Rozprawy doktorskie: „Spatio-temporal space”

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Fagerström, Daniel. "Spatio-Temporal Scale-Space Theory". Doctoral thesis, KTH, Datorseende och robotik, CVAP, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-33686.

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This thesis addresses two important topics in developing a systematic space-time geometric approach to real-time, low-level motion vision. The first one concerns measuring of image flow, while the second one focuses on how to find low level features. We argue for studying motion vision in terms of space-time geometry rather than in terms of two (or a few) consecutive image frames. The use of Galilean Geometry and Galilean similarity geometry for this purpose is motivated and relevant geometrical background is reviewed. In order to measure the visual signal in a way that respects the geometry of the situation and the causal nature of time, we argue that a time causal Galilean spatio-temporal scale-space is needed. The scale-space axioms are chosen so that they generalize popular axiomatizations of spatial scale-space to spatio-temporal geometries. To be able to derive the scale-space, an infinitesimal framework for scale-spaces that respects a more general class of Lie groups (compared to previous theory) is developed and applied. Perhaps surprisingly, we find that with the chosen axiomatization, a time causal Galilean scale-space is not possible as an evolution process on space and time. However, it is possible on space and memory. We argue that this actually is a more accurate and realistic model of motion vision. While the derivation of the time causal Galilean spatio-temporal scale-spaces requires some exotic mathematics, the end result is as simple as one possibly could hope for and a natural extension of spatial scale-spaces. The unique infinitesimally generated scale-space is an ordinary diffusion equation with drift on memory and a diffusion equation on space. The drift is used for velocity adaption, the "velocity adaption" part of Galilean geometry (the Galilean boost) and the temporal scale-space acts as memory. Lifting the restriction of infinitesimally generated scale spaces, we arrive at a new family of scale-spaces. These are generated by a family of fractional differential evolution equations that generalize the ordinary diffusion equation. The same type of evolution equations have recently become popular in research in e.g. financial and physical modeling. The second major topic in this thesis is extraction of features from an image flow. A set of low-level features can be derived by classifying basic Galilean differential invariants. We proceed to derive invariants for two main cases: when the spatio-temporal gradient cuts the image plane and when it is tangent to the image plane. The former case corresponds to isophote curve motion and the later to creation and disappearance of image structure, a case that is not well captured by the theory of optical flow. The Galilean differential invariants that are derived are equivalent with curl, divergence, deformation and acceleration. These invariants are normally calculated in terms of optical flow, but here they are instead calculated directly from the the spatio-temporal image.
QC 20110518

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Apichayakul, Phisut. "Spatio-temporal State Space Model Estimation for Smart structures". Thesis, University of Sheffield, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522539.

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Baskaran, Savitha. "Visualization of spatio-temporal data in two dimensional space". Thesis, Purdue University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10246961.

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Spatio-temporal data is becoming very popular in the recent times, as there are large number of datasets that collect both location and temporal information in the real time. The main challenge is that extracting useful insights from such large data set is extremely complex and laborious. In this thesis, we have proposed a novel 2D technique to visualize the spatio-temporal big data. The visualization of the combined interaction between the spatial and temporal data is of high importance to uncover the insights and identify the trends within the data.

Maps have been a successful way to represent the spatial information. Additionally, in this work, colors are used to represent the temporal data. Every data point has the time information which is converted into relevant color, based on the HSV color model. The variation in the time is represented by transition from one color to another and hence provide smooth interpolation. The proposed solution will help the user to quickly understand the data and gain insights.

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Laptev, Ivan. "Local spatio-temporal image features for motion interpretation". Doctoral thesis, KTH, Numerical Analysis and Computer Science, NADA, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3797.

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Visual motion carries information about the dynamics of ascene. Automatic interpretation of this information isimportant when designing computer systems forvisualnavigation, surveillance, human-computer interaction, browsingof video databases and other growing applications.

In this thesis, we address the issue of motionrepresentation for the purpose of detecting and recognizingmotion patterns in video sequences. We localize the motion inspace and time and propose to use local spatio-temporal imagefeatures as primitives when representing and recognizingmotions. To detect such features, we propose to maximize ameasure of local variation of the image function over space andtime and show that such a method detects meaningful events inimage sequences. Due to its local nature, the proposed methodavoids the in.uence of global variations in the scene andovercomes the need for spatial segmentation and tracking priorto motion recognition. These properties are shown to be highlyuseful when recognizing human actions in complexscen es.

Variations in scale and in relative motions of the cameramay strongly in.uence the structure of image sequences andtherefore the performance of recognition schemes. To addressthis problem, we develop a theory of local spatio-temporaladaptation and show that this approach provides invariance whenanalyzing image sequences under scaling and velocitytransformations. To obtain discriminative representations ofmotion patterns, we also develop several types of motiondescriptors and use them for classifying and matching localfeatures in image sequences. An extensive evaluation of thisapproach is performed and results in the context of the problemof human action recognition are presented. I

n summary, this thesis provides the following contributions:(i) it introduces the notion of local features in space-timeand demonstrates the successful application of such featuresfor motion interpretation; (ii) it presents a theory and anevaluation of methods for local adaptation with respect toscale and velocity transformations in image sequences and (iii)it presents and evaluates a set of local motion descriptors,which in combination with methods for feature detection andfeature adaptation allow for robust recognition of humanactions in complexs cenes with cluttered and non-stationarybackgrounds as well as camera motion.

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Wu, Elizabeth. "Spatio-Temporal Data Mining and Analysis of Precipitation Extremes". Thesis, The University of Sydney, 2008. https://hdl.handle.net/2123/28120.

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The work in this thesis aimed to discover new ways to analyse and mine information about precipitation extremes in South America from spatio-temporal data. This was done in two ways. First, analysis was performed through the use of statistical measures that provided insight into the behaviour of precipitation extremes between regions. Second, a new spatio-temporal outlier detection algorithm was introduced to discover moving outliers from the data.

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Krishan, Kapilanjan. "Characterizations of spatio-temporal complex systems". Diss., Available online, Georgia Institute of Technology, 2005, 2005. http://etd.gatech.edu/theses/available/etd-05162005-071906/.

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Thesis (Ph. D.)--Physics, Georgia Institute of Technology, 2006.
Schatz, Michael, Committee Chair ; Cvitanovic, Predrag, Committee Member ; Uzer, Turgay, Committee Member ; Grigoriev, Roman, Committee Member ; Mischaikow, Konstantin, Committee Member.

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Noga, Jacek Leszek. "Bayesian state-space modelling of spatio-temporal non-Gaussian radar returns". Thesis, University of Cambridge, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.624278.

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Uhl, Philip J. "A Spatio-Temporal Data Model for Zoning". BYU ScholarsArchive, 2002. https://scholarsarchive.byu.edu/etd/1.

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Planning departments are besieged with temporal/historical information. While for many institutions historical information can be relegated to archives, planning departments have a constant need to access and query their historical information, particularly their historical spatial information such as zoning. This can be a cumbersome process fraught with inaccuracies due to the changing organizational methods and the extended historical legacies of most municipalities. Geographic Information Systems can be a tool to provide a solution to the difficulties in querying spatio-temporal planning data. Using a data model designed specifically to facilitate the querying of historical zoning information, queries can be performed to answer basic zoning questions such as "what is the zoning history for a specific parcel of land?" This work outlines this zoning data model, its implementation, and its testing using queries basic to the needs of planning departments.

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Samant, Gajanan Balkrishna. "Verification of the "Energy Accumulation in Waves Travelling through a Checkerboard Dielectric Material Structure in Space-time" Using Spice Simulations". Digital WPI, 2009. https://digitalcommons.wpi.edu/etd-theses/1210.

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"Recently, there has been some good interest in the field of Dynamic Materials, also referred to as Spatio-Temporal Composites. These materials have been theoretically attributed to show ability to switch their electromagnetic properties in time, as contrast to the spatial variations shown by regular materials of non-dynamic nature, existing naturally. Though there is no exhibition of dynamic material in nature yet, there are suggestions for its synthesis. This paper follows the idea of using standard lossless transmission line model approximating a material substance. Such a material though not truly homogeneous, could be made to vary its properties in time. The aim of this work is to test this idea for its functional efficiency in comparison to analytical results obtained from earlier works on the subject. We make use of Spice simulation for this. An important aspect of this work is to facilitate the dynamic operations in a static environment. Almost all the simulators available today like Spice, ADS, etc intrinsically provide no ability for parameter variations in time. Nonetheless, we make use of certain popular tricks to implement circuits imitating the dynamic circuit components we need. Such implementations are separately tested to demonstrate their success in providing us with the dynamic environment we desire. Finally, within the limitations of the computing capabilities, we could successfully show an agreement between the results obtained and the existing theory. "

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Gabolde, Pablo. "Measurements of the spatio-temporal profiles of femtosecond laser pulses". Diss., Available online, Georgia Institute of Technology, 2007, 2007. http://etd.gatech.edu/theses/available/etd-06272007-101312/.

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Thesis (Ph. D.)--Physics, Georgia Institute of Technology, 2008.
Trebino, Rick, Committee Chair ; Kennedy, Brian, Committee Member ; Kuzmich, Alex, Committee Member ; Curtis, Jennifer, Committee Member ; Buck, John, Committee Member.

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Oechsler, Monika. "Interstitial space : the Eagle Document : performativity and spatio-temporal assemblage in the contemporary moving image installation space (MIS)". Thesis, University of Brighton, 2011. https://research.brighton.ac.uk/en/studentTheses/08db6f60-822e-4ed3-96ac-577b307369d7.

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This study investigates the perceptive modalities constituted by the multi-screen projection installation and its architectural and spatial forms. The thesis explores the hypothesis that the affective spatial experience created by the spatially configured moving image space (MIS) produces changed spectatorial positions from that of the single screen (or one-directional multi screen). The research asks: How can we understand the spatially configured multi-screen projection installation as constituting a changed aesthetic perception for the spectator? The MIS comprises two major temporalities, the multi-image structure and the mobile spectator whose physical interaction articulates the receptive process. Thus, perception in the MIS is not purely constructed through vision. Instead, the spatio-temporal assemblage shapes other direct sense phenomena. These are the spatial intensities produced by the spatial field, i.e. variable sensations of spatial receptivity, which are not mediated by the gaze but experienced directly by the body. Further, the mobile gaze of the moving spectator intersects with the moving images, and constructs vision as fluid and transient. Merleau-Ponty (2006), first published 1945, proposed that the mobile orientation of the body in space creates changing points of reference and alters both vision and embodied experience of space. The analysis of two inter-related concepts aims to contribute to a new understanding of the MIS. The first is the interstitial modality established by the screen architecture which engenders temporal and spatial gaps that interrupt the process of perception. Interstitiality here serves as an analytical tool in discerning the specific spectatorial positions instituted by individual installations. The second is the process of inter-animation which is constituted by the corporeal interaction of the spectator in the animated space of the MIS. Spatial and temporal effects act upon the body of the viewer. In turn, the viewer negotiates the unfamiliar space and multiple interstices through intuitive reaction. Deleuze (1994), first published 1968, suggested that spatial intensities and differences create immediate sensory impact which is experienced as intuition. Intuition intervenes in the visual process and expands perception from observation and reflection to intuitive action.

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Zahid, Kazi. "Space-time Processsing for the Wideband-CDMA System". Thesis, Virginia Tech, 2000. http://hdl.handle.net/10919/30783.

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Deployment of antenna arrays is a very promising solution to reduce the Multiple Access Interference (MAI) from high data rate users in the Wideband Code Division Multiple Access (W-CDMA) system. Combining the antenna array with a RAKE receiver, both of which exploits multipath diversity, can significantly improve the system performance. In this research, we investigate the performance of these beamformer-RAKE receivers, also known as two-dimensional (2-D) RAKE receiver, for the reverse link of the W-CDMA system. We consider three different Pilot Symbol Assisted (PSA) beamforming techniques, Direct Matrix Inversion (DMI), Least-Mean Square (LMS) and Recursive Least Square (RLS) adaptive algorithms. Two different Geometrically Based Single Bounce (GBSB) statistical channel models are considered, one, which is more suitable for array processing, and the other is conductive to RAKE combining. The performances of the 2-D RAKE receivers are evaluated in these two channel models as a function of the number of antenna elements and RAKE fingers. It is shown that, in both the cases, the 2-D RAKE receiver outperforms the conventional RAKE receiver and the conventional beamformer by a significant margin. Also, the output SINR expression of a 2-D RAKE receiver with the general optimum beamformer is derived.
Master of Science

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Marshall, J. Brooke. "Prospective Spatio-Temporal Surveillance Methods for the Detection of Disease Clusters". Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/29639.

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In epidemiology it is often useful to monitor disease occurrences prospectively to determine the location and time when clusters of disease are forming. This aids in the prevention of illness and injury of the public and is the reason spatio-temporal disease surveillance methods are implemented. Care must be taken in the design and implementation of these types of surveillance methods so that the methods provide accurate information on the development of clusters. Here two spatio-temporal methods for prospective disease surveillance are considered. These include the local Knox monitoring method and a new wavelet-based prospective monitoring method. The local Knox surveillance method uses a cumulative sum (CUSUM) control chart for monitoring the local Knox statistic, which tests for space-time clustering each time there is an incoming observation. The detection of clusters of events occurring close together both temporally and spatially is important in finding outbreaks of disease within a specified geographic region. The local Knox surveillance method is based on the Knox statistic, which is often used in epidemiology to test for space-time clustering retrospectively. In this method, a local Knox statistic is developed for use with the CUSUM chart for prospective monitoring so that epidemics can be detected more quickly. The design of the CUSUM chart used in this method is considered by determining the in-control average run length (ARL) performance for different space and time closeness thresholds as well as for different control limit values. The effect of nonuniform population density and region shape on the in-control ARL is explained and some issues that should be considered when implementing this method are also discussed. In the wavelet-based prospective monitoring method, a surface of incidence counts is modeled over time in the geographical region of interest. This surface is modeled using Poisson regression where the regressors are wavelet functions from the Haar wavelet basis. The surface is estimated each time new incidence data is obtained using both past and current observations, weighing current observations more heavily. The flexibility of this method allows for the detection of changes in the incidence surface, increases in the overall mean incidence count, and clusters of disease occurrences within individual areas of the region, through the use of control charts. This method is also able to incorporate information on population size and other covariates as they change in the geographical region over time. The control charts developed for use in this method are evaluated based on their in-control and out-of-control ARL performance and recommendations on the most appropriate control chart to use for different monitoring scenarios is provided.
Ph. D.

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Janoos, Firdaus H. "Spatio-Temporal Representations and Analysis of Brain Function from fMRI". The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1295643292.

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Bevanda, Mirjana [Verfasser], i Björn [Akademischer Betreuer] Reineking. "Animals in space and time : spatio-temporal movement pattern analysis / Mirjana Bevanda. Betreuer: Björn Reineking". Bayreuth : Universität Bayreuth, 2015. http://d-nb.info/1088513395/34.

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Reyes-Castro, Pablo A., Robin B. Harris, Heidi E. Brown, Gary L. Christopherson i Kacey C. Ernst. "Spatio-temporal and neighborhood characteristics of two dengue outbreaks in two arid cities of Mexico". ELSEVIER SCIENCE BV, 2017. http://hdl.handle.net/10150/623139.

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Little is currently known about the spatial-temporal dynamics of dengue epidemics in arid areas. This study assesses dengue outbreaks that occurred in two arid cities of Mexico, Hermosillo and Navojoa, located in northern state of Sonora. Laboratory confirmed dengue cases from Hermosillo (N=2730) and Navojoa (N=493) were geocoded by residence and assigned neighborhood-level characteristics from the 2010 Mexican census. Kernel density and Space-time cluster analysis was performed to detect high density areas and space-time clusters of dengue. Ordinary Least Square regression was used to assess the changing socioeconomic characteristics of cases over the course of the outbreaks. Both cities exhibited contiguous patterns of space-time clustering. Initial areas of dissemination were characterized in both cities by high population density, high percentage of occupied houses, and lack of healthcare. Future research and control efforts in these regions should consider these space-time and socioeconomic patterns.

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González, Monsalve Jonatan A. "Statistical tests for comparisons of spatial and spatio-temporal point patterns". Doctoral thesis, Universitat Jaume I, 2018. http://hdl.handle.net/10803/462034.

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We mainly introduce a new set of tests to compare functional descriptors in point processes context. Firstly, since the study of spatio-temporal point processes has not been widely covered in the literature, a complete review is made. The review is a reference paper of the available techniques and approaches regarding the spatio-temporal context. Secondly, a studentized permutation test is developed in the spatio-temporal case. This test is motivated by locations of tornadoes in the U.S. in a period of 36 years. Some tools have been developed as a non-separable estimator of the first-order intensity, which allows a realistic analysis of the phenomenon through the new test. Finally, a factorial two-way design is considered, where the observations are spatial point patterns in presence of replication. This methodology is motivated by a minerals engineering experiment. We develop statistics to test the influence of the factors and the possible interaction effects.
Desarrollamos un nuevo conjunto de pruebas para comparar descriptores funcionales en el contexto de procesos puntuales. Puesto que el estudio de los procesos puntuales espacio-temporales no ha sido muy exhaustivo en la literatura, hemos hecho un artículo de resumen. Introducimos un test de permutación para grupos de patrones puntuales espacio-temporales motivado por las ubicaciones de ocurrencias de tornados en EE.UU. durante 36 años. Hemos desarrollado algunas técnicas como la estimación de la intensidad de primer-orden sin suponer separabilidad, lo que permite un tratamiento más realista del fenómeno climático en sí mismo a través del nuevo test.Finalmente, hemos desarrollado algunas técnicas para el análisis de la varianza de experimentos de dos factores en presencia de réplicas cuando las observaciones son patrones puntuales espaciales. Esta metodología está motivada por un experimento de ingeniería de minerales. Desarrollamos algunos estadísticos adecuados para probar la influencia de los factores y su posible interacción.

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Paulionienė, Laura. "Statistical modelling of spatio-temporal data based on spatial interpolation of time series parameters". Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2013~D_20140117_113114-31261.

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Space – time data modeling problem is analysed. Often spatial data sets are relatively small, and the points, where observations are taken, are located irregularly. When solving spatial task, usually we are interpolating or estimating the spatial average. Time series data usually are used to predict future values. Meanwhile, the space - time tasks combines both types of tasks. Few original modeling methods of spatial time series are proposed. The proposed methods firstly analyzes the univariate time series, and after removing temporal dependence, spatial dependence in the time series of residuals is measured. Aim of this dissertational work - to create time series model at new unobserved location by incorporating spatial interaction thru spatial interpolation of estimated time series parameters. Such a model is based on the spatial interpolation of time series parameters.
Disertaciniame darbe nagrinėjama erdvės – laiko duomenų modeliavimo problema. Dažnai erdvinių duomenų rinkiniai yra gana nedideli, o taškai, kuriuose pasklidę stebėjimai, išsidėstę netaisyklingai. Sprendžiant „erdvinį“ uždavinį, paprastai siekiama inerpoliuoti arba įvertinti erdvinį vidurkį. Laiko eilučių duomenys dažniausiai naudojami ateities reikšmėms prognozuoti. Tuo tarpu erdvės – laiko uždaviniai jungia abu uždavinių tipus. Pasiūlyta keletas originalių erdvinių laiko eilučių modeliavimo metodų. Siūlomi metodai pirmiausia analizuoja vienmates laiko eilutes, o pašalinus laikinę priklausomybė jose, laiko eilučių liekanoms vertinama erdvinė priklausomybė. Tikslas – sudaryti modelį, leidžiantį prognozuoti požymio reikšmę naujame, nestebėtame taške, nauju laiko momentu. Tokio modelio sudarymas remiasi laiko eilučių parametrų erdviniu interpoliavimu.

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Demel, Samuel Seth. "Modeling and computations of multivariate datasets in space and time". Diss., Kansas State University, 2013. http://hdl.handle.net/2097/15578.

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Doctor of Philosophy
Department of Statistics
Juan Du
Spatio-temporal and/or multivariate dependence naturally occur in datasets obtained in various disciplines; such as atmospheric sciences, meteorology, engineering and agriculture. There is a great deal of need to effectively model the complex dependence and correlated structure exhibited in these datasets. For this purpose, this dissertation studies methods and application of the spatio-temporal modeling and multivariate computation. First, a collection of spatio-temporal functions is proposed to model spatio-temporal processes which are continuous in space and discrete over time. Theoretically, we derived the necessary and sufficient conditions to ensure the model validity. On the other hand, the possibility of taking the advantage of well-established time series and spatial statistics tools makes it relatively easy to identify and fit the proposed model in practice. The spatio-temporal models with some ARMA discrete temporal margin are fitted to Kansas precipitation and Irish wind datasets for estimation or prediction, and compared with some general existing parametric models in terms of likelihood and mean squared prediction error. Second, to deal with the immense computational burden of statistical inference for multi- ple attributes recorded at a large number of locations, we develop Wendland-type compactly supported covariance matrix function models and propose multivariate covariance tapering technique with those functions for computation reduction. Simulation studies and US tem- perature data are used to illustrate applications of the proposed multivariate tapering and computational gain in spatial cokriging. Finally, to study the impact of weather change on corn yield in Kansas, we develop a spatial functional linear regression model accounting for the fact that weather data were recorded daily or hourly as opposed to the yearly crop yield data and the underlying spatial autocorrelation. The parameter function is estimated under the functional data analysis framework and its characteristics are investigated to show the influential factor and critical period of weather change dictating crop yield during the growing season.

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Thomas, Zachary Micah. "Bayesian Hierarchical Space-Time Clustering Methods". The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1435324379.

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Yang, Ying. "Source-Space Analyses in MEG/EEG and Applications to Explore Spatio-temporal Neural Dynamics in Human Vision". Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1016.

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Human cognition involves dynamic neural activities in distributed brain areas. For studying such neural mechanisms, magnetoencephalography (MEG) and electroencephalography (EEG) are two important techniques, as they non-invasively detect neural activities with a high temporal resolution. Recordings by MEG/EEG sensors can be approximated as a linear transformation of the neural activities in the brain space (i.e., the source space). However, we only have a limited number sensors compared with the many possible locations in the brain space; therefore it is challenging to estimate the source neural activities from the sensor recordings, in that we need to solve the underdetermined inverse problem of the linear transformation. Moreover, estimating source activities is typically an intermediate step, whereas the ultimate goal is to understand what information is coded and how information flows in the brain. This requires further statistical analysis of source activities. For example, to study what information is coded in different brain regions and temporal stages, we often regress neural activities on some external covariates; to study dynamic interactions between brain regions, we often quantify the statistical dependence among the activities in those regions through “connectivity” analysis. Traditionally, these analyses are done in two steps: Step 1, solve the linear problem under some regularization or prior assumptions, (e.g., each source location being independent); Step 2, do the regression or connectivity analysis. However, biases induced in the regularization in Step 1 can not be adapted in Step 2 and thus may yield inaccurate regression or connectivity results. To tackle this issue, we present novel one-step methods of regression or connectivity analysis in the source space, where we explicitly modeled the dependence of source activities on the external covariates (in the regression analysis) or the cross-region dependence (in the connectivity analysis), jointly with the source-to-sensor linear transformation. In simulations, we observed better performance by our models than by commonly used two-step approaches, when our model assumptions are reasonably satisfied. Besides the methodological contribution, we also applied our methods in a real MEG/EEG experiment, studying the spatio-temporal neural dynamics in the visual cortex. The human visual cortex is hypothesized to have a hierarchical organization, where low-level regions extract low-level features such as local edges, and high-level regions extract semantic features such as object categories. However, details about the spatio-temporal dynamics are less understood. Here, using both the two-step and our one-step regression models in the source space, we correlated neural responses to naturalistic scene images with the low-level and high-level features extracted from a well-trained convolutional neural network. Additionally, we also studied the interaction between regions along the hierarchy using the two-step and our one-step connectivity models. The results from the two-step and the one-step methods were generally consistent; however, the one-step methods demonstrated some intriguing advantages in the regression analysis, and slightly different patterns in the connectivity analysis. In the consistent results, we not only observed an early-to-late shift from low-level to high-level features, which support feedforward information flow along the hierarchy, but also some novel evidence indicating non-feedforward information flow (e.g., topdown feedback). These results can help us better understand the neural computation in the visual cortex. Finally, we compared the empirical sensitivity between MEG and EEG in this experiment, in detecting dependence between neural responses and visual features. Our results show that the less costly EEG was able to achieve comparable sensitivity with that in MEG when the number of observations was about twice of that in MEG. These results can help researchers empirically choose between MEG and EEG when planning their experiments with limited budgets.

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Oh, Chang-Geun. "The Effects of Proximity Compatibility and Graphics on Spatio-Temporal SituationAwareness for Navigation". Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1453218915.

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Santiago, Jessica de. "Extracting informative spatio-temporal features from fMRI dynamics : a model-based characterization of timescales". Doctoral thesis, Universitat Pompeu Fabra, 2021. http://hdl.handle.net/10803/671346.

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In neuropsychiatry, the development of brain imaging and dedicated data analysis for personalized medicine promises to predict both the evolution of diseases and responses of treatments. The ability to estimate the time course of the disease is the first step to understand the response to potential treatments, which implies the development of methods able to capture subject-specific features in addition to the discrimination between pathological conditions. However, methods that effectively characterize the neuronal activity at the whole-brain level are still lacking, and many efforts are currently made in the fields of clinical research and neuroscience to fill this gap. The above is particularly problematic to interpret functional Magnetic Resonance Imaging (fMRI) data, which are indirectly coupled with neuronal activity because of hemodynamics, yielding much slower signals than neuronal activity. We propose a multiscale method that combines a computational whole-brain model with machine learning to solve this issue. In our approach, the model relates the neuronal activity and the fMRI signals in a mechanistic fashion, allowing for access to neuronal activity down to millisecond precision. Specifically, we use a novel methodology that allows the extraction of space-time motifs at different timescales through binned time windows. Then, we use machine learning to study which range of timescales in the modeled neuronal activity is most informative to separate the brain's dynamics during rest, distinguishing subjects, tasks, and neuropsychiatric conditions. Our multiscale computational approach is a further step to study the multiple timescales of brain dynamics and predict the dynamical interactions between brain regions. Overall, this method raises outlooks to detect biomarkers and predict responses of treatments.
En neuropsiquiatría, el desarrollo de imágenes cerebrales y el análisis de datos dedicados a la medicina personalizada prometen predecir tanto la evolución de las enfermedades como las respuestas a los tratamientos. La capacidad de estimar el curso temporal de la enfermedad es el primer paso para comprender la respuesta a posibles tratamientos, lo que implica el desarrollo de métodos capaces de capturar características específicas del sujeto, además de la discriminación entre condiciones patológicas. Sin embargo, todavía faltan métodos que caractericen eficazmente la actividad neuronal a nivel de todo el cerebro, y actualmente se están haciendo muchos esfuerzos en los campos de la investigación clínica y la neurociencia. Lo anterior es particularmente problemático para interpretar los datos funcionales de las imágenes de resonancia magnética (fMRI por sus siglas en inglés), que están acoplados indirectamente con la actividad neuronal debido a la hemodinámica, lo que produce señales mucho más lentas que la actividad neuronal. En este trabajo, proponemos un método multiescala que combina un modelo computacional de cerebro completo con aprendizaje automático para resolver este problema. En nuestro enfoque, el modelo relaciona la actividad neuronal y las señales de resonancia magnética funcional de manera mecanicista, lo que permite el acceso a la actividad neuronal con una precisión de milisegundos. Específicamente, utilizamos una nueva metodología que permite la extracción de patrones espacio-temporales en diferentes escalas temporales a través de ventanas de tiempo. Después, usamos aprendizaje automático para estudiar qué rango de escalas de tiempo en la actividad neuronal modelada es más informativo, para separar la dinámica del cerebro durante el descanso, distinguiendo sujetos, tareas y condiciones neuropsiquiátricas. Nuestro enfoque computacional multiescala es un paso más para estudiar las múltiples escalas de tiempo de la dinámica del cerebro y predecir las interacciones dinámicas entre las regiones del cerebro. En general, este método aumenta las perspectivas para detectar biomarcadores y predecir la respuesta de tratamientos.

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Hautala, J. (Johanna). "Academic knowledge creation as a spatio-temporal process:the case of international research groups in Finland". Doctoral thesis, Oulun yliopisto, 2011. http://urn.fi/urn:isbn:9789514296499.

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Abstract The thesis is an investigation into the spatio-temporal knowledge creation of international research groups. Knowledge is spatial: it is created in geographical places, shared in communicative space and analyzed in the cognitive space of the mind. Knowledge is a process that evolves in the interactions and interpretations of people. It is created in the tempo of working days that is experienced as flowing or disrupted time. These dimensions of spaces and times come together as the contexts of knowledge creation. In current internationalizing universities, work groups increasingly consist of members from different countries and with varying professional backgrounds. In their endeavor to create globally renowned results, the international research groups are both challenged and enabled by their diversity. Multiple views can benefit the creation of novelty, but reaching a common understanding in a diverse group might not be easy. Current research calls for a more detailed understanding of the processes and contexts of knowledge creation in international groups. The empirical part of the thesis is comprised of two case study compilations. Case A focuses on the University of Oulu, with its foreign employees and three international research groups from the humanist, scientific and technical fields. Four international research groups from the fields of science and technology, led by top foreign professors and funded by the Finland Distinguished Professor Programme, form case B. Altogether, the main materials include 37 interviews, 123 weekly diaries, observation and a survey (67 replies). The main methods applied are mental mapping, discourse analysis and content analysis. According to the results, knowledge creation is a complex spatio-temporal process that often leads to unexpected results. Processes of interaction and interpretation enable the group to reach cognitive friction. This fertile ground of knowledge creation can be reached when a group possesses enough common understanding and enough diversity in their individual interpretations. Humanist, scientific and technical knowledges are created in multiple contexts through group-specific discourses. Knowledge itself is a spatio-temporal process of stages, flows and disruptions towards the not-yet-known. Knowledge is inseparable from individuals, groups, the processes and contexts of creation
Tiivistelmä Väitöskirjassa tarkastellaan tiedon jalostamista tilassa ja ajassa kansainvälisissä tutkimusryhmissä. Tieto on tilallista: sitä jalostetaan maantieteellisissä paikoissa, jaetaan kommunikaation tilassa ja analysoidaan mielen kognitiivisessa tilassa. Tieto on prosessi, jota ihmiset jalostavat vuorovaikutuksen ja tulkinnan kautta. Tietoa jalostetaan aikarajojen ja työpäivien määrittelemässä ajassa, joka voi tuntua virtaavan tai pysähtelevän. Nämä tilan ja ajan ulottuvuudet yhdistyvät tiedon jalostamisen kontekstiksi. Kansainvälistyvissä yliopistoissa työryhmät koostuvat enenevissä määrin jäsenistä, jotka edustavat eri kansallisuuksia ja joiden ammatilliset taustat ovat erilaiset. Monimuotoisuus on sekä haaste että mahdollisuus ryhmien tavoitteelle luoda kansainvälisesti merkittäviä tuloksia. Monipuoliset näkökulmat voivat edesauttaa tiedon jalostamista, mutta yhteisymmärryksen muodostaminen ryhmässä voi olla haastavaa. Nykytutkimuksessa tarvitaan syvällisempää ymmärrystä tiedon jalostamisen prosesseista ja konteksteista kansainvälisissä ryhmissä. Väitöskirjan empiirinen osa sisältää kaksi tapaustutkimuskokonaisuutta. Tapaus A koskee Oulun yliopiston ulkomaalaisia työntekijöitä ja kolmea kansainvälistä tutkimusryhmää humanistiselta, luonnontieteelliseltä ja tekniseltä alalta. Tapauksen B muodostavat neljä kansainvälistä luonnontieteen ja tekniikan alan tutkimusryhmää, joiden johtajat ovat ulkomaalaisia huippuprofessoreita ja jotka ovat Finland Distinguished Professor -ohjelman rahoittamia. Yhteensä pääaineisto koostuu 37 haastattelusta, 123 viikkopäiväkirjasta, havainnoinnista ja kyselystä (67 vastausta). Aineiston käsittelyn päämenetelmät ovat mentaalikartta-, diskurssi- ja sisällönanalyysi. Tulosten mukaan tiedon jalostaminen on monimutkainen prosessi, joka usein johtaa odottamattomiin tuloksiin. Vuorovaikutuksen ja tulkitsemisen prosessit mahdollistavat kognitiivisen kitkan muodostumisen, joka on hedelmällinen maaperä tiedon jalostamiselle. Sen saavuttamiseksi ryhmässä tarvitaan riittävästi yhteisymmärrystä ja riittävästi monimuotoisuutta jäsenten yksilöllisissä tulkinnoissa. Humanistiset, luonnontieteelliset ja tekniset tiedot jalostetaan ryhmän sisäisissä diskursseissa monipuolisissa konteksteissa. Tieto on tilallinen ja ajallinen prosessi kohti toistaiseksi tietämätöntä. Tieto on erottamaton osa ihmisiä, ryhmiä ja jalostamisen prosesseja ja konteksteja

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Aydin, Umit. "Solution Of Inverse Problem Of Electrocardiography Using State Space Models". Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12611027/index.pdf.

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Heart is a vital organ that pumps blood to whole body. Synchronous contraction of the heart muscles assures that the required blood flow is supplied to organs. But sometimes the synchrony between those muscles is distorted, which results in reduced cardiac output that might lead to severe diseases, and even death. The most common of heart diseases are myocardial infarction and arrhythmias. The contraction of heart muscles is controlled by the electrical activity of the heart, therefore determination of that electrical activity could give us the information regarding the severeness and type of the disease. In order to diagnose heart diseases, classical 12 lead electrocardiogram (ECG) is the standard clinical tool. Although many cardiac diseases could be diagnosed with the 12 lead ECG, measurements from sparse electrode locations limit the interpretations. The main objective of this thesis is to determine the cardiac electrical activity from dense body surface measurements. This problem is called the inverse problem of electrocardiography. The high resolution maps of epicardial potentials could supply the physician the information that could not be obtained with any other method. But the calculation of those epicardial potentials are not easy
the problem is severely ill-posed due to the discretization and attenuation within the thorax. To overcome this ill-posedness, the solution should be constrained using prior information on the epicardial potential distributions. In this thesis, spatial and spatio-temporal Bayesian maximum a posteriori estimation (MAP), Tikhonov regularization and Kalman filter and Kalman smoother approaches are used to overcome the ill-posedness that is associated with the inverse problem of ECG. As part of the Kalman filter approach, the state transition matrix (STM) that determines the evolution of epicardial potentials over time is also estimated, both from the true epicardial potentials and previous estimates of the epicardial potentials. An activation time based approach was developed to overcome the computational complexity of the STM estimation problem. Another objective of this thesis is to study the effects of geometric errors to the solutions, and modify the inverse solution algorithms to minimize these effects. Geometric errors are simulated by changing the size and the location of the heart in the mathematical torso model. These errors are modeled as additive Gaussian noise in the inverse problem formulation. Residual-based and expectation maximization methods are implemented to estimate the measurement and process noise variances, as well as the geometric noise.

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De, Groeve Johannes. "A wildlife journey in space and time: methodological advancements in the assessment and analysis of spatio-temporal patterns of animal movement across European landscapes". Doctoral thesis, country:BE, 2018. http://hdl.handle.net/10449/52251.

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Movement is one of the most fundamental processes for living entities on earth at the core of scientific disciplines such as ecology and geography. In animal ecology, ongoing progress in tracking and remote sensing technologies has spurred an explosion of movement and environmental data collected at high spatial and temporal resolution, at a large scale, so that the interaction between animal movement and habitat features can now be investigated in much more detail. As a result, in recent years the field of animal ecology has produced a growing body of studies on movement-based patterns leading to habitat use and selection. In this regard, GIScience has contributed with several visual analytical approaches to study animals in relation to their environment and habitat. However, the pat - terns behind the sequential use of different habitat classes have remained largely unexplored. Sequential habitat use is defined as the consecutive use of habitat features along the trajectory of an animal, extracted from the context of its spatial movement. By account - ing for the sequence of use, it is possible to distinguish fundamentally different behavioural habitat use strategies that are important for the survival and fitness of an animal, such as habitat alternation versus random sequential use. Such distinctions would remain undetected by only considering the proportion of use. Sequential habitat use patterns occur in a spatial context, meaning sequential patterns are affected by what is actually available to the animal. In this dissertation we merge knowledge from different fields to present an innovative method to study the relation between animals and their environment by accounting for the sequential use of habitats, and animal movement rules. We developed a visually effective method to analyse and visualise sequential habitat use patterns of animals at multiple spatio- temporal scales by combining real and simulated sequences of habitat use. To study sequential habitat use patterns we use Sequence Analysis Methods (SAM), an approach widely applied in molecular biology, as well as many applications in different fields, to measure dissimilarity between sequences of characters. In brief, we use dissimilarity algorithms to measure the distance between all pairs of sequences, and then apply a cluster - ing algorithm to investigate how these sequences group together, which are visualised as dissimilarity trees. We propose a procedure consisting of three steps, including explo- ration, simulation and classification. In the exploration phase, we build exploratory trees, which visualise real sequential habitat use patterns. Second, by applying animal movement models we simulate expected sequential habitat use patterns, and assess how spatial context, and especially habitat availability, affects the clustering of sequential patterns. Third, we combine real and simulated sequences to identify which simulated pattern is most parsimonious with the real sequences. The research progress has been presented in three main chapters. In Chapter 3 we present seminal methodological development where SAM was applied to animal movement data. In Chapter 4 we introduce further methodological advancements to extend the applicability of SAM to animal ecology. In Chapter 5 we present a large-scale multi-population ecological application. All research was performed using GPS movement data of roe deer and environmental data provided by the Euroungulates database project. Chapter 3 presents the first application of SAM to identify ecologically relevant sequential patterns in animal habitat use. We exemplify the method using ecological data consisting of simulated and real trajectories from a roe deer population (Capreolus capreolus) in the Italian Alps, expressed as ordered sequences of four habitat use classes, i.e. high/open, high/closed, low/open, low/closed. In essence, the SAM framework identifies relevant sequential patterns in real trajectories by measuring their similarity to spatially-explicit simulated trajectories with known sequential patterns. Simulation trajectories were generated in arenas resembling the landscape structure of the roe deer population. Chapter 4 extends SAM to an individual-based approach (i.e. IM-SAM, Individual Movement – Sequence Analysis Methods), that is applicable over multiple populations. Specifically, instead of performing simulations in landscape-like arenas, we use real individual home ranges, thus accounting for individual spatial context, and landscape composition and structure. To assess usability of our advanced framework we investigate the sequential use of open and forest habitats for nine roe deer populations ranging in landscapes with different geographic contexts and anthropogenic disturbance. We also discuss implications for conservation and management. Chapter 5 addresses the functional role of landscapes throughout seasons by identifying both population level and individual level variability in the sequential habitat use patterns of roe deer, identified in the former nine roe deer populations. We show how identified sequential habitat use patterns can be treated as variables, and analysed with standard and well-accepted statistical methods. While the (IM-)SAM framework was developed for studying sequential habitat use in specific, we highlight that its methodological steps and study design can easily be gener- alised. Indeed, its dissimilarity and clustering algorithms, temporal resolution, sampling units, and number of classes for which sequential patterns are investigated can all be customised for the specific research questions in mind. (IM-)SAM is easily applicable to different types of sequential data that describe aspects of an animal's internal (e.g. heart rate) or external state (e.g. temperature). Through improvements in technology, including the growing number of information that can be collected through sensors (GPS trackers, biologgers and satellites), improving database infrastructures and the instant availability of advanced R packages dedicated to animal movement, (IM-)SAM could be easily integrated in a wide range of both local and broad-scaled behavioural spatio-temporal studies.

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Duncan, Earl W. "Bayesian approaches to issues arising in spatial modelling". Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/112356/1/Earl_Duncan_Thesis.pdf.

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This thesis addressed several contemporary issues arising in the analysis of spatial data and the broader statistical methodology. Two state-of-the-art statistical models are developed for the purpose of identifying unusual trends, a new algorithm to deal with label switching is devised which outperforms existing solutions, and new approaches to spatial smoothing are explored. The outcomes from this thesis should be of interest to managers in the health sector, biostatisticians, and researchers who deal with spatial data.

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Porter, Jeremy Reed. "The spatial demography of reported crime an examination of urban-rural crime articulation and associated spatio-temporal diffusion processes, U.S. 1990 - 2000 /". Diss., Mississippi State : Mississippi State University, 2008. http://library.msstate.edu/etd/show.asp?etd=etd-10272008-083903.

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Campos, Medina Fernando Andrés Verfasser], Hartmut [Akademischer Betreuer] [Rosa i Benno [Akademischer Betreuer] Werlen. "The spatio-temporal structures of society : modernity and ecological modernization as restructurations of time and space / Fernando Andrés Campos Medina. Gutachter: Hartmut Rosa ; Benno Werlen". Jena : Thüringer Universitäts- und Landesbibliothek Jena, 2015. http://d-nb.info/1066238618/34.

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Campos, Medina Fernando Andrés [Verfasser], Hartmut [Akademischer Betreuer] Rosa i Benno [Akademischer Betreuer] Werlen. "The spatio-temporal structures of society : modernity and ecological modernization as restructurations of time and space / Fernando Andrés Campos Medina. Gutachter: Hartmut Rosa ; Benno Werlen". Jena : Thüringer Universitäts- und Landesbibliothek Jena, 2015. http://d-nb.info/1066238618/34.

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Sichtig, Heike. "The SGE framework discovering spatio-temporal patterns in biological systems with spiking neural networks (S), a genetic algorithm (G) and expert knowledge (E) /". Diss., Online access via UMI:, 2009.

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Thesis (Ph. D.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Bioengineering, Biomedical Engineering, 2009.
Includes bibliographical references.

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Zheng, Jiaqi. "Interactive Visual Analytics for Agent-Based simulation : Street-Crossing Behavior at Signalized Pedestrian Crossing". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264991.

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To design a pedestrian crossing area reasonably can be a demanding task for traffic planners. There are several challenges, including determining the appropriate dimensions, and ensuring that pedestrians are exposed to the least risks. Pedestrian safety is especially obscure to analyze, given that many people in Stockholm cross the street illegally by running against the red light. To cope with these challenges, computational approaches of trajectory data visual analytics can be used to support the analytical reasoning process. However, it remains an unexplored field regarding how to visualize and communicate the street-crossing spatio-temporal data effectively. Moreover, the rendering also needs to deal with a growing data size for a more massive number of people. This thesis proposes a web-based interactive visual analytics tool for pedestrians' street-crossing behavior under various flow rates. The visualization methodology is also presented, which is then evaluated to have achieved satisfying communication and rendering effectiveness for maximal 180 agents over 100 seconds. In terms of the visualization scenario, pedestrians either wait for the red light or cross the street illegally; all people can choose to stop by a buffer island before they finish crossing. The visualization enables the analysis under multiple flow rates for 1) pedestrian movement, 2) space utilization, 3) crossing frequency in time-series, and 4) illegal frequency. Additionally, to acquire the initial trajectory data, Optimal Reciprocal Collision Avoidance (ORCA) algorithm is engaged in the crowd simulation. Then different visualization techniques are utilized to comply with user demands, including map animation, data aggregation, and time-series graph.
Att konstruera ett gångvägsområde kan rimligen vara en krävande uppgift för trafikplanerare. Det finns flera utmaningar, bland annat att bestämma lämpliga dimensioner och se till att fotgängare utsätts för minst risker. Fotgängarnas säkerhet är särskilt obskyrlig att analysera, eftersom många människor i Stockholm korsar gatan olagligt genom att springa mot det röda ljuset. För att klara av dessa utmaningar kan beräkningsmetoder för bana data visuell analys användas för att stödja den analytiska resonemangsprocessen. Det är emellertid ett oexplorerat fält om hur man visualiserar och kommunicerar gataövergången spatio-temporal data effektivt. Dessutom måste rendering också hantera en växande datastorlek för ett mer massivt antal människor. Denna avhandling föreslår ett webbaserat interaktivt visuellt analysverktyg för fotgängares gatöverföring under olika flödeshastigheter. Visualiseringsmetoden presenteras också, som sedan utvärderas för att ha uppnått tillfredsställande kommunikation och effektivitet för maximal 180 agenter över 100 sekunder. Vad beträffar visualiseringsscenariot, väntar fotgängare antingen på det röda ljuset eller tvärs över gatan; alla människor kan välja att stanna vid en buffertö innan de slutar korsa. Visualiseringen möjliggör analysen under flera flödeshastigheter för 1) fotgängarrörelse, 2) rymdutnyttjande, 3) korsfrekvens i tidsserier och 4) olaglig frekvens. För att förvärva den ursprungliga bana-data är Optimal Reciprocal Collision Avoidance (ORCA) algoritmen förknippad med folkmassimuleringen. Därefter utnyttjas olika visualiseringstekniker för att uppfylla användarnas krav, inklusive kartanimering, dataaggregering och tidsserier.

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Filho, NarcÃlio de SÃ Pereira. "AnÃlise da dinÃmica espaÃo-temporal (1973 a 2014) das dunas de Jericoacoara, CearÃ, Brasil". Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=15910.

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CoordenaÃÃo de AperfeÃoamento de Pessoal de NÃvel Superior
Dunas costeiras exercem um importante papel na manutenÃÃo do fluxo de sedimentos da zona costeira. O Parque Nacional de Jericoacoara, localizado no estado do CearÃ, regiÃo Nordeste do Brasil, possui uma morfologia pouco frequente, trata-se de um promontÃrio associado com um campo de dunas mÃveis denominadas barcanas, dunas individuais, de grande porte com formato de ferraduras que se deslocam em direÃÃo L â O. Elas realizam o by-pass, o transporte de sedimentos, essencial para a manutenÃÃo da linha de costa. Neste trabalho, foi priorizada a definiÃÃo da evoluÃÃo morfodinÃmica de dunas mÃveis isoladas (dunas Papai Noel, PÃr-do-Sol e Arraia), tendo como referencial teÃrico a anÃlise das paisagens e como procdimento tÃcnico principal a anÃlise espaÃo-temporal do recobrimento de imagens multitemporais dos satÃlites Landsat e Quickbird entre os anos de 1973 a 2014. AtravÃs da comparaÃÃo da distribuiÃÃo espaÃo temporal das morfologias dunares, nesse perÃodo de 41 anos, evidenciaram-se mudanÃas significativas na Ãrea, perÃmetro e deslocamento das dunas. Foi possÃvel constatar a aÃÃo dos fluxos de matÃria e energia vinculados com migraÃÃo continuada direcionada para a faixa de praia (setor de bypassing de sedimentos). A dinÃmica de migraÃÃo das dunas, quando analisadas apÃs as imagens de 2000, evidenciou possibilidades de alteraÃÃes dos aspectos morfolÃgicos influenciados pelo incremento do fluxo turÃstico, quando instituÃdo o PARNA de Jericoacoara. As mudanÃas foram mais significativas, sobretudo, entre os anos de 2001 a 2005, o que pode estar relacionado a uma maior intervenÃÃo humana (fluxo de turistas). A utilizaÃÃo das tÃcnicas de geoprocessamento para o mapeamento da evoluÃÃo morfodinÃmica do campo de dunas do Parque Nacional de Jericoacoara constituiu- se uma ferramenta essencial para a produÃÃo de informaÃÃes que certamente subsidiarÃo a continuidade do planejamento ambiental da referida, que se constitui como uma Unidade de ConservaÃÃo de ProteÃÃo Integral.
Coastal dunes play an important role in the sediment flow of the coastal zone. The unique morphology of the Jericoacoara National Park in the northeastern Brazilian state of CearÃ consists of a promontory covered by a mobile dune field consisting of large, horseshoe-shaped dunes known locally as barcanas that migrate from east to west. These dunes are responsible for the by-pass, the transport of sediments essential for the maintenance of the coastline. The present study focused on the morphodynamic evolution of these isolated mobile dunes through the recovery of multitemporal Landsat and Quickbird satellite images from the years between 1975 and 2014. The comparison of the spatio-temporal distribution of the morphology of these dunes over this 41-year period revealed significant shifts in their area, perimeter, and movement. It was possible to confirm that the flow of material and energy were linked to a process of continuous migration in the direction of the beach (sediment bypassing sector). The dynamics of the dune migration in the years following 2000, when the national park was established, indicate possible impacts of the increase in tourism within the area on the morphology of the dunes. The changes were most significant between 2001 and 2005, possibly reflecting a greater influx of tourists and thus more intense anthropogenic impacts. The different geoprocessing techniques applied to the mapping of the morphodynamic evolution of the dune field of the Jericoacoara National Park proved to be an essential tool for the production of information that will guarantee the long-term environmental planning of this integral conservation unit.

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Law, Man-yee Caroline, i 羅敏儀. "Dynamics of urban green spaces: assessing spatio-temporal changes in Shenzhen (China)". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43223898.

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Law, Man-yee Caroline. "Dynamics of urban green spaces assessing spatio-temporal changes in Shenzhen (China) /". Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43223898.

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Muscat, Laurent. "Coupling of time integration schemes for compressible unsteady flows". Thesis, Toulouse, INPT, 2019. http://www.theses.fr/2019INPT0010/document.

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Dans ce travail, on s'intéresse au développement d'une méthode hybride qui couple spatialement les schémas d'intégration temporelle explicite et implicite. Afin de répondre aux contraintes du solveur industriel FLUSEPA, les schémas explicite Heun et implicite Crank-Nicolson ont été hybridés via un paramètre de transition : l'approche mise en place est appelée schéma AION. Cette dernière est étudiée en détails avec une attention particulière sur son comportement spectral et sa capacité à maintenir l'ordre de précision. On montre que le traitement hybride a d'intéressants comportements dissipatif et dispersif tout en empêchant la réflexion d'ondes parasites et en maintenant la précision attendue. De plus, l'approche hybride est validée sur plusieurs cas académiques à la fois pour les flux convectifs et pour les flux diffusifs. Et comme espéré, la méthode est plus intéressante en terme de temps de calcul que les méthodes standards d'intégration temporelle. Pour l'extension de cette approche à la méthode temporelle adaptative présente dans FLUSEPA, il a été nécessaire d'améliorer le traitement qui permet à la méthode d’être conservative tout en obtenant des propriétés spectrales acceptables. Finalement l'approche hybride a été aussi étendue pour la modélisation RANS/LES de la turbulence avec des temps de calcul intéressants tout en capturant la physique de l'écoulement
This work deals with the design of a hybrid time integrator that couples spatially explicit and implicit time integrators. In order to cope with the industrial solver of Ariane Group called FLUSEPA, the explicit scheme of Heun and the implicit scheme of Crank-Nicolson are hybridized using the transition parameter : the whole technique is called AION time integration. The latter is studied into details with special focus on spectral behaviour and on its ability to keep the accuracy. It is shown that the hybrid technique has interesting dissipation and dispersion properties while maintaining precision and avoiding spurious waves. Moreover, this hybrid approach is validated on several academic test cases for both convective and diffusive fluxes. And as expected the method is more interesting in term of computational time than standard time integrators. For the extension of this hybrid approach to the temporal adaptive method implemented in FLUSEPA, it was necessary to improve some treatments in order to maintain conservation and acceptable spectral properties. Finally the hybrid time integration was also applied to a RANS/LES turbulent test case with interesting computational time while capturing the flow physics

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Noordhuis-Fairfax, Sarina. "Field | Guide: John Berger and the diagrammatic exploration of place". Phd thesis, Canberra, ACT : The Australian National University, 2018. http://hdl.handle.net/1885/154278.

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Positioned between writing and drawing, the diagram is proposed by John Berger as an alternative strategy for articulating encounters with landscape. A diagrammatic approach offers a schematic vocabulary that can compress time and offer a spatial reading of information. Situated within the contemporary field of direct data visualisation, my practice-led research interprets Berger’s ‘Field’ essay as a guide to producing four field | studies within a suburban park in Canberra. My seasonal investigations demonstrate how applying the conventions of the pictorial list, dot-distribution map, routing diagram and colour-wheel reveals subtle ecological and biographical narratives.

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Chin-HsiengFang i 方競賢. "Spatio-temporal Space Learning for Human Action Recognition". Thesis, 2010. http://ndltd.ncl.edu.tw/handle/12099050671437137549.

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碩士
國立成功大學
資訊工程學系碩博士班
98
In this thesis we propose a novel framework for human action recognition. In our framework, the major concept is that we would like to add the temporal information into the action recognition process. Based on this purpose, here we propose five kinds of temporal information in chapter 2.2, and we do have some progress by adding temporal concept. Moreover, we introduce a new dimensionality reduction method “Adaptive Locality Preserving Projections” (ALPP) that learns a better spatial subspace. And the experimental results demonstrate that our method can recognize the actions well. Especially with our DTM and DTMWB framework, they can achieve good accuracy rates. We also test noisy data to see if our framework has flexibility or not. The results show that our framework still has great accuracies toward noise, that proves our framework do have good tolerance for noise.

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Khetarpaul, Sonia. "Analizing moving objects data in spatio-temporal space and social networks". Thesis, 2017. http://localhost:8080/iit/handle/2074/7511.

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Baskaran, Savitha. "Visualization of spatio-temporal data in two dimensional space". Thesis, 2016. https://doi.org/10.7912/C2GQ01.

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Indiana University-Purdue University Indianapolis (IUPUI)
Spatio-temporal data is becoming very popular in the recent times, as there are large number of datasets that collect both location and temporal information in the real time. The main challenge is that extracting useful insights from such large data set is extremely complex and laborious. In this thesis, we have proposed a novel 2D technique to visualize the spatio-temporal big data. The visualization of the combined interaction between the spatial and temporal data is of high importance to uncover the insights and identify the trends within the data. Maps have been a successful way to represent the spatial information. Addition- ally, in this work, colors are used to represent the temporal data. Every data point has the time information which is converted into relevant color, based on the HSV color model. The variation in the time is represented by transition from one color to another and hence provide smooth interpolation. The proposed solution will help the user to quickly understand the data and gain insights.

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Oliveira, João Ricardo Leite Mota. "Spatio-temporal SNN : integrating time and space in the clustering process". Master's thesis, 2013. http://hdl.handle.net/1822/29459.

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Dissertação de mestrado em Engenharia e Gestão de Sistemas de Informação
Spatio-temporal clustering is a new subfield of data mining that is increasingly gaining scientific attention due to the technical advances of location-based or environmental devices that register position, time and, in some cases, other semantic attributes. This process intends to group objects based in their spatial and temporal similarity helping to discover interesting patterns and correlations in large datasets. One of the main challenges of this area is that there are different types of spatio-temporal data and there is no general approach to treat all these types. Another challenge still unresolved is the ability to integrate several dimensions in the clustering process with a general-purpose approach. Moreover, it was also possible to verify that few works address their implementations under the SNN (Shared Nearest Neighbour) algorithm, which gives the opportunity to propose an innovative extension of this particular algorithm. This work intends to implement in the SNN clustering algorithm the ability to deal with spatio-temporal data allowing the integration of space, time and one or more semantic attributes in the clustering process. In this document, background knowledge about clustering, spatial clustering and spatio-temporal clustering are presented along with a summary of the main approaches followed to cluster spatio-temporal data with different clustering algorithms. Based on those approaches, and in the analysis of their advantages and disadvantages, the boundaries of this work are defined in order to incorporate the space, time and semantic attribute dimensions in the SNN algorithm and thus propose the 4D+SNN approach. The results presented in this work are very promising as the approach proposed is able to identify interesting patterns on spatio-temporal data. Concretely, it can identify clusters taking into account simultaneously the spatial and temporal dimension and it also has good results when adding one or more semantic attributes.
O clustering espaço-temporal é uma nova área do data mining que está a ganhar crescente atenção por parte da comunidade científica devido aos avanços tecnológicos dos dispositivos de localização ou monitorização ambiental que registam posição, tempo e, em alguns casos, outros atributos semânticos. Este processo pretende agrupar objectos segundo as suas similaridades espaciais e temporais ajudando assim a descobrir padrões interessantes e correlações em grandes conjuntos de dados. Um dos grandes desafios nesta área é a existência de vários tipos de dados espaço-temporais e não existe uma abordagem geral para tratar todos estes tipos. Outro desafio ainda por resolver é a capacidade para integrar várias dimensões no processo de clustering com uma abordagem geral. Além disso, foi possível verificar que poucos trabalhos de investigação usam o algoritmo SNN (Shared Nearest Neighbour) nas suas implementações o que dá a oportunidade para propor uma extensão inovadora para este algoritmo em particular. Este trabalho pretende implementar no algoritmo de clustering SNN a capacidade para lidar com dados espaço-temporais permitindo assim a integração do espaço, tempo e um ou mais atributos semânticos no processo de clustering. Neste documento, serão apresentados alguns conceitos sobre clustering, clustering espacial e clustering espaço-temporal assim como um resumo das principais abordagens usadas para fazer o clustering de dados espaço-temporais com algoritmos de clustering diferentes. Baseado nestas abordagens e na análise das suas vantagens e desvantagens, serão definidos os limites deste trabalho de modo a incorporar as dimensões espaço, tempo e atributo semântico no algoritmo SNN e, assim, propor a abordagem 4D+SNN. Os resultados apresentados neste trabalho são bastante promissores pois a abordagem proposta é capaz de identificar padrões interessantes em dados espaço-temporais. Concretamente, consegue identificar clusters tendo em consideração simultaneamente as dimensões espaço e tempo e também obtém bons resultados quando adicionando um ou mais atributos semânticos.

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Zhou, Yi-Fan, i 周一帆. "Ruin Space Narrative: Spatio-Temporal Perception of J. L. Borges’ Fictions". Thesis, 2016. http://ndltd.ncl.edu.tw/handle/v7jg53.

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碩士
淡江大學
建築學系碩士班
104
What the two themes are discussed in this thesis, one is about the fabled narratives in the fictions by Argentina fictionist Jorge Luis Borges, the other one is about the real existing ruins. His fantastic and abstruse ways of narrative, reflect the chaos of the world and the sense of unreality in literature. And his works are full of metaphor, symbolizing, dreamlands, mirrors, mazes, etc, referring to the architectural spatio-temporal perception. With the author’s meticulous fabrication, they distribute an air of mystery. Ruins, as the “Heterotopia” mentioned by Michel Foucault, although exist actually somewhere in the world with some real conditions, nevertheless seemingly always stay far away from the real world, being on the margin of human’s consciousness stilly. These places of nowhere, also have the properties of spatio-temporal uniqueness. This thesis aims through analyzing the commonness between literature and architecture, fictionists and architects, to measure the possibilities of transcending the disciplinary limits of each other. So ruins are no more unused spaces with uselessness, but heterotopias with the existential importance along the whole time span. Therefore, this thesis consists of two phases. The first phase is “Fiction - Space”, taking Borges’ works as the discussion objects. On one hand, through interpreting four of his fictions referring to ruins and spatio-temporal narratives, unveil his unique perspective of the word’s nihility. On the other hand, through attempts of spatial narratives, represent these four texts in the form of spaces. The second phase is “Space – Fiction - Space”, taking Kowloon Walled City in Hong Kong existing during the year of 1987~1992 as the discussion object. On one hand, analyzing the ruins’ physical and heterogeneous properties, and its functions as well. On the other hand, use design narratives with the script, paths and scenes, to reveal the experiences being compressed within Kowloon Walled City in a way of surrealism.

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Salvaña, Mary Lai O. "Lagrangian Spatio-Temporal Covariance Functions for Multivariate Nonstationary Random Fields". Thesis, 2021. http://hdl.handle.net/10754/669674.

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In geostatistical analysis, we are faced with the formidable challenge of specifying a valid spatio-temporal covariance function, either directly or through the construction of processes. This task is di cult as these functions should yield positive de nite covariance matrices. In recent years, we have seen a ourishing of methods and theories on constructing spatiotemporal covariance functions satisfying the positive de niteness requirement. The current state-of-the-art when modeling environmental processes are those that embed the associated physical laws of the system. The class of Lagrangian spatio-temporal covariance functions ful lls this requirement. Moreover, this class possesses the allure that they turn already established purely spatial covariance functions into spatio-temporal covariance functions by a direct application of the concept of Lagrangian reference frame. In the three main chapters that comprise this dissertation, several developments are proposed and new features are provided to this special class. First, the application of the Lagrangian reference frame on transported purely spatial random elds with second-order nonstationarity is explored, an appropriate estimation methodology is proposed, and the consequences of model misspeci cation is tackled. Furthermore, the new Lagrangian models and the new estimation technique are used to analyze particulate matter concentrations over Saudi Arabia. Second, a multivariate version of the Lagrangian framework is established, catering to both secondorder stationary and nonstationary spatio-temporal random elds. The capabilities of the Lagrangian spatio-temporal cross-covariance functions are demonstrated on a bivariate reanalysis climate model output dataset previously analyzed using purely spatial covariance functions. Lastly, the class of Lagrangian spatio-temporal cross-covariance functions with multiple transport behaviors is presented, its properties are explored, and its use is demonstrated on a bivariate pollutant dataset of particulate matter in Saudi Arabia. Moreover, the importance of accounting for multiple transport behaviors is discussed and validated via numerical experiments. Together, these three extensions to the Lagrangian framework makes it a more viable geostatistical approach in modeling realistic transport scenarios.

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SEIDENARI, LORENZO. "Supervised and Semi-supervised Event Detection with Local Spatio-Temporal Features". Doctoral thesis, 2012. http://hdl.handle.net/2158/609165.

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This thesis deals with various aspects of the analysis of video sequences. The first problem this work deals with is the detection of known categories of events. The second problem this work addresses is the retrieval of events of interest regardless of their specific nature. Finally we propose a method that can leverage both semantic high level features and low level image features to reduce the disk space and bandwidth needed.

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Cochran, Benjamin Mark 1982. "A framework for spatio-temporal querying amongst mobile devices". Thesis, 2012. http://hdl.handle.net/2152/ETD-UT-2012-05-5286.

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With mobile web browsers holding around eight percent of the global browser market share in terms of usage, web development for these platforms is becoming critically important as usage moves from the desktop towards mobile devices. Recent advances in client side browser technology like HTML5 and WebSockets have allowed web browser applications to approach feature parity with thick client desktop applications. This paper explores the possibility of a real-time online multiplayer game playable from just a mobile device's web browser. It does not focus on gameplay or graphics, rather it focuses on the backend infrastructure needed to support such a game. The framework devised to support this sort of interaction, Marionette, is well suited towards addressing sharing of location-specific, short-lived information between people using their smartphones without the use of any external software or proprietary software packages on the client side.
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"Developing a Cohesive Space-Time Information Framework for Analyzing Movement Trajectories in Real and Simulated Environments". Doctoral diss., 2011. http://hdl.handle.net/2286/R.I.9514.

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abstract: In today's world, unprecedented amounts of data of individual mobile objects have become more available due to advances in location aware technologies and services. Studying the spatio-temporal patterns, processes, and behavior of mobile objects is an important issue for extracting useful information and knowledge about mobile phenomena. Potential applications across a wide range of fields include urban and transportation planning, Location-Based Services, and logistics. This research is designed to contribute to the existing state-of-the-art in tracking and modeling mobile objects, specifically targeting three challenges in investigating spatio-temporal patterns and processes; 1) a lack of space-time analysis tools; 2) a lack of studies about empirical data analysis and context awareness of mobile objects; and 3) a lack of studies about how to evaluate and test agent-based models of complex mobile phenomena. Three studies are proposed to investigate these challenges; the first study develops an integrated data analysis toolkit for exploration of spatio-temporal patterns and processes of mobile objects; the second study investigates two movement behaviors, 1) theoretical random walks and 2) human movements in urban space collected by GPS; and, the third study contributes to the research challenge of evaluating the form and fit of Agent-Based Models of human movement in urban space. The main contribution of this work is the conceptualization and implementation of a Geographic Knowledge Discovery approach for extracting high-level knowledge from low-level datasets about mobile objects. This allows better understanding of space-time patterns and processes of mobile objects by revealing their complex movement behaviors, interactions, and collective behaviors. In detail, this research proposes a novel analytical framework that integrates time geography, trajectory data mining, and 3D volume visualization. In addition, a toolkit that utilizes the framework is developed and used for investigating theoretical and empirical datasets about mobile objects. The results showed that the framework and the toolkit demonstrate a great capability to identify and visualize clusters of various movement behaviors in space and time.
Dissertation/Thesis
Ph.D. Geography 2011

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Wang, Shen-Zheng, i 王舜正. "Real-time License Plate Recognition based on Cascaded Rejection Mechanisms to Reduce Spatio-temporal Search Space in Video Sequences". Thesis, 2008. http://ndltd.ncl.edu.tw/handle/12389523070181011363.

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博士
國立交通大學
資訊科學與工程研究所
96
In surveillance applications, search space reduction (SSR) is an essential element to efficient algorithms. In this study, spatial and temporal SSRs are integrated for license plate recognition (LPR) in video sequences. However, as more features are measured, the computational load may increase significantly. When regard to the fact that most input patterns are negatives, it is apparently efficient to reject a majority of negatives as soon as possible. Therefore, we propose a realtime LPR based on a cascaded rejection framework to reduce spatiotemporal search space rapidly, while ensuring that the performance is high. To extract plates accurately even in complicated situations, two representations, compact plate regions and repeated regions, are first presented. Compact plate regions, which bound the top and bottom of plate characters, could be extracted in the first stage to avoid the use of additional removal procedures. Our method started from spatial SSR by algorithms of one-pass compact plate extraction, bi-level plate character segmentation, and adaptive machine learning. Region candidates of compact plates or plate characters are extracted and verified by these algorithms performed on effectively calculated features, such as vertical gradients and extended Haar-like features. Moreover, we proposed to exclude repeated patterns with the similar appearances in the same location of consecutive frames, which usually include stopped vehicles or regular backgrounds and could be excluded from repeated classification. For efficiency, repeated patterns were detected only on the plate candidates, named spatiotemporal SSR, based on a block-based mechanism by estimating the tangent distance, which is invariant to the variations in positions, sizes, rotations, or brightness. In our experiments, the search space could be reduced up to 87.9% by the spatiotemporal SSR; the LPR system can recognize plates over 38 frames per second with a resolution of 640 x 480 pixels on a 3-GHz Intel P-IV PC.

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Nketia, Kwabena Abrefa. "Space-time modelling of seasonal soil moisture for improved crop production – the case of the Guinea savannah region, Ghana". Thesis, 2020. http://hdl.handle.net/21.11130/00-1735-0000-0005-1527-8.

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