Matching Items (159)
Description
In this thesis I introduce a new direction to computing using nonlinear chaotic dynamics. The main idea is rich dynamics of a chaotic system enables us to (1) build better computers that have a flexible instruction set, and (2) carry out computation that conventional computers are not good at it. Here I start from the theory, explaining how one can build a computing logic block using a chaotic system, and then I introduce a new theoretical analysis for chaos computing. Specifically, I demonstrate how unstable periodic orbits and a model based on them explains and predicts how and how well a chaotic system can do computation. Furthermore, since unstable periodic orbits and their stability measures in terms of eigenvalues are extractable from experimental times series, I develop a time series technique for modeling and predicting chaos computing from a given time series of a chaotic system. After building a theoretical framework for chaos computing I proceed to architecture of these chaos-computing blocks to build a sophisticated computing system out of them. I describe how one can arrange and organize these chaos-based blocks to build a computer. I propose a brand new computer architecture using chaos computing, which shifts the limits of conventional computers by introducing flexible instruction set. Our new chaos based computer has a flexible instruction set, meaning that the user can load its desired instruction set to the computer to reconfigure the computer to be an implementation for the desired instruction set. Apart from direct application of chaos theory in generic computation, the application of chaos theory to speech processing is explained and a novel application for chaos theory in speech coding and synthesizing is introduced. More specifically it is demonstrated how a chaotic system can model the natural turbulent flow of the air in the human speech production system and how chaotic orbits can be used to excite a vocal tract model. Also as another approach to build computing system based on nonlinear system, the idea of Logical Stochastic Resonance is studied and adapted to an autoregulatory gene network in the bacteriophage λ.
ContributorsKia, Behnam (Author) / Ditto, William (Thesis advisor) / Huang, Liang (Committee member) / Lai, Ying-Cheng (Committee member) / Helms Tillery, Stephen (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
Detection of extruded features like rooftops and trees in aerial images automatically is a very active area of research. Elevated features identified from aerial imagery have potential applications in urban planning, identifying cover in military training or flight training. Detection of such features using commonly available geospatial data like orthographic aerial imagery is very challenging because rooftop and tree textures are often camouflaged by similar looking features like roads, ground and grass. So, additonal data such as LIDAR, multispectral imagery and multiple viewpoints are exploited for more accurate detection. However, such data is often not available, or may be improperly registered or inacurate. In this thesis, we discuss a novel framework that only uses orthographic images for detection and modeling of rooftops. A segmentation scheme that initializes by assigning either foreground (rooftop) or background labels to certain pixels in the image based on shadows is proposed. Then it employs grabcut to assign one of those two labels to the rest of the pixels based on initial labeling. Parametric model fitting is performed on the segmented results in order to create a 3D scene and to facilitate roof-shape and height estimation. The framework can also benefit from additional geospatial data such as streetmaps and LIDAR, if available.
ContributorsKhanna, Kunal (Author) / Femiani, John (Thesis advisor) / Wonka, Peter (Thesis advisor) / Razdan, Anshuman (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2013
Description
The Cape Floral Region (CFR) in southwestern South Africa is one of the most diverse in the world, with >9,000 plant species, 70% of which are endemic, in an area of only ~90,000 km2. Many have suggested that the CFR's heterogeneous environment, with respect to landscape gradients, vegetation, rainfall, elevation, and soil fertility, is responsible for the origin and maintenance of this biodiversity. While studies have struggled to link species diversity with these features, no study has attempted to associate patterns of gene flow with environmental data to determine how CFR biodiversity evolves on different scales. Here, a molecular population genetic data is presented for a widespread CFR plant, Leucadendron salignum, across 51 locations with 5-kb of chloroplast (cpDNA) and 6-kb of unlinked nuclear (nuDNA) DNA sequences in a dataset of 305 individuals. In the cpDNA dataset, significant genetic structure was found to vary on temporal and spatial scales, separating Western and Eastern Capes - the latter of which appears to be recently derived from the former - with the highest diversity in the heart of the CFR in a central region. A second study applied a statistical model using vegetation and soil composition and found fine-scale genetic divergence is better explained by this landscape resistance model than a geographic distance model. Finally, a third analysis contrasted cpDNA and nuDNA datasets, and revealed very little geographic structure in the latter, suggesting that seed and pollen dispersal can have different evolutionary genetic histories of gene flow on even small CFR scales. These three studies together caution that different genomic markers need to be considered when modeling the geographic and temporal origin of CFR groups. From a greater perspective, the results here are consistent with the hypothesis that landscape heterogeneity is one driving influence in limiting gene flow across the CFR that can lead to species diversity on fine-scales. Nonetheless, while this pattern may be true of the widespread L. salignum, the extension of this approach is now warranted for other CFR species with varying ranges and dispersal mechanisms to determine how universal these patterns of landscape genetic diversity are.
ContributorsTassone, Erica (Author) / Verrelli, Brian C (Thesis advisor) / Dowling, Thomas (Committee member) / Cartwright, Reed (Committee member) / Rosenberg, Michael S. (Committee member) / Wojciechowski, Martin (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
With the rapid development of mobile sensing technologies like GPS, RFID, sensors in smartphones, etc., capturing position data in the form of trajectories has become easy. Moving object trajectory analysis is a growing area of interest these days owing to its applications in various domains such as marketing, security, traffic monitoring and management, etc. To better understand movement behaviors from the raw mobility data, this doctoral work provides analytic models for analyzing trajectory data. As a first contribution, a model is developed to detect changes in trajectories with time. If the taxis moving in a city are viewed as sensors that provide real time information of the traffic in the city, a change in these trajectories with time can reveal that the road network has changed. To detect changes, trajectories are modeled with a Hidden Markov Model (HMM). A modified training algorithm, for parameter estimation in HMM, called m-BaumWelch, is used to develop likelihood estimates under assumed changes and used to detect changes in trajectory data with time. Data from vehicles are used to test the method for change detection. Secondly, sequential pattern mining is used to develop a model to detect changes in frequent patterns occurring in trajectory data. The aim is to answer two questions: Are the frequent patterns still frequent in the new data? If they are frequent, has the time interval distribution in the pattern changed? Two different approaches are considered for change detection, frequency-based approach and distribution-based approach. The methods are illustrated with vehicle trajectory data. Finally, a model is developed for clustering and outlier detection in semantic trajectories. A challenge with clustering semantic trajectories is that both numeric and categorical attributes are present. Another problem to be addressed while clustering is that trajectories can be of different lengths and also have missing values. A tree-based ensemble is used to address these problems. The approach is extended to outlier detection in semantic trajectories.
ContributorsKondaveeti, Anirudh (Author) / Runger, George C. (Thesis advisor) / Mirchandani, Pitu (Committee member) / Pan, Rong (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2012
Description
This dissertation addresses the research challenge of developing efficient new methods for discovering useful patterns and knowledge in large volumes of electronically collected spatiotemporal activity data. I propose to analyze three types of such spatiotemporal activity data in a methodological framework that integrates spatial analysis, data mining, machine learning, and geovisualization techniques. Three different types of spatiotemporal activity data were collected through different data collection approaches: (1) crowd sourced geo-tagged digital photos, representing people's travel activity, were retrieved from the website Panoramio.com through information retrieval techniques; (2) the same techniques were used to crawl crowd sourced GPS trajectory data and related metadata of their daily activities from the website OpenStreetMap.org; and finally (3) preschool children's daily activities and interactions tagged with time and geographical location were collected with a novel TabletPC-based behavioral coding system. The proposed methodology is applied to these data to (1) automatically recommend optimal multi-day and multi-stay travel itineraries for travelers based on discovered attractions from geo-tagged photos, (2) automatically detect movement types of unknown moving objects from GPS trajectories, and (3) explore dynamic social and socio-spatial patterns of preschool children's behavior from both geographic and social perspectives.
ContributorsLi, Xun (Author) / Anselin, Luc (Thesis advisor) / Koschinsky, Julia (Committee member) / Maciejewski, Ross (Committee member) / Rey, Sergio (Committee member) / Griffin, William (Committee member) / Arizona State University (Publisher)
Created2012
Description
Critical infrastructures in healthcare, power systems, and web services, incorporate cyber-physical systems (CPSes), where the software controlled computing systems interact with the physical environment through actuation and monitoring. Ensuring software safety in CPSes, to avoid hazards to property and human life as a result of un-controlled interactions, is essential and challenging. The principal hurdle in this regard is the characterization of the context driven interactions between software and the physical environment (cyber-physical interactions), which introduce multi-dimensional dynamics in space and time, complex non-linearities, and non-trivial aggregation of interaction in case of networked operations. Traditionally, CPS software is tested for safety either through experimental trials, which can be expensive, incomprehensive, and hazardous, or through static analysis of code, which ignore the cyber-physical interactions. This thesis considers model based engineering, a paradigm widely used in different disciplines of engineering, for safety verification of CPS software and contributes to three fundamental phases: a) modeling, building abstractions or models that characterize cyberphysical interactions in a mathematical framework, b) analysis, reasoning about safety based on properties of the model, and c) synthesis, implementing models on standard testbeds for performing preliminary experimental trials. In this regard, CPS modeling techniques are proposed that can accurately capture the context driven spatio-temporal aggregate cyber-physical interactions. Different levels of abstractions are considered, which result in high level architectural models, or more detailed formal behavioral models of CPSes. The outcomes include, a well defined architectural specification framework called CPS-DAS and a novel spatio-temporal formal model called Spatio-Temporal Hybrid Automata (STHA) for CPSes. Model analysis techniques are proposed for the CPS models, which can simulate the effects of dynamic context changes on non-linear spatio-temporal cyberphysical interactions, and characterize aggregate effects. The outcomes include tractable algorithms for simulation analysis and for theoretically proving safety properties of CPS software. Lastly a software synthesis technique is proposed that can automatically convert high level architectural models of CPSes in the healthcare domain into implementations in high level programming languages. The outcome is a tool called Health-Dev that can synthesize software implementations of CPS models in healthcare for experimental verification of safety properties.
ContributorsBanerjee, Ayan (Author) / Gupta, Sandeep K.S. (Thesis advisor) / Poovendran, Radha (Committee member) / Fainekos, Georgios (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2012
Description
Identifying important variation patterns is a key step to identifying root causes of process variability. This gives rise to a number of challenges. First, the variation patterns might be non-linear in the measured variables, while the existing research literature has focused on linear relationships. Second, it is important to remove noise from the dataset in order to visualize the true nature of the underlying patterns. Third, in addition to visualizing the pattern (preimage), it is also essential to understand the relevant features that define the process variation pattern. This dissertation considers these variation challenges. A base kernel principal component analysis (KPCA) algorithm transforms the measurements to a high-dimensional feature space where non-linear patterns in the original measurement can be handled through linear methods. However, the principal component subspace in feature space might not be well estimated (especially from noisy training data). An ensemble procedure is constructed where the final preimage is estimated as the average from bagged samples drawn from the original dataset to attenuate noise in kernel subspace estimation. This improves the robustness of any base KPCA algorithm. In a second method, successive iterations of denoising a convex combination of the training data and the corresponding denoised preimage are used to produce a more accurate estimate of the actual denoised preimage for noisy training data. The number of primary eigenvectors chosen in each iteration is also decreased at a constant rate. An efficient stopping rule criterion is used to reduce the number of iterations. A feature selection procedure for KPCA is constructed to find the set of relevant features from noisy training data. Data points are projected onto sparse random vectors. Pairs of such projections are then matched, and the differences in variation patterns within pairs are used to identify the relevant features. This approach provides robustness to irrelevant features by calculating the final variation pattern from an ensemble of feature subsets. Experiments are conducted using several simulated as well as real-life data sets. The proposed methods show significant improvement over the competitive methods.
ContributorsSahu, Anshuman (Author) / Runger, George C. (Thesis advisor) / Wu, Teresa (Committee member) / Pan, Rong (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2013