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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


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

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

In recent years, there has been an increased interest in sharing available bandwidth to avoid spectrum congestion. With an ever-increasing number wireless users, it is critical to develop signal processing based spectrum sharing algorithms to achieve cooperative use of the allocated spectrum among multiple systems in order to reduce interference between systems. This work studies the radar and communications systems coexistence problem using two main approaches. The first approach develops methodologies to increase radar target tracking performance under low signal-to-interference-plus-noise ratio (SINR) conditions due to the coexistence of strong communications interference. The second approach jointly optimizes the performance of both …

Contributors
Kota, John Stephen, Papandreou-Suppappola, Antonia, Berisha, Visar, et al.
Created Date
2016

The increased risk of falling and the worse ability to perform other daily physical activities in the elderly cause concern about monitoring and correcting basic everyday movement. In this thesis, a Kinect-based system was designed to assess one of the most important factors in balance control of human body when doing Sit-to-Stand (STS) movement: the postural symmetry in mediolateral direction. A symmetry score, calculated by the data obtained from a Kinect RGB-D camera, was proposed to reflect the mediolateral postural symmetry degree and was used to drive a real-time audio feedback designed in MAX/MSP to help users adjust themselves to …

Contributors
Zhou, Henghao, Turaga, Pavan, Ingalls, Todd, et al.
Created Date
2016

This work examines two main areas in model-based time-varying signal processing with emphasis in speech processing applications. The first area concentrates on improving speech intelligibility and on increasing the proposed methodologies application for clinical practice in speech-language pathology. The second area concentrates on signal expansions matched to physical-based models but without requiring independent basis functions; the significance of this work is demonstrated with speech vowels. A fully automated Vowel Space Area (VSA) computation method is proposed that can be applied to any type of speech. It is shown that the VSA provides an efficient and reliable measure and is correlated …

Contributors
Sandoval, Steven P., Papandreou-Suppappola, Antonia, Liss, Julie M, et al.
Created Date
2016

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