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Functional magnetic resonance imaging (fMRI) has been widely used to measure the retinotopic organization of early visual cortex in the human brain. Previous studies have identified multiple visual field maps (VFMs) based on statistical analysis of fMRI signals, but the resulting geometry has not been fully characterized with mathematical models. This thesis explores using concepts from computational conformal geometry to create a custom software framework for examining and generating quantitative mathematical models for characterizing the geometry of early visual areas in the human brain. The software framework includes a graphical user interface built on top of a selected core conformal flattening algorithm and various software tools compiled specifically for processing and examining retinotopic data. Three conformal flattening algorithms were implemented and evaluated for speed and how well they preserve the conformal metric. All three algorithms performed well in preserving the conformal metric but the speed and stability of the algorithms varied. The software framework performed correctly on actual retinotopic data collected using the standard travelling-wave experiment. Preliminary analysis of the Beltrami coefficient for the early data set shows that selected regions of V1 that contain reasonably smooth eccentricity and polar angle gradients do show significant local conformality, warranting further investigation of this approach for analysis of early and higher visual cortex.
ContributorsTa, Duyan (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Wonka, Peter (Committee member) / Arizona State University (Publisher)
Created2013
Description
In blindness research, the corpus callosum (CC) is the most frequently studied sub-cortical structure, due to its important involvement in visual processing. While most callosal analyses from brain structural magnetic resonance images (MRI) are limited to the 2D mid-sagittal slice, we propose a novel framework to capture a complete set of 3D morphological differences in the corpus callosum between two groups of subjects. The CCs are segmented from whole brain T1-weighted MRI and modeled as 3D tetrahedral meshes. The callosal surface is divided into superior and inferior patches on which we compute a volumetric harmonic field by solving the Laplace's equation with Dirichlet boundary conditions. We adopt a refined tetrahedral mesh to compute the Laplacian operator, so our computation can achieve sub-voxel accuracy. Thickness is estimated by tracing the streamlines in the harmonic field. We combine areal changes found using surface tensor-based morphometry and thickness information into a vector at each vertex to be used as a metric for the statistical analysis. Group differences are assessed on this combined measure through Hotelling's T2 test. The method is applied to statistically compare three groups consisting of: congenitally blind (CB), late blind (LB; onset > 8 years old) and sighted (SC) subjects. Our results reveal significant differences in several regions of the CC between both blind groups and the sighted groups; and to a lesser extent between the LB and CB groups. These results demonstrate the crucial role of visual deprivation during the developmental period in reshaping the structural architecture of the CC.
ContributorsXu, Liang (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
Description
Over 2 billion people are using online social network services, such as Facebook, Twitter, Google+, LinkedIn, and Pinterest. Users update their status, post their photos, share their information, and chat with others in these social network sites every day; however, not everyone shares the same amount of information. This thesis explores methods of linking publicly available data sources as a means of extrapolating missing information of Facebook. An application named "Visual Friends Income Map" has been created on Facebook to collect social network data and explore geodemographic properties to link publicly available data, such as the US census data. Multiple predictors are implemented to link data sets and extrapolate missing information from Facebook with accurate predictions. The location based predictor matches Facebook users' locations with census data at the city level for income and demographic predictions. Age and relationship based predictors are created to improve the accuracy of the proposed location based predictor utilizing social network link information. In the case where a user does not share any location information on their Facebook profile, a kernel density estimation location predictor is created. This predictor utilizes publicly available telephone record information of all people with the same surname of this user in the US to create a likelihood distribution of the user's location. This is combined with the user's IP level information in order to narrow the probability estimation down to a local regional constraint.
ContributorsMao, Jingxian (Author) / Maciejewski, Ross (Thesis advisor) / Farin, Gerald (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2012
Description
This document presents a new implementation of the Smoothed Particles Hydrodynamics algorithm using DirectX 11 and DirectCompute. The main goal of this document is to present to the reader an alternative solution to the largely studied and researched problem of fluid simulation. Most other solutions have been implemented using the NVIDIA CUDA framework; however, the proposed solution in this document uses the Microsoft general-purpose computing on graphics processing units API. The implementation allows for the simulation of a large number of particles in a real-time scenario. The solution presented here uses the Smoothed Particles Hydrodynamics algorithm to calculate the forces within the fluid; this algorithm provides a Lagrangian approach for discretizes the Navier-Stockes equations into a set of particles. Our solution uses the DirectCompute compute shaders to evaluate each particle using the multithreading and multi-core capabilities of the GPU increasing the overall performance. The solution then describes a method for extracting the fluid surface using the Marching Cubes method and the programmable interfaces exposed by the DirectX pipeline. Particularly, this document presents a method for using the Geometry Shader Stage to generate the triangle mesh as defined by the Marching Cubes method. The implementation results show the ability to simulate over 64K particles at a rate of 900 and 400 frames per second, not including the surface reconstruction steps and including the Marching Cubes steps respectively.
ContributorsFigueroa, Gustavo (Author) / Farin, Gerald (Thesis advisor) / Maciejewski, Ross (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2012
Description
Alzheimer's Disease (AD) is the most common form of dementia observed in elderly patients and has significant social-economic impact. There are many initiatives which aim to capture leading causes of AD. Several genetic, imaging, and biochemical markers are being explored to monitor progression of AD and explore treatment and detection options. The primary focus of this thesis is to identify key biomarkers to understand the pathogenesis and prognosis of Alzheimer's Disease. Feature selection is the process of finding a subset of relevant features to develop efficient and robust learning models. It is an active research topic in diverse areas such as computer vision, bioinformatics, information retrieval, chemical informatics, and computational finance. In this work, state of the art feature selection algorithms, such as Student's t-test, Relief-F, Information Gain, Gini Index, Chi-Square, Fisher Kernel Score, Kruskal-Wallis, Minimum Redundancy Maximum Relevance, and Sparse Logistic regression with Stability Selection have been extensively exploited to identify informative features for AD using data from Alzheimer's Disease Neuroimaging Initiative (ADNI). An integrative approach which uses blood plasma protein, Magnetic Resonance Imaging, and psychometric assessment scores biomarkers has been explored. This work also analyzes the techniques to handle unbalanced data and evaluate the efficacy of sampling techniques. Performance of feature selection algorithm is evaluated using the relevance of derived features and the predictive power of the algorithm using Random Forest and Support Vector Machine classifiers. Performance metrics such as Accuracy, Sensitivity and Specificity, and area under the Receiver Operating Characteristic curve (AUC) have been used for evaluation. The feature selection algorithms best suited to analyze AD proteomics data have been proposed. The key biomarkers distinguishing healthy and AD patients, Mild Cognitive Impairment (MCI) converters and non-converters, and healthy and MCI patients have been identified.
ContributorsDubey, Rashmi (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Wu, Tong (Committee member) / Arizona State University (Publisher)
Created2012
Description
The development of stab-resistant Kevlar armor has been an ongoing field of research
since the late 1990s, with the ultimate goal of improving the multi-threat capabilities of
traditional soft-body armor while significantly improving its protective efficiency - the amount
of layers of armor material required to defeat threats. To create a novel, superior materials
system to reinforce Kevlar armor for the Norica Capstone project, this thesis set out to
synthesize, recover, and characterize zinc oxide nanowire colloids.
The materials synthesized were successfully utilized in the wider Capstone effort to
dramatically enhance the protective abilities of Kevlar, while the data obtained on the 14
hydrothermal synthesis attempts and numerous challenges at recovery provided critical
information on the synthesis parameters involved in the reliable, scalable mass production of the
nanomaterial additive. Additionally, recovery was unconventionally facilitated in the absence of
a vacuum filtration apparatus with nanoscale filters by intentionally inducing electrostatic
agglomeration of the nanowires during standard gravity filtration. The subsequent application of
these nanowires constituted a pioneering use in the production of nanowire-reinforced
STF-based Kevlar coatings, and support the future development and, ultimately, the
commercialization of lighter and more-protective soft armor systems.
since the late 1990s, with the ultimate goal of improving the multi-threat capabilities of
traditional soft-body armor while significantly improving its protective efficiency - the amount
of layers of armor material required to defeat threats. To create a novel, superior materials
system to reinforce Kevlar armor for the Norica Capstone project, this thesis set out to
synthesize, recover, and characterize zinc oxide nanowire colloids.
The materials synthesized were successfully utilized in the wider Capstone effort to
dramatically enhance the protective abilities of Kevlar, while the data obtained on the 14
hydrothermal synthesis attempts and numerous challenges at recovery provided critical
information on the synthesis parameters involved in the reliable, scalable mass production of the
nanomaterial additive. Additionally, recovery was unconventionally facilitated in the absence of
a vacuum filtration apparatus with nanoscale filters by intentionally inducing electrostatic
agglomeration of the nanowires during standard gravity filtration. The subsequent application of
these nanowires constituted a pioneering use in the production of nanowire-reinforced
STF-based Kevlar coatings, and support the future development and, ultimately, the
commercialization of lighter and more-protective soft armor systems.
ContributorsDurso, Michael Nathan (Author) / Tongay, Sefaattin (Thesis director) / Zhuang, Houlong (Committee member) / Materials Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Graphene is a very strong two-dimensional material with a lot of potential applications in microelectromechanical systems (MEMS). In this research, graphene is being optimized for use in a 5 m x 5 m graphene resonator. To work properly, this graphene resonator must have a uniform strain across all manufactured devices. To reduce strain induced in graphene sheets grown for use in these resonators, evaporated platinum has been used in this investigation due to its relatively lower surface roughness compared to copper films. The final goal is to have the layer of ultrathin platinum (<=200 nm) deposited on the MEMS graphene resonator and used to grow graphene directly onto the devices to remove the manual transfer step due to its inscalability. After growth, graphene is coated with polymer and the platinum is then etched. This investigation concentrated on the transfer process of graphene onto Si/SiO2 substrate from the platinum films. It was determined that the ideal platinum etchant was aqua regia at a volumetric ratio of 6:3:1 (H2O:HCl:HNO3). This concentration was dilute enough to preserve the polymer and graphene layer, but strong enough to etch within a day. Type and thickness of polymer support layers were also investigated. PMMA at a thickness of 200 nm was ideal because it was easy to remove with acetone and strong enough to support the graphene during the etch process. A reference growth recipe was used in this investigation, but now that the transfer has been demonstrated, growth can be optimized for even thinner films.
ContributorsCayll, David Richard (Author) / Tongay, Sefaattin (Thesis director) / Lee, Hyunglae (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Solid-state lithium-ion batteries are a major area of research due to their increased safety characteristics over conventional liquid electrolyte batteries. Lithium lanthanum zirconate (LLZO) is a promising garnet-type ceramic for use as a solid-state electrolyte due to its high ionic conductivity. The material exists in two dierent phases, one that is cubic in structure and one that is tetragonal. One potential synthesis method that results in LLZO in the more useful, cubic phase, is electrospinning, where a mat of nanowires is spun and then calcined into LLZO. A phase containing lanthanum zirconate (LZO) and amorphous lithium occursas an intermediate during the calcination process. LZO has been shown to be a sintering aid for LLZO, allowing for lower sintering temperatures. Here it is shown the eects of internal LZO on the sintered pellets. This is done by varying the 700C calcination time to transform diering amounts of LZO and LLZO in electrospun nanowires, and then using the same sintering parameters for each sample. X-ray diraction was used to get structural and compositional analysis of both the calcined powders and sintered pellets. Pellets formed from wires calcined at 1 hour or longer contained only LLZO even if the calcined powder had only undergone the rst phase transformation. The relative density of the pellet with no initial LLZO of 61.0% was higher than that of the pellet with no LZO, which had a relative density of 57.7%. This allows for the same, or slightly higher, quality material to be synthesized with a shorter amount of processing time.
ContributorsLondon, Nathan Harry (Author) / Chan, Candace (Thesis director) / Tongay, Sefaattin (Committee member) / Department of Physics (Contributor) / Materials Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Open source image analytics and data mining software are widely available but can be overly-complicated and non-intuitive for medical physicians and researchers to use. The ASU-Mayo Clinic Imaging Informatics Lab has developed an in-house pipeline to process medical images, extract imaging features, and develop multi-parametric models to assist disease staging and diagnosis. The tools have been extensively used in a number of medical studies including brain tumor, breast cancer, liver cancer, Alzheimer's disease, and migraine. Recognizing the need from users in the medical field for a simplified interface and streamlined functionalities, this project aims to democratize this pipeline so that it is more readily available to health practitioners and third party developers.
ContributorsBaer, Lisa Zhou (Author) / Wu, Teresa (Thesis director) / Wang, Yalin (Committee member) / Computer Science and Engineering Program (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12