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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.


Resource Type
  • Doctoral Dissertation
Status
  • Public
Date Range
2010 2019


This thesis presents a gas sensor readout IC for amperometric and conductometric electrochemical sensors. The Analog Front-End (AFE) readout circuit enables tracking long term exposure to hazardous gas fumes in diesel and gasoline equipments, which may be correlated to diseases. Thus, the detection and discrimination of gases using microelectronic gas sensor system is required. This thesis describes the research, development, implementation and test of a small and portable based prototype platform for chemical gas sensors to enable a low-power and low noise gas detection system. The AFE reads out the outputs of eight conductometric sensor array and eight amperometric sensor …

Contributors
Kim, Hyuntae, Bakkaloglu, Bertan, Vermeire, Bert, et al.
Created Date
2011

Motion estimation is a core task in computer vision and many applications utilize optical flow methods as fundamental tools to analyze motion in images and videos. Optical flow is the apparent motion of objects in image sequences that results from relative motion between the objects and the imaging perspective. Today, optical flow fields are utilized to solve problems in various areas such as object detection and tracking, interpolation, visual odometry, etc. In this dissertation, three problems from different areas of computer vision and the solutions that make use of modified optical flow methods are explained. The contributions of this dissertation …

Contributors
Kanberoglu, Berkay, Frakes, David, Turaga, Pavan, et al.
Created Date
2018

Image resolution limits the extent to which zooming enhances clarity, restricts the size digital photographs can be printed at, and, in the context of medical images, can prevent a diagnosis. Interpolation is the supplementing of known data with estimated values based on a function or model involving some or all of the known samples. The selection of the contributing data points and the specifics of how they are used to define the interpolated values influences how effectively the interpolation algorithm is able to estimate the underlying, continuous signal. The main contributions of this dissertation are three fold: 1) Reframing edge-directed …

Contributors
Zwart, Christine M., Frakes, David H, Karam, Lina, et al.
Created Date
2013

Human movement is a complex process influenced by physiological and psychological factors. The execution of movement is varied from person to person, and the number of possible strategies for completing a specific movement task is almost infinite. Different choices of strategies can be perceived by humans as having different degrees of quality, and the quality can be defined with regard to aesthetic, athletic, or health-related ratings. It is useful to measure and track the quality of a person's movements, for various applications, especially with the prevalence of low-cost and portable cameras and sensors today. Furthermore, based on such measurements, feedback …

Contributors
Wang, Qiao, Turaga, Pavan, Spanias, Andreas, et al.
Created Date
2018

Distributed wireless sensor networks (WSNs) have attracted researchers recently due to their advantages such as low power consumption, scalability and robustness to link failures. In sensor networks with no fusion center, consensus is a process where all the sensors in the network achieve global agreement using only local transmissions. In this dissertation, several consensus and consensus-based algorithms in WSNs are studied. Firstly, a distributed consensus algorithm for estimating the maximum and minimum value of the initial measurements in a sensor network in the presence of communication noise is proposed. In the proposed algorithm, a soft-max approximation together with a non-linear …

Contributors
Zhang, Sai, Tepedelenlioglu, Cihan, Spanias, Andreas, et al.
Created Date
2017

Audio signals, such as speech and ambient sounds convey rich information pertaining to a user’s activity, mood or intent. Enabling machines to understand this contextual information is necessary to bridge the gap in human-machine interaction. This is challenging due to its subjective nature, hence, requiring sophisticated techniques. This dissertation presents a set of computational methods, that generalize well across different conditions, for speech-based applications involving emotion recognition and keyword detection, and ambient sounds-based applications such as lifelogging. The expression and perception of emotions varies across speakers and cultures, thus, determining features and classification methods that generalize well to different conditions …

Contributors
Shah, Mohit, Spanias, Andreas, Chakrabarti, Chaitali, et al.
Created Date
2015

Modern machine learning systems leverage data and features from multiple modalities to gain more predictive power. In most scenarios, the modalities are vastly different and the acquired data are heterogeneous in nature. Consequently, building highly effective fusion algorithms is at the core to achieve improved model robustness and inferencing performance. This dissertation focuses on the representation learning approaches as the fusion strategy. Specifically, the objective is to learn the shared latent representation which jointly exploit the structural information encoded in all modalities, such that a straightforward learning model can be adopted to obtain the prediction. We first consider sensor fusion, …

Contributors
Song, Huan, Spanias, Andreas, Thiagarajan, Jayaraman, et al.
Created Date
2018

From time immemorial, epilepsy has persisted to be one of the greatest impediments to human life for those stricken by it. As the fourth most common neurological disorder, epilepsy causes paroxysmal electrical discharges in the brain that manifest as seizures. Seizures have the effect of debilitating patients on a physical and psychological level. Although not lethal by themselves, they can bring about total disruption in consciousness which can, in hazardous conditions, lead to fatality. Roughly 1\% of the world population suffer from epilepsy and another 30 to 50 new cases per 100,000 increase the number of affected annually. Controlling seizures …

Contributors
Shafique, Md Ashfaque Bin, Tsakalis, Konstantinos, Rodriguez, Armando, et al.
Created Date
2016

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

Distributed estimation uses many inexpensive sensors to compose an accurate estimate of a given parameter. It is frequently implemented using wireless sensor networks. There have been several studies on optimizing power allocation in wireless sensor networks used for distributed estimation, the vast majority of which assume linear radio-frequency amplifiers. Linear amplifiers are inherently inefficient, so in this dissertation nonlinear amplifiers are examined to gain efficiency while operating distributed sensor networks. This research presents a method to boost efficiency by operating the amplifiers in the nonlinear region of operation. Operating amplifiers nonlinearly presents new challenges. First, nonlinear amplifier characteristics change across …

Contributors
Santucci, Robert W., Spanias, Andreas, Tepedelenlioðlu, Cihan, et al.
Created Date
2013

Feature representations for raw data is one of the most important component in a machine learning system. Traditionally, features are \textit{hand crafted} by domain experts which can often be a time consuming process. Furthermore, they do not generalize well to unseen data and novel tasks. Recently, there have been many efforts to generate data-driven representations using clustering and sparse models. This dissertation focuses on building data-driven unsupervised models for analyzing raw data and developing efficient feature representations. Simultaneous segmentation and feature extraction approaches for silicon-pores sensor data are considered. Aggregating data into a matrix and performing low rank and sparse …

Contributors
Sattigeri, Prasanna, Spanias, Andreas, Thornton, Trevor, et al.
Created Date
2014

In the last few years, significant advances in nanofabrication have allowed tailoring of structures and materials at a molecular level enabling nanofabrication with precise control of dimensions and organization at molecular length scales, a development leading to significant advances in nanoscale systems. Although, the direction of progress seems to follow the path of microelectronics, the fundamental physics in a nanoscale system changes more rapidly compared to microelectronics, as the size scale is decreased. The changes in length, area, and volume ratios due to reduction in size alter the relative influence of various physical effects determining the overall operation of a …

Contributors
Joshi, Punarvasu, Thornton, Trevor J, Goryll, Michael, et al.
Created Date
2011

Information divergence functions, such as the Kullback-Leibler divergence or the Hellinger distance, play a critical role in statistical signal processing and information theory; however estimating them can be challenge. Most often, parametric assumptions are made about the two distributions to estimate the divergence of interest. In cases where no parametric model fits the data, non-parametric density estimation is used. In statistical signal processing applications, Gaussianity is usually assumed since closed-form expressions for common divergence measures have been derived for this family of distributions. Parametric assumptions are preferred when it is known that the data follows the model, however this is …

Contributors
Wisler, Alan, Berisha, Visar, Spanias, Andreas, et al.
Created Date
2017

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

Reliable extraction of human pose features that are invariant to view angle and body shape changes is critical for advancing human movement analysis. In this dissertation, the multifactor analysis techniques, including the multilinear analysis and the multifactor Gaussian process methods, have been exploited to extract such invariant pose features from video data by decomposing various key contributing factors, such as pose, view angle, and body shape, in the generation of the image observations. Experimental results have shown that the resulting pose features extracted using the proposed methods exhibit excellent invariance properties to changes in view angles and body shapes. Furthermore, …

Contributors
Peng, Bo, Qian, Gang, Ye, Jieping, et al.
Created Date
2011

Recently, the location of the nodes in wireless networks has been modeled as point processes. In this dissertation, various scenarios of wireless communications in large-scale networks modeled as point processes are considered. The first part of the dissertation considers signal reception and detection problems with symmetric alpha stable noise which is from an interfering network modeled as a Poisson point process. For the signal reception problem, the performance of space-time coding (STC) over fading channels with alpha stable noise is studied. We derive pairwise error probability (PEP) of orthogonal STCs. For general STCs, we propose a maximum-likelihood (ML) receiver, and …

Contributors
Lee, Junghoon, Tepedelenlioglu, Cihan, Spanias, Andreas, et al.
Created Date
2014

In many applications, measured sensor data is meaningful only when the location of sensors is accurately known. Therefore, the localization accuracy is crucial. In this dissertation, both location estimation and location detection problems are considered. In location estimation problems, sensor nodes at known locations, called anchors, transmit signals to sensor nodes at unknown locations, called nodes, and use these transmissions to estimate the location of the nodes. Specifically, the location estimation in the presence of fading channels using time of arrival (TOA) measurements with narrowband communication signals is considered. Meanwhile, the Cramer-Rao lower bound (CRLB) for localization error under different …

Contributors
Zhang, Xue, Tepedelenlioglu, Cihan, Spanias, Andreas, et al.
Created Date
2016

Using stereo vision for 3D reconstruction and depth estimation has become a popular and promising research area as it has a simple setup with passive cameras and relatively efficient processing procedure. The work in this dissertation focuses on locally adaptive stereo vision methods and applications to different imaging setups and image scenes. Solder ball height and substrate coplanarity inspection is essential to the detection of potential connectivity issues in semi-conductor units. Current ball height and substrate coplanarity inspection tools are expensive and slow, which makes them difficult to use in a real-time manufacturing setting. In this dissertation, an automatic, stereo …

Contributors
Li, Jinjin, Karam, Lina, Chakrabarti, Chaitali, et al.
Created Date
2017

Autonomous vehicle control systems utilize real-time kinematic Global Navigation Satellite Systems (GNSS) receivers to provide a position within two-centimeter of truth. GNSS receivers utilize the satellite signal time of arrival estimates to solve for position; and multipath corrupts the time of arrival estimates with a time-varying bias. Time of arrival estimates are based upon accurate direct sequence spread spectrum (DSSS) code and carrier phase tracking. Current multipath mitigating GNSS solutions include fixed radiation pattern antennas and windowed delay-lock loop code phase discriminators. A new multipath mitigating code tracking algorithm is introduced that utilizes a non-symmetric correlation kernel to reject multipath. …

Contributors
Miller, Steven R., Spanias, Andreas, Tepedelenlioglu, Cihan, et al.
Created Date
2013

Effective modeling of high dimensional data is crucial in information processing and machine learning. Classical subspace methods have been very effective in such applications. However, over the past few decades, there has been considerable research towards the development of new modeling paradigms that go beyond subspace methods. This dissertation focuses on the study of sparse models and their interplay with modern machine learning techniques such as manifold, ensemble and graph-based methods, along with their applications in image analysis and recovery. By considering graph relations between data samples while learning sparse models, graph-embedded codes can be obtained for use in unsupervised, …

Contributors
Natesan Ramamurthy, Karthikeyan, Spanias, Andreas, Tsakalis, Konstantinos, et al.
Created Date
2013