Matching Items (146)
Description
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 fashion industry dubs couture as high fashion, yet couture never reaches the finish line when it comes to comfort. Most of the brand name high heels on the market are too painful to wear for long periods of time. For this project, I have developed 3D printed high heels with detachable insoles that will relieve tired feet based on the principle of reflexology. The product integrates traditional flexible insoles with Arduino computing and the result is a functional surface that can ease the pain of the wearer. This paper introduces the product and with it, under-explored opportunities to customize your own high heels at home. Essentially, each consumer will have the ability to personalize and switch out their style without sacrificing comfort. Soon, a consumer will be a designer.
ContributorsNguyen, Nhi N. (Author) / Ingalls, Todd (Thesis director) / Gigantino, Josh (Committee member) / Barrett, The Honors College (Contributor) / Herberger Institute for Design and the Arts (Contributor) / School of Arts, Media and Engineering (Contributor)
Created2015-05
Description
Modern technologies have allowed for new ways to tell stories. Specifically, interactive film is a medium that has existed for quite some time, but has never really been a widely used or accepted means of conveying narratives in an engaging and effective way. After reviewing some of the most popular interactive films to date, I sought to create an interactive film that takes some of the most effective tools implemented by these works of art. I ultimately created If: an Interactive Film with the knowledge I gained. If follows a date between a couple and gives a single audience member the ability to choose the way the narrative progresses at key moments in the narrative. Cycling ‘74’s Max object-oriented programming environment facilitates this interactive film through an audio-driven video feedback system.
ContributorsConte, Salvatore Anthony (Author) / Sha, Xin Wei (Thesis director) / Connell, Ellery (Committee member) / Barrett, The Honors College (Contributor) / School of Arts, Media and Engineering (Contributor)
Created2015-05
Description
2 and a half years ago a group of ASU men heard the startling statistic that 1 in 4 women will experience completed or attempted sexual assault by the time they graduate college. This fact along with their experience of seeing how women are treated at ASU prompted them to create a culture where men respect women. Shortly after, a group of women noticed the impact these men were having on the campus and formed the club WoW Factor to come alongside Man Up. In 2013 both clubs came together to form The Respect Movement to combat the issue of sexual assault by building a culture of respect between men and women. The issue of sexual assault is not unique to just ASU. This is an epidemic on campuses all across the nation affecting thousands of lives in a very destructive way. I truly believe that the issue of sexual assault is this generation's civil rights issue, but I am excited because the solution has started right here at ASU. The Respect Movement is not trying to address every symptom of the culture of disrespect. We boiled down the culture of disrespect to arguably the most tangible and visible symptom which is the epidemic of sexual assault on our campus. Our response is to create a safe place, which is a culture of respect on how men and women relate, and it is based on timeless transcendent truths and core values. The problem of sexual assault exists because there is a culture of disrespect; it is between how men and women relate, and that's fed by what men believe about themselves and women, and what women believe about themselves and men. Very few cultural messages are saying that a man is someone who takes responsibility, rejects passivity, leads courageously, and initiates respect - which we define respect as putting others needs above our own. If we can get men believing different about who they are and what they value, their attitudes will change, followed by behavior - meanwhile they have people walking this out next to them modeling the right behavior ultimately experiencing real character change. It is also counter cultural to say that a woman is someone who takes responsibility, rejects devaluing messages, declares the worth of others, and influences through respect. If men and women are both going after those things, they will build healthy relationships and the issue of sexual assault will start to chip away. It has to be both going towards core values that challenge and change character. Because if positive character change happens we will see positive culture change. Our main goal is to build a culture of respect and end sexual assault. But you can't build a culture without leaders. Yes, this movement is seeking relationship with every student, but in order to change the culture and prevent crisis from happening, we have to not only build a hospital at the bottom, but a fence at the top. How we do that is by investing our mentorship into what we have identified as the leaders on both the High School and College campuses. That is, athletes on the high school campuses and Greek life on college campuses. We seek to train leaders to become relational mentors through life on life mentorship and modeling of the right behavior based on core values.
ContributorsMaenner, Justin Alan (Author) / Koretz, Lora (Thesis director) / Smith, Sr. (Committee member) / Novicky, Colton (Committee member) / Barrett, The Honors College (Contributor) / School of Arts, Media and Engineering (Contributor)
Created2015-05
Description
This thesis is a technical project management overview of development of software for a student startup venture named TableTop. There were many changes made throughout the process in order to address new conditions as to keep the vision of the project on the most direct and reasonable path. Development followed the agile product development model in order to adapt as the concept pivoted or as hurdles presented themselves. The goal at the end of the project was to have a well-defined business model based off of the lean startup business model canvas. The business model would shape development of the software that needed to be functional and ready for testing by the completion of the thesis. Functional was defined as having completed the user and merchant side of the application including communication between both. Ultimately, a user needed to be able to complete a transaction that a merchant had submitted on the software.
ContributorsMaloney, Ryan Daniel (Author) / Mayron, Liam (Thesis director) / Peck, Sidnee (Committee member) / Barrett, The Honors College (Contributor) / School of Arts, Media and Engineering (Contributor)
Created2015-05