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.
Displaying 1 - 10 of 24
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- Genre: Masters Thesis
Description
A potential new class of cancer chemotherapeutic agents has been synthesized by varying the 2 position of a benzimidazole based extended amidine. Compounds 6-amino-2-chloromethyl-4-imino-1-(2-methansulfonoxyethyl)-5-methyl-1H-benzimidazole-7-one (1A) and 6-amino-2-hydroxypropyl-4-imino-1-(2-methansulfonoxyethyl)-5-methyl-1H-benzimidazole-7-one (1B) were assayed at the National Cancer Institute's (NCI) Developmental Therapeutic Program (DTP) and found to be cytotoxic at sub-micromolar concentrations, and have shown between a 100 and a 1000-fold increase in specificity towards lung, colon, CNS, and melanoma cell lines. These ATP mimics have been found to correlate with sequestosome 1 (SQSTM1), a protein implicated in drug resistance and cell survival in various cancer cell lines. Using the DTP COMPARE algorithm, compounds 1A and 1B were shown to correlate to each other at 77%, but failed to correlate with other benzimidazole based extended amidines previously synthesized in this laboratory suggesting they operate through a different biological mechanism.
ContributorsDarzi, Evan (Author) / Skibo, Edward (Thesis advisor) / Gould, Ian (Committee member) / Francisco, Wilson (Committee member) / Arizona State University (Publisher)
Created2011
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
Alkali treated aluminosilicate (geopolymer) was functionalized by surfactant to increase the hydrophobicity for making Pickering emulsion for the first part of this work. In the first part of this study, alkali treated metakaolin was functionalized with cetyltrimethylammonium bromide ((C16H33)N(CH3)3Br, CTAB). The electrostatic interaction between this quaternary ammonium and the surface of the aluminosilicate which has negative charge has taken place. The particles then were used to prepare Pickering emulsion. The resulting stable dispersions, obtained very fast at very simple conditions with low ratio of aluminosilicate to liquid phase. In the second part, the interaction between geopolymer and glycerol was studied to see the covalent grafting of the geopolymer for making geopolymer composite. The composite material would be the basis material to be used as support catalyst, thin coating reagent and flame retardant material and so on, Variety of techniques, Thermogravimetric (TGA), Particle-induced X-ray emission (PIXE), FTIR, Solid state NMR, Powder X-ray diffraction (PXRD), BET surface area, Elemental analysis (CHN), TEM, SEM and Optical microscopy were used to characterize the functionalized geopolymer.
ContributorsMesgar, Milad (Author) / Seo, Dong-Kuyn (Thesis advisor) / Petuskey, William (Committee member) / Gould, Ian (Committee member) / Arizona State University (Publisher)
Created2012
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
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
Description
Parkinson’s Disease is one of the most complicated and abundantneurodegenerative diseases in the world. Previous analysis of Parkinson’s disease has
identified both speech and gait deficits throughout progression of the disease. There has
been minimal research looking into the correlation between both the speech and gait
deficits in those diagnosed with Parkinson’s. There is high indication that there is a
correlation between the two given the similar pathology and origins of both deficits. This
exploratory study aims to establish correlation between both the gait and speech deficits
in those diagnosed with Parkinson’s disease. Using previously identified motor and
speech measurements and tasks, I conducted a correlational study of individuals with
Parkinson’s disease at baseline. There were correlations between multiple speech and gait
variability outcomes. The expected correlations ranged from average harmonics-to-noise
ratio values against anticipatory postural adjustments-lateral peak distance to average
shimmer values against anticipatory postural adjustments-lateral peak distance. There
were also unexpected outcomes that ranged from F2 variability against the average
number of steps in a turn to intensity variability against step duration variability. I also
analyzed the speech changes over 1 year as a secondary outcome of the study. Finally, I
found that averages and variabilities increased over 1 year regarding speech primary
outcomes. This study serves as a basis for further treatment that may be able to
simultaneously treat both speech and gait deficits in those diagnosed with Parkinson’s.
The exploratory study also indicates multiple targets for further investigation to better
understand cohesive and compensatory mechanisms.
ContributorsBelnavis, Alexander Salvador (Author) / Peterson, Daniel (Thesis advisor) / Daliri, Ayoub (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2022
Description
The poor spectral and temporal resolution of cochlear implants (CIs) limit their users’ music enjoyment. Remixing music by boosting vocals while attenuating spectrally complex instruments has been shown to benefit music enjoyment of postlingually deaf CI users. However, the effectiveness of music remixing in prelingually deaf CI users is still unknown. This study compared the music-remixing preferences of nine postlingually deaf, late-implanted CI users and seven prelingually deaf, early-implanted CI users, as well as their ratings of song familiarity and vocal pleasantness. Twelve songs were selected from the most streamed tracks on Spotify for testing. There were six remixed versions of each song: Original, Music-6 (6-dB attenuation of all instruments), Music-12 (12-dB attenuation of all instruments), Music-3-3-12 (3-dB attenuation of bass and drums and 12-dB attenuation of other instruments), Vocals-6 (6-dB attenuation of vocals), and Vocals-12 (12-dB attenuation of vocals). It was found that the prelingual group preferred the Music-6 and Original versions over the other versions, while the postlingual group preferred the Vocals-12 version over the Music-12 version. The prelingual group was more familiar with the songs than the postlingual group. However, the song familiarity rating did not significantly affect the patterns of preference ratings in each group. The prelingual group also had higher vocal pleasantness ratings than the postlingual group. For the prelingual group, higher vocal pleasantness led to higher preference ratings for the Music-12 version. For the postlingual group, their overall preference for the Vocals-12 version was driven by their preference ratings for songs with very unpleasant vocals. These results suggest that the patient factor of auditory experience and stimulus factor of vocal pleasantness may affect the music-remixing preferences of CI users. As such, the music-remixing strategy needs to be customized for individual patients and songs.
ContributorsVecellio, Amanda Paige (Author) / Luo, Xin (Thesis advisor) / Ringenbach, Shannon (Committee member) / Berisha, Visar (Committee member) / Zhou, Yi (Committee member) / Arizona State University (Publisher)
Created2024
Description
This study aimed to evaluate the efficacy of Apple AirPods pro (2nd generation) Live Listen feature in enhancing word recognition and memory retention among individuals with varying degrees of hearing loss, as determined by their Signal-to-Noise Ratio (SNR) loss. Utilizing a single-group experimental design, the research measured participants' performance on word recognition and memory retention tasks with and without the Live Listen feature. Statistical analysis, including paired t-tests and linear regression, revealed significant improvements in word recognition (from 81.8% to 94.4%) and memory retention (from 43.8% to 59.4%) scores when the Live Listen feature was activated. Moreover, a positive correlation between SNR loss and recognition score improvements suggested a greater benefit for those with higher levels of hearing loss. However, the relationship with memory retention improvements was less pronounced. These findings underscore the potential of the Live Listen feature as an effective assistive listening device, highlighting its importance in enhancing auditory experiences for individuals with hearing impairments and encouraging further research into personalized auditory assistance technologies in noisy healthcare environments.
ContributorsForoogozar, Mehdi (Author) / Liss, Julie (Thesis advisor) / Berisha, Visar (Committee member) / Luo, Xin (Committee member) / Arizona State University (Publisher)
Created2024
Description
This dissertation is focused on developing an algorithm to provide current state estimation and future state predictions for biomechanical human walking features. The goal is to develop a system which is capable of evaluating the current action a subject is taking while walking and then use this to predict the future states of biomechanical features.
This work focuses on the exploration and analysis of Interaction Primitives (Amor er al, 2014) and their relevance to biomechanical prediction for human walking. Built on the framework of Probabilistic Movement Primitives, Interaction Primitives utilize an EKF SLAM algorithm to localize and map a distribution over the weights of a set of basis functions. The prediction properties of Bayesian Interaction Primitives were utilized to predict real-time foot forces from a 9 degrees of freedom IMUs mounted to a subjects tibias. This method shows that real-time human biomechanical features can be predicted and have a promising link to real-time controls applications.
This work focuses on the exploration and analysis of Interaction Primitives (Amor er al, 2014) and their relevance to biomechanical prediction for human walking. Built on the framework of Probabilistic Movement Primitives, Interaction Primitives utilize an EKF SLAM algorithm to localize and map a distribution over the weights of a set of basis functions. The prediction properties of Bayesian Interaction Primitives were utilized to predict real-time foot forces from a 9 degrees of freedom IMUs mounted to a subjects tibias. This method shows that real-time human biomechanical features can be predicted and have a promising link to real-time controls applications.
ContributorsClark, Geoffrey Mitchell (Author) / Ben Amor, Heni (Thesis advisor) / Si, Jennie (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2018
Description
Deep neural networks (DNN) have shown tremendous success in various cognitive tasks, such as image classification, speech recognition, etc. However, their usage on resource-constrained edge devices has been limited due to high computation and large memory requirement.
To overcome these challenges, recent works have extensively investigated model compression techniques such as element-wise sparsity, structured sparsity and quantization. While most of these works have applied these compression techniques in isolation, there have been very few studies on application of quantization and structured sparsity together on a DNN model.
This thesis co-optimizes structured sparsity and quantization constraints on DNN models during training. Specifically, it obtains optimal setting of 2-bit weight and 2-bit activation coupled with 4X structured compression by performing combined exploration of quantization and structured compression settings. The optimal DNN model achieves 50X weight memory reduction compared to floating-point uncompressed DNN. This memory saving is significant since applying only structured sparsity constraints achieves 2X memory savings and only quantization constraints achieves 16X memory savings. The algorithm has been validated on both high and low capacity DNNs and on wide-sparse and deep-sparse DNN models. Experiments demonstrated that deep-sparse DNN outperforms shallow-dense DNN with varying level of memory savings depending on DNN precision and sparsity levels. This work further proposed a Pareto-optimal approach to systematically extract optimal DNN models from a huge set of sparse and dense DNN models. The resulting 11 optimal designs were further evaluated by considering overall DNN memory which includes activation memory and weight memory. It was found that there is only a small change in the memory footprint of the optimal designs corresponding to the low sparsity DNNs. However, activation memory cannot be ignored for high sparsity DNNs.
To overcome these challenges, recent works have extensively investigated model compression techniques such as element-wise sparsity, structured sparsity and quantization. While most of these works have applied these compression techniques in isolation, there have been very few studies on application of quantization and structured sparsity together on a DNN model.
This thesis co-optimizes structured sparsity and quantization constraints on DNN models during training. Specifically, it obtains optimal setting of 2-bit weight and 2-bit activation coupled with 4X structured compression by performing combined exploration of quantization and structured compression settings. The optimal DNN model achieves 50X weight memory reduction compared to floating-point uncompressed DNN. This memory saving is significant since applying only structured sparsity constraints achieves 2X memory savings and only quantization constraints achieves 16X memory savings. The algorithm has been validated on both high and low capacity DNNs and on wide-sparse and deep-sparse DNN models. Experiments demonstrated that deep-sparse DNN outperforms shallow-dense DNN with varying level of memory savings depending on DNN precision and sparsity levels. This work further proposed a Pareto-optimal approach to systematically extract optimal DNN models from a huge set of sparse and dense DNN models. The resulting 11 optimal designs were further evaluated by considering overall DNN memory which includes activation memory and weight memory. It was found that there is only a small change in the memory footprint of the optimal designs corresponding to the low sparsity DNNs. However, activation memory cannot be ignored for high sparsity DNNs.
ContributorsSrivastava, Gaurav (Author) / Seo, Jae-Sun (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2018