Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 18 of 18 for “"Spatiotemporal Modeling"”.
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Spatial and Spatiotemporal Modeling of Epidemiological Data
<p>This dissertation focuses on modeling approach for spatial and spatiotemporal data with epidemiological applications. Chapter one gives the general overview of spatial and spatiotemporal data and challenges in the statistical analysis of spatial and spatiotemporal data, and motivation and …
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Spatiotemporal Modeling for Wildlife Demographic Analysis: Bridging Analysis to Waterfowl Conservation
Examining variation in ecological systems is critical to understanding the fundamental demographic processes (e.g. reproduction, survival, growth, and dispersal) that govern populations and manage them in an increasingly altered world. Population dynamics often respond to environmental alterations, …
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Spatiotemporal modeling and model restructuration approaches in studies of intracellular signalling pathways
… and cell biology in a single cell. Mathematical modeling and experimental evaluation are widely used approaches for this kind of research. Firstly, A multiscale framework for protein-protein interaction has been established using Brownian dynamics algorithm. Sit specific feature, steric …
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Toward Robust and Generalizable Spatiotemporal Modeling for Tasks beyond Forecasting and Classification
In spatiotemporal data mining, building models that are robust and generalizable across complex, non-ideal conditions is crucial for real-world deployment. While many existing methods perform well on benchmark datasets, they often assume clean, stationary, and uniformly sampled data, limiting their …
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Heterogeneous Sensor Data based Online Quality Assurance for Advanced Manufacturing using Spatiotemporal Modeling
… (2) Spatial Dirichlet process (SDP) for modeling complex multimodal wafer thickness profiles and exploring their clustering effects. The SDP-based statistical control scheme can effectively detect out-of-control wafers and achieve wafer thickness quality assurance for the slicing process …
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Examining the neighborhood effect averaging problem (NEAP) in people’s exposure to mobility-dependent environmental factors: A spatiotemporal modeling approach
The neighborhood effect averaging problem (NEAP) is a major methodological problem that might affect the accuracy of assessments of individual exposure to mobility-dependent environmental factors, such as air/noise pollution, traffic congestion, green/blue spaces, healthy food environments, ethnic …
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Tweets2Cube: Interactive spatiotemporal knowledge acquisition from massive social media
… that uncovers the patterns underlying people's spatiotemporal activities from massive online social media. Tweets2Cube organizes unstructured social media records into a multi-dimensional data cube along three dimensions: (1) what is the user's activity; (2) where does that activity occur; and …
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Using Data Augmentation and Stochastic Differential Equations in Spatio Temporal Modeling
<p>One of the biggest challenges in spatiotemporal modeling is indeed how to manage the large amount of missing information. Data augmentation techniques are frequently used to infer about missing values, unobserved or latent processes, approximation of continuous time processes that are discretely …
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Design of interactive maps for ocean dynamics data
Comprehensive spatiotemporal modeling and forecasting systems for ocean dynamics necessitate robust and efficient data delivery and visualization techniques. The multi-disciplinary simulation, estimation, and assimilation systems group at MIT (MSEAS) focuses on capturing and predicting diverse …
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Embodied Representation of Time in Virtual Reality
… contributes a novel representational approach to spatiotemporal modeling in immersive systems. By doing so, we create new opportunities for architectural visualization, interactive simulations, game design, and reimagining how we perceive and construct digital spaces.
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Optimizing resource allocation in computational sustainability: Models, algorithms and tools
… elements of discrete optimization, large-scale spatiotemporal modeling and prediction, and stochastic models. This dissertation leverages network models as a flexible family of computational tools for building prediction and optimization models in three sustainability-related domain areas: 1) …
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Topics in Bayesian Spatiotemporal Prediction of Environmental Exposure
<p>We address predictive modeling for spatial and spatiotemporal modeling in a variety of settings. First, we discuss spatial and spatiotemporal data and corresponding model types used in later chapters. Specifically, we discuss Markov random fields, Gaussian processes, and Bayesian inference. …
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Quantitative modeling of spatiotemporal systems: Simulation of biological systems and analysis of error metric effects on model fitting
… question.^ This dissertation documents the modeling, parameter estimation, and simulation of two spatiotemporal modeling studies. Each study addresses an unanswered research question in the respective experimental system. The former is a 3D model of a nanoscale amperometric glucose …
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Analysis of the organization and dynamics of proteins in cell membranes
… understanding these changes. Biologists in the Spatiotemporal Modeling of Cell Signaling Center (STMC) have generated a large amount of data about the high affinity receptor FceRI, that is found in mast cells and basophils. The activation of this receptor starts when IgE bound to FceRI is …
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Predictive Model Fusion: A Modular Approach to Big, Unstructured Data
… disparate sources inhibits joint processing and modeling. Rather modular segmentation is required, in which a set of models process (potentially overlapping) partitions of the data to independently construct predictions. This framework enables individuals models to be tailored for specific …
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MODELING AND RESOURCE ALLOCATION IN MOBILE WIRELESS NETWORKS
… the emerging deep learning techniques for spatiotemporal modeling and prediction in cellular networks, based on big system data. We present a hybrid deep learning model for spatiotemporal prediction, which includes a novel autoencoder-based deep model for spatial modeling and Long …
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Dynamic speech imaging with low-rank approximation
… However, conventional MRI suffers from low spatiotemporal resolution, which limits its applica-tion in dynamic speech imaging. This thesis presents a novel model-based dynamic MR imaging method to capture speech dynamics in high spatiotemporal resolution. Specifically, high spatiotemporal …