused by user and/or path specific Doppler scaling effects; (3) proposal of using orthogonal frequency division multiple access (OFDMA) as an effective multiple access scheme for UWA communications; (4) the capacity evaluation for single-resampling versus multiple-resampling receiver designs. All of the proposed receiver designs have been verified both through simulations and emulations based on data collected in real-life UWA communications experiments. Particularly, the frequency domain equalizers are shown to be effective with significantly reduced pilot overhead and offer robustness against Doppler and timing estimation errors. The multiple-resampling designs, where each branch is tasked with the Doppler distortion of different paths and/or users, overcome the disadvantages of the commonly-used single-resampling receivers and yield significant performance gains. Multiple-resampling receivers are also demonstrated to be necessary for UWA OFDMA systems. The unique design effectively mitigates interuser interference (IUI), opening up the possibility to exploit advanced user subcarrier assignment schemes. Finally, the benefits of the multiple-resampling receivers are further demonstrated through channel capacity evaluation results.
ContributorsTu, Kai (Author) / Duman, Tolga M. (Thesis advisor) / Zhang, Junshan (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2011
Description
Following the success in incorporating perceptual models in audio coding algorithms, their application in other speech/audio processing systems is expanding. In general, all perceptual speech/audio processing algorithms involve minimization of an objective function that directly/indirectly incorporates properties of human perception. This dissertation primarily investigates the problems associated with directly embedding an auditory model in the objective function formulation and proposes possible solutions to overcome high complexity issues for use in real-time speech/audio algorithms. Specific problems addressed in this dissertation include: 1) the development of approximate but computationally efficient auditory model implementations that are consistent with the principles of psychoacoustics, 2) the development of a mapping scheme that allows synthesizing a time/frequency domain representation from its equivalent auditory model output. The first problem is aimed at addressing the high computational complexity involved in solving perceptual objective functions that require repeated application of auditory model for evaluation of different candidate solutions. In this dissertation, a frequency pruning and a detector pruning algorithm is developed that efficiently implements the various auditory model stages. The performance of the pruned model is compared to that of the original auditory model for different types of test signals in the SQAM database. Experimental results indicate only a 4-7% relative error in loudness while attaining up to 80-90 % reduction in computational complexity. Similarly, a hybrid algorithm is developed specifically for use with sinusoidal signals and employs the proposed auditory pattern combining technique together with a look-up table to store representative auditory patterns. The second problem obtains an estimate of the auditory representation that minimizes a perceptual objective function and transforms the auditory pattern back to its equivalent time/frequency representation. This avoids the repeated application of auditory model stages to test different candidate time/frequency vectors in minimizing perceptual objective functions. In this dissertation, a constrained mapping scheme is developed by linearizing certain auditory model stages that ensures obtaining a time/frequency mapping corresponding to the estimated auditory representation. This paradigm was successfully incorporated in a perceptual speech enhancement algorithm and a sinusoidal component selection task.
ContributorsKrishnamoorthi, Harish (Author) / Spanias, Andreas (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2011
Description
Structural health management (SHM) is emerging as a vital methodology to help engineers improve the safety and maintainability of critical structures. SHM systems are designed to reliably monitor and test the health and performance of structures in aerospace, civil, and mechanical engineering applications. SHM combines multidisciplinary technologies including sensing, signal processing, pattern recognition, data mining, high fidelity probabilistic progressive damage models, physics based damage models, and regression analysis. Due to the wide application of carbon fiber reinforced composites and their multiscale failure mechanisms, it is necessary to emphasize the research of SHM on composite structures. This research develops a comprehensive framework for the damage detection, localization, quantification, and prediction of the remaining useful life of complex composite structures. To interrogate a composite structure, guided wave propagation is applied to thin structures such as beams and plates. Piezoelectric transducers are selected because of their versatility, which allows them to be used as sensors and actuators. Feature extraction from guided wave signals is critical to demonstrate the presence of damage and estimate the damage locations. Advanced signal processing techniques are employed to extract robust features and information. To provide a better estimate of the damage for accurate life estimation, probabilistic regression analysis is used to obtain a prediction model for the prognosis of complex structures subject to fatigue loading. Special efforts have been applied to the extension of SHM techniques on aerospace and spacecraft structures, such as UAV composite wings and deployable composite boom structures. Necessary modifications of the developed SHM techniques were conducted to meet the unique requirements of the aerospace structures. The developed SHM algorithms are able to accurately detect and quantify impact damages as well as matrix cracking introduced.
ContributorsLiu, Yingtao (Author) / Chattopadhyay, Aditi (Thesis advisor) / Rajadas, John (Committee member) / Dai, Lenore (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Jiang, Hanqing (Committee member) / Arizona State University (Publisher)
Created2012
Description
Damage assessment and residual useful life estimation (RULE) are essential for aerospace, civil and naval structures. Structural Health Monitoring (SHM) attempts to automate the process of damage detection and identification. Multiscale modeling is a key element in SHM. It not only provides important information on the physics of failure, such as damage initiation and growth, the output can be used as "virtual sensing" data for detection and prognosis. The current research is part of an ongoing multidisciplinary effort to develop an integrated SHM framework for metallic aerospace components. In this thesis a multiscale model has been developed by bridging the relevant length scales, micro, meso and macro (or structural scale). Micro structural representations obtained from material characterization studies are used to define the length scales and to capture the size and orientation of the grains at the micro level. Parametric studies are conducted to estimate material parameters used in this constitutive model. Numerical and experimental simulations are performed to investigate the effects of Representative Volume Element (RVE) size, defect area fraction and distribution. A multiscale damage criterion accounting for crystal orientation effect is developed. This criterion is applied for fatigue crack initial stage prediction. A damage evolution rule based on strain energy density is modified to incorporate crystal plasticity at the microscale (local). Optimization approaches are used to calculate global damage index which is used for the RVE failure prediciton. Potential cracking directions are provided from the damage criterion simultaneously. A wave propagation model is incorporated with the damage model to detect changes in sensing signals due to plastic deformation and damage growth.
ContributorsLuo, Chuntao (Author) / Chattopadhyay, Aditi (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Jiang, Hanqing (Committee member) / Dai, Lenore (Committee member) / Li, Jian (Committee member) / Arizona State University (Publisher)
Created2011
Description
Polymer and polymer matrix composites (PMCs) materials are being used extensively in different civil and mechanical engineering applications. The behavior of the epoxy resin polymers under different types of loading conditions has to be understood before the mechanical behavior of Polymer Matrix Composites (PMCs) can be accurately predicted. In many structural applications, PMC structures are subjected to large flexural loadings, examples include repair of structures against earthquake and engine fan cases. Therefore it is important to characterize and model the flexural mechanical behavior of epoxy resin materials. In this thesis, a comprehensive research effort was undertaken combining experiments and theoretical modeling to investigate the mechanical behavior of epoxy resins subject to different loading conditions. Epoxy resin E 863 was tested at different strain rates. Samples with dog-bone geometry were used in the tension tests. Small sized cubic, prismatic, and cylindrical samples were used in compression tests. Flexural tests were conducted on samples with different sizes and loading conditions. Strains were measured using the digital image correlation (DIC) technique, extensometers, strain gauges, and actuators. Effects of triaxiality state of stress were studied. Cubic, prismatic, and cylindrical compression samples undergo stress drop at yield, but it was found that only cubic samples experience strain hardening before failure. Characteristic points of tensile and compressive stress strain relation and load deflection curve in flexure were measured and their variations with strain rate studied. Two different stress strain models were used to investigate the effect of out-of-plane loading on the uniaxial stress strain response of the epoxy resin material. The first model is a strain softening with plastic flow for tension and compression. The influence of softening localization on material behavior was investigated using the DIC system. It was found that compression plastic flow has negligible influence on flexural behavior in epoxy resins, which are stronger in pre-peak and post-peak softening in compression than in tension. The second model was a piecewise-linear stress strain curve simplified in the post-peak response. Beams and plates with different boundary conditions were tested and analytically studied. The flexural over-strength factor for epoxy resin polymeric materials were also evaluated.
ContributorsYekani Fard, Masoud (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Li, Jian (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2011
Description
Many products undergo several stages of testing ranging from tests on individual components to end-item tests. Additionally, these products may be further "tested" via customer or field use. The later failure of a delivered product may in some cases be due to circumstances that have no correlation with the product's inherent quality. However, at times, there may be cues in the upstream test data that, if detected, could serve to predict the likelihood of downstream failure or performance degradation induced by product use or environmental stresses. This study explores the use of downstream factory test data or product field reliability data to infer data mining or pattern recognition criteria onto manufacturing process or upstream test data by means of support vector machines (SVM) in order to provide reliability prediction models. In concert with a risk/benefit analysis, these models can be utilized to drive improvement of the product or, at least, via screening to improve the reliability of the product delivered to the customer. Such models can be used to aid in reliability risk assessment based on detectable correlations between the product test performance and the sources of supply, test stands, or other factors related to product manufacture. As an enhancement to the usefulness of the SVM or hyperplane classifier within this context, L-moments and the Western Electric Company (WECO) Rules are used to augment or replace the native process or test data used as inputs to the classifier. As part of this research, a generalizable binary classification methodology was developed that can be used to design and implement predictors of end-item field failure or downstream product performance based on upstream test data that may be composed of single-parameter, time-series, or multivariate real-valued data. Additionally, the methodology provides input parameter weighting factors that have proved useful in failure analysis and root cause investigations as indicators of which of several upstream product parameters have the greater influence on the downstream failure outcomes.
ContributorsMosley, James (Author) / Morrell, Darryl (Committee member) / Cochran, Douglas (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Roberts, Chell (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011