Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- Creators: Berisha, Visar
Description
The processing power and storage capacity of portable devices have improved considerably over the past decade. This has motivated the implementation of sophisticated audio and other signal processing algorithms on such mobile devices. Of particular interest in this thesis is audio/speech processing based on perceptual criteria. Specifically, estimation of parameters from human auditory models, such as auditory patterns and loudness, involves computationally intensive operations which can strain device resources. Hence, strategies for implementing computationally efficient human auditory models for loudness estimation have been studied in this thesis. Existing algorithms for reducing computations in auditory pattern and loudness estimation have been examined and improved algorithms have been proposed to overcome limitations of these methods. In addition, real-time applications such as perceptual loudness estimation and loudness equalization using auditory models have also been implemented. A software implementation of loudness estimation on iOS devices is also reported in this thesis. In addition to the loudness estimation algorithms and software, in this thesis project we also created new illustrations of speech and audio processing concepts for research and education. As a result, a new suite of speech/audio DSP functions was developed and integrated as part of the award-winning educational iOS App 'iJDSP." These functions are described in detail in this thesis. Several enhancements in the architecture of the application have also been introduced for providing the supporting framework for speech/audio processing. Frame-by-frame processing and visualization functionalities have been developed to facilitate speech/audio processing. In addition, facilities for easy sound recording, processing and audio rendering have also been developed to provide students, practitioners and researchers with an enriched DSP simulation tool. Simulations and assessments have been also developed for use in classes and training of practitioners and students.
ContributorsKalyanasundaram, Girish (Author) / Spanias, Andreas S (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2013
Description
Epilepsy affects numerous people around the world and is characterized by recurring seizures, prompting the ability to predict them so precautionary measures may be employed. One promising algorithm extracts spatiotemporal correlation based features from intracranial electroencephalography signals for use with support vector machines. The robustness of this methodology is tested through a sensitivity analysis. Doing so also provides insight about how to construct more effective feature vectors.
ContributorsMa, Owen (Author) / Bliss, Daniel (Thesis director) / Berisha, Visar (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2015-05
Description
Passive radar can be used to reduce the demand for radio frequency spectrum bandwidth. This paper will explain how a MATLAB simulation tool was developed to analyze the feasibility of using passive radar with digitally modulated communication signals. The first stage of the simulation creates a binary phase-shift keying (BPSK) signal, quadrature phase-shift keying (QPSK) signal, or digital terrestrial television (DTTV) signal. A scenario is then created using user defined parameters that simulates reception of the original signal on two different channels, a reference channel and a surveillance channel. The signal on the surveillance channel is delayed and Doppler shifted according to a point target scattering profile. An ambiguity function detector is implemented to identify the time delays and Doppler shifts associated with reflections off of the targets created. The results of an example are included in this report to demonstrate the simulation capabilities.
ContributorsScarborough, Gillian Donnelly (Author) / Cochran, Douglas (Thesis director) / Berisha, Visar (Committee member) / Wang, Chao (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2014-05
Description
The marmoset monkey (Callithrix jacchus) is a new-world primate species native to South America rainforests. Because they rely on vocal communication to navigate and survive, marmosets have evolved as a promising primate model to study vocal production, perception, cognition, and social interactions. The purpose of this project is to provide an initial assessment on the vocal repertoire of a marmoset colony raised at Arizona State University and call types they use in different social conditions. The vocal production of a colony of 16 marmoset monkeys was recorded in 3 different conditions with three repeats of each condition. The positive condition involves a caretaker distributing food, the negative condition involves an experimenter taking a marmoset out of his cage to a different room, and the control condition is the normal state of the colony with no human interference. A total of 5396 samples of calls were collected during a total of 256 minutes of audio recordings. Call types were analyzed in semi-automated computer programs developed in the Laboratory of Auditory Computation and Neurophysiology. A total of 5 major call types were identified and their variants in different social conditions were analyzed. The results showed that the total number of calls and the type of calls made differed in the three social conditions, suggesting that monkey vocalization signals and depends on the social context.
ContributorsFernandez, Jessmin Natalie (Author) / Zhou, Yi (Thesis director) / Berisha, Visar (Committee member) / School of International Letters and Cultures (Contributor) / Department of Psychology (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Previous studies have shown that experimentally implemented formant perturbations result in production of compensatory responses in the opposite direction of the perturbations. In this study, we investigated how participants adapt to a) auditory perturbations that shift formants to a specific point in the vowel space and hence remove variability of formants (focused perturbations), and b) auditory perturbations that preserve the natural variability of formants (uniform perturbations). We examined whether the degree of adaptation to focused perturbations was different from adaptation to uniform adaptations. We found that adaptation magnitude of the first formant (F1) was smaller in response to focused perturbations. However, F1 adaptation was initially moved in the same direction as the perturbation, and after several trials the F1 adaptation changed its course toward the opposite direction of the perturbation. We also found that adaptation of the second formant (F2) was smaller in response to focused perturbations than F2 responses to uniform perturbations. Overall, these results suggest that formant variability is an important component of speech, and that our central nervous system takes into account such variability to produce more accurate speech output.
ContributorsDittman, Jonathan William (Author) / Daliri, Ayoub (Thesis director) / Berisha, Visar (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Telemedicine is a multipurpose tool that allows medical professionals to use technology as a means to evaluate, diagnose, and treat patients remotely. This paper focuses on the challenges that developing telemedicine programs face, specifically discussing target population, user experience, and physician adoption. Various users of telemedicine share their experiences overcoming such challenges with the greater goal of this paper being to facilitate the growth of telemedicine programs.
ContributorsPalakodaty, Shivani Venkatasri (Author) / Liss, Julie (Thesis director) / Berisha, Visar (Committee member) / School of International Letters and Cultures (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Infectious diseases spread at a rapid rate, due to the increasing mobility of the human population. It is important to have a variety of containment and assessment strategies to prevent and limit their spread. In the on-going COVID-19 pandemic, telehealth services including daily health surveys are used to study the prevalence and severity of the disease. Daily health surveys can also help to study the progression and fluctuation of symptoms as recalling, tracking, and explaining symptoms to doctors can often be challenging for patients. Data aggregates collected from the daily health surveys can be used to identify the surge of a disease in a community. This thesis enhances a well-known boosting algorithm, XGBoost, to predict COVID-19 from the anonymized self-reported survey responses provided by Carnegie Mellon University (CMU) - Delphi research group in collaboration with Facebook. Despite the tremendous COVID-19 surge in the United States, this survey dataset is highly imbalanced with 84% negative COVID-19 cases and 16% positive cases. It is tedious to learn from an imbalanced dataset, especially when the dataset could also be noisy, as seen commonly in self-reported surveys. This thesis addresses these challenges by enhancing XGBoost with a tunable loss function, ?-loss, that interpolates between the exponential loss (? = 1/2), the log-loss (? = 1), and the 0-1 loss (? = ∞). Results show that tuning XGBoost with ?-loss can enhance performance over the standard XGBoost with log-loss (? = 1).
ContributorsVikash Babu, Gokulan (Author) / Sankar, Lalitha (Thesis advisor) / Berisha, Visar (Committee member) / Zhao, Ming (Committee member) / Trieu, Ni (Committee member) / Arizona State University (Publisher)
Created2021
Description
Generative models are deep neural network-based models trained to learn the underlying distribution of a dataset. Once trained, these models can be used to sample novel data points from this distribution. Their impressive capabilities have been manifested in various generative tasks, encompassing areas like image-to-image translation, style transfer, image editing, and more. One notable application of generative models is data augmentation, aimed at expanding and diversifying the training dataset to augment the performance of deep learning models for a downstream task. Generative models can be used to create new samples similar to the original data but with different variations and properties that are difficult to capture with traditional data augmentation techniques. However, the quality, diversity, and controllability of the shape and structure of the generated samples from these models are often directly proportional to the size and diversity of the training dataset. A more extensive and diverse training dataset allows the generative model to capture overall structures present in the data and generate more diverse and realistic-looking samples.
In this dissertation, I present innovative methods designed to enhance the robustness and controllability of generative models, drawing upon physics-based, probabilistic, and geometric techniques. These methods help improve the generalization and controllability of the generative model without necessarily relying on large training datasets. I enhance the robustness of generative models by integrating classical geometric moments for shape awareness and minimizing trainable parameters. Additionally, I employ non-parametric priors for the generative model's latent space through basic probability and optimization methods to improve the fidelity of interpolated images. I adopt a hybrid approach to address domain-specific challenges with limited data and controllability, combining physics-based rendering with generative models for more realistic results.
These approaches are particularly relevant in industrial settings, where the training datasets are small and class imbalance is common. Through extensive experiments on various datasets, I demonstrate the effectiveness of the proposed methods over conventional approaches.
ContributorsSingh, Rajhans (Author) / Turaga, Pavan (Thesis advisor) / Jayasuriya, Suren (Committee member) / Berisha, Visar (Committee member) / Fazli, Pooyan (Committee member) / Arizona State University (Publisher)
Created2023
Description
This dissertation centers on the development of Bayesian methods for learning differ- ent types of variation in switching nonlinear gene regulatory networks (GRNs). A new nonlinear and dynamic multivariate GRN model is introduced to account for different sources of variability in GRNs. The new model is aimed at more precisely capturing the complexity of GRN interactions through the introduction of time-varying kinetic order parameters, while allowing for variability in multiple model parameters. This model is used as the drift function in the development of several stochastic GRN mod- els based on Langevin dynamics. Six models are introduced which capture intrinsic and extrinsic noise in GRNs, thereby providing a full characterization of a stochastic regulatory system. A Bayesian hierarchical approach is developed for learning the Langevin model which best describes the noise dynamics at each time step. The trajectory of the state, which are the gene expression values, as well as the indicator corresponding to the correct noise model are estimated via sequential Monte Carlo (SMC) with a high degree of accuracy. To address the problem of time-varying regulatory interactions, a Bayesian hierarchical model is introduced for learning variation in switching GRN architectures with unknown measurement noise covariance. The trajectory of the state and the indicator corresponding to the network configuration at each time point are estimated using SMC. This work is extended to a fully Bayesian hierarchical model to account for uncertainty in the process noise covariance associated with each network architecture. An SMC algorithm with local Gibbs sampling is developed to estimate the trajectory of the state and the indicator correspond- ing to the network configuration at each time point with a high degree of accuracy. The results demonstrate the efficacy of Bayesian methods for learning information in switching nonlinear GRNs.
ContributorsVélez-Cruz, Nayely (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Moraffah, Bahman (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2023
Description
Linear-regression estimators have become widely accepted as a reliable statistical tool in predicting outcomes. Because linear regression is a long-established procedure, the properties of linear-regression estimators are well understood and can be trained very quickly. Many estimators exist for modeling linear relationships, each having ideal conditions for optimal performance. The differences stem from the introduction of a bias into the parameter estimation through the use of various regularization strategies. One of the more popular ones is ridge regression which uses ℓ2-penalization of the parameter vector. In this work, the proposed graph regularized linear estimator is pitted against the popular ridge regression when the parameter vector is known to be dense. When additional knowledge that parameters are smooth with respect to a graph is available, it can be used to improve the parameter estimates. To achieve this goal an additional smoothing penalty is introduced into the traditional loss function of ridge regression. The mean squared error(m.s.e) is used as a performance metric and the analysis is presented for fixed design matrices having a unit covariance matrix. The specific problem setup enables us to study the theoretical conditions where the graph regularized estimator out-performs the ridge estimator. The eigenvectors of the laplacian matrix indicating the graph of connections between the various dimensions of the parameter vector form an integral part of the analysis. Experiments have been conducted on simulated data to compare the performance of the two estimators for laplacian matrices of several types of graphs – complete, star, line and 4-regular. The experimental results indicate that the theory can possibly be extended to more general settings taking smoothness, a concept defined in this work, into consideration.
ContributorsSajja, Akarshan (Author) / Dasarathy, Gautam (Thesis advisor) / Berisha, Visar (Committee member) / Yang, Yingzhen (Committee member) / Arizona State University (Publisher)
Created2022