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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.


Contributor
Date Range
2010 2019


To establish reliable wireless communication links it is critical to devise schemes to mitigate the effects of the fading channel. In this regard, this dissertation analyzes two types of systems: point-to-point, and multiuser systems. For point-to-point systems with multiple antennas, switch and stay diversity combining offers a substantial complexity reduction for a modest loss in performance as compared to systems that implement selection diversity. For the first time, the design and performance of space-time coded multiple antenna systems that employ switch and stay combining at the receiver is considered. Novel switching algorithms are proposed and upper bounds on the pairwise …

Contributors
Bangalore Narasimhamurthy, Adarsh, Tepedelenlioglu, Cihan, Duman, Tolga M, et al.
Created Date
2010

Distributed inference has applications in fields as varied as source localization, evaluation of network quality, and remote monitoring of wildlife habitats. In this dissertation, distributed inference algorithms over multiple-access channels are considered. The performance of these algorithms and the effects of wireless communication channels on the performance are studied. In a first class of problems, distributed inference over fading Gaussian multiple-access channels with amplify-and-forward is considered. Sensors observe a phenomenon and transmit their observations using the amplify-and-forward scheme to a fusion center (FC). Distributed estimation is considered with a single antenna at the FC, where the performance is evaluated using …

Contributors
Banavar, Mahesh Krishna, Tepedelenlioglu, Cihan, Spanias, Andreas, et al.
Created Date
2010

The exponential rise in unmanned aerial vehicles has necessitated the need for accurate pose estimation under any extreme conditions. Visual Odometry (VO) is the estimation of position and orientation of a vehicle based on analysis of a sequence of images captured from a camera mounted on it. VO offers a cheap and relatively accurate alternative to conventional odometry techniques like wheel odometry, inertial measurement systems and global positioning system (GPS). This thesis implements and analyzes the performance of a two camera based VO called Stereo based visual odometry (SVO) in presence of various deterrent factors like shadows, extremely bright outdoors, …

Contributors
Dhar, Anchit, Saripalli, Srikanth, Li, Baoxin, et al.
Created Date
2010

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) …

Contributors
Krishnamoorthi, Harish, Spanias, Andreas, Papandreou-Suppappola, Antonia, et al.
Created Date
2011

For synthetic aperture radar (SAR) image formation processing, the chirp scaling algorithm (CSA) has gained considerable attention mainly because of its excellent target focusing ability, optimized processing steps, and ease of implementation. In particular, unlike the range Doppler and range migration algorithms, the CSA is easy to implement since it does not require interpolation, and it can be used on both stripmap and spotlight SAR systems. Another transform that can be used to enhance the processing of SAR image formation is the fractional Fourier transform (FRFT). This transform has been recently introduced to the signal processing community, and it has …

Contributors
Northrop, Judith, Papandreou-Suppappola, Antonia, Spanias, Andreas, et al.
Created Date
2011

Spotlight mode synthetic aperture radar (SAR) imaging involves a tomo- graphic reconstruction from projections, necessitating acquisition of large amounts of data in order to form a moderately sized image. Since typical SAR sensors are hosted on mobile platforms, it is common to have limitations on SAR data acquisi- tion, storage and communication that can lead to data corruption and a resulting degradation of image quality. It is convenient to consider corrupted samples as missing, creating a sparsely sampled aperture. A sparse aperture would also result from compressive sensing, which is a very attractive concept for data intensive sen- sors such …

Contributors
Werth, Nicholas, Karam, Lina, Papandreou-Suppappola, Antonia, et al.
Created Date
2011

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 …

Contributors
Mosley, James Holton, Morrell, Darryl, Morrell, Darryl, et al.
Created Date
2011

Current economic conditions necessitate the extension of service lives for a variety of aerospace systems. As a result, there is an increased need for structural health management (SHM) systems to increase safety, extend life, reduce maintenance costs, and minimize downtime, lowering life cycle costs for these aging systems. The implementation of such a system requires a collaborative research effort in a variety of areas such as novel sensing techniques, robust algorithms for damage interrogation, high fidelity probabilistic progressive damage models, and hybrid residual life estimation models. This dissertation focuses on the sensing and damage estimation aspects of this multidisciplinary topic …

Contributors
Coelho, Clyde, Chattopadhyay, Aditi, Dai, Lenore, et al.
Created Date
2011

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 …

Contributors
Yekani Fard, Masoud, Chattopadhyay, Aditi, Dai, Lenore, et al.
Created Date
2011

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 …

Contributors
Luo, Chuntao, Chattopadhyay, Aditi, Papandreou-Suppappola, Antonia, et al.
Created Date
2011

The tracking of multiple targets becomes more challenging in complex environments due to the additional degrees of nonlinearity in the measurement model. In urban terrain, for example, there are multiple reflection path measurements that need to be exploited since line-of-sight observations are not always available. Multiple target tracking in urban terrain environments is traditionally implemented using sequential Monte Carlo filtering algorithms and data association techniques. However, data association techniques can be computationally intensive and require very strict conditions for efficient performance. This thesis investigates the probability hypothesis density (PHD) method for tracking multiple targets in urban environments. The PHD is …

Contributors
Zhou, Meng, Papandreou-Suppappola, Antonia, Tepedelenlioglu, Cihan, et al.
Created Date
2011

Genomic and proteomic sequences, which are in the form of deoxyribonucleic acid (DNA) and amino acids respectively, play a vital role in the structure, function and diversity of every living cell. As a result, various genomic and proteomic sequence processing methods have been proposed from diverse disciplines, including biology, chemistry, physics, computer science and electrical engineering. In particular, signal processing techniques were applied to the problems of sequence querying and alignment, that compare and classify regions of similarity in the sequences based on their composition. However, although current approaches obtain results that can be attributed to key biological properties, they …

Contributors
Ravichandran, Lakshminarayan, Papandreou-Suppappola, Antonia, Spanias, Andreas S, et al.
Created Date
2011

Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find better solutions. In this thesis, a novel method is proposed which uses image registration techniques to provide better image classification. This method reduces the error rate of classification by performing image registration of the images with the previously obtained images before performing classification. The motivation behind this is the fact …

Contributors
Muralidhar, Ashwini, Saripalli, Srikanth, Papandreou-Suppappola, Antonia, et al.
Created Date
2011

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 …

Contributors
Tu, Kai, Duman, Tolga M, Zhang, Junshan, et al.
Created Date
2011

In this thesis, an adaptive waveform selection technique for dynamic target tracking under low signal-to-noise ratio (SNR) conditions is investigated. The approach is integrated with a track-before-detect (TBD) algorithm and uses delay-Doppler matched filter (MF) outputs as raw measurements without setting any threshold for extracting delay-Doppler estimates. The particle filter (PF) Bayesian sequential estimation approach is used with the TBD algorithm (PF-TBD) to estimate the dynamic target state. A waveform-agile TBD technique is proposed that integrates the PF-TBD with a waveform selection technique. The new approach predicts the waveform to transmit at the next time step by minimizing the predicted …

Contributors
Piwowarski, Ryan, Papandreou-Suppappola, Antonia, Chakrabarti, Chaitali, et al.
Created Date
2011

Super-Resolution (SR) techniques are widely developed to increase image resolution by fusing several Low-Resolution (LR) images of the same scene to overcome sensor hardware limitations and reduce media impairments in a cost-effective manner. When choosing a solution for the SR problem, there is always a trade-off between computational efficiency and High-Resolution (HR) image quality. Existing SR approaches suffer from extremely high computational requirements due to the high number of unknowns to be estimated in the solution of the SR inverse problem. This thesis proposes efficient iterative SR techniques based on Visual Attention (VA) and perceptual modeling of the human visual …

Contributors
Sadaka, Nabil Gergi, Karam, Lina J, Spanias, Andreas S, et al.
Created Date
2011

Multidimensional (MD) discrete Fourier transform (DFT) is a key kernel algorithm in many signal processing applications, such as radar imaging and medical imaging. Traditionally, a two-dimensional (2-D) DFT is computed using Row-Column (RC) decomposition, where one-dimensional (1-D) DFTs are computed along the rows followed by 1-D DFTs along the columns. However, architectures based on RC decomposition are not efficient for large input size data which have to be stored in external memories based Synchronous Dynamic RAM (SDRAM). In this dissertation, first an efficient architecture to implement 2-D DFT for large-sized input data is proposed. This architecture achieves very high throughput …

Contributors
Yu, Chi-Li, Chakrabarti, Chaitali, Papandreou-Suppappola, Antonia, et al.
Created Date
2012

With increased usage of green energy, the number of photovoltaic arrays used in power generation is increasing rapidly. Many of the arrays are located at remote locations where faults that occur within the array often go unnoticed and unattended for large periods of time. Technicians sent to rectify the faults have to spend a large amount of time determining the location of the fault manually. Automated monitoring systems are needed to obtain the information about the performance of the array and detect faults. Such systems must monitor the DC side of the array in addition to the AC side to …

Contributors
Krishnan, Venkatachalam, Tepedelenlioglu, Cihan, Spanias, Andreas, et al.
Created Date
2012

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 …

Contributors
Liu, Yingtao, Chattopadhyay, Aditi, Rajadas, John, et al.
Created Date
2012

Research on developing new algorithms to improve information on brain functionality and structure is ongoing. Studying neural activity through dipole source localization with electroencephalography (EEG) and magnetoencephalography (MEG) sensor measurements can lead to diagnosis and treatment of a brain disorder and can also identify the area of the brain from where the disorder has originated. Designing advanced localization algorithms that can adapt to environmental changes is considered a significant shift from manual diagnosis which is based on the knowledge and observation of the doctor, to an adaptive and improved brain disorder diagnosis as these algorithms can track activities that might …

Contributors
Michael, Stefanos, Papandreou-Suppappola, Antonia, Chakrabarti, Chaitali, et al.
Created Date
2012