Matching Items (33)
Filtering by
- Genre: Masters Thesis
Description
A wireless hybrid device for detecting volatile organic compounds (VOCs) has been developed. The device combines a highly selective and sensitive tuning-fork based detector with a pre-concentrator and a separation column. The selectivity and sensitivity of the tuning-fork based detector is optimized for discrimination and quantification of benzene, toluene, ethylbenzene, and xylenes (BTEX) via a homemade molecular imprinted polymer, and a specific detection and control circuit. The device is a wireless, portable, battery-powered, and cell-phone operated device. The device has been calibrated and validated in the laboratory and using selected ion flow tube mass spectrometry (SFIT-MS). The capability and robustness are also demonstrated in some field tests. It provides rapid and reliable detection of BTEX in real samples, including challenging high concentrations of interferents, and it is suitable for occupational, environmental health and epidemiological applications.
ContributorsChen, Zheng (Author) / Tao, Nongjian (Thesis advisor) / Chae, Junseok (Committee member) / Forzani, Erica (Committee member) / Arizona State University (Publisher)
Created2011
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
An imaging measurement technique is developed using surface plasmon resonance. Plasmonic-based electrochemical current imaging (P-ECi) method has been developed to image the local electrochemical current optically, it allows us to measure the current density quickly and non-invasively [1, 2]. In this thesis, we solve the problems when we extand the P-ECi technique to the field of thin film system. The P-ECi signal in thin film structure was found to be directly proportional to the electrochemical current. The upper-limit of thin film thickness to use the proportional relationship between P-ECi signal and EC current was discussed by experiment and simulation. Furthermore, a new algorithm which can calculate the current density from P-ECi signal without any thickness limitation is developed and tested. Besides, surface plasmon resonance is useful phenomenon which can be used to detect the changes in the refractive index near the gold sensing surface. With the assistance of pH indicator, by applied EC potential on the gold film as the working electrode, the detection of H2 evolution reaction can be enhanced. This measurement technique is useful in analyzing local EC information and H2 evolution. References [1] S. Wang, et al., "Electrochemical Surface Plasmon Resonance: Basic Formalism and Experimental Validation," Analytical Chemistry, vol. 82, pp. 935-941, 2010/02/01 2010. [2] X. Shan, et al., "Imaging Local Electrochemical Current via Surface Plasmon Resonance," Science, vol. 327, pp. 1363-1366, March 12, 2010 2010.
ContributorsZhao, Yanjun (Author) / Tao, Nongjian (Thesis advisor) / Wang, Shaopeng (Committee member) / Tsow, Tsing (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
This work demonstrates the integration of a wearable particulate detector and a wireless chemical sensor into a single portable system. The detection philosophy of the chemical sensor is based on highly selective and sensitive microfabricated quartz tuning fork arrays and the particle detector detects the particulate level in real-time using a nephelometric (light scattering) approach. The device integration is realized by carefully evaluating the needs of flow rate, power and data collection. Validation test has been carried out in both laboratory and in field trials such as parking structures and highway exits with high and low traffic emissions. The integrated single portable detection system is capable of reducing the burden for a child to carry multiple devices, simplifying the task of researchers to synchronize and analyze data from different sensors, and minimizing the overall weight, size, and cost of the sensor. It also has a cell phone for data analysis, storage, and transmission as a user-friendly interface. As the chemical and particulate levels present important exposure risks that are of high interests to epidemiologists, the integrated device will provide an easier, wearable and cost effective way to monitor it.
ContributorsGao, Tianle (Author) / Tao, Nongjian (Thesis advisor) / Chae, Junseok (Committee member) / Tsow, Tsing (Committee member) / Arizona State University (Publisher)
Created2012
Description
Advances in miniaturized sensors and wireless technologies have enabled mobile health systems for efficient healthcare. A mobile health system assists the physician to monitor the patient's progress remotely and provide quick feedbacks and suggestions in case of emergencies, which reduces the cost of healthcare without the expense of hospitalization. This work involves development of an innovative mobile health system with adaptive biofeedback mechanism and demonstrates the importance of biofeedback in accurate measurements of physiological parameters to facilitate the diagnosis in mobile health systems. Resting Metabolic Rate (RMR) assessment, a key aspect in the treatment of diet related health problems is considered as a model to demonstrate the importance of adaptive biofeedback in mobile health. A breathing biofeedback mechanism has been implemented with digital signal processing techniques for real-time visual and musical guidance to accurately measure the RMR. The effects of adaptive biofeedback with musical and visual guidance were assessed on 22 healthy subjects (12 men, 10 women). Eight RMR measurements were taken for each subject on different days under same conditions. It was observed the subjects unconsciously followed breathing biofeedback, yielding consistent and accurate measurements for the diagnosis. The coefficient of variation of the measured metabolic parameters decreased significantly (p < 0.05) for 20 subjects out of 22 subjects.
ContributorsKrishnan, Ranganath (Author) / Tao, Nongjian (Thesis advisor) / Forzani, Erica (Committee member) / Yu, Hongyu (Committee member) / Arizona State University (Publisher)
Created2012
Description
Windows based mobile application for m-health and environmental monitoring sensor devices were developed and tested. With the number of smartphone users exponentially increasing, the applications developed for m-health and environmental monitoring devices are easy to reach the general public, if the applications are simple, user-friendly and personalized. The sensing device uses Bluetooth to communicate with the smartphone, providing mobility to the user. Since the device is small and hand-held, the user can put his smartphone in his pocket, connected to the device in his hand and can move anywhere with it. The data processing performed in the applications is verified against standard off the shelf software, the results of the tests are discussed in this document. The user-interface is very simple and doesn't require many inputs from the user other than during the initial setting when they have to enter their personal information for the records. The m-health application can be used by doctors as well as by patients. The response of the application is very quick and hence the patients need not wait for a long time to see the results. The environmental monitoring device has a real-time plot displayed on the screen of the smartphone showing concentrations of total volatile organic compounds and airborne particle count in the environment at the location of the device. The programming was done with Microsoft Visual Studio and was written on VB.NET platform. On the applications, the smartphone receives data as raw binary bytes from the device via Bluetooth and this data is processed to obtain the final result. The final result is the concentration of Nitric Oxide in ppb in the Asthma Analyzer device. In the environmental monitoring device, the final result is the concentration of total Volatile Organic Compounds and the count of airborne Particles.
ContributorsGanesan, Srisivapriya (Author) / Tao, Nongjian (Thesis advisor) / Zhang, Yanchao (Committee member) / Tsow, Tsing (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