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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


The radar performance of detecting a target and estimating its parameters can deteriorate rapidly in the presence of high clutter. This is because radar measurements due to clutter returns can be falsely detected as if originating from the actual target. Various data association methods and multiple hypothesis filtering approaches have been considered to solve this problem. Such methods, however, can be computationally intensive for real time radar processing. This work proposes a new approach that is based on the unsupervised clustering of target and clutter detections before target tracking using particle filtering. In particular, Gaussian mixture modeling is first used …

Contributors
Freeman, Matthew Gregory, Papandreou-Suppappola, Antonia, Bliss, Daniel, et al.
Created Date
2016

Full-duplex communication has attracted significant attention as it promises to increase the spectral efficiency compared to half-duplex. Multi-hop full-duplex networks add new dimensions and capabilities to cooperative networks by facilitating simultaneous transmission and reception and improving data rates. When a relay in a multi-hop full-duplex system amplifies and forwards its received signals, due to the presence of self-interference, the input-output relationship is determined by recursive equations. This thesis introduces a signal flow graph approach to solve the problem of finding the input-output relationship of a multi-hop amplify-and-forward full-duplex relaying system using Mason's gain formula. Even when all links have flat …

Contributors
Sureshbabu, Abhilash, Tepedelenlioglu, Cihan, Papandreou-Suppappola, Antonia, et al.
Created Date
2016

Biological and biomedical measurements, when adequately analyzed and processed, can be used to impart quantitative diagnosis during primary health care consultation to improve patient adherence to recommended treatments. For example, analyzing neural recordings from neurostimulators implanted in patients with neurological disorders can be used by a physician to adjust detrimental stimulation parameters to improve treatment. As another example, biosequences, such as sequences from peptide microarrays obtained from a biological sample, can potentially provide pre-symptomatic diagnosis for infectious diseases when processed to associate antibodies to specific pathogens or infectious agents. This work proposes advanced statistical signal processing and machine learning methodologies …

Contributors
Maurer, Alexander, Papandreou-Suppappola, Antonia, Bliss, Daniel, et al.
Created Date
2016

In-situ fatigue damage diagnosis and prognosis is a challenging problem for both metallic and composite materials and structures. There are various uncertainties arising from material properties, component geometries, measurement noise, feature extraction techniques, and modeling errors. It is essential to manage and incorporate these uncertainties in order to achieve accurate damage detection and remaining useful life (RUL) prediction. The aim of this study is to develop an integrated fatigue damage diagnosis and prognosis framework for both metallic and composite materials. First, Lamb waves are used as the in-situ damage detection technique to interrogate the damaged structures. Both experimental and numerical …

Contributors
Peng, Tishun, Liu, Yongming, Chattopadhyay, Aditi, et al.
Created Date
2016

In the last 15 years, there has been a significant increase in the number of motor neural prostheses used for restoring limb function lost due to neurological disorders or accidents. The aim of this technology is to enable patients to control a motor prosthesis using their residual neural pathways (central or peripheral). Recent studies in non-human primates and humans have shown the possibility of controlling a prosthesis for accomplishing varied tasks such as self-feeding, typing, reaching, grasping, and performing fine dexterous movements. A neural decoding system comprises mainly of three components: (i) sensors to record neural signals, (ii) an algorithm …

Contributors
Padmanaban, Subash, Greger, Bradley, Santello, Marco, et al.
Created Date
2017

This thesis addresses two problems in digital baseband design of wireless communication systems, namely, those in Internet of Things (IoT) terminals that support long range communications and those in full-duplex systems that are designed for high spectral efficiency. IoT terminals for long range communications are typically based on Orthogonal Frequency-Division Multiple Access (OFDMA) and spread spectrum technologies. In order to design an efficient baseband architecture for such terminals, the workload profiles of both systems are analyzed. Since frame detection unit has by far the highest computational load, a simple architecture that uses only a scalar datapath is proposed. To optimize …

Contributors
Wu, Shunyao, Chakrabarti, Chaitali, Papandreou-Suppappola, Antonia, et al.
Created Date
2017

Cognitive radio (CR) and device-to-device (D2D) systems are two promising dynamic spectrum access schemes in wireless communication systems to provide improved quality-of-service, and efficient spectrum utilization. This dissertation shows that both CR and D2D systems benefit from properly designed cooperation scheme. In underlay CR systems, where secondary users (SUs) transmit simultaneously with primary users (PUs), reliable communication is by all means guaranteed for PUs, which likely deteriorates SUs’ performance. To overcome this issue, cooperation exclusively among SUs is achieved through multi-user diversity (MUD), where each SU is subject to an instantaneous interference constraint at the primary receiver. Therefore, the active …

Contributors
Zeng, Ruochen, Tepedelenlioglu, Cihan, Papandreou-Suppappola, Antonia, et al.
Created Date
2017

The Internet of Things (IoT) has become a more pervasive part of everyday life. IoT networks such as wireless sensor networks, depend greatly on the limiting unnecessary power consumption. As such, providing low-power, adaptable software can greatly improve network design. For streaming live video content, Wireless Video Sensor Network Platform compatible Dynamic Adaptive Streaming over HTTP (WVSNP-DASH) aims to revolutionize wireless segmented video streaming by providing a low-power, adaptable framework to compete with modern DASH players such as Moving Picture Experts Group (MPEG-DASH) and Apple’s Hypertext Transfer Protocol (HTTP) Live Streaming (HLS). Each segment is independently playable, and does not …

Contributors
Khan, Zarah, Reisslein, Martin, Seema, Adolph, et al.
Created Date
2018

Software-defined radio provides users with a low-cost and flexible platform for implementing and studying advanced communications and remote sensing applications. Two such applications include unmanned aerial system-to-ground communications channel and joint sensing and communication systems. In this work, these applications are studied. In the first part, unmanned aerial system-to-ground communications channel models are derived from empirical data collected from software-defined radio transceivers in residential and mountainous desert environments using a small (< 20 kg) unmanned aerial system during low-altitude flight (< 130 m). The Kullback-Leibler divergence measure was employed to characterize model mismatch from the empirical data. Using this measure …

Contributors
Gutierrez, Richard, Bliss, Daniel W, Papandreou-Suppappola, Antonia, et al.
Created Date
2018

Deep learning architectures have been widely explored in computer vision and have depicted commendable performance in a variety of applications. A fundamental challenge in training deep networks is the requirement of large amounts of labeled training data. While gathering large quantities of unlabeled data is cheap and easy, annotating the data is an expensive process in terms of time, labor and human expertise. Thus, developing algorithms that minimize the human effort in training deep models is of immense practical importance. Active learning algorithms automatically identify salient and exemplar samples from large amounts of unlabeled data and can augment maximal information …

Contributors
Ranganathan, Hiranmayi, Sethuraman, Panchanathan, Papandreou-Suppappola, Antonia, et al.
Created Date
2018

Both two-way relays (TWR) and full-duplex (FD) radios are spectrally efficient, and their integration shows great potential to further improve the spectral efficiency, which offers a solution to the fifth generation wireless systems. High quality channel state information (CSI) are the key components for the implementation and the performance of the FD TWR system, making channel estimation in FD TWRs crucial. The impact of channel estimation on spectral efficiency in half-duplex multiple-input-multiple-output (MIMO) TWR systems is investigated. The trade-off between training and data energy is proposed. In the case that two sources are symmetric in power and number of antennas, …

Contributors
Li, Xiaofeng, Tepedelenlioglu, Cihan, Papandreou-Suppappola, Antonia, et al.
Created Date
2018

Growing understanding of the neural code and how to speak it has allowed for notable advancements in neural prosthetics. With commercially-available implantable systems with bi- directional neural communication on the horizon, there is an increasing imperative to develop high resolution interfaces that can survive the environment and be well tolerated by the nervous system under chronic use. The sensory encoding aspect optimally interfaces at a scale sufficient to evoke perception but focal in nature to maximize resolution and evoke more complex and nuanced sensations. Microelectrode arrays can maintain high spatial density, operating on the scale of cortical columns, and can …

Contributors
Oswalt, Denise, Greger, Bradley, Buneo, Christopher, et al.
Created Date
2018

Speech is generated by articulators acting on a phonatory source. Identification of this phonatory source and articulatory geometry are individually challenging and ill-posed problems, called speech separation and articulatory inversion, respectively. There exists a trade-off between decomposition and recovered articulatory geometry due to multiple possible mappings between an articulatory configuration and the speech produced. However, if measurements are obtained only from a microphone sensor, they lack any invasive insight and add additional challenge to an already difficult problem. A joint non-invasive estimation strategy that couples articulatory and phonatory knowledge would lead to better articulatory speech synthesis. In this thesis, a …

Contributors
Venkataramani, Adarsh Akkshai, Papandreou-Suppappola, Antonia, Bliss, Daniel W, et al.
Created Date
2018

Medical ultrasound imaging is widely used today because of it being non-invasive and cost-effective. Flow estimation helps in accurate diagnosis of vascular diseases and adds an important dimension to medical ultrasound imaging. Traditionally flow estimation is done using Doppler-based methods which only estimate velocity in the beam direction. Thus when blood vessels are close to being orthogonal to the beam direction, there are large errors in the estimation results. In this dissertation, a low cost blood flow estimation method that does not have the angle dependency of Doppler-based methods, is presented. First, a velocity estimator based on speckle tracking and …

Contributors
WEI, SIYUAN, Chakrabarti, Chaitali, Papandreou-Suppappola, Antonia, et al.
Created Date
2018

As the demand for wireless systems increases exponentially, it has become necessary for different wireless modalities, like radar and communication systems, to share the available bandwidth. One approach to realize coexistence successfully is for each system to adopt a transmit waveform with a unique nonlinear time-varying phase function. At the receiver of the system of interest, the waveform received for process- ing may still suffer from low signal-to-interference-plus-noise ratio (SINR) due to the presence of the waveforms that are matched to the other coexisting systems. This thesis uses a time-frequency based approach to increase the SINR of a system by …

Contributors
Gattani, Vineet Sunil, Papandreou-Suppappola, Antonia, Richmond, Christ, et al.
Created Date
2018

This dissertation presents the development of structural health monitoring and prognostic health management methodologies for complex structures and systems in the field of mechanical engineering. To overcome various challenges historically associated with complex structures and systems such as complicated sensing mechanisms, noisy information, and large-size datasets, a hybrid monitoring framework comprising of solid mechanics concepts and data mining technologies is developed. In such a framework, the solid mechanics simulations provide additional intuitions to data mining techniques reducing the dependence of accuracy on the training set, while the data mining approaches fuse and interpret information from the targeted system enabling the …

Contributors
Li, Guoyi, Chattopadhyay, Aditi, Mignolet, Marc, et al.
Created Date
2019

A critical problem for airborne, ship board, and land based radars operating in maritime or littoral environments is the detection, identification and tracking of targets against backscattering caused by the roughness of the sea surface. Statistical models, such as the compound K-distribution (CKD), were shown to accurately describe two separate structures of the sea clutter intensity fluctuations. The first structure is the texture that is associated with long sea waves and exhibits long temporal decorrelation period. The second structure is the speckle that accounts for reflections from multiple scatters and exhibits a short temporal decorrelation period from pulse to pulse. …

Contributors
Northrop, Judith, Papandreou-Suppappola, Antonia, Chakrabarti, Chaitali, et al.
Created Date
2019

Spike sorting is a critical step for single-unit-based analysis of neural activities extracellularly and simultaneously recorded using multi-channel electrodes. When dealing with recordings from very large numbers of neurons, existing methods, which are mostly semiautomatic in nature, become inadequate. This dissertation aims at automating the spike sorting process. A high performance, automatic and computationally efficient spike detection and clustering system, namely, the M-Sorter2 is presented. The M-Sorter2 employs the modified multiscale correlation of wavelet coefficients (MCWC) for neural spike detection. At the center of the proposed M-Sorter2 are two automatic spike clustering methods. They share a common hierarchical agglomerative modeling …

Contributors
Ma, Weichao, Si, Jennie, Papandreou-Suppappola, Antonia, et al.
Created Date
2019

The problem of multiple object tracking seeks to jointly estimate the time-varying cardinality and trajectory of each object. There are numerous challenges that are encountered in tracking multiple objects including a time-varying number of measurements, under varying constraints, and environmental conditions. In this thesis, the proposed statistical methods integrate the use of physical-based models with Bayesian nonparametric methods to address the main challenges in a tracking problem. In particular, Bayesian nonparametric methods are exploited to efficiently and robustly infer object identity and learn time-dependent cardinality; together with Bayesian inference methods, they are also used to associate measurements to objects and …

Contributors
Moraffah, Bahman, Papandreou-Suppappola, Antonia, Bliss, Daniel W., et al.
Created Date
2019

Disentangling latent spaces is an important research direction in the interpretability of unsupervised machine learning. Several recent works using deep learning are very effective at producing disentangled representations. However, in the unsupervised setting, there is no way to pre-specify which part of the latent space captures specific factors of variations. While this is generally a hard problem because of the non-existence of analytical expressions to capture these variations, there are certain factors like geometric transforms that can be expressed analytically. Furthermore, in existing frameworks, the disentangled values are also not interpretable. The focus of this work is to disentangle these …

Contributors
Koneripalli, Kaushik, Turaga, Pavan, Papandreou-Suppappola, Antonia, et al.
Created Date
2019