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Sample records for spatio-temporal point process

  1. Functional Principal Component Analysis of Spatio-Temporal Point Processes with Applications in Disease Surveillance

    PubMed Central

    Li, Yehua; Guan, Yongtao

    2014-01-01

    In disease surveillance applications, the disease events are modeled by spatio-temporal point processes. We propose a new class of semiparametric generalized linear mixed model for such data, where the event rate is related to some known risk factors and some unknown latent random effects. We model the latent spatio-temporal process as spatially correlated functional data, and propose Poisson maximum likelihood and composite likelihood methods based on spline approximations to estimate the mean and covariance functions of the latent process. By performing functional principal component analysis to the latent process, we can better understand the correlation structure in the point process. We also propose an empirical Bayes method to predict the latent spatial random effects, which can help highlight hot areas with unusually high event rates. Under an increasing domain and increasing knots asymptotic framework, we establish the asymptotic distribution for the parametric components in the model and the asymptotic convergence rates for the functional principal component estimators. We illustrate the methodology through a simulation study and an application to the Connecticut Tumor Registry data. PMID:25368436

  2. Modeling sediment transport as a spatio-temporal Markov process.

    NASA Astrophysics Data System (ADS)

    Heyman, Joris; Ancey, Christophe

    2014-05-01

    Despite a century of research about sediment transport by bedload occuring in rivers, its constitutive laws remain largely unknown. The proof being that our ability to predict mid-to-long term transported volumes within reasonable confidence interval is almost null. The intrinsic fluctuating nature of bedload transport may be one of the most important reasons why classical approaches fail. Microscopic probabilistic framework has the advantage of taking into account these fluctuations at the particle scale, to understand their effect on the macroscopic variables such as sediment flux. In this framework, bedload transport is seen as the random motion of particles (sand, gravel, pebbles...) over a two-dimensional surface (the river bed). The number of particles in motion, as well as their velocities, are random variables. In this talk, we show how a simple birth-death Markov model governing particle motion on a regular lattice accurately reproduces the spatio-temporal correlations observed at the macroscopic level. Entrainment, deposition and transport of particles by the turbulent fluid (air or water) are supposed to be independent and memoryless processes that modify the number of particles in motion. By means of the Poisson representation, we obtained a Fokker-Planck equation that is exactly equivalent to the master equation and thus valid for all cell sizes. The analysis shows that the number of moving particles evolves locally far from thermodynamic equilibrium. Several analytical results are presented and compared to experimental data. The index of dispersion (or variance over mean ratio) is proved to grow from unity at small scales to larger values at larger scales confirming the non Poisonnian behavior of bedload transport. Also, we study the one and two dimensional K-function, which gives the average number of moving particles located in a ball centered at a particle centroid function of the ball's radius.

  3. Spatio-temporal statistical models with applications to atmospheric processes

    SciTech Connect

    Wikle, C.K.

    1996-12-31

    This doctoral dissertation is presented as three self-contained papers. An introductory chapter considers traditional spatio-temporal statistical methods used in the atmospheric sciences from a statistical perspective. Although this section is primarily a review, many of the statistical issues considered have not been considered in the context of these methods and several open questions are posed. The first paper attempts to determine a means of characterizing the semiannual oscillation (SAO) spatial variation in the northern hemisphere extratropical height field. It was discovered that the midlatitude SAO in 500hPa geopotential height could be explained almost entirely as a result of spatial and temporal asymmetries in the annual variation of stationary eddies. It was concluded that the mechanism for the SAO in the northern hemisphere is a result of land-sea contrasts. The second paper examines the seasonal variability of mixed Rossby-gravity waves (MRGW) in lower stratospheric over the equatorial Pacific. Advanced cyclostationary time series techniques were used for analysis. It was found that there are significant twice-yearly peaks in MRGW activity. Analyses also suggested a convergence of horizontal momentum flux associated with these waves. In the third paper, a new spatio-temporal statistical model is proposed that attempts to consider the influence of both temporal and spatial variability. This method is mainly concerned with prediction in space and time, and provides a spatially descriptive and temporally dynamic model.

  4. The Voxel-Wise Functional Connectome Can Be Efficiently Derived from Co-activations in a Sparse Spatio-Temporal Point-Process

    PubMed Central

    Tagliazucchi, Enzo; Siniatchkin, Michael; Laufs, Helmut; Chialvo, Dante R.

    2016-01-01

    Large efforts are currently under way to systematically map functional connectivity between all pairs of millimeter-scale brain regions based on large neuroimaging databases. The exploratory unraveling of this “functional connectome” based on functional Magnetic Resonance Imaging (fMRI) can benefit from a better understanding of the contributors to resting state functional connectivity. In this work, we introduce a sparse representation of fMRI data in the form of a discrete point-process encoding high-amplitude events in the blood oxygenation level-dependent (BOLD) signal and we show it contains sufficient information for the estimation of functional connectivity between all pairs of voxels. We validate this method by replicating results obtained with standard whole-brain voxel-wise linear correlation matrices in two datasets. In the first one (n = 71), we study the changes in node strength (a measure of network centrality) during deep sleep. The second is a large database (n = 1147) of subjects in which we look at the age-related reorganization of the voxel-wise network of functional connections. In both cases it is shown that the proposed method compares well with standard techniques, despite requiring only data on the order of 1% of the original BOLD signal time series. Furthermore, we establish that the point-process approach does not reduce (and in one case increases) classification accuracy compared to standard linear correlations. Our results show how large fMRI datasets can be drastically simplified to include only the timings of large-amplitude events, while still allowing the recovery of all pair-wise interactions between voxels. The practical importance of this dimensionality reduction is manifest in the increasing number of collaborative efforts aiming to study large cohorts of healthy subjects as well as patients suffering from brain disease. Our method also suggests that the electrophysiological signals underlying the dynamics of fMRI time series

  5. The Voxel-Wise Functional Connectome Can Be Efficiently Derived from Co-activations in a Sparse Spatio-Temporal Point-Process.

    PubMed

    Tagliazucchi, Enzo; Siniatchkin, Michael; Laufs, Helmut; Chialvo, Dante R

    2016-01-01

    Large efforts are currently under way to systematically map functional connectivity between all pairs of millimeter-scale brain regions based on large neuroimaging databases. The exploratory unraveling of this "functional connectome" based on functional Magnetic Resonance Imaging (fMRI) can benefit from a better understanding of the contributors to resting state functional connectivity. In this work, we introduce a sparse representation of fMRI data in the form of a discrete point-process encoding high-amplitude events in the blood oxygenation level-dependent (BOLD) signal and we show it contains sufficient information for the estimation of functional connectivity between all pairs of voxels. We validate this method by replicating results obtained with standard whole-brain voxel-wise linear correlation matrices in two datasets. In the first one (n = 71), we study the changes in node strength (a measure of network centrality) during deep sleep. The second is a large database (n = 1147) of subjects in which we look at the age-related reorganization of the voxel-wise network of functional connections. In both cases it is shown that the proposed method compares well with standard techniques, despite requiring only data on the order of 1% of the original BOLD signal time series. Furthermore, we establish that the point-process approach does not reduce (and in one case increases) classification accuracy compared to standard linear correlations. Our results show how large fMRI datasets can be drastically simplified to include only the timings of large-amplitude events, while still allowing the recovery of all pair-wise interactions between voxels. The practical importance of this dimensionality reduction is manifest in the increasing number of collaborative efforts aiming to study large cohorts of healthy subjects as well as patients suffering from brain disease. Our method also suggests that the electrophysiological signals underlying the dynamics of fMRI time series

  6. Bayesian hierarchical models for multivariate nonlinear spatio-temporal dynamical processes in the atmosphere and ocean

    NASA Astrophysics Data System (ADS)

    Leeds, W. B.; Wikle, C. K.

    2012-12-01

    Spatio-temporal statistical models, and in particular Bayesian hierarchical models (BHMs), have become increasingly popular as means of representing natural processes such as climate and weather that evolve over space and time. Hierarchical models make it possible to specify separate, conditional probability distributions that account for uncertainty in the observations, the underlying process, and parameters in situations when specifying these sources of uncertainty in a joint probability distribution may be difficult. As a result, BHMs are a natural setting for climatologists, meteorologists, and other environmental scientists to incorporate scientific information (e.g., PDEs, IDEs, etc.) a priori into a rigorous statistical framework that accounts for error in measurements, uncertainty in the understanding of the true underlying process, and uncertainty in the parameters that describe the process. While much work has been done in the development of statistical models for linear dynamic spatio-temporal processes, statistical modeling for nonlinear (and particularly, multivariate nonlinear) spatio-temporal dynamical processes is still a relatively open area of inquiry. As a result, general statistical models for environmental scientists to model complicated nonlinear processes is limited. We address this limitation in the methodology by introducing a multivariate "general quadratic nonlinear" framework for modeling multivariate, nonlinear spatio-temporal random processes inside of a BHM in a way that is especially applicable for problems in the ocean and atmospheric sciences. We show that in addition to the fact that this model addresses the previously mentioned sources of uncertainty for a wide spectrum of multivariate, nonlinear spatio-temporal processes, it is also a natural framework for data assimilation, allowing for the fusing of observations with computer models, computer model emulators, computer model output, or "mechanistically motivated" statistical

  7. STGP: Spatio-temporal Gaussian process models for longitudinal neuroimaging data.

    PubMed

    Hyun, Jung Won; Li, Yimei; Huang, Chao; Styner, Martin; Lin, Weili; Zhu, Hongtu

    2016-07-01

    Longitudinal neuroimaging data plays an important role in mapping the neural developmental profile of major neuropsychiatric and neurodegenerative disorders and normal brain. The development of such developmental maps is critical for the prevention, diagnosis, and treatment of many brain-related diseases. The aim of this paper is to develop a spatio-temporal Gaussian process (STGP) framework to accurately delineate the developmental trajectories of brain structure and function, while achieving better prediction by explicitly incorporating the spatial and temporal features of longitudinal neuroimaging data. Our STGP integrates a functional principal component model (FPCA) and a partition parametric space-time covariance model to capture the medium-to-large and small-to-medium spatio-temporal dependence structures, respectively. We develop a three-stage efficient estimation procedure as well as a predictive method based on a kriging technique. Two key novelties of STGP are that it can efficiently use a small number of parameters to capture complex non-stationary and non-separable spatio-temporal dependence structures and that it can accurately predict spatio-temporal changes. We illustrate STGP using simulated data sets and two real data analyses including longitudinal positron emission tomography data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and longitudinal lateral ventricle surface data from a longitudinal study of early brain development. PMID:27103140

  8. Power law in random multiplicative processes with spatio-temporal correlated multipliers

    NASA Astrophysics Data System (ADS)

    Morita, Satoru

    2016-02-01

    It is well known that random multiplicative processes generate power-law probability distributions. We study how the spatio-temporal correlation of the multipliers influences the power-law exponent. We investigate two sources of the time correlation: the local environment and the global environment. In addition, we introduce two simple models through which we analytically and numerically show that the local and global environments yield different trends in the power-law exponent.

  9. Coupled map model for spatio-temporal processing in the olfactory bulb

    NASA Astrophysics Data System (ADS)

    de Almeida, L.; Idiart, M.; Quillfeldt, J. A.

    2007-02-01

    Odor processing in the animal olfactory system is still an open problem in modern neuroscience. It is a common understanding that the spatial code provided by the activity distribution of the olfactory receptor cells (ORC) due the presence of an odorant is transformed into a spatio-temporal code in the mitral cell (MC) layer in the case of mammals, or the projection neurons (PN) in the case of insects, that is decoded later along the neural path. The putative role of the spatio-temporal coding is to disambiguate the stimulus putting it in a more robust representation that allows odor separation, categorization, and recognition. Oscillations due to lateral inhibition among MC's (or PN's) may play an important part in the code as well as neural adaptation. To shed some light on their possible role in the olfaction processing, we study the properties of a simple network model. Upon the presentation of a random distributed input it respond with a rich spatio-temporal structure where two distinct phases are observed. We discuss their properties and implications in information processing.

  10. Characteristics, processes, and causes of the spatio-temporal variabilities of the East Asian monsoon system

    NASA Astrophysics Data System (ADS)

    Huang, Ronghui; Chen, Jilong; Wang, Lin; Lin, Zhongda

    2012-09-01

    Recent advances in the study of the characteristics, processes, and causes of spatio-temporal variabilities of the East Asian monsoon (EAM) system are reviewed in this paper. The understanding of the EAM system has improved in many aspects: the basic characteristics of horizontal and vertical structures, the annual cycle of the East Asian summer monsoon (EASM) system and the East Asian winter monsoon (EAWM) system, the characteristics of the spatio-temporal variabilities of the EASM system and the EAWM system, and especially the multiple modes of the EAM system and their spatio-temporal variabilities. Some new results have also been achieved in understanding the atmosphere-ocean interaction and atmosphere-land interaction processes that affect the variability of the EAM system. Based on recent studies, the EAM system can be seen as more than a circulation system, it can be viewed as an atmosphere-ocean-land coupled system, namely, the EAM climate system. In addition, further progress has been made in diagnosing the internal physical mechanisms of EAM climate system variability, especially regarding the characteristics and properties of the East Asia-Pacific (EAP) teleconnection over East Asia and the North Pacific, the "Silk Road" teleconnection along the westerly jet stream in the upper troposphere over the Asian continent, and the dynamical effects of quasi-stationary planetary wave activity on EAM system variability. At the end of the paper, some scientific problems regarding understanding the EAM system variability are proposed for further study.

  11. Processing discontinuous displacement fields by a spatio-temporal derivative technique

    NASA Astrophysics Data System (ADS)

    Sousa, A. M. R.; Xavier, J.; Morais, J. J. L.; Filipe, V. M. J.; Vaz, M.

    2011-12-01

    In this paper, a digital image correlation (DIC) method coupling cross-correlation with spatio-temporal differential techniques was proposed for assessing discontinuous displacement fields. The accuracy and robustness of the algorithm was assessed on a set of numerical tests by processing computer generated speckled-pattern images. Fracture mechanical tests in mode I were considered, in which both in-plane and out-of-plane rigid-body movements were taken into account. The ability for recovering the analytical asymptotic displacement field in mode I was analysed, and stress intensity factor, crack opening displacement and crack tip location were used as quantitative parameters for validation purposes. Throughout these tests, the results obtained with the proposed method were systematically compared to the ones from Aramis DIC-2D commercial code. Globally, the results computed from both methods are in good agreement with reference values. However, due to the high spatial resolution (point-wise characteristic), a better matching of the displacements in the neighbour of discontinuities could be obtained by the proposed method.

  12. Spatio-Temporal Metabolite Profiling of the Barley Germination Process by MALDI MS Imaging.

    PubMed

    Gorzolka, Karin; Kölling, Jan; Nattkemper, Tim W; Niehaus, Karsten

    2016-01-01

    MALDI mass spectrometry imaging was performed to localize metabolites during the first seven days of the barley germination. Up to 100 mass signals were detected of which 85 signals were identified as 48 different metabolites with highly tissue-specific localizations. Oligosaccharides were observed in the endosperm and in parts of the developed embryo. Lipids in the endosperm co-localized in dependency on their fatty acid compositions with changes in the distributions of diacyl phosphatidylcholines during germination. 26 potentially antifungal hordatines were detected in the embryo with tissue-specific localizations of their glycosylated, hydroxylated, and O-methylated derivates. In order to reveal spatio-temporal patterns in local metabolite compositions, multiple MSI data sets from a time series were analyzed in one batch. This requires a new preprocessing strategy to achieve comparability between data sets as well as a new strategy for unsupervised clustering. The resulting spatial segmentation for each time point sample is visualized in an interactive cluster map and enables simultaneous interactive exploration of all time points. Using this new analysis approach and visualization tool germination-dependent developments of metabolite patterns with single MS position accuracy were discovered. This is the first study that presents metabolite profiling of a cereals' germination process over time by MALDI MSI with the identification of a large number of peaks of agronomically and industrially important compounds such as oligosaccharides, lipids and antifungal agents. Their detailed localization as well as the MS cluster analyses for on-tissue metabolite profile mapping revealed important information for the understanding of the germination process, which is of high scientific interest. PMID:26938880

  13. Spatio-Temporal Metabolite Profiling of the Barley Germination Process by MALDI MS Imaging

    PubMed Central

    Gorzolka, Karin; Kölling, Jan; Nattkemper, Tim W.; Niehaus, Karsten

    2016-01-01

    MALDI mass spectrometry imaging was performed to localize metabolites during the first seven days of the barley germination. Up to 100 mass signals were detected of which 85 signals were identified as 48 different metabolites with highly tissue-specific localizations. Oligosaccharides were observed in the endosperm and in parts of the developed embryo. Lipids in the endosperm co-localized in dependency on their fatty acid compositions with changes in the distributions of diacyl phosphatidylcholines during germination. 26 potentially antifungal hordatines were detected in the embryo with tissue-specific localizations of their glycosylated, hydroxylated, and O-methylated derivates. In order to reveal spatio-temporal patterns in local metabolite compositions, multiple MSI data sets from a time series were analyzed in one batch. This requires a new preprocessing strategy to achieve comparability between data sets as well as a new strategy for unsupervised clustering. The resulting spatial segmentation for each time point sample is visualized in an interactive cluster map and enables simultaneous interactive exploration of all time points. Using this new analysis approach and visualization tool germination-dependent developments of metabolite patterns with single MS position accuracy were discovered. This is the first study that presents metabolite profiling of a cereals’ germination process over time by MALDI MSI with the identification of a large number of peaks of agronomically and industrially important compounds such as oligosaccharides, lipids and antifungal agents. Their detailed localization as well as the MS cluster analyses for on-tissue metabolite profile mapping revealed important information for the understanding of the germination process, which is of high scientific interest. PMID:26938880

  14. A spatio-temporal process data model for characterizing marine disasters

    NASA Astrophysics Data System (ADS)

    Jiang, B.; Zhang, X.; Huang, X.; Gao, T.

    2014-02-01

    Marine disasters are a more prevalent problem in China than in many other countries. Based on the development of a status quo of China's marine disaster the space-time process model is used. The model uses the ocean's temperature field, salinity field, water density field, surface wind field, wave field and other four-dimensional spatio-temporal quantities. This paper studies that model in detail. This study aims at using the theory to provide support during marine disasters in an effort to prevent or decrease their frequency in the future.

  15. Spatio-temporal evolution of biogeochemical processes at a landfill site

    NASA Astrophysics Data System (ADS)

    Arora, B.; Mohanty, B. P.; McGuire, J. T.

    2011-12-01

    Predictions of fate and transport of contaminants are strongly dependent on spatio-temporal variability of soil hydraulic and geochemical properties. This study focuses on time-series signatures of hydrological and geochemical properties at different locations within the Norman landfill site. Norman Landfill is a closed municipal landfill site with prevalent organic contamination. Monthly data at the site include specific conductance, δ18O, δ2H, dissolved organic carbon (DOC) and anions (chloride, sulfate, nitrate) from 1998-2006. Column scale data on chemical concentrations, redox gradients, and flow parameters are also available on daily and hydrological event (infiltration, drainage, etc.) scales. Since high-resolution datasets of contaminant concentrations are usually unavailable, Wavelet and Fourier analyses were used to infer the dominance of different biogeochemical processes at different spatio-temporal scales and to extract linkages between transport and reaction processes. Results indicate that time variability controls the progression of reactions affecting biodegradation of contaminants. Wavelet analysis suggests that iron-sulfide reduction reactions had high seasonal variability at the site, while fermentation processes dominated at the annual time scale. Findings also suggest the dominance of small spatial features such as layered interfaces and clay lenses in driving biogeochemical reactions at both column and landfill scales. A conceptual model that caters to increased understanding and remediating structurally heterogeneous variably-saturated media is developed from the study.

  16. OFDM Radar Space-Time Adaptive Processing by Exploiting Spatio-Temporal Sparsity

    SciTech Connect

    Sen, Satyabrata

    2013-01-01

    We propose a sparsity-based space-time adaptive processing (STAP) algorithm to detect a slowly-moving target using an orthogonal frequency division multiplexing (OFDM) radar. We observe that the target and interference spectra are inherently sparse in the spatio-temporal domain. Hence, we exploit that sparsity to develop an efficient STAP technique that utilizes considerably lesser number of secondary data and produces an equivalent performance as the other existing STAP techniques. In addition, the use of an OFDM signal increases the frequency diversity of our system, as different scattering centers of a target resonate at different frequencies, and thus improves the target detectability. First, we formulate a realistic sparse-measurement model for an OFDM radar considering both the clutter and jammer as the interfering sources. Then, we apply a residual sparse-recovery technique based on the LASSO estimator to estimate the target and interference covariance matrices, and subsequently compute the optimal STAP-filter weights. Our numerical results demonstrate a comparative performance analysis of the proposed sparse-STAP algorithm with four other existing STAP methods. Furthermore, we discover that the OFDM-STAP filter-weights are adaptable to the frequency-variabilities of the target and interference responses, in addition to the spatio-temporal variabilities. Hence, by better utilizing the frequency variabilities, we propose an adaptive OFDM-waveform design technique, and consequently gain a significant amount of STAP-performance improvement.

  17. Spatio-temporal processing of massive glottic images from high-speed videoendoscopy

    NASA Astrophysics Data System (ADS)

    Yan, Yuling; Izdebski, Krzysztof; Marriott, Emma

    2011-03-01

    We present here development and application of new approaches for quantitative spatio-temporal analyses of vocal fold (VF) vibrations derived from high-speed digital imaging (HSDI) data of the glottis. We develop image processing methods to track the motion of the VF and target the analysis of HSDI-derived glottal area waveform (GAW), glottal width function (GWF) and displacements of the VF tissues for the characterization of the VF dynamic properties. In particular, a combined threshold and region growing method is used for the glottis segmentation, and an analytic signal approach and the Nyquist plot and associated parameters are used to represent and to characterize the VF vibratory behaviors in normal and specific pathologic voice productions.

  18. Big Data GPU-Driven Parallel Processing Spatial and Spatio-Temporal Clustering Algorithms

    NASA Astrophysics Data System (ADS)

    Konstantaras, Antonios; Skounakis, Emmanouil; Kilty, James-Alexander; Frantzeskakis, Theofanis; Maravelakis, Emmanuel

    2016-04-01

    Advances in graphics processing units' technology towards encompassing parallel architectures [1], comprised of thousands of cores and multiples of parallel threads, provide the foundation in terms of hardware for the rapid processing of various parallel applications regarding seismic big data analysis. Seismic data are normally stored as collections of vectors in massive matrices, growing rapidly in size as wider areas are covered, denser recording networks are being established and decades of data are being compiled together [2]. Yet, many processes regarding seismic data analysis are performed on each seismic event independently or as distinct tiles [3] of specific grouped seismic events within a much larger data set. Such processes, independent of one another can be performed in parallel narrowing down processing times drastically [1,3]. This research work presents the development and implementation of three parallel processing algorithms using Cuda C [4] for the investigation of potentially distinct seismic regions [5,6] present in the vicinity of the southern Hellenic seismic arc. The algorithms, programmed and executed in parallel comparatively, are the: fuzzy k-means clustering with expert knowledge [7] in assigning overall clusters' number; density-based clustering [8]; and a selves-developed spatio-temporal clustering algorithm encompassing expert [9] and empirical knowledge [10] for the specific area under investigation. Indexing terms: GPU parallel programming, Cuda C, heterogeneous processing, distinct seismic regions, parallel clustering algorithms, spatio-temporal clustering References [1] Kirk, D. and Hwu, W.: 'Programming massively parallel processors - A hands-on approach', 2nd Edition, Morgan Kaufman Publisher, 2013 [2] Konstantaras, A., Valianatos, F., Varley, M.R. and Makris, J.P.: 'Soft-Computing Modelling of Seismicity in the Southern Hellenic Arc', Geoscience and Remote Sensing Letters, vol. 5 (3), pp. 323-327, 2008 [3] Papadakis, S. and

  19. Evaluating Projected Changes in Mean Processes, Extreme Events, and their Spatio-Temporal Dependence Structures

    NASA Astrophysics Data System (ADS)

    Ganguly, A. R.; Steinhaeuser, K.; Kodra, E. A.; Kao, S.

    2010-12-01

    Observational datasets - both raw measurements and derived data products such as reanalysis data - are used to evaluate climate simulations run in forecast and hindcast modes. Bias and uncertainty in mean processes is quantified using statistical comparisons between observations and model-generated outputs. Weather and hydrological extremes under climate change are characterized using both event definitions and extreme value theory (EVT), and their aggregate statistics (intensity, duration and frequency) are likewise compared. The geographic variability and topographical biases are examined at continental to regional scales, and dependence structures (both spatio-temporal autocorrelation and long-range dependence) are assessed using statistical and nonlinear dynamical methods. These tools were developed primarily using the CMIP3/IPCC-AR4 archived model outputs, and are being additional tested with simulations from regional climate models which dynamically downscale the AR4 archives. This combination of traditional and novel tools is thus geared towards evaluation of multiple climate models which may handle processes or generate outputs at different spatial and temporal scales. The tools are expected to be immediately applicable to the CMIP5 data when it becomes available. The anticipated space-time resolutions will pose algorithmic challenges and computational demands, which will be addressed using analytic solutions and implementations thereof on high-performance scientific computing platforms.

  20. The Voronoi spatio-temporal data structure

    NASA Astrophysics Data System (ADS)

    Mioc, Darka

    2002-04-01

    Current GIS models cannot integrate the temporal dimension of spatial data easily. Indeed, current GISs do not support incremental (local) addition and deletion of spatial objects, and they can not support the temporal evolution of spatial data. Spatio-temporal facilities would be very useful in many GIS applications: harvesting and forest planning, cadastre, urban and regional planning, and emergency planning. The spatio-temporal model that can overcome these problems is based on a topological model---the Voronoi data structure. Voronoi diagrams are irregular tessellations of space, that adapt to spatial objects and therefore they are a synthesis of raster and vector spatial data models. The main advantage of the Voronoi data structure is its local and sequential map updates, which allows us to automatically record each event and performed map updates within the system. These map updates are executed through map construction commands that are composed of atomic actions (geometric algorithms for addition, deletion, and motion of spatial objects) on the dynamic Voronoi data structure. The formalization of map commands led to the development of a spatial language comprising a set of atomic operations or constructs on spatial primitives (points and lines), powerful enough to define the complex operations. This resulted in a new formal model for spatio-temporal change representation, where each update is uniquely characterized by the numbers of newly created and inactivated Voronoi regions. This is used for the extension of the model towards the hierarchical Voronoi data structure. In this model, spatio-temporal changes induced by map updates are preserved in a hierarchical data structure that combines events and corresponding changes in topology. This hierarchical Voronoi data structure has an implicit time ordering of events visible through changes in topology, and it is equivalent to an event structure that can support temporal data without precise temporal

  1. Spatio-temporal change modeling with array data

    NASA Astrophysics Data System (ADS)

    Lu, Meng; Pebesma, Edzer

    2015-04-01

    Spatio-temporal change modeling of our ecosystems is critical for environmental conservation. Open access to remote sensing satellite image archives provides new opportunities for change modeling, such as near real-time change monitoring with long term image time series. Newly developed time series analysis methods allow the detection of quantitative changes in trend and seasonality for each pixel of the image. A drawback of pure time series analysis is that spatial dependence is neglected. There are several spatio-temporal statistical approaches to incorporate spatial context. One method is to build hierarchical models with spatial effects for time series parameters. Other methods include representing regression parameters as spatially correlated random fields, or integrating spatial autoregressive models to time series analysis. Apart from spatio-temporal statistical modeling, the results can be further improved by qualification of detected change points with their spatio-temporal neighbors. Spatio-temporal modeling approaches are typically complex and large in scale, and call for new data management and analysis tools. Remote sensing satellite images, which are continuous and regular in space and time, can naturally be represented as three- or four-dimensional arrays for spatio-temporal data management and analysis. The developed spatio-temporal statistical algorithms can be flexibly applied within array partitions that span the relevant array-based dimensions. This study investigates the potential of array-based Data Data Management and Analytic Software (DMAS) for fast data access, data integration and large-scale complex spatio-temporal analysis. A study case is developed in near-real time deforestation monitoring in Amazonian rainforest with long-term 250 m, 8-day resolution MODIS image time series. A novel spatio-temporal change modeling process is being developed and implemented in DMAS to realize rapid and automated analysis of satellite image time series

  2. Time-Resolved and Spatio-Temporal Analysis of Complex Cognitive Processes and their Role in Disorders like Developmental Dyscalculia

    PubMed Central

    Mórocz, István Akos; Janoos, Firdaus; van Gelderen, Peter; Manor, David; Karni, Avi; Breznitz, Zvia; von Aster, Michael; Kushnir, Tammar; Shalev, Ruth

    2012-01-01

    The aim of this article is to report on the importance and challenges of a time-resolved and spatio-temporal analysis of fMRI data from complex cognitive processes and associated disorders using a study on developmental dyscalculia (DD). Participants underwent fMRI while judging the incorrectness of multiplication results, and the data were analyzed using a sequence of methods, each of which progressively provided more a detailed picture of the spatio-temporal aspect of this disease. Healthy subjects and subjects with DD performed alike behaviorally though they exhibited parietal disparities using traditional voxel-based group analyses. Further and more detailed differences, however, surfaced with a time-resolved examination of the neural responses during the experiment. While performing inter-group comparisons, a third group of subjects with dyslexia (DL) but with no arithmetic difficulties was included to test the specificity of the analysis and strengthen the statistical base with overall fifty-eight subjects. Surprisingly, the analysis showed a functional dissimilarity during an initial reading phase for the group of dyslexic but otherwise normal subjects, with respect to controls, even though only numerical digits and no alphabetic characters were presented. Thus our results suggest that time-resolved multi-variate analysis of complex experimental paradigms has the ability to yield powerful new clinical insights about abnormal brain function. Similarly, a detailed compilation of aberrations in the functional cascade may have much greater potential to delineate the core processing problems in mental disorders. PMID:22368322

  3. Multiple-Parameter Estimation Method Based on Spatio-Temporal 2-D Processing for Bistatic MIMO Radar.

    PubMed

    Yang, Shouguo; Li, Yong; Zhang, Kunhui; Tang, Weiping

    2015-01-01

    A novel spatio-temporal 2-dimensional (2-D) processing method that can jointly estimate the transmitting-receiving azimuth and Doppler frequency for bistatic multiple-input multiple-output (MIMO) radar in the presence of spatial colored noise and an unknown number of targets is proposed. In the temporal domain, the cross-correlation of the matched filters' outputs for different time-delay sampling is used to eliminate the spatial colored noise. In the spatial domain, the proposed method uses a diagonal loading method and subspace theory to estimate the direction of departure (DOD) and direction of arrival (DOA), and the Doppler frequency can then be accurately estimated through the estimation of the DOD and DOA. By skipping target number estimation and the eigenvalue decomposition (EVD) of the data covariance matrix estimation and only requiring a one-dimensional search, the proposed method achieves low computational complexity. Furthermore, the proposed method is suitable for bistatic MIMO radar with an arbitrary transmitted and received geometrical configuration. The correction and efficiency of the proposed method are verified by computer simulation results. PMID:26694385

  4. Multiple-Parameter Estimation Method Based on Spatio-Temporal 2-D Processing for Bistatic MIMO Radar

    PubMed Central

    Yang, Shouguo; Li, Yong; Zhang, Kunhui; Tang, Weiping

    2015-01-01

    A novel spatio-temporal 2-dimensional (2-D) processing method that can jointly estimate the transmitting-receiving azimuth and Doppler frequency for bistatic multiple-input multiple-output (MIMO) radar in the presence of spatial colored noise and an unknown number of targets is proposed. In the temporal domain, the cross-correlation of the matched filters’ outputs for different time-delay sampling is used to eliminate the spatial colored noise. In the spatial domain, the proposed method uses a diagonal loading method and subspace theory to estimate the direction of departure (DOD) and direction of arrival (DOA), and the Doppler frequency can then be accurately estimated through the estimation of the DOD and DOA. By skipping target number estimation and the eigenvalue decomposition (EVD) of the data covariance matrix estimation and only requiring a one-dimensional search, the proposed method achieves low computational complexity. Furthermore, the proposed method is suitable for bistatic MIMO radar with an arbitrary transmitted and received geometrical configuration. The correction and efficiency of the proposed method are verified by computer simulation results. PMID:26694385

  5. Processing of facial emotional expression: spatio-temporal data as assessed by scalp event-related potentials.

    PubMed

    Krolak-Salmon, P; Fischer, C; Vighetto, A; Mauguière, F

    2001-03-01

    Event-related potentials (ERPs) were recorded in 10 adult volunteers, who were asked to view pictures of faces with different emotional expressions, i.e. fear, happiness, disgust, surprise and neutral expression [Ekman, P. & Friesen, W.V. (1975). Pictures of Facial Affect. Consulting Psychologist Press, Palo Alto, CA]. ERPs were recorded during two different tasks with the same stimuli. Firstly, subjects were instructed to pay attention to the gender of the faces by counting males or females. Secondly, they had to focus on facial expressions by counting faces who looked surprised. The classical scalp 'face-related potentials', i.e. a vertex-positive potential and a bilateral temporal negativity, were recorded 150 ms after the stimulus onset. Significant differences were found, firstly between late-latency ERPs to emotional faces and to neutral faces, between 250 and 550 ms of latency and, secondly, among the ERPs to the different facial expressions between 550 and 750 ms of latency. These differences appeared only during the expression discrimination task, not during the gender discrimination task. Topographic maps of these differences showed a specific right temporal activity related to each emotional expression, some particularities being observed for each expression. This study provides new data concerning the spatio-temporal features of facial expression processing, particularly a late-latency activity related to specific attention to facial expressions. PMID:11264671

  6. Spatio-temporal dynamics of automatic processing of phonological information in visual words.

    PubMed

    Wang, Xiao-Dong; Wu, Yin-Yuan; A-Ping Liu; Wang, Peng

    2013-01-01

    Sensory-specific cortices appear to be sensitive to information from another modality. Here we investigate whether the human brain automatically extracts the phonological information in visual words in early visual processing. We continuously presented native Chinese speakers peripherally with Chinese homophone characters in an oddball paradigm, while they performed a visual detection task presented in the centre of the visual field. We found the lexical tone phonology embedded in the characters is processed automatically by the brain of native speakers, as revealed by whole-head electrical recordings of the mismatch negativity (MMN). Source solution further revealed the MMN involved the neural activations from the visual cortex to the auditory cortex (130-460 ms). The spatial-temporal dynamics indicate a visual-auditory interaction in the early, automatic processing of phonological information in visual words. PMID:24336606

  7. Synthesis of neural networks for spatio-temporal spike pattern recognition and processing

    PubMed Central

    Tapson, Jonathan C.; Cohen, Greg K.; Afshar, Saeed; Stiefel, Klaus M.; Buskila, Yossi; Wang, Runchun Mark; Hamilton, Tara J.; van Schaik, André

    2013-01-01

    The advent of large scale neural computational platforms has highlighted the lack of algorithms for synthesis of neural structures to perform predefined cognitive tasks. The Neural Engineering Framework (NEF) offers one such synthesis, but it is most effective for a spike rate representation of neural information, and it requires a large number of neurons to implement simple functions. We describe a neural network synthesis method that generates synaptic connectivity for neurons which process time-encoded neural signals, and which makes very sparse use of neurons. The method allows the user to specify—arbitrarily—neuronal characteristics such as axonal and dendritic delays, and synaptic transfer functions, and then solves for the optimal input-output relationship using computed dendritic weights. The method may be used for batch or online learning and has an extremely fast optimization process. We demonstrate its use in generating a network to recognize speech which is sparsely encoded as spike times. PMID:24009550

  8. Spatio-temporal learning with the online finite and infinite echo-state Gaussian processes.

    PubMed

    Soh, Harold; Demiris, Yiannis

    2015-03-01

    Successful biological systems adapt to change. In this paper, we are principally concerned with adaptive systems that operate in environments where data arrives sequentially and is multivariate in nature, for example, sensory streams in robotic systems. We contribute two reservoir inspired methods: 1) the online echostate Gaussian process (OESGP) and 2) its infinite variant, the online infinite echostate Gaussian process (OIESGP) Both algorithms are iterative fixed-budget methods that learn from noisy time series. In particular, the OESGP combines the echo-state network with Bayesian online learning for Gaussian processes. Extending this to infinite reservoirs yields the OIESGP, which uses a novel recursive kernel with automatic relevance determination that enables spatial and temporal feature weighting. When fused with stochastic natural gradient descent, the kernel hyperparameters are iteratively adapted to better model the target system. Furthermore, insights into the underlying system can be gleamed from inspection of the resulting hyperparameters. Experiments on noisy benchmark problems (one-step prediction and system identification) demonstrate that our methods yield high accuracies relative to state-of-the-art methods, and standard kernels with sliding windows, particularly on problems with irrelevant dimensions. In addition, we describe two case studies in robotic learning-by-demonstration involving the Nao humanoid robot and the Assistive Robot Transport for Youngsters (ARTY) smart wheelchair. PMID:25720008

  9. Advanced spatio-temporal filtering techniques for photogrammetric image sequence analysis in civil engineering material testing

    NASA Astrophysics Data System (ADS)

    Liebold, F.; Maas, H.-G.

    2016-01-01

    The paper shows advanced spatial, temporal and spatio-temporal filtering techniques which may be used to reduce noise effects in photogrammetric image sequence analysis tasks and tools. As a practical example, the techniques are validated in a photogrammetric spatio-temporal crack detection and analysis tool applied in load tests in civil engineering material testing. The load test technique is based on monocular image sequences of a test object under varying load conditions. The first image of a sequence is defined as a reference image under zero load, wherein interest points are determined and connected in a triangular irregular network structure. For each epoch, these triangles are compared to the reference image triangles to search for deformations. The result of the feature point tracking and triangle comparison process is a spatio-temporally resolved strain value field, wherein cracks can be detected, located and measured via local discrepancies. The strains can be visualized as a color-coded map. In order to improve the measuring system and to reduce noise, the strain values of each triangle must be treated in a filtering process. The paper shows the results of various filter techniques in the spatial and in the temporal domain as well as spatio-temporal filtering techniques applied to these data. The best results were obtained by a bilateral filter in the spatial domain and by a spatio-temporal EOF (empirical orthogonal function) filtering technique.

  10. Analysing the spatio-temporal organization of sediment dynamics at the hillslope scale using a process based erosion model

    NASA Astrophysics Data System (ADS)

    Scherer, U.; Zehe, E.; Ehret, U.; Kleidon, A.

    2012-04-01

    The morphology of soil covered hillslopes tends to a characteristic convex-concave shape. In hilly landscapes, where erosion rates do not exceed the weathering rates of bedrock material, the form of hillslopes is convex near the hilltop and becomes increasingly planar further downslope with the steepest descent in the middle of the slope. This typical shape is the result of long term erosion and sediment redistribution processes driven by a topographic gradient and climatic forcing. Erosion rates depend on slope, soil properties, vegetation cover and rainfall/runoff rates. The morphology of hillslopes is thus the result of a trade-off between all these parameters controlling the relation of detachment, transport and deposition rates of sediments as well as feedback mechanisms on the driving gradient. Sediment flux increases with increasing slope but higher sediment transport rates deplete the driving gradient and thus reduce sediment export. We hypothesize that sediment export is maximized under the condition of maintaining the driving gradient and that this trade-off implies a typical shape and steepness of slopes. We used the process based model CATFLOW-SED to verify this hypothesis and to better understand the spatio-temporal organisation of sediment dynamics at the hillslope scale. CATFLOW-SED is a continuous, dynamic, spatially distributed model. Soil water dynamics is described by the Richards equation, including an effective approach for preferential flow that is numerically solved by an implicit mass conservative Picard iteration. Evaporation and transpiration is simulated, using an advanced approach based on the Penman-Monteith equation. The model simulates overland flow as sheet flow using the diffusion wave equation. Soil detachment is related to the attacking forces of rainfall and overland flow. The detachment rate further depends on the model parameter erosion resistance, which is characterized by soil properties, land use and management practice

  11. Estimating global natural wetland methane emissions using process modelling: spatio-temporal patterns and contributions to atmospheric methane fluctuations

    USGS Publications Warehouse

    Zhu, Qiuan; Peng, Changhui; Chen, Huai; Fang, Xiuqin; Liu, Jinxun; Jiang, Hong; Yang, Yanzheng; Yang, Gang

    2015-01-01

    Aim The fluctuations of atmospheric methane (CH4) that have occurred in recent decades are not fully understood, particularly with regard to the contribution from wetlands. The application of spatially explicit parameters has been suggested as an effective method for reducing uncertainties in bottom-up approaches to wetland CH4 emissions, but has not been included in recent studies. Our goal was to estimate spatio-temporal patterns of global wetland CH4 emissions using a process model and then to identify the contribution of wetland emissions to atmospheric CH4fluctuations. Location Global. Methods A process-based model integrated with full descriptions of methanogenesis (TRIPLEX-GHG) was used to simulate global wetland CH4emissions. Results Global annual wetland CH4 emissions ranged from 209 to 245 Tg CH4 year−1 between 1901 and 2012, with peaks occurring in 1991 and 2012. There is a decreasing trend between 1990 and 2010 with a rate of approximately 0.48 Tg CH4 year−1, which was largely caused by emissions from tropical wetlands showing a decreasing trend of 0.44 Tg CH4 year−1 since the 1970s. Emissions from tropical, temperate and high-latitude wetlands comprised 59, 26 and 15% of global emissions, respectively. Main conclusion Global wetland CH4 emissions, the interannual variability of which was primary controlled by tropical wetlands, partially drive the atmosphericCH4 burden. The stable to decreasing trend in wetland CH4 emissions, a result of a balance of emissions from tropical and extratropical wetlands, was a particular factor in slowing the atmospheric CH4 growth rate during the 1990s. The rapid decrease in tropical wetland CH4emissions that began in 2000 was supposed to offset the increase in anthropogenic emissions and resulted in a relatively stable level of atmospheric CH4 from 2000 to 2006. Increasing wetland CH4 emissions, particularly after 2010, should be an important contributor to the growth in

  12. Spatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series

    NASA Astrophysics Data System (ADS)

    Lu, Meng; Pebesma, Edzer; Sanchez, Alber; Verbesselt, Jan

    2016-07-01

    Growing availability of long-term satellite imagery enables change modeling with advanced spatio-temporal statistical methods. Multidimensional arrays naturally match the structure of spatio-temporal satellite data and can provide a clean modeling process for complex spatio-temporal analysis over large datasets. Our study case illustrates the detection of breakpoints in MODIS imagery time series for land cover change in the Brazilian Amazon using the BFAST (Breaks For Additive Season and Trend) change detection framework. BFAST includes an Empirical Fluctuation Process (EFP) to alarm the change and a change point time locating process. We extend the EFP to account for the spatial autocorrelation between spatial neighbors and assess the effects of spatial correlation when applying BFAST on satellite image time series. In addition, we evaluate how sensitive EFP is to the assumption that its time series residuals are temporally uncorrelated, by modeling it as an autoregressive process. We use arrays as a unified data structure for the modeling process, R to execute the analysis, and an array database management system to scale computation. Our results point to BFAST as a robust approach against mild temporal and spatial correlation, to the use of arrays to ease the modeling process of spatio-temporal change, and towards communicable and scalable analysis.

  13. Annotating spatio-temporal datasets for meaningful analysis in the Web

    NASA Astrophysics Data System (ADS)

    Stasch, Christoph; Pebesma, Edzer; Scheider, Simon

    2014-05-01

    More and more environmental datasets that vary in space and time are available in the Web. This comes along with an advantage of using the data for other purposes than originally foreseen, but also with the danger that users may apply inappropriate analysis procedures due to lack of important assumptions made during the data collection process. In order to guide towards a meaningful (statistical) analysis of spatio-temporal datasets available in the Web, we have developed a Higher-Order-Logic formalism that captures some relevant assumptions in our previous work [1]. It allows to proof on meaningful spatial prediction and aggregation in a semi-automated fashion. In this poster presentation, we will present a concept for annotating spatio-temporal datasets available in the Web with concepts defined in our formalism. Therefore, we have defined a subset of the formalism as a Web Ontology Language (OWL) pattern. It allows capturing the distinction between the different spatio-temporal variable types, i.e. point patterns, fields, lattices and trajectories, that in turn determine whether a particular dataset can be interpolated or aggregated in a meaningful way using a certain procedure. The actual annotations that link spatio-temporal datasets with the concepts in the ontology pattern are provided as Linked Data. In order to allow data producers to add the annotations to their datasets, we have implemented a Web portal that uses a triple store at the backend to store the annotations and to make them available in the Linked Data cloud. Furthermore, we have implemented functions in the statistical environment R to retrieve the RDF annotations and, based on these annotations, to support a stronger typing of spatio-temporal datatypes guiding towards a meaningful analysis in R. [1] Stasch, C., Scheider, S., Pebesma, E., Kuhn, W. (2014): "Meaningful spatial prediction and aggregation", Environmental Modelling & Software, 51, 149-165.

  14. Modeling spatio-temporal field evolution

    NASA Astrophysics Data System (ADS)

    Borštnik Bračič, A.; Grabec, I.; Govekar, E.

    2009-06-01

    Prediction of spatio-temporal field evolution is based on the extraction of a physical law from joint experimental data. This extraction is usually described by a set of differential equations. If the only source of information is a field record, a method of field generators based on nonparametric modeling by conditional average can successfully replace differential equations. In this article we apply the method of field generators to a two-dimensional chaotic field record that describes the asynchronous motion of high-amplitude striations. We show how to choose the model structure in order to optimize the quality of the prediction process.

  15. Spatio-temporal action localization for human action recognition in large dataset

    NASA Astrophysics Data System (ADS)

    Megrhi, Sameh; Jmal, Marwa; Beghdadi, Azeddine; Mseddi, Wided

    2015-03-01

    Human action recognition has drawn much attention in the field of video analysis. In this paper, we develop a human action detection and recognition process based on the tracking of Interest Points (IP) trajectory. A pre-processing step that performs spatio-temporal action detection is proposed. This step uses optical flow along with dense speed-up-robust-features (SURF) in order to detect and track moving humans in moving fields of view. The video description step is based on a fusion process that combines displacement and spatio-temporal descriptors. Experiments are carried out on the big data-set UCF-101. Experimental results reveal that the proposed techniques achieve better performances compared to many existing state-of-the-art action recognition approaches.

  16. Nonlinear wave interactions between short pulses of different spatio-temporal extents

    PubMed Central

    Sivan, Y.; Rozenberg, S.; Halstuch, A.; Ishaaya, A. A.

    2016-01-01

    We study the nonlinear wave interactions between short pulses of different spatio-temporal extents. Unlike the well-understood mixing of quasi-monochromatic waves, this configuration is highly non-intuitive due to the complex coupling between the spatial and temporal degrees of freedom of the interacting pulses. We illustrate the process intuitively with transitions between different branches of the dispersion curves and interpret it in terms of spectral exchange between the interacting pulses. We verify our interpretation with an example whereby a spectrally-narrow pulse “inherits” the wide spectrum of a pump pulse centered at a different wavelength, using exact numerical simulations, as well as a simplified coupled mode analysis and an asymptotic analytical solution. The latter also provides a simple and intuitive quantitative interpretation. The complex wave mixing process studied here may enable flexible spatio-temporal shaping of short pulses and is the starting point of the study of more complicated systems. PMID:27381552

  17. Art, Science and Architecture: Architecture as a Dynamic Process of Structuring Matter-Energy in the Spatio-Temporal World.

    ERIC Educational Resources Information Center

    Minai, Asghar Talaye

    Developed were methods of coordinating art and science in relation to the creation of the physical form of the environment. Such an approach has been directed towards a theory of form based on point theory or field theory in architecture and deals with the problem of potentiality or dispositional properties. Part I, Towards a Sociology of…

  18. A multivariate statistical approach to identify the spatio-temporal variation of geochemical process in a hard rock aquifer.

    PubMed

    Thivya, C; Chidambaram, S; Thilagavathi, R; Prasanna, M V; Singaraja, C; Adithya, V S; Nepolian, M

    2015-09-01

    A study has been carried out in crystalline hard rock aquifers of Madurai district, Tamil Nadu, to identify the spatial and temporal variations and to understand sources responsible for hydrogeochemical processes in the region. Totally, 216 samples were collected for four seasons [premonsoon (PRM), southwest monsoon (SWM), northeast monsoon (NWM), and postmonsoon (POM)]. The Na and K ions are attributed from weathering of feldspars in charnockite and fissile hornblende gneiss. The results also indicate that monsoon leaches the U ions in the groundwater and later it is reflected in the (222)Rn levels also. The statistical relationship on the temporal data reflects the fact that Ca, Mg, Na, Cl, HCO3, and SO4 form the spinal species, which are the chief ions playing the significant role in the geochemistry of the region. The factor loadings of the temporal data reveal the fact that the predominant factor is anthropogenic process and followed by natural weathering and U dissolution. The spatial analysis of the temporal data reveals that weathering is prominent in the NW part and that of distribution of U and (222)Rn along the NE part of the study area. This is also reflected in the cluster analysis, and it is understood that lithology, land use pattern, lineaments, and groundwater flow direction determine the spatial variation of these ions with respect to season. PMID:26239570

  19. The effect of spatio-temporal distance between visual stimuli on information processing in children with Specific Language Impairment.

    PubMed

    Dispaldro, Marco; Corradi, Nicola

    2015-01-01

    The purpose of this study is to evaluate whether children with Specific Language Impairment (SLI) have a deficit in processing a sequence of two visual stimuli (S1 and S2) presented at different inter-stimulus intervals and in different spatial locations. In particular, the core of this study is to investigate whether S1 identification is disrupted due to a retroactive interference of S2. To this aim, two experiments were planned in which children with SLI and children with typical development (TD), matched by age and non-verbal IQ, were compared (Experiment 1: SLI n=19; TD n=19; Experiment 2: SLI n=16; TD n=16). Results show group differences in the ability to identify a single stimulus surrounded by flankers (Baseline level). Moreover, children with SLI show a stronger negative interference of S2, both for temporal and spatial modulation. These results are discussed in the light of an attentional processing limitation in children with SLI. PMID:26277740

  20. Spatio-temporal modelling of electrical supply systems to optimize the site planning process for the "power to mobility" technology

    NASA Astrophysics Data System (ADS)

    Karl, Florian; Zink, Roland

    2016-04-01

    The transformation of the energy sector towards decentralized renewable energies (RE) requires also storage systems to ensure security of supply. The new "Power to Mobility" (PtM) technology is one potential solution to use electrical overproduction to produce methane for i.e. gas vehicles. Motivated by these fact, the paper presents a methodology for a GIS-based temporal modelling of the power grid, to optimize the site planning process for the new PtM-technology. The modelling approach is based on a combination of the software QuantumGIS for the geographical and topological energy supply structure and OpenDSS for the net modelling. For a case study (work in progress) of the city of Straubing (Lower Bavaria) the parameters of the model are quantified. The presentation will discuss the methodology as well as the first results with a view to the application on a regional scale.

  1. A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases

    PubMed Central

    Ke, Shengnan; Gong, Jun; Li, Songnian; Zhu, Qing; Liu, Xintao; Zhang, Yeting

    2014-01-01

    In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type. PMID:25051028

  2. A hybrid spatio-temporal data indexing method for trajectory databases.

    PubMed

    Ke, Shengnan; Gong, Jun; Li, Songnian; Zhu, Qing; Liu, Xintao; Zhang, Yeting

    2014-01-01

    In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type. PMID:25051028

  3. A semiparametric spatio-temporal model for solar irradiance data

    DOE PAGESBeta

    Patrick, Joshua D.; Harvill, Jane L.; Hansen, Clifford W.

    2016-03-01

    Here, we evaluate semiparametric spatio-temporal models for global horizontal irradiance at high spatial and temporal resolution. These models represent the spatial domain as a lattice and are capable of predicting irradiance at lattice points, given data measured at other lattice points. Using data from a 1.2 MW PV plant located in Lanai, Hawaii, we show that a semiparametric model can be more accurate than simple interpolation between sensor locations. We investigate spatio-temporal models with separable and nonseparable covariance structures and find no evidence to support assuming a separable covariance structure. These results indicate a promising approach for modeling irradiance atmore » high spatial resolution consistent with available ground-based measurements. Moreover, this kind of modeling may find application in design, valuation, and operation of fleets of utility-scale photovoltaic power systems.« less

  4. a Spatio-Temporal Framework for Modeling Active Layer Thickness

    NASA Astrophysics Data System (ADS)

    Touyz, J.; Streletskiy, D. A.; Nelson, F. E.; Apanasovich, T. V.

    2015-07-01

    The Arctic is experiencing an unprecedented rate of environmental and climate change. The active layer (the uppermost layer of soil between the atmosphere and permafrost that freezes in winter and thaws in summer) is sensitive to both climatic and environmental changes, and plays an important role in the functioning, planning, and economic activities of Arctic human and natural ecosystems. This study develops a methodology for modeling and estimating spatial-temporal variations in active layer thickness (ALT) using data from several sites of the Circumpolar Active Layer Monitoring network, and demonstrates its use in spatial-temporal interpolation. The simplest model's stochastic component exhibits no spatial or spatio-temporal dependency and is referred to as the naïve model, against which we evaluate the performance of the other models, which assume that the stochastic component exhibits either spatial or spatio-temporal dependency. The methods used to fit the models are then discussed, along with point forecasting. We compare the predicted fit of the various models at key study sites located in the North Slope of Alaska and demonstrate the advantages of space-time models through a series of error statistics such as mean squared error, mean absolute and percent deviance from observed data. We find the difference in performance between the spatio-temporal and remaining models is significant for all three error statistics. The best stochastic spatio-temporal model increases predictive accuracy, compared to the naïve model, of 33.3%, 36.2% and 32.5% on average across the three error metrics at the key sites for a one-year hold out period.

  5. Inverse hydrological modelling of spatio-temporal rainfall patterns

    NASA Astrophysics Data System (ADS)

    Grundmann, Jens; Hörning, Sebastian; Bárdossy, András

    2016-04-01

    Distributed hydrological models are commonly used for simulating the non-linear response of a watershed to rainfall events for addressing different hydrological properties of the landscape. Such models are driven by spatial rainfall patterns for consecutive time steps, which are normally generated from point measurements using spatial interpolation methods. However, such methods fail in reproducing the true spatio-temporal rainfall patterns especially in data scarce regions with poorly gauged catchments or for highly dynamic, small scaled rainstorms which are not well recorded by existing monitoring networks. Consequently, uncertainties are associated with poorly identified spatio-temporal rainfall distribution in distributed rainfall-runoff-modelling since the amount of rainfall received by a catchment as well as the dynamics of the runoff generation of flood waves are underestimated. For addressing these challenges a novel methodology for inverse hydrological modelling is proposed using a Markov-Chain-Monte-Carlo framework. Thereby, potential candidates of spatio-temporal rainfall patterns are generated and selected according their ability to reproduce the observed surface runoff at the catchment outlet for a given transfer function in a best way. The Methodology combines the concept of random mixing of random spatial fields with a grid-based spatial distributed rainfall runoff model. The conditional target rainfall field is obtained as a linear combination of unconditional spatial random fields. The corresponding weights of the linear combination are selected such that the spatial variability of the rainfall amounts as well as the actual observed rainfall values are reproduced. The functionality of the methodology is demonstrated on a synthetic example. Thereby, the known spatio-temporal distribution of rainfall is reproduced for a given number of point observations of rainfall and the integral catchment response at the catchment outlet for a synthetic catchment

  6. Spatio-temporal variation of erosion-type non-point source pollution in a small watershed of hilly and gully region, Chinese Loess Plateau.

    PubMed

    Wu, Lei; Liu, Xia; Ma, Xiao-Yi

    2016-06-01

    Loss of nitrogen and phosphorus in the hilly and gully region of Chinese Loess Plateau not only decreases the utilization rate of fertilizer but also is a potential threat to aquatic environments. In order to explore the process of erosion-type non-point source (NPS) pollution in Majiagou watershed of Loess Plateau, a distributed, dynamic, and integrated NPS pollution model was established to investigate impacts of returning farmland on erosion-type NPS pollution load from 1995 to 2012. Results indicate that (1) the integrated model proposed in this study was verified to be reasonable; the general methodology is universal and can be applicable to the hilly and gully region, Loess Plateau; (2) the erosion-type NPS total nitrogen (TN) and total phosphorus (TP) load showed an overall decreasing trend; the average nitrogen and phosphorus load modulus in the last four years (2009-2012) were 1.23 and 1.63 t/km(2) · a, respectively, which were both decreased by about 35.4 % compared with the initial treatment period (1995-1998); and (3) The spatial variations of NPS pollution are closely related to spatial characteristics of rainfall, topography, and soil and land use types; the peak regions of TN and TP loss mainly occurred along the main river banks of the Yanhe River watershed from northeast to southeast, and gradually decreased with the increase of distance to the left and right river banks, respectively. Results may provide scientific basis for the watershed-scale NPS pollution control of the Loess Plateau. PMID:26898934

  7. Chaotic itinerancy, temporal segmentation and spatio-temporal combinatorial codes

    NASA Astrophysics Data System (ADS)

    Dias, Juliana R.; Oliveira, Rodrigo F.; Kinouchi, Osame

    2008-01-01

    We study a deterministic dynamics with two time scales in a continuous state attractor network. To the usual (fast) relaxation dynamics towards point attractors (“patterns”) we add a slow coupling dynamics that makes the visited patterns lose stability, leading to an itinerant behavior in the form of punctuated equilibria. One finds that the transition frequency matrix for transitions between patterns shows non-trivial statistical properties in the chaotic itinerant regime. We show that mixture input patterns can be temporally segmented by the itinerant dynamics. The viability of a combinatorial spatio-temporal neural code is also demonstrated.

  8. Mapping spatio-temporal filtering algorithms used in fluoroscopy to single core and multicore DSP architectures

    NASA Astrophysics Data System (ADS)

    Dasgupta, Udayan; Ali, Murtaza

    2011-03-01

    Low dose X-ray image sequences, as obtained in fluoroscopy, exhibit high levels of noise that must be suppressed in real-time, while preserving diagnostic structures. Multi-step adaptive filtering approaches, often involving spatio-temporal filters, are typically used to achieve this goal. In this work typical fluoroscopic image sequences, corrupted with Poisson noise, were processed using various filtering schemes. The noise suppression of the schemes was evaluated using objective image quality measures. Two adaptive spatio-temporal schemes, the first one using object detection and the second one using unsharp masking, were chosen as representative approaches for different fluoroscopy procedures and mapped on to Texas Instrument's (TI) high performance digital signal processors (DSP). The paper explains the fixed point design of these algorithms and evaluates its impact on overall system performance. The fixed point versions of these algorithms are mapped onto the C64x+TM core using instruction-level parallelism to effectively use its VLIW architecture. The overall data flow was carefully planned to reduce cache and data movement overhead, while working with large medical data sets. Apart from mapping these algorithms on to TI's single core DSP architecture, this work also distributes the operations to leverage multi-core DSP architectures. The data arrangement and flow were optimized to minimize inter-processor messaging and data movement overhead.

  9. Application of Geostatistical Methods and Machine Learning for spatio-temporal Earthquake Cluster Analysis

    NASA Astrophysics Data System (ADS)

    Schaefer, A. M.; Daniell, J. E.; Wenzel, F.

    2014-12-01

    Earthquake clustering tends to be an increasingly important part of general earthquake research especially in terms of seismic hazard assessment and earthquake forecasting and prediction approaches. The distinct identification and definition of foreshocks, aftershocks, mainshocks and secondary mainshocks is taken into account using a point based spatio-temporal clustering algorithm originating from the field of classic machine learning. This can be further applied for declustering purposes to separate background seismicity from triggered seismicity. The results are interpreted and processed to assemble 3D-(x,y,t) earthquake clustering maps which are based on smoothed seismicity records in space and time. In addition, multi-dimensional Gaussian functions are used to capture clustering parameters for spatial distribution and dominant orientations. Clusters are further processed using methodologies originating from geostatistics, which have been mostly applied and developed in mining projects during the last decades. A 2.5D variogram analysis is applied to identify spatio-temporal homogeneity in terms of earthquake density and energy output. The results are mitigated using Kriging to provide an accurate mapping solution for clustering features. As a case study, seismic data of New Zealand and the United States is used, covering events since the 1950s, from which an earthquake cluster catalogue is assembled for most of the major events, including a detailed analysis of the Landers and Christchurch sequences.

  10. Spatio-temporal dynamics in the origin of genetic information

    NASA Astrophysics Data System (ADS)

    Kim, Pan-Jun; Jeong, Hawoong

    2005-04-01

    We study evolutionary processes induced by spatio-temporal dynamics in prebiotic evolution. Using numerical simulations, we demonstrate that hypercycles emerge from complex interaction structures in multispecies systems. In this work, we also find that ‘hypercycle hybrid’ protects the hypercycle from its environment during the growth process. There is little selective advantage for one hypercycle to maintain coexistence with others. This brings the possibility of the outcompetition between hypercycles resulting in the negative effect on information diversity. To enrich the information in hypercycles, symbiosis with parasites is suggested. It is shown that symbiosis with parasites can play an important role in the prebiotic immunology.

  11. Spatio-temporal dynamics induced by competing instabilities in two asymmetrically coupled nonlinear evolution equations

    SciTech Connect

    Schüler, D.; Alonso, S.; Bär, M.; Torcini, A.

    2014-12-15

    Pattern formation often occurs in spatially extended physical, biological, and chemical systems due to an instability of the homogeneous steady state. The type of the instability usually prescribes the resulting spatio-temporal patterns and their characteristic length scales. However, patterns resulting from the simultaneous occurrence of instabilities cannot be expected to be simple superposition of the patterns associated with the considered instabilities. To address this issue, we design two simple models composed by two asymmetrically coupled equations of non-conserved (Swift-Hohenberg equations) or conserved (Cahn-Hilliard equations) order parameters with different characteristic wave lengths. The patterns arising in these systems range from coexisting static patterns of different wavelengths to traveling waves. A linear stability analysis allows to derive a two parameter phase diagram for the studied models, in particular, revealing for the Swift-Hohenberg equations, a co-dimension two bifurcation point of Turing and wave instability and a region of coexistence of stationary and traveling patterns. The nonlinear dynamics of the coupled evolution equations is investigated by performing accurate numerical simulations. These reveal more complex patterns, ranging from traveling waves with embedded Turing patterns domains to spatio-temporal chaos, and a wide hysteretic region, where waves or Turing patterns coexist. For the coupled Cahn-Hilliard equations the presence of a weak coupling is sufficient to arrest the coarsening process and to lead to the emergence of purely periodic patterns. The final states are characterized by domains with a characteristic length, which diverges logarithmically with the coupling amplitude.

  12. Assessing global vegetation activity using spatio-temporal Bayesian modelling

    NASA Astrophysics Data System (ADS)

    Mulder, Vera L.; van Eck, Christel M.; Friedlingstein, Pierre; Regnier, Pierre A. G.

    2016-04-01

    This work demonstrates the potential of modelling vegetation activity using a hierarchical Bayesian spatio-temporal model. This approach allows modelling changes in vegetation and climate simultaneous in space and time. Changes of vegetation activity such as phenology are modelled as a dynamic process depending on climate variability in both space and time. Additionally, differences in observed vegetation status can be contributed to other abiotic ecosystem properties, e.g. soil and terrain properties. Although these properties do not change in time, they do change in space and may provide valuable information in addition to the climate dynamics. The spatio-temporal Bayesian models were calibrated at a regional scale because the local trends in space and time can be better captured by the model. The regional subsets were defined according to the SREX segmentation, as defined by the IPCC. Each region is considered being relatively homogeneous in terms of large-scale climate and biomes, still capturing small-scale (grid-cell level) variability. Modelling within these regions is hence expected to be less uncertain due to the absence of these large-scale patterns, compared to a global approach. This overall modelling approach allows the comparison of model behavior for the different regions and may provide insights on the main dynamic processes driving the interaction between vegetation and climate within different regions. The data employed in this study encompasses the global datasets for soil properties (SoilGrids), terrain properties (Global Relief Model based on SRTM DEM and ETOPO), monthly time series of satellite-derived vegetation indices (GIMMS NDVI3g) and climate variables (Princeton Meteorological Forcing Dataset). The findings proved the potential of a spatio-temporal Bayesian modelling approach for assessing vegetation dynamics, at a regional scale. The observed interrelationships of the employed data and the different spatial and temporal trends support

  13. Spatio-temporal Hotelling observer for signal detection from image sequences

    PubMed Central

    Caucci, Luca; Barrett, Harrison H.; Rodríguez, Jeffrey J.

    2010-01-01

    Detection of signals in noisy images is necessary in many applications, including astronomy and medical imaging. The optimal linear observer for performing a detection task, called the Hotelling observer in the medical literature, can be regarded as a generalization of the familiar prewhitening matched filter. Performance on the detection task is limited by randomness in the image data, which stems from randomness in the object, randomness in the imaging system, and randomness in the detector outputs due to photon and readout noise, and the Hotelling observer accounts for all of these effects in an optimal way. If multiple temporal frames of images are acquired, the resulting data set is a spatio-temporal random process, and the Hotelling observer becomes a spatio-temporal linear operator. This paper discusses the theory of the spatio-temporal Hotelling observer and estimation of the required spatio-temporal covariance matrices. It also presents a parallel implementation of the observer on a cluster of Sony PLAYSTATION 3 gaming consoles. As an example, we consider the use of the spatio-temporal Hotelling observer for exoplanet detection. PMID:19550494

  14. Spatio-temporal Hotelling observer for signal detection from image sequences.

    PubMed

    Caucci, Luca; Barrett, Harrison H; Rodriguez, Jeffrey J

    2009-06-22

    Detection of signals in noisy images is necessary in many applications, including astronomy and medical imaging. The optimal linear observer for performing a detection task, called the Hotelling observer in the medical literature, can be regarded as a generalization of the familiar prewhitening matched filter. Performance on the detection task is limited by randomness in the image data, which stems from randomness in the object, randomness in the imaging system, and randomness in the detector outputs due to photon and readout noise, and the Hotelling observer accounts for all of these effects in an optimal way. If multiple temporal frames of images are acquired, the resulting data set is a spatio-temporal random process, and the Hotelling observer becomes a spatio-temporal linear operator. This paper discusses the theory of the spatio-temporal Hotelling observer and estimation of the required spatio-temporal covariance matrices. It also presents a parallel implementation of the observer on a cluster of Sony PLAYSTATION 3 gaming consoles. As an example, we consider the use of the spatio-temporal Hotelling observer for exoplanet detection. PMID:19550494

  15. Proposal for a Non-Interceptive Spatio-Temporal Correlation Monitor

    SciTech Connect

    Maxwell, T.; Piot, P.; /Northern Illinois U. /Fermilab

    2009-05-01

    Designs toward TeV-range electron-positron linear colliders include a non-zero crossing angle colliding scheme at the interaction point to mitigate instabilities and possible background. Maximizing the luminosity when operating with non-zero crossing angles requires the use of 'crab' cavities to impart a well-defined spatio-temporal correlation. In this paper we propose a novel noninterceptive diagnostic capable of measuring and monitoring the spatio-temporal correlation, i.e. the transverse position of sub-picosecond time slices, within bunch. An analysis of the proposed scheme, its spatio-temporal resolution and its limitations are quantified. Finally, the design of a proof-of-principle experiment in preparation for the Fermilab's A0 photoinjector is presented.

  16. Standards-Based Services for Big Spatio-Temporal Data

    NASA Astrophysics Data System (ADS)

    Baumann, P.; Merticariu, V.; Dumitru, A.; Misev, D.

    2016-06-01

    With the unprecedented availability of continuously updated measured and generated data there is an immense potential for getting new and timely insights - yet, the value is not fully leveraged as of today. The quest is up for high-level service interfaces for dissecting datasets and rejoining them with other datasets - ultimately, to allow users to ask "any question, anytime, on any size" enabling them to "build their own product on the go". With OGC Coverages, a concrete, interoperable data model has been established which unifies n-D spatio-temporal regular and irregular grids, point clouds, and meshes. The Web Coverage Service (WCS) suite provides versatile streamlined coverage functionality ranging from simple access to flexible spatio-temporal analytics. Flexibility and scalability of the WCS suite has been demonstrated in practice through massive services run by large-scale data centers. We present the current status in OGC Coverage data and service models, contrast them to related work, and describe a scalable implementation based on the rasdaman array engine.

  17. Multiscale recurrence analysis of spatio-temporal data

    NASA Astrophysics Data System (ADS)

    Riedl, M.; Marwan, N.; Kurths, J.

    2015-12-01

    The description and analysis of spatio-temporal dynamics is a crucial task in many scientific disciplines. In this work, we propose a method which uses the mapogram as a similarity measure between spatially distributed data instances at different time points. The resulting similarity values of the pairwise comparison are used to construct a recurrence plot in order to benefit from established tools of recurrence quantification analysis and recurrence network analysis. In contrast to other recurrence tools for this purpose, the mapogram approach allows the specific focus on different spatial scales that can be used in a multi-scale analysis of spatio-temporal dynamics. We illustrate this approach by application on mixed dynamics, such as traveling parallel wave fronts with additive noise, as well as more complicate examples, pseudo-random numbers and coupled map lattices with a semi-logistic mapping rule. Especially the complicate examples show the usefulness of the multi-scale consideration in order to take spatial pattern of different scales and with different rhythms into account. So, this mapogram approach promises new insights in problems of climatology, ecology, or medicine.

  18. Spatio-Temporal Oscillations in Predator-Prey Systems

    NASA Astrophysics Data System (ADS)

    Tomé, T.; de Carvalho, K. Cristina

    2005-10-01

    In recent years a particularly great effort has been made to understand the role of space given by a spatial structure and local interactions in the characterization of the dynamics of competing biological species. Irreversible stochastic lattice models have been studied to mimic predator-prey systems with Markovian local rules based in the Lotka-Volterra model. One of the problems being studied is the stability of the temporal oscillations of the population of two-species systems-whether they are synchronized. Here we study the temporal oscillations of a two-species system by considering two probabilistic cellular automata defined in regular lattices where each site can be in three states: empty, occupied by a prey, or occupied by a predator. One of them, the isotropic model, has local rules similar to those of the contact process and try to mimic the Lotka-Volterra model mechanisms. The other automaton, the anisotropic model, is based in rules that are similar to the isotropic model, but a anisotropic neighborhood is considered. This model was introduced to explore the effect of spatial anisotropy in temporal oscillations. In fact, it has been pointed out that temporally periodic states can be stable in spatial anisotropic irreversible systems whose anisotropy is exploited conveniently. We show Monte Carlo simulations performed on square lattices for both automata. Our results indicate that, in the thermodynamic limit, oscillations can occur only at a local level, even in the anisotropic model. We observe that for given sets of control parameters a spatio-temporal oscillation occurs in the system. These structures are analyzed.

  19. Spatio-temporal aggregation of European air quality observations in the Sensor Web

    NASA Astrophysics Data System (ADS)

    Stasch, Christoph; Foerster, Theodor; Autermann, Christian; Pebesma, Edzer

    2012-10-01

    An increasing amount of observations from different applications such as long-term environmental monitoring or disaster management is published in the Web using Sensor Web technologies. The standardization of these technologies eases the integration of heterogeneous observations into several applications. However, as observations differ in spatio-temporal coverage and resolution, aggregation of observations in space and time is needed. We present an approach for spatio-temporal aggregation in the Sensor Web using the Geoprocessing Web. In particular, we define a tailored observation model for different aggregation levels, a process model for aggregation processes and a Spatio-Temporal Aggregation Service. The presented approach is demonstrated by a case study of delivering aggregated air quality observations on-demand in the Sensor Web.

  20. Plant diversity increases spatio-temporal niche complementarity in plant-pollinator interactions.

    PubMed

    Venjakob, Christine; Klein, Alexandra-Maria; Ebeling, Anne; Tscharntke, Teja; Scherber, Christoph

    2016-04-01

    Ongoing biodiversity decline impairs ecosystem processes, including pollination. Flower visitation, an important indicator of pollination services, is influenced by plant species richness. However, the spatio-temporal responses of different pollinator groups to plant species richness have not yet been analyzed experimentally. Here, we used an experimental plant species richness gradient to analyze plant-pollinator interactions with an unprecedented spatio-temporal resolution. We observed four pollinator functional groups (honeybees, bumblebees, solitary bees, and hoverflies) in experimental plots at three different vegetation strata between sunrise and sunset. Visits were modified by plant species richness interacting with time and space. Furthermore, the complementarity of pollinator functional groups in space and time was stronger in species-rich mixtures. We conclude that high plant diversity should ensure stable pollination services, mediated via spatio-temporal niche complementarity in flower visitation. PMID:27069585

  1. A hierarchical spatio-temporal data model for dynamic monitoring of land use

    NASA Astrophysics Data System (ADS)

    Wang, Hao; Liu, Yaolin

    2007-06-01

    Dynamic monitoring of land use is a perennial and persistent process now in Shanghai. Therefore, the cumulated amount of monitoring data will be very large. It is an exigent problem how to manage and use this data effectively. The key issue is finding a suitable spatio-temporal data model that must take into account space, time and attribute factor adequately. In dynamic monitoring of land use, it is change that is of direct interest. With analyzing the feature of land use dynamic monitoring and the shortage of some spatio-temporal data models when they are used in the field, this paper proposes a Hierarchical Spatio-Temporal Data Model (HSDM) that stores elements of change and makes these available for direct query and analysis.

  2. The Relationship between Filling-in Induction and Spatio-Temporal Frequency of Sorrounding Dynamic Textures

    NASA Astrophysics Data System (ADS)

    Yokota, Masae; Yokota, Yasunari

    To elucidate perceptual filling-in mechanisms in peripheral vision, we investigated dependency of filling-in occurrence on spatio-temporal frequency of dynamic textures surrounding the filling-in target. We first measured spatial frequency sensitivity of the filling-in target in static texture. Then, the time to filling-in, when dynamic textures which have variously limited spatio-temporal frequency are surrounding the filling-in target, were measured. According to the hypothesis of filling-in process which has already proposed by the authors, the tendency of inducing filling-in, i.e., the attenuation factor of perceptual power for filling-in target in dynamic textures, is estimated as a function of spatio-temporal frequency. It was suggested that surrounding texture with stronger perception promotes filling-in more intensively.

  3. Stochastic spatio-temporal modelling with PCRaster Python

    NASA Astrophysics Data System (ADS)

    Karssenberg, D.; Schmitz, O.; de Jong, K.

    2012-04-01

    PCRaster Python is a software framework for building spatio-temporal models of land surface processes (Karssenberg, Schmitz, Salamon, De Jong, & Bierkens, 2010; PCRaster, 2012). Building blocks of models are spatial operations on raster maps, including a large suite of operations for water and sediment routing. These operations, developed in C++, are available to model builders as Python functions. Users create models by combining these functions in a Python script. As construction of large iterative models is often difficult and time consuming for non-specialists in programming, the software comes with a set of Python framework classes that provide control flow for static modelling, temporal modelling, stochastic modelling using Monte Carlo simulation, and data assimilation techniques including the Ensemble Kalman filter and the Particle Filter. A framework for integrating model components with different time steps and spatial discretization is currently available as a prototype (Schmitz, de Jong, & Karssenberg, in review). The software includes routines for visualisation of stochastic spatio-temporal data for prompt, interactive, visualisation of model inputs and outputs. Visualisation techniques include animated maps, time series, probability distributions, and animated maps with exceedance probabilities. The PCRaster Python software is used by researchers from a large range of disciplines, including hydrology, ecology, sedimentology, and land use change studies. Applications include global scale hydrological modelling and error propagation in large-scale land use change models. The software runs on MS Windows and Linux operating systems, and OS X (under development).

  4. In-situ measurements of pore water stable isotope composition in a semi-arid environment with implications for spatio-temporal variability of unsaturated zone processes.

    NASA Astrophysics Data System (ADS)

    Gaj, Marcel; Beyer, Matthias; Koeniger, Paul; Hamutoko, Yosefina; Himmelsbach, Thomas

    2015-04-01

    Northern Namibia is a region with high population growth, limited water resources and a transboundary aquifer system where groundwater recharge and groundwater flow processes are not yet well understood. Our study is an interdisciplinary approach to improve the understanding of links between hydrological, geochemical and ecological processes to estimate areas that contribute to recharge a shallow aquifer system. To determine the spatial variability of infiltration and evapotranspiration processes within the critical interface and between the atmosphere and the groundwater nine plots in an area of 9,000 m2 were investigated. Stable isotopes (deuterium, δ2H, and oxygen-18, δ18O) of soil pore water were measured directly in the field using a LGR-DLT100 and commercially available soil gas probes (BGL-30, UMS). Additionally, soil moisture and temperature were recorded. After drift correction of the isotope data the long term precision using a quality check standard for 221 measurement points of a two week period was between 6.3 - 7.4 o for δ2H and 1.3 - 3.6 o for δ18O. Each point was measured with six repetitions were the mean standard deviation for all quality check standards was 1.3 - 1.6 o for δ2H and 0.23 - 0.30 o for δ18O. It could be observed that the quality of the measurements decreased with increasing number of measurements due to a memory effect of the probes enhanced by organic contamination of the membrane pores. However, results support the applicability of an in-situ system for the determination of stable isotopes in soil pore water. Spatially and temporal variability affected by intermitted light rainfall events can be deduced with the observed data.

  5. Spatio-Temporal Detection of the Thiomonas Population and the Thiomonas Arsenite Oxidase Involved in Natural Arsenite Attenuation Processes in the Carnoulès Acid Mine Drainage.

    PubMed

    Hovasse, Agnès; Bruneel, Odile; Casiot, Corinne; Desoeuvre, Angélique; Farasin, Julien; Hery, Marina; Van Dorsselaer, Alain; Carapito, Christine; Arsène-Ploetze, Florence

    2016-01-01

    The acid mine drainage (AMD) impacted creek of the Carnoulès mine (Southern France) is characterized by acid waters with a high heavy metal content. The microbial community inhabiting this AMD was extensively studied using isolation, metagenomic and metaproteomic methods, and the results showed that a natural arsenic (and iron) attenuation process involving the arsenite oxidase activity of several Thiomonas strains occurs at this site. A sensitive quantitative Selected Reaction Monitoring (SRM)-based proteomic approach was developed for detecting and quantifying the two subunits of the arsenite oxidase and RpoA of two different Thiomonas groups. Using this approach combined with FISH and pyrosequencing-based 16S rRNA gene sequence analysis, it was established here for the first time that these Thiomonas strains are ubiquitously present in minor proportions in this AMD and that they express the key enzymes involved in natural remediation processes at various locations and time points. In addition to these findings, this study also confirms that targeted proteomics applied at the community level can be used to detect weakly abundant proteins in situ. PMID:26870729

  6. Spatio-Temporal Detection of the Thiomonas Population and the Thiomonas Arsenite Oxidase Involved in Natural Arsenite Attenuation Processes in the Carnoulès Acid Mine Drainage

    PubMed Central

    Hovasse, Agnès; Bruneel, Odile; Casiot, Corinne; Desoeuvre, Angélique; Farasin, Julien; Hery, Marina; Van Dorsselaer, Alain; Carapito, Christine; Arsène-Ploetze, Florence

    2016-01-01

    The acid mine drainage (AMD) impacted creek of the Carnoulès mine (Southern France) is characterized by acid waters with a high heavy metal content. The microbial community inhabiting this AMD was extensively studied using isolation, metagenomic and metaproteomic methods, and the results showed that a natural arsenic (and iron) attenuation process involving the arsenite oxidase activity of several Thiomonas strains occurs at this site. A sensitive quantitative Selected Reaction Monitoring (SRM)-based proteomic approach was developed for detecting and quantifying the two subunits of the arsenite oxidase and RpoA of two different Thiomonas groups. Using this approach combined with FISH and pyrosequencing-based 16S rRNA gene sequence analysis, it was established here for the first time that these Thiomonas strains are ubiquitously present in minor proportions in this AMD and that they express the key enzymes involved in natural remediation processes at various locations and time points. In addition to these findings, this study also confirms that targeted proteomics applied at the community level can be used to detect weakly abundant proteins in situ. PMID:26870729

  7. Risk management in spatio-temporally varying field by true slime mold

    NASA Astrophysics Data System (ADS)

    Ito, Kentaro; Sumpter, David; Nakagaki, Toshiyuki

    Revealing how lower organisms solve complicated problems is a challenging research area, which could reveal the evolutionary origin of biological information processing. Here we report on the ability of a single-celled organism, true slime mold, to find a smart solution of risk management under spatio-temporally varying conditions. We designed test conditions under which there were three food-locations at vertices of equilateral triangle and a toxic light illuminated the organism on alternating halves of the triangle. We found that the organism behavior depended on the period of the repeated illumination, even though the total exposure time was kept the same . A simple mathematical model for the experimental results is proposed from a dynamical system point of view. We discuss our results in the context of a strategy of risk management by Physarum.

  8. Spatio-temporal properties of letter crowding.

    PubMed

    Chung, Susana T L

    2016-04-01

    Crowding between adjacent letters has been investigated primarily as a spatial effect. The purpose of this study was to investigate the spatio-temporal properties of letter crowding. Specifically, we examined the systematic changes in the degradation effects in letter identification performance when adjacent letters were presented with a temporal asynchrony, as a function of letter separation and between the fovea and the periphery. We measured proportion-correct performance for identifying the middle target letter in strings of three lowercase letters at the fovea and 10° in the inferior visual field, for a range of center-to-center letter separations and a range of stimulus onset asynchronies (SOA) between the target and flanking letters (positive SOAs: target preceded flankers). As expected, the accuracy for identifying the target letters reduces with decreases in letter separation. This crowding effect shows a strong dependency on SOAs, such that crowding is maximal between 0 and ∼100 ms (depending on conditions) and diminishes for larger SOAs (positive or negative). Maximal crowding does not require the target and flanking letters to physically coexist for the entire presentation duration. Most importantly, crowding can be minimized even for closely spaced letters if there is a large temporal asynchrony between the target and flankers. The reliance of letter identification performance on SOAs and how it changes with letter separations imply that the crowding effect can be traded between space and time. Our findings are consistent with the notion that crowding should be considered as a spatio-temporal, and not simply a spatial, effect. PMID:27088895

  9. Spatio-temporal properties of letter crowding

    PubMed Central

    Chung, Susana T. L.

    2016-01-01

    Crowding between adjacent letters has been investigated primarily as a spatial effect. The purpose of this study was to investigate the spatio-temporal properties of letter crowding. Specifically, we examined the systematic changes in the degradation effects in letter identification performance when adjacent letters were presented with a temporal asynchrony, as a function of letter separation and between the fovea and the periphery. We measured proportion-correct performance for identifying the middle target letter in strings of three lowercase letters at the fovea and 10° in the inferior visual field, for a range of center-to-center letter separations and a range of stimulus onset asynchronies (SOA) between the target and flanking letters (positive SOAs: target preceded flankers). As expected, the accuracy for identifying the target letters reduces with decreases in letter separation. This crowding effect shows a strong dependency on SOAs, such that crowding is maximal between 0 and ∼100 ms (depending on conditions) and diminishes for larger SOAs (positive or negative). Maximal crowding does not require the target and flanking letters to physically coexist for the entire presentation duration. Most importantly, crowding can be minimized even for closely spaced letters if there is a large temporal asynchrony between the target and flankers. The reliance of letter identification performance on SOAs and how it changes with letter separations imply that the crowding effect can be traded between space and time. Our findings are consistent with the notion that crowding should be considered as a spatio-temporal, and not simply a spatial, effect. PMID:27088895

  10. A Flexible Spatio-Temporal Model for Air Pollution with Spatial and Spatio-Temporal Covariates.

    PubMed

    Lindström, Johan; Szpiro, Adam A; Sampson, Paul D; Oron, Assaf P; Richards, Mark; Larson, Tim V; Sheppard, Lianne

    2014-09-01

    The development of models that provide accurate spatio-temporal predictions of ambient air pollution at small spatial scales is of great importance for the assessment of potential health effects of air pollution. Here we present a spatio-temporal framework that predicts ambient air pollution by combining data from several different monitoring networks and deterministic air pollution model(s) with geographic information system (GIS) covariates. The model presented in this paper has been implemented in an R package, SpatioTemporal, available on CRAN. The model is used by the EPA funded Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) to produce estimates of ambient air pollution; MESA Air uses the estimates to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. In this paper we use the model to predict long-term average concentrations of NOx in the Los Angeles area during a ten year period. Predictions are based on measurements from the EPA Air Quality System, MESA Air specific monitoring, and output from a source dispersion model for traffic related air pollution (Caline3QHCR). Accuracy in predicting long-term average concentrations is evaluated using an elaborate cross-validation setup that accounts for a sparse spatio-temporal sampling pattern in the data, and adjusts for temporal effects. The predictive ability of the model is good with cross-validated R (2) of approximately 0.7 at subject sites. Replacing four geographic covariate indicators of traffic density with the Caline3QHCR dispersion model output resulted in very similar prediction accuracy from a more parsimonious and more interpretable model. Adding traffic-related geographic covariates to the model that included Caline3QHCR did not further improve the prediction accuracy. PMID:25264424

  11. A Flexible Spatio-Temporal Model for Air Pollution with Spatial and Spatio-Temporal Covariates

    PubMed Central

    Lindström, Johan; Szpiro, Adam A; Sampson, Paul D; Oron, Assaf P; Richards, Mark; Larson, Tim V; Sheppard, Lianne

    2013-01-01

    The development of models that provide accurate spatio-temporal predictions of ambient air pollution at small spatial scales is of great importance for the assessment of potential health effects of air pollution. Here we present a spatio-temporal framework that predicts ambient air pollution by combining data from several different monitoring networks and deterministic air pollution model(s) with geographic information system (GIS) covariates. The model presented in this paper has been implemented in an R package, SpatioTemporal, available on CRAN. The model is used by the EPA funded Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) to produce estimates of ambient air pollution; MESA Air uses the estimates to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. In this paper we use the model to predict long-term average concentrations of NOx in the Los Angeles area during a ten year period. Predictions are based on measurements from the EPA Air Quality System, MESA Air specific monitoring, and output from a source dispersion model for traffic related air pollution (Caline3QHCR). Accuracy in predicting long-term average concentrations is evaluated using an elaborate cross-validation setup that accounts for a sparse spatio-temporal sampling pattern in the data, and adjusts for temporal effects. The predictive ability of the model is good with cross-validated R2 of approximately 0.7 at subject sites. Replacing four geographic covariate indicators of traffic density with the Caline3QHCR dispersion model output resulted in very similar prediction accuracy from a more parsimonious and more interpretable model. Adding traffic-related geographic covariates to the model that included Caline3QHCR did not further improve the prediction accuracy. PMID:25264424

  12. Spatio-Temporal Clustering of Monitoring Network

    NASA Astrophysics Data System (ADS)

    Hussain, I.; Pilz, J.

    2009-04-01

    Pakistan has much diversity in seasonal variation of different locations. Some areas are in desserts and remain very hot and waterless, for example coastal areas are situated along the Arabian Sea and have very warm season and a little rainfall. Some areas are covered with mountains, have very low temperature and heavy rainfall; for instance Karakoram ranges. The most important variables that have an impact on the climate are temperature, precipitation, humidity, wind speed and elevation. Furthermore, it is hard to find homogeneous regions in Pakistan with respect to climate variation. Identification of homogeneous regions in Pakistan can be useful in many aspects. It can be helpful for prediction of the climate in the sub-regions and for optimizing the number of monitoring sites. In the earlier literature no one tried to identify homogeneous regions of Pakistan with respect to climate variation. There are only a few papers about spatio-temporal clustering of monitoring network. Steinhaus (1956) presented the well-known K-means clustering method. It can identify a predefined number of clusters by iteratively assigning centriods to clusters based. Castro et al. (1997) developed a genetic heuristic algorithm to solve medoids based clustering. Their method is based on genetic recombination upon random assorting recombination. The suggested method is appropriate for clustering the attributes which have genetic characteristics. Sap and Awan (2005) presented a robust weighted kernel K-means algorithm incorporating spatial constraints for clustering climate data. The proposed algorithm can effectively handle noise, outliers and auto-correlation in the spatial data, for effective and efficient data analysis by exploring patterns and structures in the data. Soltani and Modarres (2006) used hierarchical and divisive cluster analysis to categorize patterns of rainfall in Iran. They only considered rainfall at twenty-eight monitoring sites and concluded that eight clusters

  13. Cortical Spatio-Temporal Dynamics Underlying Phonological Target Detection in Humans

    ERIC Educational Resources Information Center

    Chang, Edward F.; Edwards, Erik; Nagarajan, Srikantan S.; Fogelson, Noa; Dalal, Sarang S.; Canolty, Ryan T.; Kirsch, Heidi E.; Barbaro, Nicholas M.; Knight, Robert T.

    2011-01-01

    Selective processing of task-relevant stimuli is critical for goal-directed behavior. We used electrocorticography to assess the spatio-temporal dynamics of cortical activation during a simple phonological target detection task, in which subjects press a button when a prespecified target syllable sound is heard. Simultaneous surface potential…

  14. A Spatio-Temporal Downscaler for Output From Numerical Models

    PubMed Central

    Berrocal, Veronica J.; Gelfand, Alan E.; Holland, David M.

    2010-01-01

    Often, in environmental data collection, data arise from two sources: numerical models and monitoring networks. The first source provides predictions at the level of grid cells, while the second source gives measurements at points. The first is characterized by full spatial coverage of the region of interest, high temporal resolution, no missing data but consequential calibration concerns. The second tends to be sparsely collected in space with coarser temporal resolution, often with missing data but, where recorded, provides, essentially, the true value. Accommodating the spatial misalignment between the two types of data is of fundamental importance for both improved predictions of exposure as well as for evaluation and calibration of the numerical model. In this article we propose a simple, fully model-based strategy to downscale the output from numerical models to point level. The static spatial model, specified within a Bayesian framework, regresses the observed data on the numerical model output using spatially-varying coefficients which are specified through a correlated spatial Gaussian process. As an example, we apply our method to ozone concentration data for the eastern U.S. and compare it to Bayesian melding (Fuentes and Raftery 2005) and ordinary kriging (Cressie 1993; Chilès and Delfiner 1999). Our results show that our method outperforms Bayesian melding in terms of computing speed and it is superior to both Bayesian melding and ordinary kriging in terms of predictive performance; predictions obtained with our method are better calibrated and predictive intervals have empirical coverage closer to the nominal values. Moreover, our model can be easily extended to accommodate for the temporal dimension. In this regard, we consider several spatio-temporal versions of the static model. We compare them using out-of-sample predictions of ozone concentration for the eastern U.S. for the period May 1–October 15, 2001. For the best choice, we present a

  15. Automatic calibration of a parsimonious ecohydrological model in a sparse basin using the spatio-temporal variation of the NDVI

    NASA Astrophysics Data System (ADS)

    Ruiz-Pérez, Guiomar; Manfreda, Salvatore; Caylor, Kelly; Francés, Félix

    2016-04-01

    Drylands are extensive, covering 30% of the Earth's land surface and 50% of Africa. In these water-controlled areas, vegetation plays a key role in the water cycle. Ecohydrological models provide a tool to investigate the relationships between vegetation and water resources. However, studies in Africa often face the problem that many ecohydrological models have quite extensive parametrical requirements, while available data are scarce. Therefore, there is a need for searching new sources of information such as satellite data. The advantages of the use of satellite data in dry regions has been deeply demonstrated and studied. But, the use of this kind of data forces to introduce the concept of spatio-temporal information. In this context, we have to deal with the fact that there is a lack in terms of statistics and methodologies to incorporate the spatio-temporal data during the calibration and validation processes. This research wants to be a contribution in that sense. The used ecohydrological model was calibrated in the Upper Ewaso river basin in Kenya only using NDVI (Normalized Difference Vegetation Index) data from MODIS. An automatic calibration methodology based on Singular Value Decomposition techniques was proposed in order to calibrate the model taking into account the temporal variation and, also, the spatial pattern of the observed NDVI and the simulated LAI. The obtained results have demonstrated: (1) the satellite data is an extraordinary useful tool of information and it can be used to implement ecohydrological models in dry regions; (2) the proposed model calibrated only using satellite data is able to reproduce the vegetation dynamics (in time and in space) and, also, the observed discharge at the outlet point; and (3) the proposed automatic calibration methodology works satisfactorily and it includes spatio-temporal data, in other words, it takes into account the temporal variation and the spatial pattern of the analyzed data.

  16. Research on spatio-temporal ontology based on description logic

    NASA Astrophysics Data System (ADS)

    Huang, Yongqi; Ding, Zhimin; Zhao, Zhui; Ouyang, Fucheng

    2008-10-01

    DL, short for Description Logic, is aimed at getting a balance between describing ability and reasoning complexity. Users can adopt DL to write clear and formalized concept description for domain model, which makes ontology description possess well-defined syntax and semantics and helps to resolve the problem of spatio-temporal reasoning based on ontology. This paper studies on basic theory of DL and relationship between DL and OWL at first. By analyzing spatio-temporal concepts and relationship of spatio-temporal GIS, the purpose of this paper is adopting ontology language based on DL to express spatio-temporal ontology, and employing suitable ontology-building tool to build spatio-temporal ontology. With regard to existing spatio-temporal ontology based on first-order predicate logic, we need to transform it into spatio-temporal ontology based on DL so as to make the best of existing research fruits. This paper also makes a research on translating relationships between DL and first-order predicate logic.

  17. Spatio-temporal characteristics of Trichel pulse at low pressure

    NASA Astrophysics Data System (ADS)

    He, Shoujie; Jing, Ha

    2014-01-01

    Trichel pulses are investigated using a needle-to-plane electrode geometry at low pressure. The evolution of current and voltage, the spatio-temporal discharge images of Trichel pulse are measured. The rising time and duration time in a pulse are about 10 μs and several tens of microseconds, respectively. One period of pulse can be divided into three stages: the stage preceding cathode breakdown, cathode glow formation, and discharge decaying process. Besides a cathode glow and a dark space, an anode glow is also observed. The emission spectra mainly originate from the C3Пu → B3Пg transition for nitrogen. In addition, the capacitances in parallel connected with the discharge cell have important influence on the pulsing frequency.

  18. Target tracking based on spatio-temporal fractal error

    NASA Astrophysics Data System (ADS)

    Allen, Brian S.

    2007-04-01

    This paper presents a novel approach to target tracking using a measurement process based on spatio-temporal fractal error. Moving targets are automatically detected using one-dimensional temporal fractal error. A template derived from the two-dimensional spatial fractal error is then extracted for a designated target to allow for correlation-based template matching in subsequent frames. The outputs of both the spatial and temporal fractal error components are combined and presented as input to a kinematic tracking filter. It is shown that combining the two outputs provides improved tracking performance in the presence of noise, occlusion, other moving objects, and when the target of interest stops moving. Furthermore, reconciliation of the spatial and temporal components also provides a useful mechanism for detecting occlusion and avoiding template drift, a problem typically present in correlation-based trackers. Results are demonstrated using airborne MWIR sequences from the DARPA VIVID dataset.

  19. Spatio-temporal characteristics of Trichel pulse at low pressure

    SciTech Connect

    He, Shoujie; Jing, Ha

    2014-01-15

    Trichel pulses are investigated using a needle-to-plane electrode geometry at low pressure. The evolution of current and voltage, the spatio-temporal discharge images of Trichel pulse are measured. The rising time and duration time in a pulse are about 10 μs and several tens of microseconds, respectively. One period of pulse can be divided into three stages: the stage preceding cathode breakdown, cathode glow formation, and discharge decaying process. Besides a cathode glow and a dark space, an anode glow is also observed. The emission spectra mainly originate from the C{sup 3}Π{sub u} → B{sup 3}Π{sub g} transition for nitrogen. In addition, the capacitances in parallel connected with the discharge cell have important influence on the pulsing frequency.

  20. Numerical spatio-temporal characterization of Listeria monocytogenes biofilms.

    PubMed

    Mosquera-Fernández, M; Rodríguez-López, P; Cabo, M L; Balsa-Canto, E

    2014-07-16

    As the structure of biofilms plays a key role in their resistance and persistence, this work presents for the first time the numerical characterization of the temporal evolution of biofilm structures formed by three Listeria monocytogenes strains on two types of stainless-steel supports, AISI 304 SS No. 2B and AISI 316 SS No. 2R. Counting methods, motility tests, fluorescence microscopy and image analysis were combined to study the dynamic evolution of biofilm formation and structure. Image analysis was performed with several well-known parameters as well as a newly defined parameter to quantify spatio-temporal distribution. The results confirm the interstrain variability of L. monocytogenes species regarding biofilm structure and structure evolution. Two types of biofilm were observed: homogeneous or flat and heterogeneous or clustered. Differences in clusters and in attachment and detachment processes were due mainly to the topography and composition of the two surfaces although an effect due to motility was also found. PMID:24858448

  1. A spatio-temporal extension to the map cube operator

    NASA Astrophysics Data System (ADS)

    Alzate, Juan C.; Moreno, Francisco J.; Echeverri, Jaime

    2012-09-01

    OLAP (On Line Analytical Processing) is a set of techniques and operators to facilitate the data analysis usually stored in a data warehouse. In this paper, we extend the functionality of an OLAP operator known as Map Cube with the definition and incorporation of a function that allows the formulation of spatio-temporal queries. For example, consider a data warehouse about crimes that includes data about the places where the crimes were committed. Suppose we want to find and visualize the trajectory (a trajectory is just the path that an object follows through space as a function of time) of the crimes of a suspect beginning with his oldest crime and ending with his most recent one. In order to meet this requirement, we extend the Map Cube operator.

  2. Evaluation of Bayesian spatio-temporal latent models in small area health data.

    PubMed

    Choi, Jungsoon; Lawson, Andrew B; Cai, Bo; Hossain, Md Monir

    2011-12-01

    Health outcomes are linked to air pollution, demographic, or socioeconomic factors which vary across space and time. Thus, it is often found that relative risks in space-time health data have locally different temporal patterns. In such cases, latent modeling is useful in the disaggregation of risk profiles. In particular, spatio-temporal mixture models can help to isolate spatial clusters each of which has a homogeneous temporal pattern in relative risks. In mixture modeling, various weight structures can be used and two situations can be considered: the number of underlying components is known or unknown. In this paper, we compare spatio-temporal mixture models with different weight structures in both situations. In addition, spatio-temporal Dirichlet process mixture models are compared to them when the number of components is unknown. For comparison, we propose a set of spatial cluster detection diagnostics based on the posterior distribution of the weights. We also develop new accuracy measures to assess the recovery of true relative risks. Based on the simulation study, we examine the performance of various spatio-temporal mixture models in terms of proposed methods and goodness-of-fit measures. We apply our models to a county-level chronic obstructive pulmonary disease data set from the state of Georgia. PMID:22184483

  3. Spatio-temporal fluctuations in immiscible polymeric binary mixtures: towards the realization of a signal/information processing device with hierarchical instabilities

    NASA Astrophysics Data System (ADS)

    Maruyama, Ryota; Asakawa, Naoki

    2014-09-01

    A design of a bio-inspired signal/information processing device and the fabrication of a stochastic delay-derivative element (SDDE) using an immiscible polymer binary mixture of poly(L-lactic acid) with poly(ɛ-caprolactone) are described. A functional aspect of bio-inspired signal/information processing using both analogue electric circuits and numerical simulations are shown. Nano-thin films of polymeric binary mixtures were explored to realize the SDDE.

  4. Sensor Web for Spatio-Temporal Monitoring of a Hydrological Environment

    NASA Technical Reports Server (NTRS)

    Delin, K. A.; Jackson, S. P.; Johnson, D. W.; Burleigh, S. C.; Woodrow, R. R.; McAuley, M.; Britton, J. T.; Dohm, J. M.; Ferre, T. P. A.; Ip, Felipe

    2004-01-01

    The Sensor Web is a macroinstrument concept that allows for the spatio-temporal understanding of an environment through coordinated efforts between multiple numbers and types of sensing platforms, including, in its most general form, both orbital and terrestrial and both fixed and mobile. Each of these platforms, or pods, communicates within its local neighborhood and thus distributes information to the instrument as a whole. The result of sharing and continual processing of this information among all the Sensor Web elements will result in an information flow and a global perception of and reactive capability to the environment. As illustrated, the Sensor Web concept also allows for the recursive notion of a web of webs with individual distributed instruments possibly playing the role of a single node point on a larger Sensor Web instrument. In particular, the fusion of inexpensive, yet sophisticated, commercial technology from both the computation and telecommunication revolutions has enabled the development of practical, fielded, and embedded in situ systems that have been the focus of the NASA/JPL Sensor Webs Project (http://sensorwebs.jpl.nasa.gov/). These Sensor Webs are complete systems consisting of not only the pod elements that wirelessly communicate among themselves, but also interfacing and archiving software that allows for easy use by the end-user. Previous successful deployments have included environments as diverse as coastal regions, Antarctica, and desert areas. The Sensor Web has broad implications for Earth and planetary science and will revolutionize the way experiments and missions are conceived and performed. As part of our current efforts to develop a macrointelligence within the system, we have deployed a Sensor Web at the Central Avra Valley Storage and Recovery Project (CAVSARP) facility located west of Tucson, AZ. This particular site was selected because it is ideal for studying spatio-temporal phenomena and for providing a test site for

  5. Hirarchical Bayesian Spatio-Temporal Interpolation including Covariates

    NASA Astrophysics Data System (ADS)

    Hussain, Ijaz; Mohsin, Muhammad; Spoeck, Gunter; Pilz, Juergen

    2010-05-01

    The space-time interpolation of precipitation has significant contribution to river control,reservoir operations, forestry interest and flash flood watches etc. The changes in environmental covariates and spatial covariates make space-time estimation of precipitation a challenging task. In our earlier paper [1], we used transformed hirarchical Bayesian sapce-time interpolation method for predicting the amount of precipiation. In present paper, we modified the [2] method to include covarites which varaies with respect to space-time. The proposed method is applied to estimating space-time monthly precipitation in the monsoon periods during 1974 - 2000. The 27-years monthly average data of precipitation, temperature, humidity and wind speed are obtained from 51 monitoring stations in Pakistan. The average monthly precipitation is used response variable and temperature, humidity and wind speed are used as time varying covariates. Moreovere the spatial covarites elevation, latitude and longitude of same monitoring stations are also included. The cross-validation method is used to compare the results of transformed hierarchical Bayesian spatio-temporal interpolation with and without including environmental and spatial covariates. The software of [3] is modified to incorprate enviornmental covariates and spatil covarites. It is observed that the transformed hierarchical Bayesian method including covarites provides more accuracy than the transformed hierarchical Bayesian method without including covarites. Moreover, the five potential monitoring cites are selected based on maximum entropy sampaling design approach. References [1] I.Hussain, J.Pilz,G. Spoeck and H.L.Yu. Spatio-Temporal Interpolation of Precipitation during Monsoon Periods in Pakistan. submitted in Advances in water Resources,2009. [2] N.D. Le, W. Sun, and J.V. Zidek, Bayesian multivariate spatial interpolation with data missing by design. Journal of the Royal Statistical Society. Series B (Methodological

  6. Spatio-temporal population estimates for risk management

    NASA Astrophysics Data System (ADS)

    Cockings, Samantha; Martin, David; Smith, Alan; Martin, Rebecca

    2013-04-01

    . Unusual patterns associated with special events can also be modelled and the framework is fully volume preserving. Outputs from the model are gridded population surfaces for the specified time slice, either for total population or by sub-groups (e.g. age). Software to implement the models (SurfaceBuilder247) has been developed and pre-processed layers for typical time slices for England and Wales in 2001 and 2006 are available for UK academic purposes. The outputs and modelling framework from the Pop24/7 programme provide significant opportunities for risk management applications. For estimates of mid- to long-term cumulative population exposure to hazards, such as in flood risk mapping, populations can be produced for numerous time slices and integrated with flood models. For applications in emergency response/ management, time-specific population models can be used as seeds for agent-based models or other response/behaviour models. Estimates for sub-groups of the population also permit exploration of vulnerability through space and time. This paper outlines the requirements for effective spatio-temporal population models for risk management. It then describes the Pop24/7 framework and illustrates its potential for risk management through presentation of examples from natural and anthropogenic hazard applications. The paper concludes by highlighting key challenges for future research in this area.

  7. Unavoidable Errors: A Spatio-Temporal Analysis of Time-Course and Neural Sources of Evoked Potentials Associated with Error Processing in a Speeded Task

    ERIC Educational Resources Information Center

    Vocat, Roland; Pourtois, Gilles; Vuilleumier, Patrik

    2008-01-01

    The detection of errors is known to be associated with two successive neurophysiological components in EEG, with an early time-course following motor execution: the error-related negativity (ERN/Ne) and late positivity (Pe). The exact cognitive and physiological processes contributing to these two EEG components, as well as their functional…

  8. Spatio-temporal registration of multiple trajectories.

    PubMed

    Padoy, Nicolas; Hager, Gregory D

    2011-01-01

    A growing number of medical datasets now contain both a spatial and a temporal dimension. Trajectories, from tools or body features, are thus becoming increasingly important for their analysis. In this paper, we are interested in recovering the spatial and temporal differences between trajectories coming from different datasets. In particular, we address the case of surgical gestures, where trajectories contain both spatial transformations and speed differences in the execution. We first define the spatio-temporal registration problem between multiple trajectories. We then propose an optimization method to jointly recover both the rigid spatial motions and the non-linear time warpings. The optimization generates also a generic trajectory template, in which spatial and temporal differences have been factored out. This approach can be potentially used to register and compare gestures side-by-side for training sessions, to build gesture trajectory models for automation by a robot, or to register the trajectories of natural or artificial markers which follow similar motions. We demonstrate its usefulness with synthetic and real experiments. In particular, we register and analyze complex surgical gestures performed by tele-manipulation using the da Vinci robot. PMID:22003611

  9. Spatio-temporal correlations in Coulomb clusters

    NASA Astrophysics Data System (ADS)

    Ghosal, Amit; Ash, Biswarup; Chakrabarti, Jaydeb

    Dynamical response of Coulomb-particles in nanoclusters are investigated at different temperatures characterizing their solid-like (Wigner molecule) and liquid-like behavior. The density correlations probe spatio-temporal relaxation, uncovering distinct behavior at multiple time scales in these systems. They show a stretched-Gaussian or stretched-exponential spatial decay at long times in circular and irregular traps. Interplay of confinement and long-range nature of interactions yields spatially correlated motion of the particles in string-like paths, leaving the system heterogeneous even at long times. While particles in a `solid' flow producing dynamic heterogeneities, their random motion in `liquid' defies central limit theorem. Distinguishing the two confinements, temperature dependent motional signatures serve as a criterion for the crossover between `solid' and `liquid'. The irregular Wigner molecule turns into a nearly homogeneous liquid over a much wider temperature window compared to the circular case. The temperature dependence of different relaxation time scales builds crucial insights. A phenomenological model, relating the unusual dynamics to the heterogeneous nature of the diffusivities in the system, captures much of the subtleties of our numerical simulations.

  10. From upright to upside-down presentation: A spatio-temporal ERP study of the parametric effect of rotation on face and house processing

    PubMed Central

    Jemel, Boutheina; Coutya, Julie; Langer, Caroline; Roy, Sylvain

    2009-01-01

    Background While there is a general agreement that picture-plane inversion is more detrimental to face processing than to other seemingly complex visual objects, the origin of this effect is still largely debatable. Here, we address the question of whether face inversion reflects a quantitative or a qualitative change in processing mode by investigating the pattern of event-related potential (ERP) response changes with picture plane rotation of face and house pictures. Thorough analyses of topographical (Scalp Current Density maps, SCD) and dipole source modeling were also conducted. Results We find that whilst stimulus orientation affected in a similar fashion participants' response latencies to make face and house decisions, only the ERPs in the N170 latency range were modulated by picture plane rotation of faces. The pattern of N170 amplitude and latency enhancement to misrotated faces displayed a curvilinear shape with an almost linear increase for rotations from 0° to 90° and a dip at 112.5° up to 180° rotations. A similar discontinuity function was also described for SCD occipito-temporal and temporal current foci with no topographic distribution changes, suggesting that upright and misrotated faces activated similar brain sources. This was confirmed by dipole source analyses showing the involvement of bilateral sources in the fusiform and middle occipital gyri, the activity of which was differentially affected by face rotation. Conclusion Our N170 findings provide support for both the quantitative and qualitative accounts for face rotation effects. Although the qualitative explanation predicted the curvilinear shape of N170 modulations by face misrotations, topographical and source modeling findings suggest that the same brain regions, and thus the same mechanisms, are probably at work when processing upright and rotated faces. Taken collectively, our results indicate that the same processing mechanisms may be involved across the whole range of face

  11. Spatio-temporal characteristics of metabotropic glutamate receptor 5 traffic at or near the plasma membrane in astrocytes.

    PubMed

    Lee, William; Parpura, Vladimir

    2016-06-01

    Astrocytes can sense extracellular glutamate and respond to it by elevating their intracellular Ca(2+) levels via the activation of G-protein coupled receptors, such as metabotropic glutamate receptor 5 (mGluR5), which, during early postnatal development, is the primary receptor responsible for glutamatergic signaling in astrocytes. However, the detailed spatio-temporal characteristics of mGluR5 traffic at or near the plasma membrane of astrocytes are not well understood. To address this issue, we expressed recombinant fluorescent protein chimera of mGluR5 and used total internal reflection fluorescence microscopy on rat visual cortical astrocytes in culture. We used astrocytes lacking major processes, otherwise posing as a diffusion barrier, to infer into the general dynamics of this receptor. We found that plasmalemmal mGluR5 clusters in distinct areas, the size, and initial spatio-temporal level of occupancy of which dictated mGluR5 trafficking characteristics upon glutamate stimulation. These findings will be valuable in the interpretation of point-to-point information transfer and volume transmission between astrocytes and neurons, as well as that of paracrine signaling within astrocytic networks. PMID:27014856

  12. SuperFly: a comparative database for quantified spatio-temporal gene expression patterns in early dipteran embryos.

    PubMed

    Cicin-Sain, Damjan; Pulido, Antonio Hermoso; Crombach, Anton; Wotton, Karl R; Jiménez-Guri, Eva; Taly, Jean-François; Roma, Guglielmo; Jaeger, Johannes

    2015-01-01

    We present SuperFly (http://superfly.crg.eu), a relational database for quantified spatio-temporal expression data of segmentation genes during early development in different species of dipteran insects (flies, midges and mosquitoes). SuperFly has a special focus on emerging non-drosophilid model systems. The database currently includes data of high spatio-temporal resolution for three species: the vinegar fly Drosophila melanogaster, the scuttle fly Megaselia abdita and the moth midge Clogmia albipunctata. At this point, SuperFly covers up to 9 genes and 16 time points per species, with a total of 1823 individual embryos. It provides an intuitive web interface, enabling the user to query and access original embryo images, quantified expression profiles, extracted positions of expression boundaries and integrated datasets, plus metadata and intermediate processing steps. SuperFly is a valuable new resource for the quantitative comparative study of gene expression patterns across dipteran species. Moreover, it provides an interesting test set for systems biologists interested in fitting mathematical gene network models to data. Both of these aspects are essential ingredients for progress toward a more quantitative and mechanistic understanding of developmental evolution. PMID:25404137

  13. SuperFly: a comparative database for quantified spatio-temporal gene expression patterns in early dipteran embryos

    PubMed Central

    Cicin-Sain, Damjan; Pulido, Antonio Hermoso; Crombach, Anton; Wotton, Karl R.; Jiménez-Guri, Eva; Taly, Jean-François; Roma, Guglielmo; Jaeger, Johannes

    2015-01-01

    We present SuperFly (http://superfly.crg.eu), a relational database for quantified spatio-temporal expression data of segmentation genes during early development in different species of dipteran insects (flies, midges and mosquitoes). SuperFly has a special focus on emerging non-drosophilid model systems. The database currently includes data of high spatio-temporal resolution for three species: the vinegar fly Drosophila melanogaster, the scuttle fly Megaselia abdita and the moth midge Clogmia albipunctata. At this point, SuperFly covers up to 9 genes and 16 time points per species, with a total of 1823 individual embryos. It provides an intuitive web interface, enabling the user to query and access original embryo images, quantified expression profiles, extracted positions of expression boundaries and integrated datasets, plus metadata and intermediate processing steps. SuperFly is a valuable new resource for the quantitative comparative study of gene expression patterns across dipteran species. Moreover, it provides an interesting test set for systems biologists interested in fitting mathematical gene network models to data. Both of these aspects are essential ingredients for progress toward a more quantitative and mechanistic understanding of developmental evolution. PMID:25404137

  14. A LANGUAGE FOR MODULAR SPATIO-TEMPORAL SIMULATION (R824766)

    EPA Science Inventory

    Creating an effective environment for collaborative spatio-temporal model development will require computational systems that provide support for the user in three key areas: (1) Support for modular, hierarchical model construction and archiving/linking of simulation modules; (2)...

  15. Spatio-temporal dynamics of biogeochemical processes and air-sea CO2 fluxes in the Western English Channel based on two years of FerryBox deployment

    NASA Astrophysics Data System (ADS)

    Marrec, P.; Cariou, T.; Latimier, M.; Macé, E.; Morin, P.; Vernet, M.; Bozec, Y.

    2014-12-01

    From January 2011 to January 2013, a FerryBox system was installed on a Voluntary Observing Ship (VOS), which crossed the Western English Channel (WEC) between Roscoff (France) and Plymouth (UK) up to 3 times a day. The FerryBox continuously measured sea surface temperature (SST), sea surface salinity (SSS), dissolved oxygen (DO), fluorescence and partial pressure of CO2 (from April 2012) along the ferry track. Sensors were calibrated based on 714 bimonthly surface samplings with precisions of 0.016 for SSS, 3.3 μM for DO, 0.40 μg L- 1 for Chlorophyll-a (Chl-a) (based on fluorescence measurements) and 5.2 μatm for pCO2. Over the 2 years of deployment (900 crossings), we reported 9% of data lost due to technical issues and quality checked data was obtained to allow investigation of the dynamics of biogeochemical processes related to air-sea CO2 fluxes in the WEC. Based on this unprecedented high-frequency dataset, the physical structure of the WEC was assessed using SST anomalies and the presence of a thermal front was observed around the latitude 49.5°N, which divided the WEC in two main provinces: the seasonally stratified northern WEC (nWEC) and the all-year well-mixed southern WEC (sWEC). These hydrographical properties strongly influenced the spatial and inter-annual distributions of phytoplankton blooms, which were mainly limited by nutrients and light availability in the nWEC and the sWEC, respectively. Air-sea CO2 fluxes were also highly related to hydrographical properties of the WEC between late April and early September 2012, with the sWEC a weak source of CO2 to the atmosphere of 0.9 mmol m- 2 d- 1, whereas the nWEC acted as a sink for atmospheric CO2 of 6.9 mmol m- 2 d- 1. The study of short time-scale dynamics of air-sea CO2 fluxes revealed that an intense and short (less than 10 days) summer bloom in the nWEC contributed to 29% of the CO2 sink during the productive period, highlighting the necessity for high frequency observations in coastal

  16. Parallel indexing technique for spatio-temporal data

    NASA Astrophysics Data System (ADS)

    He, Zhenwen; Kraak, Menno-Jan; Huisman, Otto; Ma, Xiaogang; Xiao, Jing

    2013-04-01

    The requirements for efficient access and management of massive multi-dimensional spatio-temporal data in geographical information system and its applications are well recognized and researched. The most popular spatio-temporal access method is the R-Tree and its variants. However, it is difficult to use them for parallel access to multi-dimensional spatio-temporal data because R-Trees, and variants thereof, are in hierarchical structures which have severe overlapping problems in high dimensional space. We extended a two-dimensional interval space representation of intervals to a multi-dimensional parallel space, and present a set of formulae to transform spatio-temporal queries into parallel interval set operations. This transformation reduces problems of multi-dimensional object relationships to simpler two-dimensional spatial intersection problems. Experimental results show that the new parallel approach presented in this paper has superior range query performance than R*-trees for handling multi-dimensional spatio-temporal data and multi-dimensional interval data. When the number of CPU cores is larger than that of the space dimensions, the insertion performance of this new approach is also superior to R*-trees. The proposed approach provides a potential parallel indexing solution for fast data retrieval of massive four-dimensional or higher dimensional spatio-temporal data.

  17. Spatio-Temporal Variability of Water Vapor in the Free Troposphere Investigated by Dial and Ftir Vertical Soundings

    NASA Astrophysics Data System (ADS)

    Vogelmann, H.; Sussmann, R.; Trickl, T.; Reichert, A.

    2016-06-01

    We report on the free tropospheric spatio-temporal variability of water vapor investigated by the analysis of a five-year period of water vapor vertical soundings above Mt. Zugspitze (2962 m a.s.l., Germany). Our results are obtained from a combination of measurements of vertically integrated water vapor (IWV), recorded with a solar Fourier Transform InfraRed (FTIR) spectrometer and of water vapor profiles recorded with the nearby differential absorption lidar (DIAL). The special geometrical arrangement of one zenith-viewing and one sun-pointing instrument and the temporal resolution of both optical instruments allow for an investigation of the spatio-temporal variability of IWV on a spatial scale of less than one kilometer and on a time scale of less than one hour. We investigated the short-term variability of both IWV and water vapor profiles from statistical analyses. The latter was also examined by case studies with a clear assignment to certain atmospheric processes as local convection or long-range transport. This study is described in great detail in our recent publication [1].

  18. Out of equilibrium spatio-temporal correlations in the Bose-Hubbard model

    NASA Astrophysics Data System (ADS)

    Kennett, Malcolm; Fitzpatrick, Matthew

    2016-05-01

    The Bose-Hubbard model (BHM) provides a model system to study quench dynamics across a quantum phase transition. Theoretically, it has proven challenging to study spatio-temporal correlations in the BHM in dimensions higher than one. We use the Schwinger-Keldysh technique and a strong-coupling expansion to develop a two-particle irreducible formalism to allow us to study spatio-temporal correlations in both the superfluid (SF) and Mott-insulating (MI) regimes during a quantum quench for dimensions higher than one. We obtain equations of motion for both the superfluid order parameter and two-point correlation functions and present numerical results for the evolution of two-time correlation functions. We relate our results to experiments on cold atoms in optical lattices. Supported by NSERC.

  19. Analysis of non-ergodic behaviour in spatio-temporal coherence properties of speckle light

    NASA Astrophysics Data System (ADS)

    Réfrégier, Philippe

    Spatio-temporal coherence properties of light scattered by rough surfaces that leads to speckle fluctuations are analysed. It is demonstrated that the scattered light is non-ergodic with the disorder due to the scattering process. Although the mutual coherence matrix vanishes with isotropic polarization fluctuations, it is shown that spatio-temporal coherence properties can be described with interference experiments that can be obtained between different speckles of the scattered light. For non-singular scattering processes, the maximal value of the modulus of the Wolf degree of coherence is analysed in the spatial time domain. This approach is also applied to totally unpolarized incident light with an isotropic and spatially independent scattering process. The mean value and the standard deviation of the Wolf degree of coherence are then determined from the coherence properties of the incident light.

  20. Dynamic design of ecological monitoring networks for non-Gaussian spatio-temporal data

    USGS Publications Warehouse

    Wikle, C.K.; Royle, J. Andrew

    2005-01-01

    Many ecological processes exhibit spatial structure that changes over time in a coherent, dynamical fashion. This dynamical component is often ignored in the design of spatial monitoring networks. Furthermore, ecological variables related to processes such as habitat are often non-Gaussian (e.g. Poisson or log-normal). We demonstrate that a simulation-based design approach can be used in settings where the data distribution is from a spatio-temporal exponential family. The key random component in the conditional mean function from this distribution is then a spatio-temporal dynamic process. Given the computational burden of estimating the expected utility of various designs in this setting, we utilize an extended Kalman filter approximation to facilitate implementation. The approach is motivated by, and demonstrated on, the problem of selecting sampling locations to estimate July brood counts in the prairie pothole region of the U.S.

  1. Spatio-temporal networks: reachability, centrality and robustness

    PubMed Central

    Musolesi, Mirco

    2016-01-01

    Recent advances in spatial and temporal networks have enabled researchers to more-accurately describe many real-world systems such as urban transport networks. In this paper, we study the response of real-world spatio-temporal networks to random error and systematic attack, taking a unified view of their spatial and temporal performance. We propose a model of spatio-temporal paths in time-varying spatially embedded networks which captures the property that, as in many real-world systems, interaction between nodes is non-instantaneous and governed by the space in which they are embedded. Through numerical experiments on three real-world urban transport systems, we study the effect of node failure on a network's topological, temporal and spatial structure. We also demonstrate the broader applicability of this framework to three other classes of network. To identify weaknesses specific to the behaviour of a spatio-temporal system, we introduce centrality measures that evaluate the importance of a node as a structural bridge and its role in supporting spatio-temporally efficient flows through the network. This exposes the complex nature of fragility in a spatio-temporal system, showing that there is a variety of failure modes when a network is subject to systematic attacks. PMID:27429776

  2. Spatio-temporal networks: reachability, centrality and robustness.

    PubMed

    Williams, Matthew J; Musolesi, Mirco

    2016-06-01

    Recent advances in spatial and temporal networks have enabled researchers to more-accurately describe many real-world systems such as urban transport networks. In this paper, we study the response of real-world spatio-temporal networks to random error and systematic attack, taking a unified view of their spatial and temporal performance. We propose a model of spatio-temporal paths in time-varying spatially embedded networks which captures the property that, as in many real-world systems, interaction between nodes is non-instantaneous and governed by the space in which they are embedded. Through numerical experiments on three real-world urban transport systems, we study the effect of node failure on a network's topological, temporal and spatial structure. We also demonstrate the broader applicability of this framework to three other classes of network. To identify weaknesses specific to the behaviour of a spatio-temporal system, we introduce centrality measures that evaluate the importance of a node as a structural bridge and its role in supporting spatio-temporally efficient flows through the network. This exposes the complex nature of fragility in a spatio-temporal system, showing that there is a variety of failure modes when a network is subject to systematic attacks. PMID:27429776

  3. Methods for detecting saddle-type objects from spatio-temporal data: A comparative analysis

    NASA Astrophysics Data System (ADS)

    Donner, S.; Padberg, K.; Donner, R.; Kurths, J.

    2009-04-01

    The positions of saddle points and their associated invariant manifolds are known to play a crucial role in understanding transport processes in two-dimensional steady flows. Since the relevant data are often only coarsely gridded, a reliable detection of fixed points in spatio-temporal discretised vector fields is a problem of contemporary interest. Unsteady flows may additionally exhibit distinguished hyperbolic trajectories and associated manifolds - often only defined on a finite-time span - that continue to organise particle transport. As these objects are known to be Lagrangian their exact position can typically not be deduced from analysing single velocity fields. In the literature, several methods are described for approximating hyperbolic objects in steady and unsteady flows, but it is usually not clear how well they perform for a specific data set. This contribution presents a comparative analysis of the performance of three common methods (finite-time Lyapunov exponents, hyperbolicity time, leaking). In addition, a simple statistic approach based on a gradient approximation of the velocity fields is used to approximate instantaneous stagnation points. The results are evaluated with respect to the errors in the total number and location of analytically known saddle points for two different two-dimensional steady velocity fields. The reliability is statistically tested by applying multiplicative Gaussian white noise to the original data and repeating all procedures. In a second step of analysis, the different methods are applied to time-dependent versions of these velocity fields, where candidates for hyperbolic trajectories are detected and compared.

  4. Spatio-temporal correlation-based fast coding unit depth decision for high efficiency video coding

    NASA Astrophysics Data System (ADS)

    Zhou, Chengtao; Zhou, Fan; Chen, Yaowu

    2013-10-01

    The exhaustive block partition search process in high efficiency video coding (HEVC) imposes a very high computational complexity on test module of HEVC encoder (HM). A fast coding unit (CU) depth algorithm using the spatio-temporal correlation of the depth information to fasten the search process is proposed. The depth of the coding tree unit (CTU) is predicted first by using the depth information of the spatio-temporal neighbor CTUs. Then, the depth information of the adjacent CU is incorporated to skip some specific depths when encoding the sub-CTU. As compared with the original HM encoder, experimental results show that the proposed algorithm can save more than 20% encoding time on average for intra-only, low-delay, low-delay P slices, and random access cases with almost the same rate-distortion performance.

  5. Spatio-temporally smoothed coherence factor for ultrasound imaging.

    PubMed

    Xu, Mengling; Yang, Xin; Ding, Mingyue; Yuchi, Ming

    2014-01-01

    Coherence-factor-like beamforming methods, such as the coherence factor (CF), the phase coherence factor (PCF), or the sign coherence factor (SCF), have been applied to suppress side and/or grating lobes and clutter in ultrasound imaging. These adaptive weighting factors can be implemented effectively with low computational complexity to improve image contrast properties. However, because of low SNR, the resulting images may suffer from deficiencies, including reduced overall image brightness, increased speckle variance, black-region artifacts surrounding hyperechoic objects, and underestimated magnitudes of point targets. To overcome these artifacts, a new spatio-temporal smoothing procedure is introduced to the CF method. It results in a smoothed coherence factor which measures the signal coherence among the beamsums of the divided subarrays over the duration of a transmit pulse. In addition, the procedure is extended to the SCF using the sign bits of the received signals. Simulated and real experimental data sets demonstrate that the proposed methods can improve the robustness of the CF and SCF with reduced speckle variance and significant removal of black-region artifacts, while preserving the ability to suppress clutter. Consequently, image contrast can be enhanced, especially for anechoic cysts. PMID:24402905

  6. Decomposition of the complex system into nonlinear spatio-temporal modes: algorithm and application to climate data mining

    NASA Astrophysics Data System (ADS)

    Feigin, Alexander; Gavrilov, Andrey; Loskutov, Evgeny; Mukhin, Dmitry

    2015-04-01

    Proper decomposition of the complex system into well separated "modes" is a way to reveal and understand the mechanisms governing the system behaviour as well as discover essential feedbacks and nonlinearities. The decomposition is also natural procedure that provides to construct adequate and concurrently simplest models of both corresponding sub-systems, and of the system in whole. In recent works two new methods of decomposition of the Earth's climate system into well separated modes were discussed. The first method [1-3] is based on the MSSA (Multichannel Singular Spectral Analysis) [4] for linear expanding vector (space-distributed) time series and makes allowance delayed correlations of the processes recorded in spatially separated points. The second one [5-7] allows to construct nonlinear dynamic modes, but neglects delay of correlations. It was demonstrated [1-3] that first method provides effective separation of different time scales, but prevent from correct reduction of data dimension: slope of variance spectrum of spatio-temporal empirical orthogonal functions that are "structural material" for linear spatio-temporal modes, is too flat. The second method overcomes this problem: variance spectrum of nonlinear modes falls essentially sharply [5-7]. However neglecting time-lag correlations brings error of mode selection that is uncontrolled and increases with growth of mode time scale. In the report we combine these two methods in such a way that the developed algorithm allows constructing nonlinear spatio-temporal modes. The algorithm is applied for decomposition of (i) multi hundreds years globally distributed data generated by the INM RAS Coupled Climate Model [8], and (ii) 156 years time series of SST anomalies distributed over the globe [9]. We compare efficiency of different methods of decomposition and discuss the abilities of nonlinear spatio-temporal modes for construction of adequate and concurrently simplest ("optimal") models of climate systems

  7. Reaction diffusion equation with spatio-temporal delay

    NASA Astrophysics Data System (ADS)

    Zhao, Zhihong; Rong, Erhua

    2014-07-01

    We investigate reaction-diffusion equation with spatio-temporal delays, the global existence, uniqueness and asymptotic behavior of solutions for which in relation to constant steady-state solution, included in the region of attraction of a stable steady solution. It is shown that if the delay reaction function satisfies some conditions and the system possesses a pair of upper and lower solutions then there exists a unique global solution. In terms of the maximal and minimal constant solutions of the corresponding steady-state problem, we get the asymptotic stability of reaction-diffusion equation with spatio-temporal delay. Applying this theory to Lotka-Volterra model with spatio-temporal delay, we get the global solution asymptotically tend to the steady-state problem's steady-state solution.

  8. Diverse spatio-temporal dynamical patterns of p53 and cell fate decisions

    NASA Astrophysics Data System (ADS)

    Clairambault, Jean; Eliaš, Ján

    2016-06-01

    The protein p53 as a tumour suppressor protein accumulates in cells in response to DNA damage and transactivates a large variety of genes involved in apoptosis, cell cycle regulation and numerous other processes. Recent biological observations suggest that specific spatio-temporal dynamical patterns of p53 may be associated with specific cellular response, and thus the spatio-temporal heterogeneity of the p53 dynamics contributes to the overall complexity of p53 signalling. Reaction-diffusion equations taking into account spatial representation of the cell and motion of the species inside the cell can be used to model p53 protein network and could be thus of some help to biologists and pharmacologists in anticancer treatment.

  9. Bayesian spatio-temporal modeling of particulate matter concentrations in Peninsular Malaysia

    NASA Astrophysics Data System (ADS)

    Manga, Edna; Awang, Norhashidah

    2016-06-01

    This article presents an application of a Bayesian spatio-temporal Gaussian process (GP) model on particulate matter concentrations from Peninsular Malaysia. We analyze daily PM10 concentration levels from 35 monitoring sites in June and July 2011. The spatiotemporal model set in a Bayesian hierarchical framework allows for inclusion of informative covariates, meteorological variables and spatiotemporal interactions. Posterior density estimates of the model parameters are obtained by Markov chain Monte Carlo methods. Preliminary data analysis indicate information on PM10 levels at sites classified as industrial locations could explain part of the space time variations. We include the site-type indicator in our modeling efforts. Results of the parameter estimates for the fitted GP model show significant spatio-temporal structure and positive effect of the location-type explanatory variable. We also compute some validation criteria for the out of sample sites that show the adequacy of the model for predicting PM10 at unmonitored sites.

  10. A spatio-temporal model of housing prices based on individual sales transactions over time

    NASA Astrophysics Data System (ADS)

    Smith, Tony E.; Wu, Peggy

    2009-12-01

    A spatio-temporal model of housing price trends is developed that focuses on individual housing sales over time. The model allows for both the spatio-temporal lag effects of previous sales in the vicinity of each housing sale, and for general autocorrelation effects over time. A key feature of this model is the recognition of the unequal spacing between individual housing sales over time. Hence the residuals are modeled as a first-order autoregressive process with unequally spaced events. The maximum-likelihood estimation of this model is developed in detail, and tested in terms of simulations based on selected data. In addition, the model is applied to a small data set in the Philadelphia area.

  11. Time reversal and the spatio-temporal matched filter

    SciTech Connect

    Lehman, S K; Poggio, A J; Kallman, J S; Meyer, A W; Candy, J V

    2004-03-08

    It is known that focusing of an acoustic field by a time-reversal mirror (TRM) is equivalent to a spatio-temporal matched filter under conditions where the Green's function of the field satisfies reciprocity and is time invariant, i.e. the Green's function is independent of the choice of time origin. In this letter, it is shown that both reciprocity and time invariance can be replaced by a more general constraint on the Green's function that allows a TRM to implement the spatio-temporal matched filter even when conditions are time varying.

  12. Real-Time Spatio-Temporal Twice Whitening for MIMO Energy Detector

    SciTech Connect

    Humble, Travis S; Mitra, Pramita; Barhen, Jacob; Schleck, Bryan

    2010-01-01

    While many techniques exist for local spectrum sensing of a primary user, each represents a computationally demanding task to secondary user receivers. In software-defined radio, computational complexity lengthens the time for a cognitive radio to recognize changes in the transmission environment. This complexity is even more significant for spatially multiplexed receivers, e.g., in SIMO and MIMO, where the spatio-temporal data sets grow in size with the number of antennae. Limits on power and space for the processor hardware further constrain SDR performance. In this report, we discuss improvements in spatio-temporal twice whitening (STTW) for real-time local spectrum sensing by demonstrating a form of STTW well suited for MIMO environments. We implement STTW on the Coherent Logix hx3100 processor, a multicore processor intended for low-power, high-throughput software-defined signal processing. These results demonstrate how coupling the novel capabilities of emerging multicore processors with algorithmic advances can enable real-time, software-defined processing of large spatio-temporal data sets.

  13. Ontology Driven Analysis of Spatio-temporal Phenomena, Aimed At Spatial Planning And Environmental Forecasting

    NASA Astrophysics Data System (ADS)

    Iwaniak, A.; Łukowicz, J.; Strzelecki, M.; Kaczmarek, I.

    2013-10-01

    Spatial planning is a crucial area for balancing civilization development with environmental protection. Spatial planning has a multidisciplinary nature. It must take into account the dynamics of the processes, which could affect the integrity of the environmental system. That is why we need a new approach to modelling phenomena occurring in space. Such approach is offered by ontologies, based on Description Logic (DL) and related to inference systems. Ontology is a system for the knowledge representation, including conceptual scheme and based on this scheme representation of reality. Ontologies can be enriched with additional logical systems. The authors present a way of building domain ontologies for spatial planning, including the representation of spatio-temporal phenomena. Description Logic is supplemented by structures of temporal logic. As a result, the analysis for exploring the topological relations between spatial objects will be extended to include temporal relationships: coincidence, precedence and succession, cause and effect relationship. Spatio-temporal models with temporal logic structures, encoded in ontologies, could be a subject of inference process, performed by semantic reasoners (reasoner engines). Spatio-temporal representations are offered, by so-called upper ontologies, such as GFO, BFO, OCHRE and others. Temporal structures provided in such ontologies, are useful for the analysis of data obtained from environmental and development monitoring systems and for description and representation of historical phenomena. They allow creating the models and scenarios of expected spatial transformation. They will support analysis for spatial development design, decision-making in spatial planning and forecasting of environmental impact.

  14. Impaired Spatio-Temporal Predictive Motor Timing Associated with Spinocerebellar Ataxia Type 6.

    PubMed

    Broersen, Robin; Onuki, Yoshiyuki; Abdelgabar, Abdel R; Owens, Cullen B; Picard, Samuel; Willems, Jessica; Boele, Henk-Jan; Gazzola, Valeria; Van der Werf, Ysbrand D; De Zeeuw, Chris I

    2016-01-01

    Many daily life activities demand precise integration of spatial and temporal information of sensory inputs followed by appropriate motor actions. This type of integration is carried out in part by the cerebellum, which has been postulated to play a central role in learning and timing of movements. Cerebellar damage due to atrophy or lesions may compromise forward-model processing, in which both spatial and temporal cues are used to achieve prediction for future motor states. In the present study we sought to further investigate the cerebellar contribution to predictive and reactive motor timing, as well as to learning of sequential order and temporal intervals in these tasks. We tested patients with spinocerebellar ataxia type 6 (SCA6) and healthy controls for two related motor tasks; one requiring spatio-temporal prediction of dynamic visual stimuli and another one requiring reactive timing only. We found that healthy controls established spatio-temporal prediction in their responses with high temporal precision, which was absent in the cerebellar patients. SCA6 patients showed lower predictive motor timing, coinciding with a reduced number of correct responses during the 'anticipatory' period on the task. Moreover, on the task utilizing reactive motor timing functions, control participants showed both sequence order and temporal interval learning, whereas patients only showed sequence order learning. These results suggest that SCA6 affects predictive motor timing and temporal interval learning. Our results support and highlight cerebellar contribution to timing and argue for cerebellar engagement during spatio-temporal prediction of upcoming events. PMID:27571363

  15. Impaired Spatio-Temporal Predictive Motor Timing Associated with Spinocerebellar Ataxia Type 6

    PubMed Central

    Onuki, Yoshiyuki; Abdelgabar, Abdel R.; Owens, Cullen B.; Picard, Samuel; Willems, Jessica; Boele, Henk-Jan; Gazzola, Valeria; Van der Werf, Ysbrand D.; De Zeeuw, Chris I.

    2016-01-01

    Many daily life activities demand precise integration of spatial and temporal information of sensory inputs followed by appropriate motor actions. This type of integration is carried out in part by the cerebellum, which has been postulated to play a central role in learning and timing of movements. Cerebellar damage due to atrophy or lesions may compromise forward-model processing, in which both spatial and temporal cues are used to achieve prediction for future motor states. In the present study we sought to further investigate the cerebellar contribution to predictive and reactive motor timing, as well as to learning of sequential order and temporal intervals in these tasks. We tested patients with spinocerebellar ataxia type 6 (SCA6) and healthy controls for two related motor tasks; one requiring spatio-temporal prediction of dynamic visual stimuli and another one requiring reactive timing only. We found that healthy controls established spatio-temporal prediction in their responses with high temporal precision, which was absent in the cerebellar patients. SCA6 patients showed lower predictive motor timing, coinciding with a reduced number of correct responses during the ‘anticipatory’ period on the task. Moreover, on the task utilizing reactive motor timing functions, control participants showed both sequence order and temporal interval learning, whereas patients only showed sequence order learning. These results suggest that SCA6 affects predictive motor timing and temporal interval learning. Our results support and highlight cerebellar contribution to timing and argue for cerebellar engagement during spatio-temporal prediction of upcoming events. PMID:27571363

  16. Spatio-Temporal Change Modeling of Lulc: a Semantic Kriging Approach

    NASA Astrophysics Data System (ADS)

    Bhattacharjee, S.; Ghosh, S. K.

    2015-07-01

    Spatio-temporal land-use/ land-cover (LULC) change modeling is important to forecast the future LULC distribution, which may facilitate natural resource management, urban planning, etc. The spatio-temporal change in LULC trend often exhibits non-linear behavior, due to various dynamic factors, such as, human intervention (e.g., urbanization), environmental factors, etc. Hence, proper forecasting of LULC distribution should involve the study and trend modeling of historical data. Existing literatures have reported that the meteorological attributes (e.g., NDVI, LST, MSI), are semantically related to the terrain. Being influenced by the terrestrial dynamics, the temporal changes of these attributes depend on the LULC properties. Hence, incorporating meteorological knowledge into the temporal prediction process may help in developing an accurate forecasting model. This work attempts to study the change in inter-annual LULC pattern and the distribution of different meteorological attributes of a region in Kolkata (a metropolitan city in India) during the years 2000-2010 and forecast the future spread of LULC using semantic kriging (SemK) approach. A new variant of time-series SemK is proposed, namely Rev-SemKts to capture the multivariate semantic associations between different attributes. From empirical analysis, it may be observed that the augmentation of semantic knowledge in spatio-temporal modeling of meteorological attributes facilitate more precise forecasting of LULC pattern.

  17. Studies on spatio-temporal filtering of GNSS-derived coordinates

    NASA Astrophysics Data System (ADS)

    Gruszczynski, Maciej; Bogusz, Janusz; Kłos, Anna; Figurski, Mariusz

    2015-04-01

    The information about lithospheric deformations may be obtained nowadays by analysis of velocity field derived from permanent GNSS (Global Navigation Satellite System) observations. Despite developing more and more reliable models, the permanent stations residuals must still be considered as coloured noise. Meeting the GGOS (Global Geodetic Observing System) requirements, we are obliged to investigate the correlations between residuals, which are the result of common mode error (CME). This type of error may arise from mismodelling of: satellite orbits, the Earth Orientation Parameters, satellite antenna phase centre variations or unmodelling of large scale atmospheric effects. The above described together cause correlations between stochastic parts of coordinate time series obtained at stations located of even few thousands kilometres from each other. Permanent stations that meet the aforementioned terms form the regional (EPN - EUREF Permanent Network) or local sub-networks of global (IGS - International GNSS Service) network. Other authors (Wdowinski et al., 1997; Dong et al., 2006) dealt with spatio-temporal filtering and indicated three major regional filtering approaches: the stacking, the Principal Component Analysis (PCA) based on the empirical orthogonal function and the Karhunen-Loeve expansion. The need for spatio-temporal filtering is evident today, but the question whether the size of the network affects the accuracy of station's position and its velocity still remains unanswered. With the aim to determine the network's size, for which the assumption of spatial uniform distribution of CME is retained, we used stacking approach. We analyzed time series of IGS stations with daily network solutions processed by the Military University of Technology EPN Local Analysis Centre in Bernese 5.0 software and compared it with the JPL (Jet Propulsion Laboratory) PPP (Precice Point Positioning). The method we propose is based on the division of local GNSS networks

  18. Robust visual tracking with dual spatio-temporal context trackers

    NASA Astrophysics Data System (ADS)

    Sun, Shiyan; Zhang, Hong; Yuan, Ding

    2015-12-01

    Visual tracking is a challenging problem in computer vision. Recent years, significant numbers of trackers have been proposed. Among these trackers, tracking with dense spatio-temporal context has been proved to be an efficient and accurate method. Other than trackers with online trained classifier that struggle to meet the requirement of real-time tracking task, a tracker with spatio-temporal context can run at hundreds of frames per second with Fast Fourier Transform (FFT). Nevertheless, the performance of the tracker with Spatio-temporal context relies heavily on the learning rate of the context, which restricts the robustness of the tracker. In this paper, we proposed a tracking method with dual spatio-temporal context trackers that hold different learning rate during tracking. The tracker with high learning rate could track the target smoothly when the appearance of target changes, while the tracker with low learning rate could percepts the occlusion occurring and continues to track when the target starts to emerge again. To find the target among the candidates from these two trackers, we adopt Normalized Correlation Coefficient (NCC) to evaluate the confidence of each sample. Experimental results show that the proposed algorithm performs robustly against several state-of-the-art tracking methods.

  19. Fast Spatio-Temporal Data Mining from Large Geophysical Datasets

    NASA Technical Reports Server (NTRS)

    Stolorz, P.; Mesrobian, E.; Muntz, R.; Santos, J. R.; Shek, E.; Yi, J.; Mechoso, C.; Farrara, J.

    1995-01-01

    Use of the UCLA CONQUEST (CONtent-based Querying in Space and Time) is reviewed for performance of automatic cyclone extraction and detection of spatio-temporal blocking conditions on MPP. CONQUEST is a data analysis environment for knowledge and data mining to aid in high-resolution modeling of climate modeling.

  20. Visual Experience Modulates Spatio-Temporal Dynamics of Circuit Activation

    PubMed Central

    Wang, Lang; Fontanini, Alfredo; Maffei, Arianna

    2011-01-01

    Persistent reduction in sensory drive in early development results in multiple plastic changes of different cortical synapses. How these experience-dependent modifications affect the spatio-temporal dynamics of signal propagation in neocortical circuits is poorly understood. Here we demonstrate that brief visual deprivation significantly affects the propagation of electrical signals in the primary visual cortex. The spatio-temporal spread of circuit activation upon direct stimulation of its input layer (Layer 4) is reduced, as is the activation of L2/3 – the main recipient of the output from L4. Our data suggest that the decrease in spatio-temporal activation of L2/3 depends on reduced L4 output, and is not intrinsically generated within L2/3. The data shown here suggest that changes in the synaptic components of the visual cortical circuit result not only in alteration of local integration of excitatory and inhibitory inputs, but also in a significant decrease in overall circuit activation. Furthermore, our data indicate a differential effect of visual deprivation on L4 and L2/3, suggesting that while feedforward activation of L2/3 is reduced, its activation by long range, within layer inputs is unaltered. Thus, brief visual deprivation induces experience-dependent circuit re-organization by modulating not only circuit excitability, but also the spatio-temporal patterns of cortical activation within and between layers. PMID:21743804

  1. Spatio-temporal analysis of environmental radiation in Korea

    SciTech Connect

    Kim, J.Y.; Lee, B.C.; Shin, H.K.

    2007-07-01

    Geostatistical visualization of environmental radiation is a very powerful approach to explore and understand spatio-temporal variabilities of environmental radiation data. Spatial patterns of environmental radiation can be described quantitatively in terms of variogram and kriging, which are based on the idea that statistical variation of data are functions of distance. (authors)

  2. Spatio-temporal evaluation matrices for geospatial data

    NASA Astrophysics Data System (ADS)

    Triglav, Joc; Petrovič, Dušan; Stopar, Bojan

    2011-02-01

    The global geospatial community is investing substantial effort in providing tools for geospatial data-quality information analysis and systematizing the criteria for geospatial data quality. The importance of these activities is increasing, especially in the last decade, which has witnessed an enormous expansion of geospatial data use in general and especially among mass users. Although geospatial data producers are striving to define and present data-quality standards to users and users increasingly need to assess the fitness for use of the data, the success of these activities is still far from what is expected or required. As a consequence, neglect or misunderstanding of data quality among users results in misuse or risks. This paper presents an aid in spatio-temporal quality evaluation through the use of spatio-temporal evaluation matrices (STEM) and the index of spatio-temporal anticipations (INSTANT) matrices. With the help of these two simple tools, geospatial data producers can systematically categorize and visualize the granularity of their spatio-temporal data, and users can present their requirements in the same way using business intelligence principles and a Web 2.0 approach. The basic principles and some examples are presented in the paper, and potential further applied research activities are briefly described.

  3. Finding Spatio-Temporal Patterns in Large Sensor Datasets

    ERIC Educational Resources Information Center

    McGuire, Michael Patrick

    2010-01-01

    Spatial or temporal data mining tasks are performed in the context of the relevant space, defined by a spatial neighborhood, and the relevant time period, defined by a specific time interval. Furthermore, when mining large spatio-temporal datasets, interesting patterns typically emerge where the dataset is most dynamic. This dissertation is…

  4. Cubic map algebra functions for spatio-temporal analysis

    USGS Publications Warehouse

    Mennis, J.; Viger, R.; Tomlin, C.D.

    2005-01-01

    We propose an extension of map algebra to three dimensions for spatio-temporal data handling. This approach yields a new class of map algebra functions that we call "cube functions." Whereas conventional map algebra functions operate on data layers representing two-dimensional space, cube functions operate on data cubes representing two-dimensional space over a third-dimensional period of time. We describe the prototype implementation of a spatio-temporal data structure and selected cube function versions of conventional local, focal, and zonal map algebra functions. The utility of cube functions is demonstrated through a case study analyzing the spatio-temporal variability of remotely sensed, southeastern U.S. vegetation character over various land covers and during different El Nin??o/Southern Oscillation (ENSO) phases. Like conventional map algebra, the application of cube functions may demand significant data preprocessing when integrating diverse data sets, and are subject to limitations related to data storage and algorithm performance. Solutions to these issues include extending data compression and computing strategies for calculations on very large data volumes to spatio-temporal data handling.

  5. Kernel Averaged Predictors for Spatio-Temporal Regression Models.

    PubMed

    Heaton, Matthew J; Gelfand, Alan E

    2012-12-01

    In applications where covariates and responses are observed across space and time, a common goal is to quantify the effect of a change in the covariates on the response while adequately accounting for the spatio-temporal structure of the observations. The most common approach for building such a model is to confine the relationship between a covariate and response variable to a single spatio-temporal location. However, oftentimes the relationship between the response and predictors may extend across space and time. In other words, the response may be affected by levels of predictors in spatio-temporal proximity to the response location. Here, a flexible modeling framework is proposed to capture such spatial and temporal lagged effects between a predictor and a response. Specifically, kernel functions are used to weight a spatio-temporal covariate surface in a regression model for the response. The kernels are assumed to be parametric and non-stationary with the data informing the parameter values of the kernel. The methodology is illustrated on simulated data as well as a physical data set of ozone concentrations to be explained by temperature. PMID:24010051

  6. Spatio-Temporal Signal Twice-Whitening Algorithms on the hx3100 Ultra-Low Power Multicore Processor

    SciTech Connect

    Humble, Travis S; Mitra, Pramita; Barhen, Jacob; Schleck, Bryan; Polcari, John; Traweek, Michael

    2010-01-01

    While modern signal detection theory fully accounts for spatially distributed sensors, exploiting these techniques for real-time sensing using large, underwater acoustic arrays requires advances in the spatio-temporal signal processing algorithms. In particular, the computational complexity of many spatio-temporal processing techniques is so large that conventional computer processors lack sufficient throughput to provide real-time processing of large spatio-temporal data sets. These limits are exacerbated when constraints, such as power consumption or footprint, reduce the available computational resources. In this report, we demonstrate an implementation of a signal twice-whitening algorithm that is better suited for processing spatio-temporal data in real time. We emphasize these advances by implementing data whitening on the Coherent Logix hx3100 processor, a programmable multicore processor intended for low-power and high-throughput signal processing. These results serve as an example of how the novel capabilities available from emerging multicore processor platforms can provide real-time, software-defined processing of large data sets acquired by spatially distributed sensing.

  7. Inferring social network structure in ecological systems from spatio-temporal data streams

    PubMed Central

    Psorakis, Ioannis; Roberts, Stephen J.; Rezek, Iead; Sheldon, Ben C.

    2012-01-01

    We propose a methodology for extracting social network structure from spatio-temporal datasets that describe timestamped occurrences of individuals. Our approach identifies temporal regions of dense agent activity and links are drawn between individuals based on their co-occurrences across these ‘gathering events’. The statistical significance of these connections is then tested against an appropriate null model. Such a framework allows us to exploit the wealth of analytical and computational tools of network analysis in settings where the underlying connectivity pattern between interacting agents (commonly termed the adjacency matrix) is not given a priori. We perform experiments on two large-scale datasets (greater than 106 points) of great tit Parus major wild bird foraging records and illustrate the use of this approach by examining the temporal dynamics of pairing behaviour, a process that was previously very hard to observe. We show that established pair bonds are maintained continuously, whereas new pair bonds form at variable times before breeding, but are characterized by a rapid development of network proximity. The method proposed here is general, and can be applied to any system with information about the temporal co-occurrence of interacting agents. PMID:22696481

  8. An Accessible Method for Implementing Hierarchical Models with Spatio-Temporal Abundance Data

    PubMed Central

    Ross, Beth E.; Hooten, Mevin B.; Koons, David N.

    2012-01-01

    A common goal in ecology and wildlife management is to determine the causes of variation in population dynamics over long periods of time and across large spatial scales. Many assumptions must nevertheless be overcome to make appropriate inference about spatio-temporal variation in population dynamics, such as autocorrelation among data points, excess zeros, and observation error in count data. To address these issues, many scientists and statisticians have recommended the use of Bayesian hierarchical models. Unfortunately, hierarchical statistical models remain somewhat difficult to use because of the necessary quantitative background needed to implement them, or because of the computational demands of using Markov Chain Monte Carlo algorithms to estimate parameters. Fortunately, new tools have recently been developed that make it more feasible for wildlife biologists to fit sophisticated hierarchical Bayesian models (i.e., Integrated Nested Laplace Approximation, ‘INLA’). We present a case study using two important game species in North America, the lesser and greater scaup, to demonstrate how INLA can be used to estimate the parameters in a hierarchical model that decouples observation error from process variation, and accounts for unknown sources of excess zeros as well as spatial and temporal dependence in the data. Ultimately, our goal was to make unbiased inference about spatial variation in population trends over time. PMID:23166658

  9. An Accessible Method for Implementing Hierarchical Models with Spatio-Temporal Abundance Data

    USGS Publications Warehouse

    Ross, Beth E.; Hooten, Melvin B.; Koons, David N.

    2012-01-01

    A common goal in ecology and wildlife management is to determine the causes of variation in population dynamics over long periods of time and across large spatial scales. Many assumptions must nevertheless be overcome to make appropriate inference about spatio-temporal variation in population dynamics, such as autocorrelation among data points, excess zeros, and observation error in count data. To address these issues, many scientists and statisticians have recommended the use of Bayesian hierarchical models. Unfortunately, hierarchical statistical models remain somewhat difficult to use because of the necessary quantitative background needed to implement them, or because of the computational demands of using Markov Chain Monte Carlo algorithms to estimate parameters. Fortunately, new tools have recently been developed that make it more feasible for wildlife biologists to fit sophisticated hierarchical Bayesian models (i.e., Integrated Nested Laplace Approximation, ‘INLA’). We present a case study using two important game species in North America, the lesser and greater scaup, to demonstrate how INLA can be used to estimate the parameters in a hierarchical model that decouples observation error from process variation, and accounts for unknown sources of excess zeros as well as spatial and temporal dependence in the data. Ultimately, our goal was to make unbiased inference about spatial variation in population trends over time.

  10. Spatio-temporal properties and evolution of the 2013 Aigion earthquake swarm (Corinth Gulf, Greece)

    NASA Astrophysics Data System (ADS)

    Mesimeri, M.; Karakostas, V.; Papadimitriou, E.; Schaff, D.; Tsaklidis, G.

    2016-04-01

    The 2013 Aigion earthquake swarm that took place in the west part of Corinth Gulf is investigated for revealing faulting and seismicity properties of the activated area. The activity started on May 21 and was appreciably intense in the next 3 months. The recordings of the Hellenic Unified Seismological Network (HUSN), which is adequately dense around the affected area, were used to accurately locate 1501 events. The double difference ( hypoDD) technique was employed for the manually picked P and S phases along with differential times derived from waveform cross-correlation for improving location accuracy. The activated area with dimensions 6 × 2 km is located approximately 5 km SE of Aigion. Focal mechanisms of 77 events with M ≥ 2.0 were determined from P wave first motions and used for the geometry identification of the ruptured segments. Spatio-temporal distribution of earthquakes revealed an eastward and westward hypocentral migration from the starting point suggesting the division of the seismic swarm into four major clusters. The hypocentral migration was corroborated by the Coulomb stress change calculation, indicating that four fault segments involved in the rupture process successively failed by stress change encouragement. Examination of fluid flow brought out that it cannot be unambiguously considered as the driving mechanism for the successive failures.

  11. Identifying spatio-temporal patterns of transboundary disease spread: examples using avian influenza H5N1 outbreaks

    PubMed Central

    Farnsworth, Matthew L.; Ward, Michael P.

    2009-01-01

    Characterizing spatio-temporal patterns among epidemics in which the mechanism of spread is uncertain is important for generating disease spread hypotheses, which may in turn inform disease control and prevention strategies. Using a dataset representing three phases of highly pathogenic avian influenza H5N1 outbreaks in village poultry in Romania, 2005–2006, spatio-temporal patterns were characterized. We first fit a set of hierarchical Bayesian models that quantified changes in the spatio-temporal relative risk for each of the 23 affected counties. We then modeled spatial synchrony in each of the three epidemic phases using non-parametric covariance functions and Thin Plate Spline regression models. We found clear differences in the spatio-temporal patterns among the epidemic phases (local versus regional correlated processes), which may indicate differing spread mechanisms (for example wild bird versus human-mediated). Elucidating these patterns allowed us to postulate that a shift in the primary mechanism of disease spread may have taken place between the second and third phases of this epidemic. Information generated by such analyses could assist affected countries in determining the most appropriate control programs to implement, and to allocate appropriate resources to preventing contact between domestic poultry and wild birds versus enforcing bans on poultry movements and quarantine. The methods used in this study could be applied in many different situations to analyze transboundary disease data in which only location and time of occurrence data are reported. PMID:19210952

  12. High spatio-temporal resolution observations of crater lake temperatures at Kawah Ijen volcano, East Java, Indonesia

    NASA Astrophysics Data System (ADS)

    Lewicki, Jennifer L.; Caudron, Corentin; van Hinsberg, Vincent J.; Hilley, George E.

    2016-08-01

    The crater lake of Kawah Ijen volcano, East Java, Indonesia, has displayed large and rapid changes in temperature at point locations during periods of unrest, but measurement techniques employed to date have not resolved how the lake's thermal regime has evolved over both space and time. We applied a novel approach for mapping and monitoring variations in crater lake apparent surface ("skin") temperatures at high spatial (˜32 cm) and temporal (every 2 min) resolution at Kawah Ijen on 18 September 2014. We used a ground-based FLIR T650sc camera with digital and thermal infrared (TIR) sensors from the crater rim to collect (1) a set of visible imagery around the crater during the daytime and (2) a time series of co-located visible and TIR imagery at one location from pre-dawn to daytime. We processed daytime visible imagery with the Structure-from-Motion photogrammetric method to create a digital elevation model onto which the time series of TIR imagery was orthorectified and georeferenced. Lake apparent skin temperatures typically ranged from ˜21 to 33 °C. At two locations, apparent skin temperatures were ˜4 and 7 °C less than in situ lake temperature measurements at 1.5 and 5-m depth, respectively. These differences, as well as the large spatio-temporal variations observed in skin temperatures, were likely largely associated with atmospheric effects such as the evaporative cooling of the lake surface and infrared absorption by water vapor and SO2. Calculations based on orthorectified TIR imagery thus yielded underestimates of volcanic heat fluxes into the lake, whereas volcanic heat fluxes estimated based on in situ temperature measurements (68 to 111 MW) were likely more representative of Kawah Ijen in a quiescent state. The ground-based imaging technique should provide a valuable tool to continuously monitor crater lake temperatures and contribute insight into the spatio-temporal evolution of these temperatures associated with volcanic activity.

  13. Nonlinear Spatio-Temporal Dynamics and Chaos in Semiconductors

    NASA Astrophysics Data System (ADS)

    Schöll, Eckehard

    2005-08-01

    Nonlinear transport phenomena are an increasingly important aspect of modern semiconductor research. This volume deals with complex nonlinear dynamics, pattern formation, and chaotic behavior in such systems. It bridges the gap between two well-established fields: the theory of dynamic systems and nonlinear charge transport in semiconductors. This unified approach helps reveal important electronic transport instabilities. The initial chapters lay a general framework for the theoretical description of nonlinear self-organized spatio-temporal patterns, such as current filaments, field domains, fronts, and analysis of their stability. Later chapters consider important model systems in detail: impact ionization induced impurity breakdown, Hall instabilities, superlattices, and low-dimensional structures. State-of-the-art results include chaos control, spatio-temporal chaos, multistability, pattern selection, activator-inhibitor kinetics, and global coupling, linking fundamental issues to electronic device applications. This book will be of great value to semiconductor physicists and nonlinear scientists alike.

  14. Nonlinear Spatio-Temporal Dynamics and Chaos in Semiconductors

    NASA Astrophysics Data System (ADS)

    Schöll, Eckehard

    2001-02-01

    Nonlinear transport phenomena are an increasingly important aspect of modern semiconductor research. This volume deals with complex nonlinear dynamics, pattern formation, and chaotic behavior in such systems. It bridges the gap between two well-established fields: the theory of dynamic systems and nonlinear charge transport in semiconductors. This unified approach helps reveal important electronic transport instabilities. The initial chapters lay a general framework for the theoretical description of nonlinear self-organized spatio-temporal patterns, such as current filaments, field domains, fronts, and analysis of their stability. Later chapters consider important model systems in detail: impact ionization induced impurity breakdown, Hall instabilities, superlattices, and low-dimensional structures. State-of-the-art results include chaos control, spatio-temporal chaos, multistability, pattern selection, activator-inhibitor kinetics, and global coupling, linking fundamental issues to electronic device applications. This book will be of great value to semiconductor physicists and nonlinear scientists alike.

  15. A Bayesian spatio-temporal method for disease outbreak detection

    PubMed Central

    Cooper, Gregory F

    2010-01-01

    A system that monitors a region for a disease outbreak is called a disease outbreak surveillance system. A spatial surveillance system searches for patterns of disease outbreak in spatial subregions of the monitored region. A temporal surveillance system looks for emerging patterns of outbreak disease by analyzing how patterns have changed during recent periods of time. If a non-spatial, non-temporal system could be converted to a spatio-temporal one, the performance of the system might be improved in terms of early detection, accuracy, and reliability. A Bayesian network framework is proposed for a class of space-time surveillance systems called BNST. The framework is applied to a non-spatial, non-temporal disease outbreak detection system called PC in order to create the spatio-temporal system called PCTS. Differences in the detection performance of PC and PCTS are examined. The results show that the spatio-temporal Bayesian approach performs well, relative to the non-spatial, non-temporal approach. PMID:20595315

  16. Low-rank matrix decomposition and spatio-temporal sparse recovery for STAP radar

    DOE PAGESBeta

    Sen, Satyabrata

    2015-08-04

    We develop space-time adaptive processing (STAP) methods by leveraging the advantages of sparse signal processing techniques in order to detect a slowly-moving target. We observe that the inherent sparse characteristics of a STAP problem can be formulated as the low-rankness of clutter covariance matrix when compared to the total adaptive degrees-of-freedom, and also as the sparse interference spectrum on the spatio-temporal domain. By exploiting these sparse properties, we propose two approaches for estimating the interference covariance matrix. In the first approach, we consider a constrained matrix rank minimization problem (RMP) to decompose the sample covariance matrix into a low-rank positivemore » semidefinite and a diagonal matrix. The solution of RMP is obtained by applying the trace minimization technique and the singular value decomposition with matrix shrinkage operator. Our second approach deals with the atomic norm minimization problem to recover the clutter response-vector that has a sparse support on the spatio-temporal plane. We use convex relaxation based standard sparse-recovery techniques to find the solutions. With extensive numerical examples, we demonstrate the performances of proposed STAP approaches with respect to both the ideal and practical scenarios, involving Doppler-ambiguous clutter ridges, spatial and temporal decorrelation effects. As a result, the low-rank matrix decomposition based solution requires secondary measurements as many as twice the clutter rank to attain a near-ideal STAP performance; whereas the spatio-temporal sparsity based approach needs a considerably small number of secondary data.« less

  17. Low-rank matrix decomposition and spatio-temporal sparse recovery for STAP radar

    SciTech Connect

    Sen, Satyabrata

    2015-08-04

    We develop space-time adaptive processing (STAP) methods by leveraging the advantages of sparse signal processing techniques in order to detect a slowly-moving target. We observe that the inherent sparse characteristics of a STAP problem can be formulated as the low-rankness of clutter covariance matrix when compared to the total adaptive degrees-of-freedom, and also as the sparse interference spectrum on the spatio-temporal domain. By exploiting these sparse properties, we propose two approaches for estimating the interference covariance matrix. In the first approach, we consider a constrained matrix rank minimization problem (RMP) to decompose the sample covariance matrix into a low-rank positive semidefinite and a diagonal matrix. The solution of RMP is obtained by applying the trace minimization technique and the singular value decomposition with matrix shrinkage operator. Our second approach deals with the atomic norm minimization problem to recover the clutter response-vector that has a sparse support on the spatio-temporal plane. We use convex relaxation based standard sparse-recovery techniques to find the solutions. With extensive numerical examples, we demonstrate the performances of proposed STAP approaches with respect to both the ideal and practical scenarios, involving Doppler-ambiguous clutter ridges, spatial and temporal decorrelation effects. As a result, the low-rank matrix decomposition based solution requires secondary measurements as many as twice the clutter rank to attain a near-ideal STAP performance; whereas the spatio-temporal sparsity based approach needs a considerably small number of secondary data.

  18. Spatio-temporal filtration of dynamic CT data using diffusion filters

    NASA Astrophysics Data System (ADS)

    Bruder, H.; Raupach, R.; Klotz, E.; Stierstorfer, K.; Flohr, T.

    2009-02-01

    We present a method for spatio-temporal filtration of dynamic CT data, to increase the signal-to-noise ratio (SNR) of image data at the same time maintaining image quality, in particular spatial and temporal sharpness of the images. Alternatively, the radiation dose applied to the patient can be reduced at the same time maintaining the noise level and the image sharpness. In contrast to classical methods, which generally operate on the three spatial dimensions of image data, noise statistics is improved by extending the filtration to the temporal dimension. Our approach is based on nonlinear and anisotropic diffusion filters, which are based on a model of heat diffusion adapted to medical CT data. Bilateral filters are a special class of diffusion filters, which do not need iteration to reach a convergence image, but represent the fixed point of a dedicated diffusion filter. Spatio-temporal, anisotropic bilateral filters are developed and applied to dynamic CT image data. The potential was evaluated using data from perfusion CT and cardiac dual source CT (DSCT) data, respectively. It was shown, that in perfusion CT, SNR can be improved by a factor of 4 at the same radiation dose. On basis of clinical data it was shown, that alternatively the radiation dose to the patient can be reduced by a factor of at least 2. A more accurate evaluation of the perfusion parameters blood flow, blood volume and time-to-peak is supported. In DSCT noise statistics can be improved using more projection data than needed for image reconstruction, however, as a consequence the temporal resolution is significantly impaired. Due to the anisotropy of the spatio-temporal bilateral filter temporal contrast edges between adjacent time samples are preserved, at the same time substantially smoothing image data in homogeneous regions. Also temporal contrast edges are preserved, maintaining the very high temporal resolution of DSCT acquisitions (~ 80 ms). CT examinations of the heart require

  19. A Hierarchical Bayesian Approach for Learning Sparse Spatio-Temporal Decomposition of Multichannel EEG

    PubMed Central

    Wu, Wei; Chen, Zhe; Gao, Shangkai; Brown, Emery N.

    2011-01-01

    Multichannel electroencephalography (EEG) offers a non-invasive tool to explore spatio-temporal dynamics of brain activity. With EEG recordings consisting of multiple trials, traditional signal processing approaches that ignore inter-trial variability in the data may fail to accurately estimate the underlying spatio-temporal brain patterns. Moreover, precise characterization of such inter-trial variability per se can be of high scientific value in establishing the relationship between brain activity and behavior. In this paper, a statistical modeling framework is introduced for learning spatiotemporal decomposition of multiple-trial EEG data recorded under two contrasting experimental conditions. By modeling the variance of source signals as random variables varying across trials, the proposed two-stage hierarchical Bayesian model is able to capture inter-trial amplitude variability in the data in a sparse way where a parsimonious representation of the data can be obtained. A variational Bayesian (VB) algorithm is developed for statistical inference of the hierarchical model. The efficacy of the proposed modeling framework is validated with the analysis of both synthetic and real EEG data. In the simulation study we show that even at low signal-to-noise ratios our approach is able to recover with high precision the underlying spatiotemporal patterns and the evolution of source amplitude across trials; on two brain-computer interface (BCI) data sets we show that our VB algorithm can extract physiologically meaningful spatio-temporal patterns and make more accurate predictions than other two widely used algorithms: the common spatial patterns (CSP) algorithm and the Infomax algorithm for independent component analysis (ICA). The results demonstrate that our statistical modeling framework can serve as a powerful tool for extracting brain patterns, characterizing trial-to-trial brain dynamics, and decoding brain states by exploiting useful structures in the data. PMID

  20. A general science-based framework for dynamical spatio-temporal models

    USGS Publications Warehouse

    Wikle, C.K.; Hooten, M.B.

    2010-01-01

    Spatio-temporal statistical models are increasingly being used across a wide variety of scientific disciplines to describe and predict spatially-explicit processes that evolve over time. Correspondingly, in recent years there has been a significant amount of research on new statistical methodology for such models. Although descriptive models that approach the problem from the second-order (covariance) perspective are important, and innovative work is being done in this regard, many real-world processes are dynamic, and it can be more efficient in some cases to characterize the associated spatio-temporal dependence by the use of dynamical models. The chief challenge with the specification of such dynamical models has been related to the curse of dimensionality. Even in fairly simple linear, first-order Markovian, Gaussian error settings, statistical models are often over parameterized. Hierarchical models have proven invaluable in their ability to deal to some extent with this issue by allowing dependency among groups of parameters. In addition, this framework has allowed for the specification of science based parameterizations (and associated prior distributions) in which classes of deterministic dynamical models (e. g., partial differential equations (PDEs), integro-difference equations (IDEs), matrix models, and agent-based models) are used to guide specific parameterizations. Most of the focus for the application of such models in statistics has been in the linear case. The problems mentioned above with linear dynamic models are compounded in the case of nonlinear models. In this sense, the need for coherent and sensible model parameterizations is not only helpful, it is essential. Here, we present an overview of a framework for incorporating scientific information to motivate dynamical spatio-temporal models. First, we illustrate the methodology with the linear case. We then develop a general nonlinear spatio-temporal framework that we call general quadratic

  1. Working with Spatio-Temporal Data Type

    NASA Astrophysics Data System (ADS)

    Raza, A.

    2012-07-01

    Several aspects of spatiotemporal databases have been explored in past decades, ranging from basic data structure to query processing and indexing. But today, operational temporal GIS does not exist. The key impediments have been the complexity of integrating space and time and the lack of standards. OpenGIS standards for simple feature access (spatial type) do exist, but unlike the spatial type, standards for spatiotemporal data type do not exist. This paper explores a new approach to modeling space and time to provide the basis for implementing a temporal GIS. This approach is based on the concept of data types in databases. A data type provides constructors, accessors, and operators. Most commercial and open source databases provide data types to deal with the spatial component of a GIS, called spatial type. Oracle Spatial, DB2 Spatial Extender and Informix Spatial DataBlade, ST_Geometry for PostgreSQL and Oracle from Esri, PostGIS for PostgreSQL, etc., are some examples. This new spatiotemporal data type is called spatiotemporal type (STT). This STT is implemented in PostgreSQL, an open source relational database management system. The STT is an extension of Esri's ST_Geometry type for PostgreSQL, in which each spatial object has a life span. Constructors, accessors, and relational functions are provided to create STT and support spatial, spatiotemporal, and temporal queries. Some functions are extended based on OpenGIS standards for the spatial type. Examples are provided to demonstrate the application of these functions. The paper concludes with limitations and challenges of implementing STT.

  2. Macroscopic hotspots identification: A Bayesian spatio-temporal interaction approach.

    PubMed

    Dong, Ni; Huang, Helai; Lee, Jaeyoung; Gao, Mingyun; Abdel-Aty, Mohamed

    2016-07-01

    This study proposes a Bayesian spatio-temporal interaction approach for hotspot identification by applying the full Bayesian (FB) technique in the context of macroscopic safety analysis. Compared with the emerging Bayesian spatial and temporal approach, the Bayesian spatio-temporal interaction model contributes to a detailed understanding of differential trends through analyzing and mapping probabilities of area-specific crash trends as differing from the mean trend and highlights specific locations where crash occurrence is deteriorating or improving over time. With traffic analysis zones (TAZs) crash data collected in Florida, an empirical analysis was conducted to evaluate the following three approaches for hotspot identification: FB ranking using a Poisson-lognormal (PLN) model, FB ranking using a Bayesian spatial and temporal (B-ST) model and FB ranking using a Bayesian spatio-temporal interaction (B-ST-I) model. The results show that (a) the models accounting for space-time effects perform better in safety ranking than does the PLN model, and (b) the FB approach using the B-ST-I model significantly outperforms the B-ST approach in correctly identifying hotspots by explicitly accounting for the space-time variation in addition to the stable spatial/temporal patterns of crash occurrence. In practice, the B-ST-I approach plays key roles in addressing two issues: (a) how the identified hotspots have evolved over time and (b) the identification of areas that, whilst not yet hotspots, show a tendency to become hotspots. Finally, it can provide guidance to policy decision makers to efficiently improve zonal-level safety. PMID:27110645

  3. Clifford algebra-based spatio-temporal modelling and analysis for complex geo-simulation data

    NASA Astrophysics Data System (ADS)

    Luo, Wen; Yu, Zhaoyuan; Hu, Yong; Yuan, Linwang

    2013-10-01

    The spatio-temporal data simulating Ice-Land-Ocean interaction of Antarctic are used to demonstrate the Clifford algebra-based data model construction, spatio-temporal query and data analysis. The results suggest that Clifford algebra provides a powerful mathematical tool for the whole modelling and analysis chains for complex geo-simulation data. It can also help implement spatio-temporal analysis algorithms more clearly and simply.

  4. Spontaneous bursting: From temporal to spatio-temporal intermittency

    SciTech Connect

    Platt, N.; Hammel, S.M.

    1996-06-01

    A simple model for temporal bursting is introduced. This model invokes either dynamic or random forcing of a bifurcation parameter of some simple dynamical system in a way that makes the bifurcation parameter spend suitable amounts of time below and above the bifurcation threshold. This model is extended to coupled map lattices to produce spontaneous spatio-temporal burstings. It models physical systems which are embedded in a random background that is statistically homogeneous in space and time. An application of this model to optical turbulence is discussed. {copyright} {ital 1996 American Institute of Physics.}

  5. Spatio-Temporal Equalizer for a Receiving-Antenna Feed Array

    NASA Technical Reports Server (NTRS)

    Mukai, Ryan; Lee, Dennis; Vilnrotter, Victor

    2010-01-01

    A spatio-temporal equalizer has been conceived as an improved means of suppressing multipath effects in the reception of aeronautical telemetry signals, and may be adaptable to radar and aeronautical communication applications as well. This equalizer would be an integral part of a system that would also include a seven-element planar array of receiving feed horns centered at the focal point of a paraboloidal antenna that would be nominally aimed at or near the aircraft that would be the source of the signal that one seeks to receive (see Figure 1). This spatio-temporal equalizer would consist mostly of a bank of seven adaptive finite-impulse-response (FIR) filters one for each element in the array - and the outputs of the filters would be summed (see Figure 2). The combination of the spatial diversity of the feedhorn array and the temporal diversity of the filter bank would afford better multipath-suppression performance than is achievable by means of temporal equalization alone. The seven-element feed array would supplant the single feed horn used in a conventional paraboloidal ground telemetry-receiving antenna. The radio-frequency telemetry signals re ceiv ed by the seven elements of the array would be digitized, converted to complex baseband form, and sent to the FIR filter bank, which would adapt itself in real time to enable reception of telemetry at a low bit error rate, even in the presence of multipath of the type found at many flight test ranges.

  6. Spatio-Temporal Regularization for Longitudinal Registration to Subject-Specific 3d Template

    PubMed Central

    Guizard, Nicolas; Fonov, Vladimir S.; García-Lorenzo, Daniel; Nakamura, Kunio; Aubert-Broche, Bérengère; Collins, D. Louis

    2015-01-01

    Neurodegenerative diseases such as Alzheimer's disease present subtle anatomical brain changes before the appearance of clinical symptoms. Manual structure segmentation is long and tedious and although automatic methods exist, they are often performed in a cross-sectional manner where each time-point is analyzed independently. With such analysis methods, bias, error and longitudinal noise may be introduced. Noise due to MR scanners and other physiological effects may also introduce variability in the measurement. We propose to use 4D non-linear registration with spatio-temporal regularization to correct for potential longitudinal inconsistencies in the context of structure segmentation. The major contribution of this article is the use of individual template creation with spatio-temporal regularization of the deformation fields for each subject. We validate our method with different sets of real MRI data, compare it to available longitudinal methods such as FreeSurfer, SPM12, QUARC, TBM, and KNBSI, and demonstrate that spatially local temporal regularization yields more consistent rates of change of global structures resulting in better statistical power to detect significant changes over time and between populations. PMID:26301716

  7. Spatio-Temporal Expression Pattern of Frizzled Receptors after Contusive Spinal Cord Injury in Adult Rats

    PubMed Central

    Arenas, Ernest; Rodriguez, Francisco Javier

    2012-01-01

    Background Wnt proteins are a large family of molecules that are critically involved in multiple central nervous system (CNS) developmental processes. Experimental evidences suggest a role for this family of proteins in many CNS disorders, including spinal cord injury (SCI), which is a major neuropathology owing to its high prevalence and chronic sensorimotor functional sequelae. Interestingly, most Wnt proteins and their inhibitors are expressed in the uninjured spinal cord, and their temporal expression patterns are dramatically altered after injury. However, little is known regarding the expression of their better-known receptors, the Frizzled family, after SCI. Thus, the aim of the present study was to evaluate the expression of Frizzled receptors in the damaged spinal cord. Findings Based on the evidence that Wnts are expressed in the spinal cord and are transcriptionally regulated by SCI in adulthood, we analysed the spatio-temporal mRNA and protein expression patterns of Frizzled receptors after contusive SCI using quantitative RT-PCR and single and double immunohistochemistry, respectively. Our results show that almost all of the 10 known Frizzled receptors were expressed in specific spatial patterns in the uninjured spinal cords. Moreover, the Frizzled mRNAs and proteins were expressed after SCI, although their expression patterns were altered during the temporal progression of SCI. Finally, analysis of cellular Frizzled 5 expression pattern by double immunohistochemistry showed that, in the uninjured spinal cord, this receptor was expressed in neurons, oligodendrocytes, astrocytes, microglia and NG2+ glial precursors. After injury, Frizzled 5 was not only still expressed in oligodendrocytes, astrocytes and NG2+ glial precursors but also in axons at all evaluated time points. Moreover, Frizzled 5 was expressed in reactive microglia/macrophages from 3 to 14 days post-injury. Conclusions Our data suggest the involvement of Frizzled receptors in physiological

  8. Interesting Spatio-Temporal Region Discovery Computations Over Gpu and Mapreduce Platforms

    NASA Astrophysics Data System (ADS)

    McDermott, M.; Prasad, S. K.; Shekhar, S.; Zhou, X.

    2015-07-01

    Discovery of interesting paths and regions in spatio-temporal data sets is important to many fields such as the earth and atmospheric sciences, GIS, public safety and public health both as a goal and as a preliminary step in a larger series of computations. This discovery is usually an exhaustive procedure that quickly becomes extremely time consuming to perform using traditional paradigms and hardware and given the rapidly growing sizes of today's data sets is quickly outpacing the speed at which computational capacity is growing. In our previous work (Prasad et al., 2013a) we achieved a 50 times speedup over sequential using a single GPU. We were able to achieve near linear speedup over this result on interesting path discovery by using Apache Hadoop to distribute the workload across multiple GPU nodes. Leveraging the parallel architecture of GPUs we were able to drastically reduce the computation time of a 3-dimensional spatio-temporal interest region search on a single tile of normalized difference vegetative index for Saudi Arabia. We were further able to see an almost linear speedup in compute performance by distributing this workload across several GPUs with a simple MapReduce model. This increases the speed of processing 10 fold over the comparable sequential while simultaneously increasing the amount of data being processed by 384 fold. This allowed us to process the entirety of the selected data set instead of a constrained window.

  9. Multiple dipole modeling of spatio-temporal MEG (magnetoencephalogram) data

    SciTech Connect

    Mosher, J.C. . Systems Engineering and Development Div. University of Southern California, Los Angeles, CA . Signal and Image Processing Inst.); Lewis, P.S. ); Leahy, R. . Signal and Image Processing Inst.); Singh, M. (University of Southern Californi

    1990-01-01

    An array of SQUID biomagentometers may be used to measure the spatio-temporal neuromagnetic field produced by the brain in response to a given sensory stimulus. A popular model for the neural activity that produces these fields is a set of current dipoles. We present here a common linear algebraic framework for three common spatio-temporal dipole models: moving and rotating dipoles, rotating dipoles with fixed location, and dipoles with fixed orientation and location. Our intent here is not to argue the merits of one model over another, but rather show how each model may be solved efficiently, and within the same framework as the others. In all cases, we assume that the location, orientation, and magnitude of the dipoles are unknown. We present the parameter estimation problem for these three models in a common framework, and show how, in each case, the problem may be decomposed into the estimation of the dipole locations using nonlinear minimization followed by linear estimation of the associated moment time series. Numerically efficient means of calculating the cost function are presented, and problems of model order selection and missing moments are also investigated. The methods described are demonstrated in a simulated application to a three dipole problem. 21 refs., 2 figs., 1 tab.

  10. Spatio-temporal Granger causality: a new framework

    PubMed Central

    Luo, Qiang; Lu, Wenlian; Cheng, Wei; Valdes-Sosa, Pedro A.; Wen, Xiaotong; Ding, Mingzhou; Feng, Jianfeng

    2015-01-01

    That physiological oscillations of various frequencies are present in fMRI signals is the rule, not the exception. Herein, we propose a novel theoretical framework, spatio-temporal Granger causality, which allows us to more reliably and precisely estimate the Granger causality from experimental datasets possessing time-varying properties caused by physiological oscillations. Within this framework, Granger causality is redefined as a global index measuring the directed information flow between two time series with time-varying properties. Both theoretical analyses and numerical examples demonstrate that Granger causality is a monotonically increasing function of the temporal resolution used in the estimation. This is consistent with the general principle of coarse graining, which causes information loss by smoothing out very fine-scale details in time and space. Our results confirm that the Granger causality at the finer spatio-temporal scales considerably outperforms the traditional approach in terms of an improved consistency between two resting-state scans of the same subject. To optimally estimate the Granger causality, the proposed theoretical framework is implemented through a combination of several approaches, such as dividing the optimal time window and estimating the parameters at the fine temporal and spatial scales. Taken together, our approach provides a novel and robust framework for estimating the Granger causality from fMRI, EEG, and other related data. PMID:23643924

  11. Mining fuzzy association rules in spatio-temporal databases

    NASA Astrophysics Data System (ADS)

    Shu, Hong; Dong, Lin; Zhu, Xinyan

    2008-12-01

    A huge amount of geospatial and temporal data have been collected through various networks of environment monitoring stations. For instance, daily precipitation and temperature are observed at hundreds of meteorological stations in Northeastern China. However, these massive raw data from the stations are not fully utilized for meeting the requirements of human decision-making. In nature, the discovery of geographical data mining is the computation of multivariate spatio-temporal correlations through the stages of data mining. In this paper, a procedure of mining association rules in regional climate-changing databases is introduced. The methods of Kriging interpolation, fuzzy cmeans clustering, and Apriori-based logical rules extraction are employed subsequently. Formally, we define geographical spatio-temporal transactions and fuzzy association rules. Innovatively, we make fuzzy data conceptualization by means of fuzzy c-means clustering, and transform fuzzy data items with membership grades into Boolean data items with weights by means ofλ-cut sets. When the algorithm Apriori is executed on Boolean transactions with weights, fuzzy association rules are derived. Fuzzy association rules are more nature than crisp association rules for human cognition about the reality.

  12. Gait recognition using spatio-temporal silhouette-based features

    NASA Astrophysics Data System (ADS)

    Sabir, Azhin; Al-jawad, Naseer; Jassim, Sabah

    2013-05-01

    This paper presents a new algorithm for human gait recognition based on Spatio-temporal body biometric features using wavelet transforms. The proposed algorithm extracts the Gait cycle depending on the width of boundary box from a sequence of Silhouette images. Gait recognition is based on feature level fusion of three feature vectors: the gait spatio-temporal feature represented by the distances between (feet, knees, hands, shoulders, and height); binary difference between consecutive frames of the silhouette for each leg detected separately based on hamming distance; a vector of statistical parameters captured from the wavelet low frequency domain. The fused feature vector is subjected to dimension reduction using linear discriminate analysis. The Nearest Neighbour with a certain threshold used for classification. The threshold is obtained by experiment from a set of data captured from the CASIA database. We shall demonstrate that our method provides a non-traditional identification based on certain threshold to classify the outsider members as non-classified members.

  13. The spatio-temporal spectrum of turbulent flows.

    PubMed

    Clark di Leoni, P; Cobelli, P J; Mininni, P D

    2015-12-01

    Identification and extraction of vortical structures and of waves in a disorganised flow is a mayor challenge in the study of turbulence. We present a study of the spatio-temporal behavior of turbulent flows in the presence of different restitutive forces. We show how to compute and analyse the spatio-temporal spectrum from data stemming from numerical simulations and from laboratory experiments. Four cases are considered: homogeneous and isotropic turbulence, rotating turbulence, stratified turbulence, and water wave turbulence. For homogeneous and isotropic turbulence, the spectrum allows identification of sweeping by the large-scale flow. For rotating and for stratified turbulence, the spectrum allows identification of the waves, precise quantification of the energy in the waves and in the turbulent eddies, and identification of physical mechanisms such as Doppler shift and wave absorption in critical layers. Finally, in water wave turbulence the spectrum shows a transition from gravity-capillary waves to bound waves as the amplitude of the forcing is increased. PMID:26701711

  14. Spatio-temporal Granger causality: a new framework.

    PubMed

    Luo, Qiang; Lu, Wenlian; Cheng, Wei; Valdes-Sosa, Pedro A; Wen, Xiaotong; Ding, Mingzhou; Feng, Jianfeng

    2013-10-01

    That physiological oscillations of various frequencies are present in fMRI signals is the rule, not the exception. Herein, we propose a novel theoretical framework, spatio-temporal Granger causality, which allows us to more reliably and precisely estimate the Granger causality from experimental datasets possessing time-varying properties caused by physiological oscillations. Within this framework, Granger causality is redefined as a global index measuring the directed information flow between two time series with time-varying properties. Both theoretical analyses and numerical examples demonstrate that Granger causality is a monotonically increasing function of the temporal resolution used in the estimation. This is consistent with the general principle of coarse graining, which causes information loss by smoothing out very fine-scale details in time and space. Our results confirm that the Granger causality at the finer spatio-temporal scales considerably outperforms the traditional approach in terms of an improved consistency between two resting-state scans of the same subject. To optimally estimate the Granger causality, the proposed theoretical framework is implemented through a combination of several approaches, such as dividing the optimal time window and estimating the parameters at the fine temporal and spatial scales. Taken together, our approach provides a novel and robust framework for estimating the Granger causality from fMRI, EEG, and other related data. PMID:23643924

  15. Event Detection using Twitter: A Spatio-Temporal Approach

    PubMed Central

    Cheng, Tao; Wicks, Thomas

    2014-01-01

    Background Every day, around 400 million tweets are sent worldwide, which has become a rich source for detecting, monitoring and analysing news stories and special (disaster) events. Existing research within this field follows key words attributed to an event, monitoring temporal changes in word usage. However, this method requires prior knowledge of the event in order to know which words to follow, and does not guarantee that the words chosen will be the most appropriate to monitor. Methods This paper suggests an alternative methodology for event detection using space-time scan statistics (STSS). This technique looks for clusters within the dataset across both space and time, regardless of tweet content. It is expected that clusters of tweets will emerge during spatio-temporally relevant events, as people will tweet more than expected in order to describe the event and spread information. The special event used as a case study is the 2013 London helicopter crash. Results and Conclusion A spatio-temporally significant cluster is found relating to the London helicopter crash. Although the cluster only remains significant for a relatively short time, it is rich in information, such as important key words and photographs. The method also detects other special events such as football matches, as well as train and flight delays from Twitter data. These findings demonstrate that STSS is an effective approach to analysing Twitter data for event detection. PMID:24893168

  16. A GRASS GIS based Spatio-Temporal Algebra for Raster-, 3D Raster- and Vector Time Series Data

    NASA Astrophysics Data System (ADS)

    Leppelt, Thomas; Gebbert, Sören

    2015-04-01

    Enhancing the well known and widely used map algebra proposed by Dr. Charles Dana Tomlin [1] with the time dimension is an ongoing research topic. The efficient processing of large time series of raster, 3D raster and vector datasets, e. g. raster datasets for temperature or precipitations on continental scale, requires a sophisticated spatio-temporal algebra that is capable of handling datasets with different temporal granularities and spatio-temporal extents. With the temporal enabled GRASS GIS [2] and the GRASS GIS Temporal Framework new spatio-temporal data types are available in GRASS GIS 7, called space time datasets. These space time datasets represent time series of raster, 3D raster and vector map layers. Furthermore the temporal framework provides a wide range of functionalities to support the implementation of a temporal algebra. While spatial capabilities of GRASS GIS are used to perform the spatial processing of the time stamped map layers that are registered in a space time dataset, the temporal processing is provided by the GRASS GIS temporal framework that supports time intervals and time instances. Mixing time instance and time intervals as well as gaps, overlapping or inclusion of intervals and instances is possible. Hence this framework allows an arbitrary layout of the time dimension. We implemented two ways to process space time datasets with arbitrary temporal layout, the temporal topology and the granularity based spatio-temporal algebra. The algebra provides the functionality to define complex spatio-temporal topological operators that process time and space in a single expression. The algebra includes methods to select map layers from space time datasets based on their temporal relations, to temporally shift time stamped map layers, to create temporal buffer and to snap time instances of time stamped map layers to create a valid temporal topology. In addition spatio-temporal operations can be evaluated within conditional statements. These

  17. Spatio-Temporal Dynamics of Fructan Metabolism in Developing Barley Grains[W

    PubMed Central

    Peukert, Manuela; Thiel, Johannes; Peshev, Darin; Weschke, Winfriede; Van den Ende, Wim; Mock, Hans-Peter; Matros, Andrea

    2014-01-01

    Barley (Hordeum vulgare) grain development follows a series of defined morphological and physiological stages and depends on the supply of assimilates (mainly sucrose) from the mother plant. Here, spatio-temporal patterns of sugar distributions were investigated by mass spectrometric imaging, targeted metabolite analyses, and transcript profiling of microdissected grain tissues. Distinct spatio-temporal sugar balances were observed, which may relate to differentiation and grain filling processes. Notably, various types of oligofructans showed specific distribution patterns. Levan- and graminan-type oligofructans were synthesized in the cellularized endosperm prior to the commencement of starch biosynthesis, while during the storage phase, inulin-type oligofructans accumulated to a high concentration in and around the nascent endosperm cavity. In the shrunken endosperm mutant seg8, with a decreased sucrose flux toward the endosperm, fructan accumulation was impaired. The tight partitioning of oligofructan biosynthesis hints at distinct functions of the various fructan types in the young endosperm prior to starch accumulation and in the endosperm transfer cells that accomplish the assimilate supply toward the endosperm at the storage phase. PMID:25271242

  18. A spatio-temporal database for diagnosing drought vulnerability in the Upper Colorado River Basin, Colorado

    NASA Astrophysics Data System (ADS)

    Sampson, K. M.; Wilhelmi, O.

    2009-12-01

    Effective drought planning and mitigation requires an understanding of water supply and demand, including historical biophysical and legal conditions that lead to water shortages among various end-users. With the goal of providing information that is useful for managing current drought risks and for adapting to changing climate, this project aims to fill the gaps in the knowledge about spatio-temporal variations in water demand patterns in the Upper Colorado River Basin (UCRB). This information will help to identify vulnerabilities in the water management structure for more targeted drought preparedness and early warning. Though monitoring of hydro-meteorological properties is important to the forecast of drought conditions, the availability of water is complicated by the administration of existing water rights. The picture is increasingly complicated by the common practice of transmountain diversion, in which water in one basin is transported to another basin for use. This presentation will discuss development of a water demand data model and a spatio-temporal database that will support topological relationships among water users and their respective sources of water supply, including transfers and exchanges. GIS processes for linking water supply to the end users and their water demands will be discussed.

  19. Spatio-temporal dynamics of fructan metabolism in developing barley grains.

    PubMed

    Peukert, Manuela; Thiel, Johannes; Peshev, Darin; Weschke, Winfriede; Van den Ende, Wim; Mock, Hans-Peter; Matros, Andrea

    2014-09-01

    Barley (Hordeum vulgare) grain development follows a series of defined morphological and physiological stages and depends on the supply of assimilates (mainly sucrose) from the mother plant. Here, spatio-temporal patterns of sugar distributions were investigated by mass spectrometric imaging, targeted metabolite analyses, and transcript profiling of microdissected grain tissues. Distinct spatio-temporal sugar balances were observed, which may relate to differentiation and grain filling processes. Notably, various types of oligofructans showed specific distribution patterns. Levan- and graminan-type oligofructans were synthesized in the cellularized endosperm prior to the commencement of starch biosynthesis, while during the storage phase, inulin-type oligofructans accumulated to a high concentration in and around the nascent endosperm cavity. In the shrunken endosperm mutant seg8, with a decreased sucrose flux toward the endosperm, fructan accumulation was impaired. The tight partitioning of oligofructan biosynthesis hints at distinct functions of the various fructan types in the young endosperm prior to starch accumulation and in the endosperm transfer cells that accomplish the assimilate supply toward the endosperm at the storage phase. PMID:25271242

  20. Estimating spatio-temporal dynamics of stream total phosphate concentration by soft computing techniques.

    PubMed

    Chang, Fi-John; Chen, Pin-An; Chang, Li-Chiu; Tsai, Yu-Hsuan

    2016-08-15

    This study attempts to model the spatio-temporal dynamics of total phosphate (TP) concentrations along a river for effective hydro-environmental management. We propose a systematical modeling scheme (SMS), which is an ingenious modeling process equipped with a dynamic neural network and three refined statistical methods, for reliably predicting the TP concentrations along a river simultaneously. Two different types of artificial neural network (BPNN-static neural network; NARX network-dynamic neural network) are constructed in modeling the dynamic system. The Dahan River in Taiwan is used as a study case, where ten-year seasonal water quality data collected at seven monitoring stations along the river are used for model training and validation. Results demonstrate that the NARX network can suitably capture the important dynamic features and remarkably outperforms the BPNN model, and the SMS can effectively identify key input factors, suitably overcome data scarcity, significantly increase model reliability, satisfactorily estimate site-specific TP concentration at seven monitoring stations simultaneously, and adequately reconstruct seasonal TP data into a monthly scale. The proposed SMS can reliably model the dynamic spatio-temporal water pollution variation in a river system for missing, hazardous or costly data of interest. PMID:27100003

  1. Spatio-temporal representativeness of euphotic depth in situ sampling in transitional coastal waters

    NASA Astrophysics Data System (ADS)

    Luhtala, Hanna; Tolvanen, Harri

    2016-06-01

    In dynamic coastal waters, the representativeness of spot sampling is limited to the measurement time and place due to local heterogeneity and irregular water property fluctuations. We assessed the representativeness of in situ sampling by analysing spot-sampled depth profiles of photosynthetically active radiation (PAR) in dynamic coastal archipelago waters in the south-western Finnish coast of the Baltic Sea. First, we assessed the role of spatio-temporality within the underwater light dynamics. As a part of this approach, an anomaly detection procedure was tested on a dataset including a large archipelago area and extensive temporal coverage throughout the ice-free season. The results suggest that euphotic depth variability should be treated as a spatio-temporal process rather than considering spatial and temporal dimensions separately. Second, we assessed the representativeness of spot sampling through statistical analysis of comparative data from spatially denser sampling on three test sites on two optically different occasions. The datasets revealed variability in different dimensions and scales. The suitability of a dataset to reveal wanted phenomena can usually be improved by careful planning and by clearly defining the data sampling objectives beforehand. Nonetheless, conducting a sufficient in situ sampling in dynamic coastal area is still challenging: detecting the general patterns at all the relevant dimensions is complicated by the randomness effect, which reduces the reliability of spot samples on a more detailed scale. Our results indicate that good representativeness of a euphotic depth sampling location is not a stable feature in a highly dynamic environment.

  2. Mapping Spatio-Temporal Diffusion inside the Human Brain Using a Numerical Solution of the Diffusion Equation

    PubMed Central

    Zhan, Wang; Jiang, Li; Loew, Murray; Yang, Yihong

    2008-01-01

    Diffusion is an important mechanism for molecular transport in living biological tissues. Diffusion magnetic resonance imaging (dMRI) provides a unique probe to examine microscopic structures of the tissues in vivo, but current dMRI techniques usually ignore the spatio-temporal evolution process of the diffusive medium. In the present study, we demonstrate the feasibility to reveal the spatio-temporal diffusion process inside the human brain based on a numerical solution of the diffusion equation. Normal human subjects were scanned with a diffusion tensor imaging (DTI) technique on a 3-Tesla MRI scanner, and the diffusion tensor in each voxel was calculated from the DTI data. The diffusion equation, a partial-derivative description of Fick’s Law for the diffusion process, was discretized into equivalent algebraic equations. A finite-difference method was employed to obtain the numerical solution of the diffusion equation with a Crank-Nicholson iteration scheme to enhance the numerical stability. By specifying boundary and initial conditions, the spatio-temporal evolution of the diffusion process inside the brain can be virtually reconstructed. Our results exhibit similar medium profiles and diffusion coefficients as those of light fluorescence dextrans measured in integrative optical imaging experiments. The proposed method highlights the feasibility to non-invasively estimate the macroscopic diffusive transport time for a molecule in a given region of the brain. PMID:18440744

  3. Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence.

    PubMed

    Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Torralba, Antonio; Oliva, Aude

    2016-01-01

    The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain. PMID:27282108

  4. Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence

    PubMed Central

    Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Torralba, Antonio; Oliva, Aude

    2016-01-01

    The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain. PMID:27282108

  5. Resolving Trends in Antarctic Ice Sheet Mass Loss and Glacio-isostatic Adjustment Through Spatio-temporal Source-separation

    NASA Astrophysics Data System (ADS)

    Bamber, J. L.; Schoen, N.; Zammit-Mangion, A.; Rougier, J.; Flament, T.; Luthcke, S. B.; Petrie, E. J.; Rémy, F.

    2013-12-01

    There remains considerable inconsistency between different methods and approaches for determining ice mass trends for Antarctica from satellite observations. There are three approaches that can provide near global coverage for mass trends: altimetry, gravimetry and mass budget calculations. All three approaches suffer from a source separation problem where other geophysical processes limit the capability of the method to resolve the origin and magnitude of a mass change. A fourth approach, GPS vertical motion, provides localised estimates of mass change due to elastic uplift and an indirect estimate of GIA. Each approach has different source separation issues and different spatio-temporal error characteristics. In principle, it should be possible to combine the data and process covariances to minimize the uncertainty in the solution and to produce robust, posterior errors for the trends. In practice, this is a challenging problem in statistics because of the large number of degrees of freedom, the variable spatial and temporal sampling between the different observations and the fact that some processes remain under-sampled, such as firn compaction. Here, we present a novel solution to this problem using the latest methods in statistical modelling of spatio-temporal processes. We use Bayesian hierarchical modelling and employ stochastic partial differential equations to capture our physical understanding of the key processes that influence our observations. Due to the huge number of observations involved (> 10^8) methods are required to reduce the dimensionality of the problem and care is required in treatment of the observations as they are not independent. Here, we focus mainly on the results rather than the full suite of methods and we present time evolving fields of surface mass balance, ice dynamic-driven mass loss, and firn compaction for the period 2003-2009, derived from a combination of ICESat, ENVISAT, GRACE, InSAR, GPS and regional climate model output

  6. [Spatio-temporal variability of habitat quality in Beijing-Tianjin-Hebei Area based on land use change].

    PubMed

    Wu, Jian-sheng; Cao, Qi-wen; Shi, Shu-qin; Huang, Xiu-lan; Lu, Zhi-qiang

    2015-11-01

    Land use change is the core content of global change. To achieve sustainable land use planning, it is necessary to evaluate the habitat quality pattern and its spatio-temporal variation resulted from land use change, which can provide basis for the formulation of land management policy. Based on the analysis of land use change from 2000 to 2010, this study investigated the spatio-temporal variation of habitat quality pattern of Beijing-Tianjin-Hebei Area. We used the watershed profile sampling points and spatial autocorrelation analysis based on watershed subdivision. The results showed that the main land use change types from 2000 to 2010 in this area included the transition from cultivated land to construction land, the transition between forest and grassland, and the transition from water bodies to cultivated land. This land use/cover change process led to the decrease of heterogeneity of landscape structure and increase of fragmentation. The overall spatial pattern of habitat quality was that southeast and south areas were relatively lower, while north and west areas were relatively higher. The analysis based on watershed profile showed that the habitat quality of each watershed presented significant difference in each part. Habitat quality of most sampling points degraded in a way, while some improved compared with 2000. In general, the habitat quality of the bottom part of Luanhe River basin, the medium part of Bai-Chaobai-Chaobaixin River basin, the medium and the bottom part of Yongding River basin and medium part of Laozhang-Fudongpai- Beipai River basin were poor and volatile, while other parts were relatively good. There was a decreasing agglomeration characteristic of distribution of habitat quality in Beijing-Tianjin-Hebei Area under the disturbance of human activities. Areas of high habitat quality in 2000 were mainly located in Luanhe River basin and top part of Baihe basin. Areas of low habitat quality were mainly located in medium and bottom part

  7. Unravelling spatio-temporal evapotranspiration patterns in topographically complex landscapes

    NASA Astrophysics Data System (ADS)

    Metzen, Daniel; Sheridan, Gary; Nyman, Petter; Lane, Patrick

    2016-04-01

    Vegetation co-evolves with soils and topography under a given long-term climatic forcing. Previous studies demonstrated a strong eco-hydrologic feedback between topography, vegetation and energy and water fluxes. Slope orientation (aspect and gradient) alter the magnitude of incoming solar radiation resulting in larger evaporative losses and less water availability on equator-facing slopes. Furthermore, non-local water inputs from upslope areas potentially contribute to available water at downslope positions. The combined effect of slope orientation and drainage position creates complex spatial patterns in biological productivity and pedogenesis, which in turn alter the local hydrology. In complex upland landscapes, topographic alteration of incoming radiation can cause substantial aridity index (ratio of potential evapotranspiration to precipitation) variations over small spatial extents. Most of the upland forests in south-east Australia are located in an aridity index (AI) range of 1-2, around the energy limited to water limited boundary, where forested systems are expected to be most sensitive to AI changes. In this research we aim to improve the fundamental understanding of spatio-temporal evolution of evapotranspiration (ET) patterns in complex terrain, accounting for local topographic effects on system properties (e.g. soil depth, sapwood area, leaf area) and variation in energy and water exchange processes due to slope orientation and drainage position. Six measurement plots were set-up in a mixed species eucalypt forest on a polar and equatorial-facing hillslope (AI ˜1.3 vs. 1.8) at varying drainage position (ridge, mid-slope, gully), while minimizing variations in other factors, e.g. geology and weather patterns. Sap flow, soil water content, incoming solar radiation and throughfall were continuously monitored at field sites spanning a wide range of soil depth (0.5 - >3m), maximum tree heights (17 - 51m) and LAI (1.2 - 4.6). Site-specific response curves

  8. Spatio-temporal Characterization of the Motion of Beating and Fibrillating Myocardial Cells

    NASA Astrophysics Data System (ADS)

    Koss, Jordan; Coppersmith, Susan

    2000-03-01

    We discuss our spatio-temporal analysis of video images of the motion of chicken myocyte tissue cultures. These chicken myocardial cells form a standard biological model for testing the efficacy of drugs and other clinical techniques in restoring organized contraction after a simulated event of cardiac arrest. Our analysis provides a novel means for measuring the strength of regenerated contractions. Additionally, under certain circumstances, the culture of myocardial cells can be driven into a state of fibrillation. We can quantify the visually obvious fact that both the time sequence at individual points as well as the degree of synchronization of the motion at spatially separated points in normally beating tissue are quite different than those in fibrillatory tissue. We compare our work to the results of analyzing electrocardiogram (EKG) traces of fibrillations.

  9. Geostatistical Analysis of Spatio-Temporal Forest Fire Data

    NASA Astrophysics Data System (ADS)

    Vega Orozco, Carmen D.; Kanevski, Mikhail; Tonini, Marj; Conedera, Marc

    2010-05-01

    Forest fire is one of the major phenomena causing degradation of environment, landscape, natural ecosystems, human health and economy. One of the main topic in forest fire data studies deals with the detection, analysis and modelling of spatio-temporal patterns of clustering. Spatial patterns of forest fire locations, their sizes and their sequence in time are of great interest for fire prediction and for forest fire management planning and distribution in optimal way necessary resources. Currently, fires can be analyzed and monitored by using different statistical tools, for example, Ripley's k-function, fractals, Allan factor, scan statistics, etc. Some of them are adapted to temporal or spatial data and are either local or global. In the present study the main attention is paid to the application of geostatistical tools - variography and methods for the analysis of monitoring networks (MN) clustering techniques (topological, statistical and fractal measures), in order to detect and to characterize spatio-temporal forest fire patterns. The main studies performed include: a) analysis of forest fires temporal sequences; b) spatial clustering of forest fires; c) geostatistical spatial analysis of burnt areas. Variography was carried out both for temporal and spatial data. Real case study is based on the forest-fire event data from Canton of Ticino (Switzerland) for a period of 1969 to 2008. The results from temporal analysis show the presence of clustering and seasonal periodicities. Comprehensive analysis of the variograms shows an anisotropy in the direction 30° East-North where smooth changes are detected, while on the direction 30° North-West a greater variability was identified. The research was completed with an application of different MN analysis techniques including, analysis of distributions of distances between events, Morisita Index (MI), fractal dimensions (sandbox counting and box counting methods) and functional fractal dimensions, adapted and

  10. Multilevel Methodology for Simulation of Spatio-Temporal Systems with Heterogeneous Activity; Application to Spread of Valley Fever Fungus

    USGS Publications Warehouse

    Jammalamadaka, Rajanikanth

    2009-01-01

    This report consists of a dissertation submitted to the faculty of the Department of Electrical and Computer Engineering, in partial fulfillment of the requirements for the degree of Doctor of Philosophy, Graduate College, The University of Arizona, 2008. Spatio-temporal systems with heterogeneity in their structure and behavior have two major problems associated with them. The first one is that such complex real world systems extend over very large spatial and temporal domains and consume so many computational resources to simulate that they are infeasible to study with current computational platforms. The second one is that the data available for understanding such systems is limited because they are spread over space and time making it hard to obtain micro and macro measurements. This also makes it difficult to get the data for validation of their constituent processes while simultaneously considering their global behavior. For example, the valley fever fungus considered in this dissertation is spread over a large spatial grid in the arid Southwest and typically needs to be simulated over several decades of time to obtain useful information. It is also hard to get the temperature and moisture data (which are two critical factors on which the survival of the valley fever fungus depends) at every grid point of the spatial domain over the region of study. In order to address the first problem, we develop a method based on the discrete event system specification which exploits the heterogeneity in the activity of the spatio-temporal system and which has been shown to be effective in solving relatively simple partial differential equation systems. The benefit of addressing the first problem is that it now makes it feasible to address the second problem. We address the second problem by making use of a multilevel methodology based on modeling and simulation and systems theory. This methodology helps us in the construction of models with different resolutions (base and

  11. Spatio-Temporal Structure of Hooded Gull Flocks

    PubMed Central

    Yomosa, Makoto; Mizuguchi, Tsuyoshi; Hayakawa, Yoshinori

    2013-01-01

    We analyzed the spatio-temporal structure of hooded gull flocks with a portable stereo camera system. The 3-dimensional positions of individuals were reconstructed from pairs of videos. The motions of each individual were analyzed, and both gliding and flapping motions were quantified based on the velocity time series. We analyzed the distributions of the nearest neighbor’s position in terms of coordinates based on each individual’s motion. The obtained results were consistent with the aerodynamic interaction between individuals. We characterized the leader-follower relationship between individuals by a delay time to mimic the direction of a motion. A relation between the delay time and a relative position was analyzed quantitatively, which suggested the basic properties of the formation flight that maintains order in the flock. PMID:24339960

  12. A spatio-temporal filter approach to synchronous brain activities.

    PubMed

    Nakagawa, T; Ohashi, A

    1980-01-01

    This paper presents a mathematical mechanism for neuronal synchronization in oscillatory brain activities on the basis of the layer structures with recurrent inhibition. To begin with, a linear theory reveals that the recurrent inhibition tends to cause a synchronous uniform oscillation if the loop delay increases, and that an oscillating neuron recruits neighboring neurons by delivering synchronous inputs through the recurrent inhibition loop if the frequency is that of the selfexcitatory oscillation. Then, a quasilinearized dual wave model (DWM), employing the two-sinusoids plus bias input describing functions (TSBDF), shows the competitive relationship between the synchronous oscillation and a spatial wave that is introduced to represent normal brain activity patterns. Results of computer simulations conform well to the predictions of the DWM. Thus, synchronous brain activities are suggested to be the result of the spatio-temporal filter characteristics of the brain layer structures, modified by the neural nonlinearity. PMID:7353063

  13. Sparse cortical source localization using spatio-temporal atoms.

    PubMed

    Korats, Gundars; Ranta, Radu; Le Cam, Steven; Louis-Dorr, Valérie

    2015-08-01

    This paper addresses the problem of sparse localization of cortical sources from scalp EEG recordings. Localization algorithms use propagation model under spatial and/or temporal constraints, but their performance highly depends on the data signal-to-noise ratio (SNR). In this work we propose a dictionary based sparse localization method which uses a data driven spatio-temporal dictionary to reconstruct the measurements using Single Best Replacement (SBR) and Continuation Single Best Replacement (CSBR) algorithms. We tested and compared our methods with the well-known MUSIC and RAP-MUSIC algorithms on simulated realistic data. Tests were carried out for different noise levels. The results show that our method has a strong advantage over MUSIC-type methods in case of synchronized sources. PMID:26737185

  14. SPATIO-TEMPORAL COMPLEXITY OF THE AORTIC SINUS VORTEX

    PubMed Central

    Moore, Brandon; Dasi, Lakshmi Prasad

    2014-01-01

    The aortic sinus vortex is a classical flow structure of significant importance to aortic valve dynamics and the initiation and progression of calific aortic valve disease. We characterize the spatio-temporal characteristics of aortic sinus voxtex dynamics in relation to the viscosity of blood analog solution as well as heart rate. High resolution time-resolved (2KHz) particle image velocimetry was conducted to capture 2D particle streak videos and 2D instantaneous velocity and streamlines along the sinus midplane using a physiological but rigid aorta model fitted with a porcine bioprosthetic heart valve. Blood analog fluids used include a water-glycerin mixture and saline to elucidate the sensitivity of vortex dynamics to viscosity. Experiments were conducted to record 10 heart beats for each combination of blood analog and heart rate condition. Results show that the topological characteristics of the velocity field vary in time-scales as revealed using time bin averaged vectors and corresponding instantaneous streamlines. There exist small time-scale vortices and a large time-scale main vortex. A key flow structure observed is the counter vortex at the upstream end of the sinus adjacent to the base (lower half) of the leaflet. The spatio-temporal complexity of vortex dynamics is shown to be profoundly influenced by strong leaflet flutter during systole with a peak frequency of 200Hz and peak amplitude of 4 mm observed in the saline case. While fluid viscosity influences the length and time-scales as well as the introduction of leaflet flutter, heart rate influences the formation of counter vortex at the upstream end of the sinus. Higher heart rates are shown to reduce the strength of the counter vortex that can greatly influence the directionality and strength of shear stresses along the base of the leaflet. This study demonstrates the impact of heart rate and blood analog viscosity on aortic sinus hemodynamics. PMID:25067881

  15. Spatio-temporal activity of lightnings over Greece

    NASA Astrophysics Data System (ADS)

    Nastos, P. T.; Matsangouras, I. T.; Chronis, T. G.

    2012-04-01

    Extreme precipitation events are always associated with convective weather conditions driving to intense lightning activity: Cloud to Ground (CG), Ground to Cloud (GC) and Cloud to Cloud (CC). Thus, the study of lightnings, which typically occur during thunderstorms, gives evidence of the spatio-temporal variability of intense precipitation. Lightning is a natural phenomenon in the atmosphere, being a major cause of storm related with deaths and main trigger of forest fires during dry season. Lightning affects the many electrochemical systems of the body causing nerve damage, memory loss, personality change, and emotional problems. Besides, among the various nitrogen oxides sources, the contribution from lightning likely represents the largest uncertainty. An operational lightning detection network (LDN) has been established since 2007 by HNMS, consisting of eight time-of-arrival sensors (TOA), spatially distributed across Greek territory. In this study, the spatial and temporal variability of recorded lightnings (CG, GC and CC) are analyzed over Greece, during the period from January 14, 2008 to December 31, 2009, for the first time. The data for retrieving the location and time-of-occurrence of lightning were acquired from Hellenic National Meteorological Service (HNMS). In addition to the analysis of spatio-temporal activity over Greece, the HNMS-LDN characteristics are also presented. The results of the performed analysis reveal the specific geographical sub-regions associated with lightnings incidence. Lightning activity occurs mainly during the autumn season, followed by summer and spring. Higher frequencies of flashes appear over Ionian and Aegean Sea than over land during winter period against continental mountainous regions during summer period.

  16. High Spatio-Temporal Resolution Bathymetry Estimation and Morphology

    NASA Astrophysics Data System (ADS)

    Bergsma, E. W. J.; Conley, D. C.; Davidson, M. A.; O'Hare, T. J.

    2015-12-01

    In recent years, bathymetry estimates using video images have become increasingly accurate. With the cBathy code (Holman et al., 2013) fully operational, bathymetry results with 0.5 metres accuracy have been regularly obtained at Duck, USA. cBathy is based on observations of the dominant frequencies and wavelengths of surface wave motions and estimates the depth (and hence allows inference of bathymetry profiles) based on linear wave theory. Despite the good performance at Duck, large discrepancies were found related to tidal elevation and camera height (Bergsma et al., 2014) and on the camera boundaries. A tide dependent floating pixel and camera boundary solution have been proposed to overcome these issues (Bergsma et al., under review). The video-data collection is set estimate depths hourly on a grid with resolution in the order of 10x25 meters. Here, the application of the cBathy at Porthtowan in the South-West of England is presented. Hourly depth estimates are combined and analysed over a period of 1.5 years (2013-2014). In this work the focus is on the sub-tidal region, where the best cBathy results are achieved. The morphology of the sub-tidal bar is tracked with high spatio-temporal resolution on short and longer time scales. Furthermore, the impact of the storm and reset (sudden and large changes in bathymetry) of the sub-tidal area is clearly captured with the depth estimations. This application shows that the high spatio-temporal resolution of cBathy makes it a powerful tool for coastal research and coastal zone management.

  17. Spatio-temporal clustering of wildfires in Portugal

    NASA Astrophysics Data System (ADS)

    Costa, R.; Pereira, M. G.; Caramelo, L.; Vega Orozco, C.; Kanevski, M.

    2012-04-01

    Several studies have shown that wildfires in Portugal presenthigh temporal as well as high spatial variability (Pereira et al., 2005, 2011). The identification and characterization of spatio-temporal clusters contributes to a comprehensivecharacterization of the fire regime and to improve the efficiency of fire prevention and combat activities. The main goalsin this studyare: (i) to detect the spatio-temporal clusters of burned area; and, (ii) to characterize these clusters along with the role of human and environmental factors. The data were supplied by the National Forest Authority(AFN, 2011) and comprises: (a)the Portuguese Rural Fire Database, PRFD, (Pereira et al., 2011) for the 1980-2007period; and, (b) the national mapping burned areas between 1990 and 2009. In this work, in order to complement the more common cluster analysis algorithms, an alternative approach based onscan statistics and on the permutation modelwas used. This statistical methodallows the detection of local excess events and to test if such an excess can reasonably have occurred by chance.Results obtained for different simulations performed for different spatial and temporal windows are presented, compared and interpreted.The influence of several fire factors such as (climate, vegetation type, etc.) is also assessed. Pereira, M.G., Trigo, R.M., DaCamara, C.C., Pereira, J.M.C., Leite, S.M., 2005:"Synoptic patterns associated with large summer forest fires in Portugal".Agricultural and Forest Meteorology. 129, 11-25. Pereira, M. G., Malamud, B. D., Trigo, R. M., and Alves, P. I.: The history and characteristics of the 1980-2005 Portuguese rural fire database, Nat. Hazards Earth Syst. Sci., 11, 3343-3358, doi:10.5194/nhess-11-3343-2011, 2011 AFN, 2011: AutoridadeFlorestalNacional (National Forest Authority). Available at http://www.afn.min-agricultura.pt/portal.

  18. A Spatio-temporal Model of African Animal Trypanosomosis Risk

    PubMed Central

    Dicko, Ahmadou H.; Percoma, Lassane; Sow, Adama; Adam, Yahaya; Mahama, Charles; Sidibé, Issa; Dayo, Guiguigbaza-Kossigan; Thévenon, Sophie; Fonta, William; Sanfo, Safietou; Djiteye, Aligui; Salou, Ernest; Djohan, Vincent; Cecchi, Giuliano; Bouyer, Jérémy

    2015-01-01

    Background African animal trypanosomosis (AAT) is a major constraint to sustainable development of cattle farming in sub-Saharan Africa. The habitat of the tsetse fly vector is increasingly fragmented owing to demographic pressure and shifts in climate, which leads to heterogeneous risk of cyclical transmission both in space and time. In Burkina Faso and Ghana, the most important vectors are riverine species, namely Glossina palpalis gambiensis and G. tachinoides, which are more resilient to human-induced changes than the savannah and forest species. Although many authors studied the distribution of AAT risk both in space and time, spatio-temporal models allowing predictions of it are lacking. Methodology/Principal Findings We used datasets generated by various projects, including two baseline surveys conducted in Burkina Faso and Ghana within PATTEC (Pan African Tsetse and Trypanosomosis Eradication Campaign) national initiatives. We computed the entomological inoculation rate (EIR) or tsetse challenge using a range of environmental data. The tsetse apparent density and their infection rate were separately estimated and subsequently combined to derive the EIR using a “one layer-one model” approach. The estimated EIR was then projected into suitable habitat. This risk index was finally validated against data on bovine trypanosomosis. It allowed a good prediction of the parasitological status (r2 = 67%), showed a positive correlation but less predictive power with serological status (r2 = 22%) aggregated at the village level but was not related to the illness status (r2 = 2%). Conclusions/Significance The presented spatio-temporal model provides a fine-scale picture of the dynamics of AAT risk in sub-humid areas of West Africa. The estimated EIR was high in the proximity of rivers during the dry season and more widespread during the rainy season. The present analysis is a first step in a broader framework for an efficient risk management of climate

  19. High spatio-temporal resolution observations of crater-lake temperatures at Kawah Ijen volcano, East Java, Indonesia

    USGS Publications Warehouse

    Lewicki, Jennifer L.; Corentin Caudron; Vincent van Hinsberg; George Hilley

    2016-01-01

    The crater lake of Kawah Ijen volcano, East Java, Indonesia, has displayed large and rapid changes in temperature at point locations during periods of unrest, but measurement techniques employed to-date have not resolved how the lake’s thermal regime has evolved over both space and time. We applied a novel approach for mapping and monitoring variations in crater-lake apparent surface (“skin”) temperatures at high spatial (~32 cm) and temporal (every two minutes) resolution at Kawah Ijen on 18 September 2014. We used a ground-based FLIR T650sc camera with digital and thermal infrared (TIR) sensors from the crater rim to collect (1) a set of visible imagery around the crater during the daytime and (2) a time series of co-located visible and TIR imagery at one location from pre-dawn to daytime. We processed daytime visible imagery with the Structure-from-Motion photogrammetric method to create a digital elevation model onto which the time series of TIR imagery was orthorectified and georeferenced. Lake apparent skin temperatures typically ranged from ~21 to 33oC. At two locations, apparent skin temperatures were ~ 4 and 7 oC less than in-situ lake temperature measurements at 1.5 and 5 m depth, respectively. These differences, as well as the large spatio-temporal variations observed in skin temperatures, were likely largely associated with atmospheric effects such as evaporative cooling of the lake surface and infrared absorption by water vapor and SO2. Calculations based on orthorectified TIR imagery thus yielded underestimates of volcanic heat fluxes into the lake, whereas volcanic heat fluxes estimated based on in-situ temperature measurements (68 to 111 MW) were likely more representative of Kawah Ijen in a quiescent state. The ground-based imaging technique should provide a valuable tool to continuously monitor crater-lake temperatures and contribute insight into the spatio-temporal evolution of these temperatures associated with volcanic activity.

  20. Spatio-Temporal Transmission Patterns of Black-Band Disease in a Coral Community

    PubMed Central

    Zvuloni, Assaf; Artzy-Randrup, Yael; Stone, Lewi; Kramarsky-Winter, Esti; Barkan, Roy; Loya, Yossi

    2009-01-01

    Background Transmission mechanisms of black-band disease (BBD) in coral reefs are poorly understood, although this disease is considered to be one of the most widespread and destructive coral infectious diseases. The major objective of this study was to assess transmission mechanisms of BBD in the field based on the spatio-temporal patterns of the disease. Methodology/Principal Findings 3,175 susceptible and infected corals were mapped over an area of 10×10 m in Eilat (northern Gulf of Aqaba, Red Sea) and the distribution of the disease was examined monthly throughout almost two full disease cycles (June 2006–December 2007). Spatial and spatio-temporal analyses were applied to infer the transmission pattern of the disease and to calculate key epidemiological parameters such as (basic reproduction number). We show that the prevalence of the disease is strongly associated with high water temperature. When water temperatures rise and disease prevalence increases, infected corals exhibit aggregated distributions on small spatial scales of up to 1.9 m. Additionally, newly-infected corals clearly appear in proximity to existing infected corals and in a few cases in direct contact with them. We also present and test a model of water-borne infection, indicating that the likelihood of a susceptible coral becoming infected is defined by its spatial location and by the relative spatial distribution of nearby infected corals found in the site. Conclusions/Significance Our results provide evidence that local transmission, but not necessarily by direct contact, is likely to be an important factor in the spread of the disease over the tested spatial scale. In the absence of potential disease vectors with limited mobility (e.g., snails, fireworms) in the studied site, water-borne infection is likely to be a significant transmission mechanism of BBD. Our suggested model of water-borne transmission supports this hypothesis. The spatio-temporal analysis also points out that

  1. A probe array for the investigation of spatio-temporal structures in drift wave turbulence

    SciTech Connect

    Latten, A.; Klinger, T.; Piel, A.; Pierre, T.

    1995-05-01

    A probe array with 64 azimuthally arranged Langmuir probes is presented as a new diagnostic tool for the investigation of drift waves. A parallel data acquisition system provides full spatio-temporal data of azimuthally propagating waves. For both regular and turbulent states of current-driven drift waves, the information provided by such space-time patterns is compared with results obtained from conventional two-point correlation methods. The probe array allows one to directly estimate the time-averaged wave number spectrum. In a turbulent state, the spectrum yields to a power law of {ital S}({ital k}){proportional_to}{ital k}{sup {minus}3.6{plus_minus}0.1}. {copyright} {ital 1995} {ital American} {ital Institute} {ital of} {ital Physics}.

  2. Final report: spatio-temporal data mining of scientific trajectory data

    SciTech Connect

    Gaffney, S; Smyth, P

    2001-01-10

    With the increasing availability of massive observational and experimental data sets (across a wide variety of scientific disciplines) there is an increasing need to provide scientists with efficient computational tools to explore such data in a systematic manner. For example, techniques such as classification and clustering are now being widely used in astronomy to categorize and organize stellar objects into groups and catalogs, which in turn provide the impetus for scientific hypothesis formation and discovery (e.g., see Fayyad, Djorgovski and Weir (1996); or Cheeseman and Stutz (1996) or Fayyad and Smyth (1999) in a more general context). Data-driven exploration of massive spatio-temporal data sets is an area where there is particular need of data mining techniques. Scientists are overwhelmed by the vast quantities of data which simulations, experiments, and observational instruments can produce. Analysis of spatio-temporal data is inherently challenging, yet most current research in data mining is focused on algorithms based on more traditional feature-vector data representations. Scientists are often not particularly interested in raw grid-level data, but rather in the phenomena and processes which are ''driving'' the data. In particular, they are often interested in the temporal and spatial evolution of specific ''spatially local'' structures of interest, e.g., birth-death processes for vortices and interfaces in fluid-flow simulations and experiments, trajectories of extra-tropical cyclones from sea-level pressure data over the Atlantic and Pacific oceans, and sunspot shape and size evolution over time from daily chromospheric images of the Sun. The ability to automatically detect, cluster, and catalog such objects in principle provides an important ''data reduction front-end'' which can convert 4-d data sets (3 spatial and 1 temporal dimension) on a massive grid to a much more abstract representation of local structures and their evolution. In turn, these

  3. A single glance at natural face images generate larger and qualitatively different category-selective spatio-temporal signatures than other ecologically-relevant categories in the human brain.

    PubMed

    Jacques, Corentin; Retter, Talia L; Rossion, Bruno

    2016-08-15

    Although humans discriminate natural images of faces from other categories at a single glance, clarifying the neural specificity and spatio-temporal dynamics of this process without low-level visual confounds remains a challenge. We recorded high-density scalp electroencephalogram while presenting natural images of various objects at a fast periodic rate (5.88images/s). In different stimulation sequences, numerous variable exemplars of three categories associated with cortical specialization in neuroimaging - faces, body parts, or houses - appeared every five images (5.88Hz/5=1.18Hz). In these fast periodic visual stimulation (FPVS) sequences, common low- and high-level visual processes between these categories and other objects are captured at the 5.88Hz frequency, while high-level category-selective responses are objectively quantified at the 1.18Hz frequency and harmonics. Category-selective responses differed quantitatively and qualitatively between faces, body parts and houses. First, they were much larger (2-4 times) for faces over the whole scalp. Second, specific and reliable scalp topographical maps of category-selective responses pointed to distinct principle neural sources for faces (ventral occipito-temporal), body parts (lateral occipito-temporal) and houses (dorso-medial occipital). Category-selective EEG responses were found at multiple time-windows from 110 to 600ms post-stimulus onset. Faces elicited the most complex spatio-temporal profile with up to four selective responses, although body parts and houses also elicited selective responses more complex than previously described. These observations indicate that a single glance at natural face images inserted in a rapid stream of natural objects generates a quantitatively and qualitatively unique category-selective spatio-temporal signature in occipito-temporal cortical areas of the human brain. PMID:27138205

  4. Spatio-temporal characteristics of self-pulse in hollow cathode discharge

    NASA Astrophysics Data System (ADS)

    Jing, Ha; He, Shoujie

    2015-02-01

    The characteristics of self-pulse in hollow cathode discharge at low pressure have been investigated. The voltage-current (V-I) curves, the influence of ballast resistor on the self-pulses, and the evolution of current and voltage are measured. Both the axial and radial spatio-temporal discharge images of self-pulse are recorded. The results show that there exists the hysteresis effect in the present hollow cathode discharge. The high value of ballast resistors is favourable for the observation of self-pulses. The process of the self-pulse can be divided into three stages from the temporal discharge images, i.e., the pre-discharge, the transition from mainly axial electric field to mainly radial electric field, and the decaying process. The self-pulse is suggested to originate from the mode transition of the discharge in essence.

  5. Spatio-temporal characteristics of self-pulse in hollow cathode discharge

    SciTech Connect

    Jing, Ha; He, Shoujie

    2015-02-15

    The characteristics of self-pulse in hollow cathode discharge at low pressure have been investigated. The voltage-current (V-I) curves, the influence of ballast resistor on the self-pulses, and the evolution of current and voltage are measured. Both the axial and radial spatio-temporal discharge images of self-pulse are recorded. The results show that there exists the hysteresis effect in the present hollow cathode discharge. The high value of ballast resistors is favourable for the observation of self-pulses. The process of the self-pulse can be divided into three stages from the temporal discharge images, i.e., the pre-discharge, the transition from mainly axial electric field to mainly radial electric field, and the decaying process. The self-pulse is suggested to originate from the mode transition of the discharge in essence.

  6. Big Data Standards in Action: Spatio-Temporal Analytics with EarthServer

    NASA Astrophysics Data System (ADS)

    Baumann, Peter

    2014-05-01

    In the transatlantic EarthServer initiative, a set of value-adding services on massive spatio-temporal data are being established for all Earth Sciences. Already at a volume exceeding 10 Terabyte in total, several of the services will break the 100 TB barrier this summer. The common EarthServer platform rigorously relies on the open OGC standards only. At the heart is the core OGC Big Geo Data standard, Web Coverage Service (WCS), together with its analytics extension Web Coverage Processing Service (WCPS). We present OGC's coverage data and processing model. On the example of the WCS Core Reference Implementation, rasdaman, we discuss how scalable implementations are supported. Time and Internet permitting a live demo will be included.

  7. Spatio-temporal interpolation of soil moisture in 3D+T using automated sensor network data

    NASA Astrophysics Data System (ADS)

    Gasch, C.; Hengl, T.; Magney, T. S.; Brown, D. J.; Gräler, B.

    2014-12-01

    Soil sensor networks provide frequent in situ measurements of dynamic soil properties at fixed locations, producing data in 2- or 3-dimensions and through time (2D+T and 3D+T). Spatio-temporal interpolation of 3D+T point data produces continuous estimates that can then be used for prediction at unsampled times and locations, as input for process models, and can simply aid in visualization of properties through space and time. Regression-kriging with 3D and 2D+T data has successfully been implemented, but currently the field of geostatistics lacks an analytical framework for modeling 3D+T data. Our objective is to develop robust 3D+T models for mapping dynamic soil data that has been collected with high spatial and temporal resolution. For this analysis, we use data collected from a sensor network installed on the R.J. Cook Agronomy Farm (CAF), a 37-ha Long-Term Agro-Ecosystem Research (LTAR) site in Pullman, WA. For five years, the sensors have collected hourly measurements of soil volumetric water content at 42 locations and five depths. The CAF dataset also includes a digital elevation model and derivatives, a soil unit description map, crop rotations, electromagnetic induction surveys, daily meteorological data, and seasonal satellite imagery. The soil-water sensor data, combined with the spatial and temporal covariates, provide an ideal dataset for developing 3D+T models. The presentation will include preliminary results and address main implementation strategies.

  8. Predicting gross primary production with high spatio-temporal resolution remote sensing datasets at regional scale

    NASA Astrophysics Data System (ADS)

    Fu, D.; Chen, B.

    2012-04-01

    resolution; (ii) the estimated GPP is produced by GR model using available reflectance data with high spatial resolution; (iii) the GR model's calibration process is done combined with SAFE model's pure footprint result and the observations of flux sites; (iv) the spatio-temporal distribution of GPP values at regional scale are predicted with specific parameters correspond to different ecosystem.

  9. Correlation analysis of spatio-temporal images for estimating two-dimensional flow velocity field in a rotating flow condition

    NASA Astrophysics Data System (ADS)

    Yu, Kwonkyu; Kim, Seojun; Kim, Dongsu

    2015-10-01

    Flow velocity estimation in actual rivers using image processing technique has been highlighted for hydrometric communities in the last decades, and this technique is called Large Scale Particle Image Velocimetry (LSPIV). Although LSPIV has been successfully tested in many flow conditions, it has addressed several limitations estimating mean flow field because of difficult flow conditions such as rotating, lack of light and seeds, and noisy flow conditions. Recently, an alternative technique named STIV to use spatio-temporal images based on successively recorded images has been introduced to overcome the limitations of LSPIV. The STIV was successfully applied to obtain one-dimensional flow component in the river for estimating streamflow discharge, where the main flow direction is known. Using the 5th order of central difference scheme, the STIV directly calculated the mean angle of slopes which appeared as strips in the spatio-temporal images and has been proved to be more reliable and efficient for the discharge estimation as compared with the conventional LSPIV. However, yet it has not been sufficiently qualified to derive two-dimensional flow field in the complex flow, such as rotating or locally unsteady flow conditions. We deemed that it was because the strips in the given spatio-temporal images from not properly oriented for main flow direction are not narrow enough or clearly visible, thus the direct estimating strip slope could give erroneous results. Thereby, the STIV has been mainly applied for obtaining one-dimensional flow component. In this regard, we proposed an alternative algorithm to estimate the mean slope angle for enhancing the capability of the STIV, which used correlation coefficient between odd and even image splits from the given spatio-temporal image. This method was named CASTI (Correlation Analysis of Spatio-Temporal Image). This paper described the step-by-step procedure of the CASTI and validated its capability for estimating two

  10. How geomorphology and groundwater level affect the spatio-temporal variability of riverine cold water patches?

    NASA Astrophysics Data System (ADS)

    Wawrzyniak, Vincent; Piégay, Hervé; Allemand, Pascal; Vaudor, Lise; Goma, Régis; Grandjean, Philippe

    2016-04-01

    Temperature is a key factor for river ecosystems. In summer, patches of cold water are formed in the river by groundwater seepage. These patches have strong ecological significance and extend to the surface water in a well-mixed riverine system. These patches can serve as thermal refuges for some fish species during summer. In this study, the temporal variability and spatial distribution of cold water patches were explored along a 50 km river reach (the lower Ain River, France) using thermal infrared airborne remote sensing. This study examines a new range of processes acting on cold water patches at different scales that have not previously been touched upon in the literature. Three airborne campaigns were conducted during the summers of 2010, 2011 and 2014. Based on these images, a large number of cold water patches were identified using an automated method. Four types of patches were observed: tributary plumes, cold side channels (former channels or point-bar backwater channels), side seeps (located directly in the river channel) and gravel bar seeps (occurring at the downstream end of gravel bars). Logistic regression was used to analyse the longitudinal distribution of cold water patches according to geomorphologic indicators reflecting current or past fluvial process. Side seeps were found to be related to the local geology. Cold side channels were correlated to contemporary and past lateral river mobility. Gravel bar seeps were related to the current development of bars and are more prevalent in wandering reaches than in single-bed incised and paved reaches. The logistic model was subsequently used to evaluate gravel bar seep variability in the past. The model suggests larger numbers of seeps in the mid-20th century when bar surface area was higher. Interannual variability in the occurrence and spatial extent of side seeps and gravel bar seeps appear to be related to groundwater level fluctuations. Cold side channels exhibited greater interannual stability

  11. Approach to analysis of multiscale space-distributed time series: separation of spatio-temporal modes with essentially different time scales

    NASA Astrophysics Data System (ADS)

    Feigin, Alexander; Mukhin, Dmitry; Gavrilov, Andrey; Volodin, Evgeny; Loskutov, Evgeny

    2014-05-01

    Natural systems are in general space-distributed, and their evolution represents a broad spectrum of temporal scales. The multiscale nature may be resulted from multiplicity of mechanisms governing the system behaviour, and a large number of feedbacks and nonlinearities. A way to reveal and understand the underlying mechanisms as well as to model corresponding sub-systems is decomposition of the full (complex) system into well separated spatio-temporal patterns ("modes") that evolve with essentially different time scales. In the report a new method of a similar decomposition is discussed. The method is based on generalization of the MSSA (Multichannel Singular Spectral Analysis) [1] for expanding space-distributed time series in basis of spatio-temporal empirical orthogonal functions (STEOF), which makes allowance delayed correlations of the processes recorded in spatially separated points. The method is applied to decomposition of the Earth's climate system: on the base of 156 years time series of SST anomalies distributed over the globe [2] two climatic modes possessing by noticeably different time scales (3-5 and 9-11 years) are separated. For more accurate exclusion of "too slow" (and thus not represented correctly) processes from real data the numerically produced STEOF basis is used. For doing this the time series generated by the INM RAS Coupled Climate Model [3] is utilized. Relations of separated modes to ENSO and PDO are investigated. Possible development of the suggested approach in order to the separation of the modes that are nonlinearly uncorrelated is discussed. 1. Ghil, M., R. M. Allen, M. D. Dettinger, K. Ide, D. Kondrashov, et al. (2002) "Advanced spectral methods for climatic time series", Rev. Geophys. 40(1), 3.1-3.41. 2. http://iridl.ldeo.columbia.edu/SOURCES/.KAPLAN/.EXTENDED/.v2/.ssta/ 3. http://83.149.207.89/GCM_DATA_PLOTTING/GCM_INM_DATA_XY_en.htm

  12. Mass Spectrometric Analysis of Spatio-Temporal Dynamics of Crustacean Neuropeptides

    PubMed Central

    OuYang, Chuanzi; Liang, Zhidan; Li, Lingjun

    2014-01-01

    Neuropeptides represent one of the largest classes of signaling molecules used by nervous systems to regulate a wide range of physiological processes. Over the past several years, mass spectrometry (MS)-based strategies have revolutionized the discovery of neuropeptides in numerous model organisms, especially in decapod crustaceans. Here, we focus our discussion on recent advances in the use of MS-based techniques to map neuropeptides in spatial domain and monitoring their dynamic changes in temporal domain. These MS-enabled investigations provide valuable information about the distribution, secretion and potential function of neuropeptides with high molecular specificity and sensitivity. In situ MS imaging and in vivo microdialysis are highlighted as key technologies for probing spatio-temporal dynamics of neuropeptides in the crustacean nervous system. This review summarizes the latest advancement in MS-based methodologies for neuropeptide analysis including typical workflow and sample preparation strategies as well as major neuropeptide families discovered in decapod crustaceans. PMID:25448012

  13. Spatio-temporal patterns of soil erosion and suspended sediment dynamics in the Mekong River Basin.

    PubMed

    Suif, Zuliziana; Fleifle, Amr; Yoshimura, Chihiro; Saavedra, Oliver

    2016-10-15

    Understanding of the distribution patterns of sediment erosion, concentration and transport in river basins is critically important as sediment plays a major role in river basin hydrophysical and ecological processes. In this study, we proposed an integrated framework for the assessment of sediment dynamics, including soil erosion (SE), suspended sediment load (SSL) and suspended sediment concentration (SSC), and applied this framework to the Mekong River Basin. The Revised Universal Soil Loss Equation (RUSLE) model was adopted with a geographic information system to assess SE and was coupled with a sediment accumulation and a routing scheme to simulate SSL. This framework also analyzed Landsat imagery captured between 1987 and 2000 together with ground observations to interpolate spatio-temporal patterns of SSC. The simulated SSL results from 1987 to 2000 showed the relative root mean square error of 41% and coefficient of determination (R(2)) of 0.89. The polynomial relationship of the near infrared exoatmospheric reflectance and the band 4 wavelength (760-900nm) to the observed SSC at 9 sites demonstrated the good agreement (overall relative RMSE=5.2%, R(2)=0.87). The result found that the severe SE occurs in the upper (China and Lao PDR) and lower (western part of Vietnam) regions. The SSC in the rainy season (June-November) showed increasing and decreasing trends longitudinally in the upper (China and Lao PDR) and lower regions (Cambodia), respectively, while the longitudinal profile of SSL showed a fluctuating trend along the river in the early rainy season. Overall, the results described the unique spatio-temporal patterns of SE, SSL and SSC in the Mekong River Basin. Thus, the proposed integrated framework is useful for elucidating complex process of sediment generation and transport in the land and river systems of large river basins. PMID:27338846

  14. Spatio-temporal generation regimes in quasi-CW Raman fiber lasers.

    PubMed

    Tarasov, Nikita; Sugavanam, Srikanth; Churkin, Dmitry

    2015-09-21

    We present experimental measurements of intensity spatio-temporal dynamics in quasi-CW Raman fiber laser. Depending on the power, the laser operates in different spatio-temporal regimes varying from partial mode-locking near the generation threshold to almost stochastic radiation and a generation of short-lived pulses at high power. The transitions between the generation regimes are evident in intensity spatio-temporal dynamics. Two-dimensional auto-correlation functions provide an additional insight into temporal and spatial properties of the observed regimes. PMID:26406625

  15. STSE: Spatio-Temporal Simulation Environment Dedicated to Biology

    PubMed Central

    2011-01-01

    Background Recently, the availability of high-resolution microscopy together with the advancements in the development of biomarkers as reporters of biomolecular interactions increased the importance of imaging methods in molecular cell biology. These techniques enable the investigation of cellular characteristics like volume, size and geometry as well as volume and geometry of intracellular compartments, and the amount of existing proteins in a spatially resolved manner. Such detailed investigations opened up many new areas of research in the study of spatial, complex and dynamic cellular systems. One of the crucial challenges for the study of such systems is the design of a well stuctured and optimized workflow to provide a systematic and efficient hypothesis verification. Computer Science can efficiently address this task by providing software that facilitates handling, analysis, and evaluation of biological data to the benefit of experimenters and modelers. Results The Spatio-Temporal Simulation Environment (STSE) is a set of open-source tools provided to conduct spatio-temporal simulations in discrete structures based on microscopy images. The framework contains modules to digitize, represent, analyze, and mathematically model spatial distributions of biochemical species. Graphical user interface (GUI) tools provided with the software enable meshing of the simulation space based on the Voronoi concept. In addition, it supports to automatically acquire spatial information to the mesh from the images based on pixel luminosity (e.g. corresponding to molecular levels from microscopy images). STSE is freely available either as a stand-alone version or included in the linux live distribution Systems Biology Operational Software (SB.OS) and can be downloaded from http://www.stse-software.org/. The Python source code as well as a comprehensive user manual and video tutorials are also offered to the research community. We discuss main concepts of the STSE design and

  16. Spatial Distribution of Tree Species Governs the Spatio-Temporal Interaction of Leaf Area Index and Soil Moisture across a Forested Landscape

    PubMed Central

    Naithani, Kusum J.; Baldwin, Doug C.; Gaines, Katie P.; Lin, Henry; Eissenstat, David M.

    2013-01-01

    Quantifying coupled spatio-temporal dynamics of phenology and hydrology and understanding underlying processes is a fundamental challenge in ecohydrology. While variation in phenology and factors influencing it have attracted the attention of ecologists for a long time, the influence of biodiversity on coupled dynamics of phenology and hydrology across a landscape is largely untested. We measured leaf area index (L) and volumetric soil water content (θ) on a co-located spatial grid to characterize forest phenology and hydrology across a forested catchment in central Pennsylvania during 2010. We used hierarchical Bayesian modeling to quantify spatio-temporal patterns of L and θ. Our results suggest that the spatial distribution of tree species across the landscape created unique spatio-temporal patterns of L, which created patterns of water demand reflected in variable soil moisture across space and time. We found a lag of about 11 days between increase in L and decline in θ. Vegetation and soil moisture become increasingly homogenized and coupled from leaf-onset to maturity but heterogeneous and uncoupled from leaf maturity to senescence. Our results provide insight into spatio-temporal coupling between biodiversity and soil hydrology that is useful to enhance ecohydrological modeling in humid temperate forests. PMID:23554915

  17. Spontaneous switching among multiple spatio-temporal patterns in three-oscillator systems constructed with oscillatory cells of true slime mold

    NASA Astrophysics Data System (ADS)

    Takamatsu, Atsuko

    2006-11-01

    Three-oscillator systems with plasmodia of true slime mold, Physarum polycephalum, which is an oscillatory amoeba-like unicellular organism, were experimentally constructed and their spatio-temporal patterns were investigated. Three typical spatio-temporal patterns were found: rotation ( R), partial in-phase ( PI), and partial anti-phase with double frequency ( PA). In pattern R, phase differences between adjacent oscillators were almost 120 ∘. In pattern PI, two oscillators were in-phase and the third oscillator showed anti-phase against the two oscillators. In pattern PA, two oscillators showed anti-phase and the third oscillator showed frequency doubling oscillation with small amplitude. Actually each pattern is not perfectly stable but quasi-stable. Interestingly, the system shows spontaneous switching among the multiple quasi-stable patterns. Statistical analyses revealed a characteristic in the residence time of each pattern: the histograms seem to have Gamma-like distribution form but with a sharp peak and a tail on the side of long period. That suggests the attractor of this system has complex structure composed of at least three types of sub-attractors: a “Gamma attractor”-involved with several Poisson processes, a “deterministic attractor”-the residence time is deterministic, and a “stable attractor”-each pattern is stable. When the coupling strength was small, only the Gamma attractor was observed and switching behavior among patterns R, PI, and PA almost always via an asynchronous pattern named O. A conjecture is as follows: Internal/external noise exposes each pattern of R, PI, and PA coexisting around bifurcation points: That is observed as the Gamma attractor. As coupling strength increases, the deterministic attractor appears then followed by the stable attractor, always accompanied with the Gamma attractor. Switching behavior could be caused by regular existence of the Gamma attractor.

  18. Adaptive OFDM waveform design for spatio-temporal-sparsity exploited STAP radar

    NASA Astrophysics Data System (ADS)

    Sen, Satyabrata; Barhen, Jacob

    2015-05-01

    We propose a sparsity-based space-time adaptive processing (STAP) algorithm to detect a slowly-moving target using an orthogonal frequency division multiplexing (OFDM) radar. The motivation of employing an OFDM signal is that it improves the target-detectability from the interfering signals by increasing the frequency diversity of the system. However, due to the addition of one extra dimension in terms of frequency, the adaptive degrees-of- freedom in an OFDM-STAP also increases. Therefore, to avoid the construction a fully-adaptive OFDM-STAP, we propose a sparsity-based STAP algorithm. We observe that the interference spectrum is inherently sparse in the spatio-temporal domain, as the clutter responses occupy only a diagonal ridge on the spatio-temporal plane and the jammer signals interfere only from a few spatial directions. Hence, we exploit that sparsity to develop an efficient STAP technique that utilizes considerably lesser number of secondary data compared to the other existing STAP techniques, and produces nearly optimum STAP performance. In addition to designing the STAP filter, we propose to optimally design the transmit OFDM signals by maximizing the output signal- to-interference-plus-noise ratio (SINR) in order to improve the STAP-performance. The computation of output SINR depends on the estimated value of the interference covariance matrix, which we obtain by applying the sparse recovery algorithm. Therefore, we analytically assess the effects of the synthesized OFDM coefficients on the sparse recovery of the interference covariance matrix by computing the coherence measure of the sparse measurement matrix. Our numerical examples demonstrate the achieved STAP-performance due to sparsity- based technique and adaptive waveform design.

  19. Spatio-temporal soil moisture patterns across gradients of vegetation and topography

    NASA Astrophysics Data System (ADS)

    Hassler, Sibylle; Weiler, Markus; Blume, Theresa

    2014-05-01

    Soil moisture dynamics control hydrological processes on various scales: changes in local water storage and potential activation of preferential flow paths influence connectivity and runoff from hillslopes and ultimately the discharge response of the stream. The spatio-temporal patterns of soil moisture, however, are dependent on a combination of local parameters such as soil type, vegetation and topography as well as meteorological conditions, antecedent moisture and seasonality. In an integrative monitoring study carried out within the CAOS observatory in Luxemburg (http://www.caos-project.de/), soil moisture was measured at 21 sites with 3 soil moisture profiles each. These sites include grassland as well as forest on the one hand and cover different hillslope positions on the other hand. This setup allows us to study both vegetation and topographic effects. The spatio-temporal patterns of soil moisture were analysed using two approaches: 1) we examined temporal persistence of soil moisture patterns with rank stability plots and addressed the variability within and between sites for contrasting meteorological conditions. 2) In a next step we focused on specific hydrologic events: two periods during summer recession were distinguished, first the drying out of the soils during a period of no precipitation, but also the continuing decline even after summer rains have started. Furthermore, the soil moisture response to three different rainfall events was examined, varying in intensity and antecedent moisture conditions. The emerging contrasts in patterns were put into context of site-specific characteristics such as vegetation and topographical position to identify controls on soil moisture dynamics for our range of sites. Ultimately, linking similarity in soil moisture response in landscapes to these controls can elucidate the hydrological functioning of landscape units and thus facilitate modelling efforts.

  20. How spatio-temporal habitat connectivity affects amphibian genetic structure

    USGS Publications Warehouse

    Watts, Alexander G.; Schlichting, P; Billerman, S; Jesmer, B; Micheletti, S; Fortin, M.-J.; Funk, W.C.; Hapeman, P; Muths, Erin L.; Murphy, M.A.

    2015-01-01

    Heterogeneous landscapes and fluctuating environmental conditions can affect species dispersal, population genetics, and genetic structure, yet understanding how biotic and abiotic factors affect population dynamics in a fluctuating environment is critical for species management. We evaluated how spatio-temporal habitat connectivity influences dispersal and genetic structure in a population of boreal chorus frogs (Pseudacris maculata) using a landscape genetics approach. We developed gravity models to assess the contribution of various factors to the observed genetic distance as a measure of functional connectivity. We selected (a) wetland (within-site) and (b) landscape matrix (between-site) characteristics; and (c) wetland connectivity metrics using a unique methodology. Specifically, we developed three networks that quantify wetland connectivity based on: (i) P. maculata dispersal ability, (ii) temporal variation in wetland quality, and (iii) contribution of wetland stepping-stones to frog dispersal. We examined 18 wetlands in Colorado, and quantified 12 microsatellite loci from 322 individual frogs. We found that genetic connectivity was related to topographic complexity, within- and between-wetland differences in moisture, and wetland functional connectivity as contributed by stepping-stone wetlands. Our results highlight the role that dynamic environmental factors have on dispersal-limited species and illustrate how complex asynchronous interactions contribute to the structure of spatially-explicit metapopulations.

  1. Spatio-Temporal Self-Organization in Mudstones (Invited)

    NASA Astrophysics Data System (ADS)

    Dewers, T. A.

    2010-12-01

    Shales and other mudstones are the most abundant rock types in sedimentary basins, yet have received comparatively little attention. Common as hydrocarbon seals, these are increasingly being targeted as unconventional gas reservoirs, caprocks for CO2 sequestration, and storage repositories for waste. The small pore and grain size, large specific surface areas, and clay mineral structures lend themselves to rapid reaction rates, high capillary pressures, and semi-permeable membrane behavior accompanying changes in stress, pressure, temperature and chemical conditions. Under far from equilibrium conditions, mudrocks display a variety of spatio-temporal self-organized phenomena arising from nonlinear thermo-mechano-chemo-hydro coupling. Beginning with a detailed examination of nano-scale pore network structures in mudstones, we discuss the dynamics behind such self-organized phenomena as pressure solitons in unconsolidated muds, chemically-induced flow self focusing and permeability transients, localized compaction, time dependent well-bore failure, and oscillatory osmotic fluxes as they occur in clay-bearing sediments. Examples are draw from experiments, numerical simulation, and the field. These phenomena bear on the ability of these rocks to serve as containment barriers. This work is funded by the US Department of Energy, Office of Basic Energy Sciences. Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000

  2. How spatio-temporal habitat connectivity affects amphibian genetic structure

    PubMed Central

    Watts, Alexander G.; Schlichting, Peter E.; Billerman, Shawn M.; Jesmer, Brett R.; Micheletti, Steven; Fortin, Marie-Josée; Funk, W. Chris; Hapeman, Paul; Muths, Erin; Murphy, Melanie A.

    2015-01-01

    Heterogeneous landscapes and fluctuating environmental conditions can affect species dispersal, population genetics, and genetic structure, yet understanding how biotic and abiotic factors affect population dynamics in a fluctuating environment is critical for species management. We evaluated how spatio-temporal habitat connectivity influences dispersal and genetic structure in a population of boreal chorus frogs (Pseudacris maculata) using a landscape genetics approach. We developed gravity models to assess the contribution of various factors to the observed genetic distance as a measure of functional connectivity. We selected (a) wetland (within-site) and (b) landscape matrix (between-site) characteristics; and (c) wetland connectivity metrics using a unique methodology. Specifically, we developed three networks that quantify wetland connectivity based on: (i) P. maculata dispersal ability, (ii) temporal variation in wetland quality, and (iii) contribution of wetland stepping-stones to frog dispersal. We examined 18 wetlands in Colorado, and quantified 12 microsatellite loci from 322 individual frogs. We found that genetic connectivity was related to topographic complexity, within- and between-wetland differences in moisture, and wetland functional connectivity as contributed by stepping-stone wetlands. Our results highlight the role that dynamic environmental factors have on dispersal-limited species and illustrate how complex asynchronous interactions contribute to the structure of spatially-explicit metapopulations. PMID:26442094

  3. Spatio-Temporal Multiscale Denoising of Fluoroscopic Sequence.

    PubMed

    Amiot, Carole; Girard, Catherine; Chanussot, Jocelyn; Pescatore, Jeremie; Desvignes, Michel

    2016-06-01

    In the past 20 years, a wide range of complex fluoroscopically guided procedures have shown considerable growth. Biologic effects of the exposure (radiation induced burn, cancer) lead to reduce the dose during the intervention, for the safety of patients and medical staff. However, when the dose is reduced, image quality decreases, with a high level of noise and a very low contrast. Efficient restoration and denoising algorithms should overcome this drawback. We propose a spatio-temporal filter operating in a multi-scales space. This filter relies on a first order, motion compensated, recursive temporal denoising. Temporal high frequency content is first detected and then matched over time to allow for a strong denoising in the temporal axis. We study this filter in the curvelet domain and in the dual-tree complex wavelet domain, and compare those results to state of the art methods. Quantitative and qualitative analysis on both synthetic and real fluoroscopic sequences demonstrate that the proposed filter allows a great dose reduction. PMID:26812705

  4. Spatio-temporal distribution of human lifespan in China

    PubMed Central

    Wang, Shaobin; Luo, Kunli; Liu, Yonglin

    2015-01-01

    Based on the data of latest three Chinese population censuses (1990–2010), four lifespan indicators were calculated: centenarians per one hundred thousand inhabitants (CH); longevity index (LI); the percentage of the population aged at least 80 years (ultra-octogenarian index, UOI) and life expectancy at birth (LEB). The spatio-temporal distributions of data at Chinese county level show that high-longevity areas (high values of CH and LI) and low-longevity areas (low CH and LI values) both exhibit clear non-uniformity of spatial distribution and relative immobility through time. Contrarily, the distribution of UOI and LEB shows a decline from the east to the west. The spatial autocorrelation analyses indicate less spatial dependency and several discontinuous clusters regions of high-CH and LI areas. The factors of temperature, topography and wet/dry climate lack of significant influence on CH and LI. It can be inferred that, in addition to genetic factor and living custom, some unique and long-term environmental effects may be related with high or low values of CH and LI. PMID:26346713

  5. Response-mode decomposition of spatio-temporal haemodynamics.

    PubMed

    Pang, J C; Robinson, P A; Aquino, K M

    2016-05-01

    The blood oxygen-level dependent (BOLD) response to a neural stimulus is analysed using the transfer function derived from a physiologically based poroelastic model of cortical tissue. The transfer function is decomposed into components that correspond to distinct poles, each related to a response mode with a natural frequency and dispersion relation; together these yield the total BOLD response. The properties of the decomposed components provide a deeper understanding of the nature of the BOLD response, via the components' frequency dependences, spatial and temporal power spectra, and resonances. The transfer function components are then used to separate the BOLD response to a localized impulse stimulus, termed the Green function or spatio-temporal haemodynamic response function, into component responses that are explicitly related to underlying physiological quantities. The analytical results also provide a quantitative tool to calculate the linear BOLD response to an arbitrary neural drive, which is faster to implement than direct Fourier transform methods. The results of this study can be used to interpret functional magnetic resonance imaging data in new ways based on physiology, to enhance deconvolution methods and to design experimental protocols that can selectively enhance or suppress particular responses, to probe specific physiological phenomena. PMID:27170653

  6. Workload induced spatio-temporal distortions and safety of flight

    SciTech Connect

    Barrett, C.L.; Weisgerber, S.A.; Naval Weapons Center, China Lake, CA )

    1989-01-01

    A theoretical analysis of the relationship between cognitive complexity and the perception of time and distance is presented and experimentally verified. Complex tasks produce high rates of mental representation which affect the subjective sense of duration and, through the subjective time scale, the percept of distance derived from dynamic visual cues (i.e., visual cues requiring rate integration). The analysis of the interrelationship of subjective time and subjective distance yields the prediction that, as a function of cognitive complexity, distance estimates derived from dynamic visual cues will be longer than the actual distance whereas estimates based on perceived temporal duration will be shorter than the actual distance. This prediction was confirmed in an experiment in which subjects (both pilots and non-pilots) estimated distances using either temporal cues or dynamic visual cues. The distance estimation task was also combined with secondary loading tasks in order to vary the overall task complexity. The results indicated that distance estimates based on temporal cues were underestimated while estimates based on visual cues were overestimated. This spatio-temporal distortion effect increased with increases in overall task complexity. 30 refs., 6 figs., 1 tab.

  7. Spatio-temporal self-organization in mudstones.

    SciTech Connect

    Dewers, Thomas A.

    2010-12-01

    Shales and other mudstones are the most abundant rock types in sedimentary basins, yet have received comparatively little attention. Common as hydrocarbon seals, these are increasingly being targeted as unconventional gas reservoirs, caprocks for CO2 sequestration, and storage repositories for waste. The small pore and grain size, large specific surface areas, and clay mineral structures lend themselves to rapid reaction rates, high capillary pressures, and semi-permeable membrane behavior accompanying changes in stress, pressure, temperature and chemical conditions. Under far from equilibrium conditions, mudrocks display a variety of spatio-temporal self-organized phenomena arising from nonlinear thermo-mechano-chemo-hydro coupling. Beginning with a detailed examination of nano-scale pore network structures in mudstones, we discuss the dynamics behind such self-organized phenomena as pressure solitons in unconsolidated muds, chemically-induced flow self focusing and permeability transients, localized compaction, time dependent well-bore failure, and oscillatory osmotic fluxes as they occur in clay-bearing sediments. Examples are draw from experiments, numerical simulation, and the field. These phenomena bear on the ability of these rocks to serve as containment barriers.

  8. Research of spatio-temporal analysis of agricultural pest

    NASA Astrophysics Data System (ADS)

    Wang, Changwei; Li, Deren; Hu, Yueming; Wu, Xiaofang; Qi, Yu

    2009-10-01

    The increase of agricultural pest disasters in recent years has become one of major problems in agriculture harvest; how to predict and control the disasters of agricultural pest has thus attracted great research interest. Although a series of works have been done and some achievements have been attained, the knowledge in this area remains limited. The migration of agricultural pest is not only related to the time variation, but also the space; consequently, the population of agricultural pest has complex spatio-temporal characteristics. The space factor and the temporal factor must be considered at the same time in the research of dynamics changes of the pest population. Using plant hoppers as an object of study, this study employed the biological analogy deviation model to study the distribution of pest population in different periods of time in Guangdong Province. It is demonstrated that the population distribution of plant hoppers is not only related to the space location, but also has a certain direction. The result reported here offers help to the monitor, prevention and control of plant hoppers in Guangdong Provinces.

  9. Spatio-Temporal Matching for Human Pose Estimation in Video.

    PubMed

    Zhou, Feng; Torre, Fernando De la

    2016-08-01

    Detection and tracking humans in videos have been long-standing problems in computer vision. Most successful approaches (e.g., deformable parts models) heavily rely on discriminative models to build appearance detectors for body joints and generative models to constrain possible body configurations (e.g., trees). While these 2D models have been successfully applied to images (and with less success to videos), a major challenge is to generalize these models to cope with camera views. In order to achieve view-invariance, these 2D models typically require a large amount of training data across views that is difficult to gather and time-consuming to label. Unlike existing 2D models, this paper formulates the problem of human detection in videos as spatio-temporal matching (STM) between a 3D motion capture model and trajectories in videos. Our algorithm estimates the camera view and selects a subset of tracked trajectories that matches the motion of the 3D model. The STM is efficiently solved with linear programming, and it is robust to tracking mismatches, occlusions and outliers. To the best of our knowledge this is the first paper that solves the correspondence between video and 3D motion capture data for human pose detection. Experiments on the CMU motion capture, Human3.6M, Berkeley MHAD and CMU MAD databases illustrate the benefits of our method over state-of-the-art approaches. PMID:26863647

  10. Spatio-temporal patterns in inclined layer convection

    NASA Astrophysics Data System (ADS)

    Subramanian, Priya; Brausch, Oliver; Daniels, Karen E.; Bodenschatz, Eberhard; Schneider, Tobias M.; Pesch, Werner

    2016-05-01

    This paper reports on a theoretical analysis of the rich variety of spatio-temporal patterns observed recently in inclined layer convection at medium Prandtl number when varying the inclination angle $\\gamma$ and the Rayleigh number $R$. The present numerical investigation of the inclined layer convection system is based on the standard Oberbeck-Boussinesq equations. The patterns are shown to originate from a complicated competition of buoyancy-driven and shear-flow driven pattern forming mechanisms. The former are expressed as \\rm{longitudinal} convection rolls with their axes oriented parallel to the incline, the latter as perpendicular \\rm{transverse} rolls. Along with conventional methods to study roll patterns and their stability, we employ direct numerical simulations in large spatial domains, comparable with the experimental ones. As a result, we determine the phase diagram of the characteristic complex 3D convection patterns above onset of convection in the $\\gamma-R$ plane, and find that it compares very well with the experiments. In particular we demonstrate that interactions of specific Fourier modes, characterized by a resonant interaction of their wavevectors in the layer plane, are key to understanding the pattern morphologies.

  11. Efficient Segmentation of Spatio-Temporal Data from Simulations

    SciTech Connect

    Fodor, I K; Kamath, C

    2003-01-15

    Detecting and tracking objects in spatio-temporal datasets is an active research area with applications in many domains. A common approach is to segment the 2D frames in order to separate the objects of interest from the background, then estimate the motion of the objects and track them over time. Most existing algorithms assume that the objects to be tracked are rigid. In many scientific simulations, however, the objects of interest evolve over time and thus pose additional challenges for the segmentation and tracking tasks. We investigate efficient segmentation methods in the context of scientific simulation data. Instead of segmenting each frame separately, we propose an incremental approach which incorporates the segmentation result from the previous time frame when segmenting the data at the current time frame. We start with the simple K-means method, then we study more complicated segmentation techniques based on Markov random fields. We compare the incremental methods to the corresponding sequential ones both in terms of the quality of the results, as well as computational complexity.

  12. Spatio-Temporal Measurements of Short Wind Water Waves

    NASA Astrophysics Data System (ADS)

    Rocholz, Roland; Jähne, Bernd

    2010-05-01

    Spatio-temporal measurements of wind-driven short-gravity capillary waves are reported for a wide range of experimental conditions, including wind, rain and surface slicks. The experiments were conducted in the Hamburg linear wind/wave flume in cooperation with the Institute of Oceanography at the University of Hamburg, Germany. Both components of the slope field were measured optically at a fetch of 14.4 m using a color imaging slope gauge (CISG) with a footprint of 223 x 104 mm and a resolution of 0.7 mm. The instrument was improved versus earlier versions (Jähne and Riemer (1990), Klinke (1992)) to achieve a sampling rate of 312.5 Hz, which now allows for the computation of 3D wavenumber-frequency spectra (see Rocholz (2008)). This made it possible to distinguish waves traveling in and against wind direction, which proved useful to distinguish wind waves from ring waves caused by rain drop impacts. Using a new calibration method it was possible to correct for the intrinsic nonlinearities of the instrument in the slope range up to ±1. In addition, the Modulation Transfer Function (MTF) was measured and employed for the restoration of the spectral amplitudes for wavenumbers in the range from 60 to 2300 rad/m. The spectra for pure wind conditions are generally consistent with previous measurements. But, the shape of the saturation spectra in the vicinity of k~1000 rad/m (i.e. pure capillary waves) stands in contradiction to former investigations where a sharp spectral cutoff (k^(-2) or k^(-3)) is commonly reported (e.g. Jähne and Riemer (1990)). This cutoff is reproduced by almost all semi-empirical models of the energy flux in the capillary range (e.g. Kudryavtsev et al. (1999), Apel (1994)). However, the new MTF corrected spectra show only a gentle decrease (between k^(-0.5) and k^(-1)) for k > 1000 rad/m. Therefore the question for the relative importance of different dissipation mechanisms might need a new assessment. References: J. R. Apel. An improved

  13. Spatio-Temporal Dynamics of Hypoxia during Radiotherapy.

    PubMed

    Kempf, Harald; Bleicher, Marcus; Meyer-Hermann, Michael

    2015-01-01

    Tumour hypoxia plays a pivotal role in cancer therapy for most therapeutic approaches from radiotherapy to immunotherapy. The detailed and accurate knowledge of the oxygen distribution in a tumour is necessary in order to determine the right treatment strategy. Still, due to the limited spatial and temporal resolution of imaging methods as well as lacking fundamental understanding of internal oxygenation dynamics in tumours, the precise oxygen distribution map is rarely available for treatment planing. We employ an agent-based in silico tumour spheroid model in order to study the complex, localized and fast oxygen dynamics in tumour micro-regions which are induced by radiotherapy. A lattice-free, 3D, agent-based approach for cell representation is coupled with a high-resolution diffusion solver that includes a tissue density-dependent diffusion coefficient. This allows us to assess the space- and time-resolved reoxygenation response of a small subvolume of tumour tissue in response to radiotherapy. In response to irradiation the tumour nodule exhibits characteristic reoxygenation and re-depletion dynamics which we resolve with high spatio-temporal resolution. The reoxygenation follows specific timings, which should be respected in treatment in order to maximise the use of the oxygen enhancement effects. Oxygen dynamics within the tumour create windows of opportunity for the use of adjuvant chemotherapeutica and hypoxia-activated drugs. Overall, we show that by using modelling it is possible to follow the oxygenation dynamics beyond common resolution limits and predict beneficial strategies for therapy and in vitro verification. Models of cell cycle and oxygen dynamics in tumours should in the future be combined with imaging techniques, to allow for a systematic experimental study of possible improved schedules and to ultimately extend the reach of oxygenation monitoring available in clinical treatment. PMID:26273841

  14. Spatio-temporal microseismicity clustering in the Cretan region

    NASA Astrophysics Data System (ADS)

    Becker, Dirk; Meier, Thomas; Rische, Martina; Bohnhoff, Marco; Harjes, Hans-Peter

    2006-09-01

    Spatio-temporal clustering of microseismicity in the central forearc of the Hellenic Subduction Zone in the area of Crete is investigated. Data for this study were gathered by temporary short period networks which were installed on the islands of Crete and Gavdos between 1996 and 2004. The similarity of waveforms is quantified systematically to identify clusters of microseismicity. Waveform similarities are calculated using an adaptive time window containing both the P- and S-wave onsets. The cluster detection is performed by applying a single linkage approach. Clusters are found in the interplate seismicity as well as in the intraplate seismicity of the continental crust in the region of the transtensional Ptolemy structure. The majority of the clusters are off the southern coast of Crete, in a region of elevated intraplate microseismic activity within the Aegean plate. Clusters in the Gavdos region are located at depths compatible with the plate interface while cluster activity in the region of the Ptolemy trench is distributed along a nearly vertical structure throughout the crust extending down to the plate interface. Most clusters show swarm-like behaviour with seismic activity confined to only a few hours or days, without a dominant earthquake and with a power law distribution of the interevent times. For the largest cluster, precise relocations of the events using travel time differences of P- and S-waves derived from waveform cross correlations reveal migration of the hypocenters. This cluster is located in the region of the Ptolemy trench and migration occurs along the strike of the trench at ˜ 500 m/day. Relocated hypocenters as well as subtle differences in the waveforms suggest an offset between the hypocenters and thus the activation of distinct patches on the rupture surface. The observed microseismicity patterns may be related to fluids being transported along the plate interface and escaping towards the surface in zones of crustal weakness (Ptolemy

  15. Spatio-Temporal Dynamics of Hypoxia during Radiotherapy

    PubMed Central

    Kempf, Harald; Bleicher, Marcus; Meyer-Hermann, Michael

    2015-01-01

    Tumour hypoxia plays a pivotal role in cancer therapy for most therapeutic approaches from radiotherapy to immunotherapy. The detailed and accurate knowledge of the oxygen distribution in a tumour is necessary in order to determine the right treatment strategy. Still, due to the limited spatial and temporal resolution of imaging methods as well as lacking fundamental understanding of internal oxygenation dynamics in tumours, the precise oxygen distribution map is rarely available for treatment planing. We employ an agent-based in silico tumour spheroid model in order to study the complex, localized and fast oxygen dynamics in tumour micro-regions which are induced by radiotherapy. A lattice-free, 3D, agent-based approach for cell representation is coupled with a high-resolution diffusion solver that includes a tissue density-dependent diffusion coefficient. This allows us to assess the space- and time-resolved reoxygenation response of a small subvolume of tumour tissue in response to radiotherapy. In response to irradiation the tumour nodule exhibits characteristic reoxygenation and re-depletion dynamics which we resolve with high spatio-temporal resolution. The reoxygenation follows specific timings, which should be respected in treatment in order to maximise the use of the oxygen enhancement effects. Oxygen dynamics within the tumour create windows of opportunity for the use of adjuvant chemotherapeutica and hypoxia-activated drugs. Overall, we show that by using modelling it is possible to follow the oxygenation dynamics beyond common resolution limits and predict beneficial strategies for therapy and in vitro verification. Models of cell cycle and oxygen dynamics in tumours should in the future be combined with imaging techniques, to allow for a systematic experimental study of possible improved schedules and to ultimately extend the reach of oxygenation monitoring available in clinical treatment. PMID:26273841

  16. Spatio-temporal coupling of EEG signals in epilepsy

    NASA Astrophysics Data System (ADS)

    Senger, Vanessa; Müller, Jens; Tetzlaff, Ronald

    2011-05-01

    Approximately 1% of the world's population suffer from epileptic seizures throughout their lives that mostly come without sign or warning. Thus, epilepsy is the most common chronical disorder of the neurological system. In the past decades, the problem of detecting a pre-seizure state in epilepsy using EEG signals has been addressed in many contributions by various authors over the past two decades. Up to now, the goal of identifying an impending epileptic seizure with sufficient specificity and reliability has not yet been achieved. Cellular Nonlinear Networks (CNN) are characterized by local couplings of dynamical systems of comparably low complexity. Thus, they are well suited for an implementation as highly parallel analogue processors. Programmable sensor-processor realizations of CNN combine high computational power comparable to tera ops of digital processors with low power consumption. An algorithm allowing an automated and reliable detection of epileptic seizure precursors would be a"huge step" towards the vision of an implantable seizure warning device that could provide information to patients and for a time/event specific treatment directly in the brain. Recent contributions have shown that modeling of brain electrical activity by solutions of Reaction-Diffusion-CNN as well as the application of a CNN predictor taking into account values of neighboring electrodes may contribute to the realization of a seizure warning device. In this paper, a CNN based predictor corresponding to a spatio-temporal filter is applied to multi channel EEG data in order to identify mutual couplings for different channels which lead to a enhanced prediction quality. Long term EEG recordings of different patients are considered. Results calculated for these recordings with inter-ictal phases as well as phases with seizures will be discussed in detail.

  17. Ultrashort relativistic electron bunches and spatio-temporal radiation biology

    NASA Astrophysics Data System (ADS)

    Gauduel, Y. A.; Faure, J.; Malka, V.

    2008-08-01

    The intensive developments of terawatt Ti:Sa lasers permit to extend laser-plasma interactions into the relativistic regime, providing very-short electron or proton bunches. Experimental researches developed at the interface of laser physics and radiation biology, using the combination of sub-picosecond electron beams in the energy range 2-15 MeV with femtosecond near-IR optical pulses might conjecture the real-time investigation of penetrating radiation effects. A perfect synchronization between the particle beam (pump) and optical beam at 820 nm (probe) allows subpicosecond time resolution. This emerging domain involves high-energy radiation femtochemistry (HERF) for which the early spatial energy deposition is decisive for the prediction of cellular and tissular radiation damages. With vacuum-focused intensities of 2.7 x 1019 W cm-2 and a high energy electron total charge of 2.5 nC, radiation events have been investigated in the temporal range 10-13 - 10-10s. The early radiation effects of secondary electron on biomolecular sensors may be investigated inside sub-micrometric ionisation, considering the radial direction of Gaussian electron bunches. It is shown that short range electron-biosensor interactions lower than 10 A take place in nascent track structures triggered by penetrating radiation bunches. The very high dose delivery 1013 Gy s-1 performed with laser plasma accelerator may challenge our understanding of nanodosimetry on the time scale of molecular target motions. High-quality ultrashort penetrating radiation beams open promising opportunities for the development of spatio-temporal radiation biology, a crucial domain of cancer therapy, and would favor novating applications in nanomedicine such as highly-selective shortrange pro-drug activation.

  18. Spatio-Temporal Patterns of Surface Irradiance in the Himalaya

    NASA Astrophysics Data System (ADS)

    Dobreva, I. D.; Bishop, M. P.

    2014-12-01

    Climate-glacier dynamics in the Himalaya are complex. Research indicates extreme local variability in glacier fluctuations and the presence of regional trends. The glaciers in the Karakoram Himalaya depart from world trends of glacier recession, as many are advancing or surging. Nevertheless, glacier sensitivity to climate change has yet to be quantitatively assessed given numerous controlling factors. We attempt to address part of the problem by evaluating the role of topography in explaining variations in surface irradiance. Specifically, we developed a spectral-based topographic solar radiation model that accounts for multi-scale topographic effects. We evaluate surface irradiance simulations over a multitude of glaciers across the Karakoram and Nepalese Himalaya and examine spatio-temporal patterns to determine which alpine glaciers are more susceptible to radiation forcing. Simulation results reveal that many Nepalese glaciers characterized by rapid downwasting, retreat and expanding proglacial lakes, exhibit relatively high-magnitude daily irradiance patterns spatially focused over the terminus region, while other glacier surface areas received less short-wave irradiance. These results were found to be associated with basin-scale relief conditions and topographic shielding. Altitudinal variation in glacier surface irradiance was found to increase during the later portion of the ablation season, as changes in solar geometry produce more cast shadows that protect glaciers given extreme relief. Topographic effects on surface irradiance vary significantly from glacier to glacier, demonstrating the important role of glacier and mountain geodynamics on glacier sensitivity to climate change. Spatial and altitudinal patterns, coupled with information regarding supraglacial debris distribution, depth and ice-flow velocities, may potentially explain glacier sensitivity to climate change and the local variability of glacier fluctuations in the Himalaya.

  19. Spatio-temporal representativeness of aerosol remote sensing observations

    NASA Astrophysics Data System (ADS)

    Schutgens, Nick; Gryspeerdt, Edward; Tsyro, Svetlana; Goto, Daisuke; Watson-Parris, Duncan; Weigum, Natalie; Schulz, Michael; Stier, Philip

    2016-04-01

    One characteristic of remote sensing observations is the strong intermittency with which they observe the same scene. Due to unfavourable conditions (due to e.g. low visible light, cloudiness or high surface albedo), sampling constraints (due to e.g. polar orbits) or instrument malfunction or maintenance, gaps in the observing record of hours to months exist. At the same time, satellite L3 products often are spatial aggregates over considerable distances (e.g. 1 by 1 degree). We study the impact of spatio-temporal sampling of observations on their representativeness: i.e. how well can satellite products represent the large scale (~ 100 by 100 km) aerosol field over periods of days, months, or years. This study was conducted by using diverse global and regional aerosol models as a truth and sub-sample them according to actual observations. In this way, we have been able to study the representativeness of different observing systems like MODIS, CALIOP and AERONET. Monthly and yearly averages allow serious sampling errors, that may still be present in multi-year climatologies due to recurring observing patterns. Even daily averages are affected as diurnal cycles can often not be observed. We discuss the implications these representativeness errors have for e.g. model evaluation or the construction of climatologies. We also assess similar representativeness issues in ground site in-situ observations from e.g. EMEP or IMPROVE and show that satellite datasets have distinct advantages due to their better spatial coverage provided temporal sampling is dealt with properly (i.e. through collocation of datasets). Finally, we briefly introduce a software tool (the Community Intercomparison Suite or CIS) that is designed to improve representativeness of datasets in intercomparion studies through aggregation and collocation of data.

  20. SOLAP technology: Merging business intelligence with geospatial technology for interactive spatio-temporal exploration and analysis of data

    NASA Astrophysics Data System (ADS)

    Rivest, Sonia; Bédard, Yvan; Proulx, Marie-Josée; Nadeau, Martin; Hubert, Frederic; Pastor, Julien

    To support their analytical processes, today's organizations deploy data warehouses and client tools such as OLAP (On-Line Analytical Processing) to access, visualize, and analyze their integrated, aggregated and summarized data. Since a large part of these data have a spatial component, better client tools are required to take full advantage of the geometry of the spatial phenomena or objects being analyzed. With this regard, Spatial OLAP (SOLAP) technology offers promising possibilities. A SOLAP tool can be defined as "a type of software that allows rapid and easy navigation within spatial databases and that offers many levels of information granularity, many themes, many epochs and many display modes synchronized or not: maps, tables and diagrams" [Bédard, Y., Proulx, M.J., Rivest, S., 2005. Enrichissement du OLAP pour l'analyse géographique: exemples de réalisation et différentes possibilités technologiques. In: Bentayeb, F., Boussaid, O., Darmont, J., Rabaseda, S. (Eds.), Entrepôts de Données et Analyse en ligne, RNTI B_1. Paris: Cépaduès, pp. 1-20]. SOLAP tools offer a new user interface and are meant to be client applications sitting on top of multi-scale spatial data warehouses or datacubes. As they are based on the multidimensional paradigm, they facilitate the interactive spatio-temporal exploration of data. The purpose of this paper is to discuss how SOLAP concepts support spatio-temporal exploration of data and then to present the geovisualization, interactivity, and animation features of the SOLAP software developed by our research group. This paper first reviews the general concepts behind OLAP and SOLAP systems. This is followed by a discussion of how these SOLAP concepts support spatio-temporal exploration of data. In the subsequent section, SOLAP software is introduced along with features that enable geovisualization, interactivity and animation.

  1. Mapping the spatio-temporal structure of motor cortical LFP and spiking activities during reach-to-grasp movements

    PubMed Central

    Riehle, Alexa; Wirtssohn, Sarah; Grün, Sonja; Brochier, Thomas

    2013-01-01

    Grasping an object involves shaping the hand and fingers in relation to the object’s physical properties. Following object contact, it also requires a fine adjustment of grasp forces for secure manipulation. Earlier studies suggest that the control of hand shaping and grasp force involve partially segregated motor cortical networks. However, it is still unclear how information originating from these networks is processed and integrated. We addressed this issue by analyzing massively parallel signals from population measures (local field potentials, LFPs) and single neuron spiking activities recorded simultaneously during a delayed reach-to-grasp task, by using a 100-electrode array chronically implanted in monkey motor cortex. Motor cortical LFPs exhibit a large multi-component movement-related potential (MRP) around movement onset. Here, we show that the peak amplitude of each MRP component and its latency with respect to movement onset vary along the cortical surface covered by the array. Using a comparative mapping approach, we suggest that the spatio-temporal structure of the MRP reflects the complex physical properties of the reach-to-grasp movement. In addition, we explored how the spatio-temporal structure of the MRP relates to two other measures of neuronal activity: the temporal profile of single neuron spiking activity at each electrode site and the somatosensory receptive field properties of single neuron activities. We observe that the spatial representations of LFP and spiking activities overlap extensively and relate to the spatial distribution of proximal and distal representations of the upper limb. Altogether, these data show that, in motor cortex, a precise spatio-temporal pattern of activation is involved for the control of reach-to-grasp movements and provide some new insight about the functional organization of motor cortex during reaching and object manipulation. PMID:23543888

  2. A generic regional spatio-temporal co-occurrence pattern mining model: a case study for air pollution

    NASA Astrophysics Data System (ADS)

    Akbari, Mohammad; Samadzadegan, Farhad; Weibel, Robert

    2015-07-01

    Spatio-temporal co-occurrence patterns represent subsets of object types which are located together in both space and time. Existing algorithms for co-occurrence pattern mining cannot handle complex applications such as air pollution in several ways. First, the existing models assume that spatial relationships between objects are explicitly represented in the input data, while the new method allows extracting implicitly contained spatial relationships algorithmically. Second, instead of extracting co-occurrence patterns of only point data, the proposed method deals with different feature types that is with point, line and polygon data. Thus, it becomes relevant for a wider range of real applications. Third, it also allows mining a spatio-temporal co-occurrence pattern simultaneously in space and time so that it illustrates the evolution of patterns over space and time. Furthermore, the proposed algorithm uses a Voronoi tessellation to improve efficiency. To evaluate the proposed method, it was applied on a real case study for air pollution where the objective is to find correspondences of air pollution with other parameters which affect this phenomenon. The results of evaluation confirm not only the capability of this method for co-occurrence pattern mining of complex applications, but also it exhibits an efficient computational performance.

  3. Characterizing the spatio-temporal and energy-dependent response of riometer absorption to particle precipitation

    NASA Astrophysics Data System (ADS)

    Kellerman, Adam; Makarevich, Roman; Spanswick, Emma; Donovan, Eric; Shprits, Yuri

    2016-07-01

    Energetic electrons in the 10's of keV range precipitate to the upper D- and lower E-region ionosphere, and are responsible for enhanced ionization. The same particles are important in the inner magnetosphere, as they provide a source of energy for waves, and thus relate to relativistic electron enhancements in Earth's radiation belts.In situ observations of plasma populations and waves are usually limited to a single point, which complicates temporal and spatial analysis. Also, the lifespan of satellite missions is often limited to several years which does not allow one to infer long-term climatology of particle precipitation, important for affecting ionospheric conditions at high latitudes. Multi-point remote sensing of the ionospheric plasma conditions can provide a global view of both ionospheric and magnetospheric conditions, and the coupling between magnetospheric and ionospheric phenomena can be examined on time-scales that allow comprehensive statistical analysis. In this study we utilize multi-point riometer measurements in conjunction with in situ satellite data, and physics-based modeling to investigate the spatio-temporal and energy-dependent response of riometer absorption. Quantifying this relationship may be a key to future advancements in our understanding of the complex D-region ionosphere, and may lead to enhanced specification of auroral precipitation both during individual events and over climatological time-scales.

  4. Spatio-temporal resolution of primary processes of photosynthesis.

    PubMed

    Junge, Wolfgang

    2015-01-01

    Technical progress in laser-sources and detectors has allowed the temporal and spatial resolution of chemical reactions down to femtoseconds and Å-units. In photon-excitable systems the key to chemical kinetics, trajectories across the vibrational saddle landscape, are experimentally accessible. Simple and thus well-defined chemical compounds are preferred objects for calibrating new methodologies and carving out paradigms of chemical dynamics, as shown in several contributions to this Faraday Discussion. Aerobic life on earth is powered by solar energy, which is captured by microorganisms and plants. Oxygenic photosynthesis relies on a three billion year old molecular machinery which is as well defined as simpler chemical constructs. It has been analysed to a very high precision. The transfer of excitation between pigments in antennae proteins, of electrons between redox-cofactors in reaction centres, and the oxidation of water by a Mn4Ca-cluster are solid state reactions. ATP, the general energy currency of the cell, is synthesized by a most agile, rotary molecular machine. While the efficiency of photosynthesis competes well with photovoltaics at the time scale of nanoseconds, it is lower by an order of magnitude for crops and again lower for bio-fuels. The enormous energy demand of mankind calls for engineered (bio-mimetic or bio-inspired) solar-electric and solar-fuel devices. PMID:25824647

  5. Automated Spatio-Temporal Analysis of Remotely Sensed Imagery for Water Resources Management

    NASA Astrophysics Data System (ADS)

    Bahr, Thomas

    2016-04-01

    Since 2012, the state of California faces an extreme drought, which impacts water supply in many ways. Advanced remote sensing is an important technology to better assess water resources, monitor drought conditions and water supplies, plan for drought response and mitigation, and measure drought impacts. In the present case study latest time series analysis capabilities are used to examine surface water in reservoirs located along the western flank of the Sierra Nevada region of California. This case study was performed using the COTS software package ENVI 5.3. Integration of custom processes and automation is supported by IDL (Interactive Data Language). Thus, ENVI analytics is running via the object-oriented and IDL-based ENVITask API. A time series from Landsat images (L-5 TM, L-7 ETM+, L-8 OLI) of the AOI was obtained for 1999 to 2015 (October acquisitions). Downloaded from the USGS EarthExplorer web site, they already were georeferenced to a UTM Zone 10N (WGS-84) coordinate system. ENVITasks were used to pre-process the Landsat images as follows: • Triangulation based gap-filling for the SLC-off Landsat-7 ETM+ images. • Spatial subsetting to the same geographic extent. • Radiometric correction to top-of-atmosphere (TOA) reflectance. • Atmospheric correction using QUAC®, which determines atmospheric correction parameters directly from the observed pixel spectra in a scene, without ancillary information. Spatio-temporal analysis was executed with the following tasks: • Creation of Modified Normalized Difference Water Index images (MNDWI, Xu 2006) to enhance open water features while suppressing noise from built-up land, vegetation, and soil. • Threshold based classification of the water index images to extract the water features. • Classification aggregation as a post-classification cleanup process. • Export of the respective water classes to vector layers for further evaluation in a GIS. • Animation of the classification series and export to

  6. Brazilian Amazonia Deforestation Detection Using Spatio-Temporal Scan Statistics

    NASA Astrophysics Data System (ADS)

    Vieira, C. A. O.; Santos, N. T.; Carneiro, A. P. S.; Balieiro, A. A. S.

    2012-07-01

    The spatio-temporal models, developed for analyses of diseases, can also be used for others fields of study, including concerns about forest and deforestation. The aim of this paper is to quantitatively check priority areas in order to combat deforestation on the Amazon forest, using the space-time scan statistic. The study area location is at the south of the Amazonas State and cover around 297.183 kilometre squares, including the municipality of Boca do Acre, Labrea, Canutama, Humaita, Manicore, Novo Aripuana e Apui County on the north region of Brazil. This area has showed a significant change for land cover, which has increased the number of deforestation's alerts. Therefore this situation becomes a concern and gets more investigation, trying to stop factors that increase the number of cases in the area. The methodology includes the location and year that deforestation's alert occurred. These deforestation's alerts are mapped by the DETER (Detection System of Deforestation in Real Time in Amazonia), which is carry out by the Brazilian Space Agency (INPE). The software SatScanTM v7.0 was used in order to define space-time permutation scan statistic for detection of deforestation cases. The outcome of this experiment shows an efficient model to detect space-time clusters of deforestation's alerts. The model was efficient to detect the location, the size, the order and characteristics about activities at the end of the experiments. Two clusters were considered actives and kept actives up to the end of the study. These clusters are located in Canutama and Lábrea County. This quantitative spatial modelling of deforestation warnings allowed: firstly, identifying actives clustering of deforestation, in which the environment government official are able to concentrate their actions; secondly, identifying historic clustering of deforestation, in which the environment government official are able to monitoring in order to avoid them to became actives again; and finally

  7. Hydrograph transposition to ungauged basin accounting for spatio-temporal rainfall variability

    NASA Astrophysics Data System (ADS)

    de Lavenne, Alban; Cudennec, Christophe

    2013-04-01

    Lack of measurements is one of the main issues in hydrological modelling. However, neighbours and nested gauged catchment are precious sources of information to understand the catchment behaviours within one region. Extracting the maximum of information from those points of measurements, that could be then transposed to ungauged catchment, is still a great challenge. We propose a methodology to transpose hydrological information from gauged catchments to ungauged ones, in order to simulate streamflow hydrographs. It uses geomorphology-based hydrological modelling, which is particularly well adapted to ungauged basins thanks to its robustness, generality and flexibility. We develop a geomorphology-based model on the gauged catchment which has been built in order to capture the main behaviour of the basin. Its transfer function considers the different dynamics of the catchment through the combination of velocities and width functions. Moreover, the explicit structure of the model enables to easily create a map of isochrone areas describing the time to the outlet. Therefore, spatially distributed rainfall can then be split into those isochrone areas, permitting the transfer function to deal with spatio-temporal variability of rainfall. Once the model calibrated, using a particle swarm optimisation algorithm, its transfer function is inversed to assess the net rainfall time series. In this way, we obtained a standardized variable which is used to estimate discharge in ungauged basin. Therefore, net rainfall time series is transposed and convoluted on the ungauged catchment using its own transfer function. Spatio-temporal rainfall variability between basins is considered through a correction of this net rainfall time series. This correction is based on differences between mean gross rainfall observation among those two catchments. This methodology is applied on pairs of basins among 6 gauged basins (from 5km² to 316km²) located in Brittany, France. For the benefit of

  8. Fast prototyping of wavelet spatio-temporal RS fusion with Raingauge time series with GDAL and Python-DWT

    NASA Astrophysics Data System (ADS)

    Chemin, Yann

    2013-04-01

    Availability of rainfall time-series is limited in many parts of the World, and the continuity of such records is variable. This research endeavors to extend actual daily rainfall observations to ungauged areas using vegetation response as witnessed by remote sensing data and taking into account rainfall event histograms as well as cumulative total daily rainfall, over a period of 11 years. Open Source code development permitted to gain on several aspects. The first one pertains to space, Python and its numerical part (NumPy) are scientifically concise, as a bonus to be expressive. The second is the availability of the Discrete Wavelet Transform (DWT) in Python already, which permitted to reduce the Wavelet Transform to a small set of instructions, clarifying and simplifying the understanding of the code once it reaches the Public Domain. GDAL interface permitted to load satellite imagery and write fused rainfall time-series in spatio-temporal dimensions. Other scientific tool from Numerical Python were also used in the process of developing the algorithm (scipy.stats.stats and scipy.interpolate.griddata). Due to the large amount of days (4019) and the kilometer based resolution of the vegetation RS data, it takes about a week for the code to resolve the fusion problem. An attempt at using an multicore interpolation implementation in Python (hpgl) which unfortunately was not an active project anymore, though certainly deserving interest. Results show that rainfall events histograms can be reconstructed, and that total cumulative rainfall is estimated with 85% accuracy, using a surrounding network of rain gauges at 30-50 km of distance from the point of study. This research can strengthen various types of research and applications such as ungauged basins research, regional climate modeling, agricultural insurance systems, etc. Further development aims at porting the code to distributed computing.

  9. A spatio-temporal detective quantum efficiency and its application to fluoroscopic systems

    SciTech Connect

    Friedman, S. N.; Cunningham, I. A.

    2010-11-15

    Purpose: Fluoroscopic x-ray imaging systems are used extensively in spatio-temporal detection tasks and require a spatio-temporal description of system performance. No accepted metric exists that describes spatio-temporal fluoroscopic performance. The detective quantum efficiency (DQE) is a metric widely used in radiography to quantify system performance and as a surrogate measure of patient ''dose efficiency.'' It has been applied previously to fluoroscopic systems with the introduction of a temporal correction factor. However, the use of a temporally-corrected DQE does not provide system temporal information and it is only valid under specific conditions, many of which are not likely to be satisfied by suboptimal systems. The authors propose a spatio-temporal DQE that describes performance in both space and time and is applicable to all spatio-temporal quantum-based imaging systems. Methods: The authors define a spatio-temporal DQE (two spatial-frequency axes and one temporal-frequency axis) in terms of a small-signal spatio-temporal modulation transfer function (MTF) and spatio-temporal noise power spectrum (NPS). Measurements were made on an x-ray image intensifier-based bench-top system using continuous fluoroscopy with an RQA-5 beam at 3.9 {mu}R/frame and hardened 50 kVp beam (0.8 mm Cu filtration added) at 1.9 {mu}R/frame. Results: A zero-frequency DQE value of 0.64 was measured under both conditions. Nonideal performance was noted at both larger spatial and temporal frequencies; DQE values decreased by {approx}50% at the cutoff temporal frequency of 15 Hz. Conclusions: The spatio-temporal DQE enables measurements of decreased temporal system performance at larger temporal frequencies analogous to previous measurements of decreased (spatial) performance. This marks the first time that system performance and dose efficiency in both space and time have been measured on a fluoroscopic system using DQE and is the first step toward the generalized use of DQE on

  10. An Adaptive Organization Method of Geovideo Data for Spatio-Temporal Association Analysis

    NASA Astrophysics Data System (ADS)

    Wu, C.; Zhu, Q.; Zhang, Y. T.; Du, Z. Q.; Zhou, Y.; Xie, X.; He, F.

    2015-07-01

    Public security incidents have been increasingly challenging to address with their new features, including large-scale mobility, multi-stage dynamic evolution, spatio-temporal concurrency and uncertainty in the complex urban environment, which require spatio-temporal association analysis among multiple regional video data for global cognition. However, the existing video data organizational methods that view video as a property of the spatial object or position in space dissever the spatio-temporal relationship of scattered video shots captured from multiple video channels, limit the query functions on interactive retrieval between a camera and its video clips and hinder the comprehensive management of event-related scattered video shots. GeoVideo, which maps video frames onto a geographic space, is a new approach to represent the geographic world, promote security monitoring in a spatial perspective and provide a highly feasible solution to this problem. This paper analyzes the large-scale personnel mobility in public safety events and proposes a multi-level, event-related organization method with massive GeoVideo data by spatio-temporal trajectory. This paper designs a unified object identify(ID) structure to implicitly store the spatio-temporal relationship of scattered video clips and support the distributed storage management of massive cases. Finally, the validity and feasibility of this method are demonstrated through suspect tracking experiments.

  11. Bayesian Spatio-Temporal Analysis and Geospatial Risk Factors of Human Monocytic Ehrlichiosis

    PubMed Central

    Raghavan, Ram K.; Neises, Daniel; Goodin, Douglas G.; Andresen, Daniel A.; Ganta, Roman R.

    2014-01-01

    Variations in spatio-temporal patterns of Human Monocytic Ehrlichiosis (HME) infection in the state of Kansas, USA were examined and the relationship between HME relative risk and various environmental, climatic and socio-economic variables were evaluated. HME data used in the study was reported to the Kansas Department of Health and Environment between years 2005–2012, and geospatial variables representing the physical environment [National Land cover/Land use, NASA Moderate Resolution Imaging Spectroradiometer (MODIS)], climate [NASA MODIS, Prediction of Worldwide Renewable Energy (POWER)], and socio-economic conditions (US Census Bureau) were derived from publicly available sources. Following univariate screening of candidate variables using logistic regressions, two Bayesian hierarchical models were fit; a partial spatio-temporal model with random effects and a spatio-temporal interaction term, and a second model that included additional covariate terms. The best fitting model revealed that spatio-temporal autocorrelation in Kansas increased steadily from 2005–2012, and identified poverty status, relative humidity, and an interactive factor, ‘diurnal temperature range x mixed forest area’ as significant county-level risk factors for HME. The identification of significant spatio-temporal pattern and new risk factors are important in the context of HME prevention, for future research in the areas of ecology and evolution of HME, and as well as climate change impacts on tick-borne diseases. PMID:24992684

  12. Bayesian spatio-temporal analysis and geospatial risk factors of human monocytic ehrlichiosis.

    PubMed

    Raghavan, Ram K; Neises, Daniel; Goodin, Douglas G; Andresen, Daniel A; Ganta, Roman R

    2014-01-01

    Variations in spatio-temporal patterns of Human Monocytic Ehrlichiosis (HME) infection in the state of Kansas, USA were examined and the relationship between HME relative risk and various environmental, climatic and socio-economic variables were evaluated. HME data used in the study was reported to the Kansas Department of Health and Environment between years 2005-2012, and geospatial variables representing the physical environment [National Land cover/Land use, NASA Moderate Resolution Imaging Spectroradiometer (MODIS)], climate [NASA MODIS, Prediction of Worldwide Renewable Energy (POWER)], and socio-economic conditions (US Census Bureau) were derived from publicly available sources. Following univariate screening of candidate variables using logistic regressions, two Bayesian hierarchical models were fit; a partial spatio-temporal model with random effects and a spatio-temporal interaction term, and a second model that included additional covariate terms. The best fitting model revealed that spatio-temporal autocorrelation in Kansas increased steadily from 2005-2012, and identified poverty status, relative humidity, and an interactive factor, 'diurnal temperature range x mixed forest area' as significant county-level risk factors for HME. The identification of significant spatio-temporal pattern and new risk factors are important in the context of HME prevention, for future research in the areas of ecology and evolution of HME, and as well as climate change impacts on tick-borne diseases. PMID:24992684

  13. A Spatio-Temporally Explicit Random Encounter Model for Large-Scale Population Surveys.

    PubMed

    Jousimo, Jussi; Ovaskainen, Otso

    2016-01-01

    Random encounter models can be used to estimate population abundance from indirect data collected by non-invasive sampling methods, such as track counts or camera-trap data. The classical Formozov-Malyshev-Pereleshin (FMP) estimator converts track counts into an estimate of mean population density, assuming that data on the daily movement distances of the animals are available. We utilize generalized linear models with spatio-temporal error structures to extend the FMP estimator into a flexible Bayesian modelling approach that estimates not only total population size, but also spatio-temporal variation in population density. We also introduce a weighting scheme to estimate density on habitats that are not covered by survey transects, assuming that movement data on a subset of individuals is available. We test the performance of spatio-temporal and temporal approaches by a simulation study mimicking the Finnish winter track count survey. The results illustrate how the spatio-temporal modelling approach is able to borrow information from observations made on neighboring locations and times when estimating population density, and that spatio-temporal and temporal smoothing models can provide improved estimates of total population size compared to the FMP method. PMID:27611683

  14. Transfer of spatio-temporal multifractal properties of rainfall to simulated surface runoff

    NASA Astrophysics Data System (ADS)

    Gires, Auguste; Giangola-Murzyn, Agathe; Richard, Julien; Abbes, Jean-Baptiste; Tchiguirinskaia, Ioulia; Schertzer, Daniel; Willinger, Bernard; Cardinal, Hervé; Thouvenot, Thomas

    2014-05-01

    In this paper we suggest to use scaling laws and more specifically Universal Multifractals (UM) to analyse in a spatio-temporal framework both the radar rainfall and the simulated surface runoff. Such tools have been extensively used to analyse and simulate geophysical fields extremely variable over wide range of spatio-temporal scales such as rainfall, but have not often if ever been applied to surface runoff. Such novel combined analysis helps to improve the understanding of the rainfall-runoff relationship. Two catchments of the chair "Hydrology for resilient cities" sponsored by Véolia, and of the European Interreg IV RainGain project are used. They are both located in the Paris area: a 144 ha flat urban area in the Seine-Saint-Denis County, and a 250 ha urban area with a significant portion of forest located on a steep hillside of the Bièvre River. A fully distributed urban hydrological model currently under development called Multi-Hydro is implemented to represent the catchments response. It consists in an interacting core between open source software packages, each of them representing a portion of the water cycle in urban environment. The fully distributed model is tested with pixels of size 5, 10 and 20 m. In a first step the model is validated for three rainfall events that occurred in 2010 and 2011, for which the Météo-France radar mosaic with a resolution of 1 km in space and 5 min in time is available. These events generated significant surface runoff and some local flooding. The sensitivity of the model to the rainfall resolution is briefly checked by stochastically generating an ensemble of realistic downscaled rainfall fields (obtained by continuing the underlying cascade process which is observed on the available range of scales) and inputting them into the model. The impact is significant on both the simulated sewer flow and surface runoff. Then rainfall fields are generated with the help of discrete multifractal cascades and inputted in the

  15. Spatio-Temporal Evolution and Scaling Properties of Human Settlements (Invited)

    NASA Astrophysics Data System (ADS)

    Small, C.; Milesi, C.; Elvidge, C.; Baugh, K.; Henebry, G. M.; Nghiem, S. V.

    2013-12-01

    Growth and evolution of cities and smaller settlements is usually studied in the context of population and other socioeconomic variables. While this is logical in the sense that settlements are groups of humans engaged in socioeconomic processes, our means of collecting information about spatio-temporal distributions of population and socioeconomic variables often lack the spatial and temporal resolution to represent the processes at scales which they are known to occur. Furthermore, metrics and definitions often vary with country and through time. However, remote sensing provides globally consistent, synoptic observations of several proxies for human settlement at spatial and temporal resolutions sufficient to represent the evolution of settlements over the past 40 years. We use several independent but complementary proxies for anthropogenic land cover to quantify spatio-temporal (ST) evolution and scaling properties of human settlements globally. In this study we begin by comparing land cover and night lights in 8 diverse settings - each spanning gradients of population density and degree of land surface modification. Stable anthropogenic night light is derived from multi-temporal composites of emitted luminance measured by the VIIRS and DMSP-OLS sensors. Land cover is represented as mixtures of sub-pixel fractions of rock, soil and impervious Substrates, Vegetation and Dark surfaces (shadow, water and absorptive materials) estimated from Landsat imagery with > 94% accuracy. Multi-season stability and variability of land cover fractions effectively distinguishes between spectrally similar land covers that corrupt thematic classifications based on single images. We find that temporal stability of impervious substrates combined with persistent shadow cast between buildings results in temporally stable aggregate reflectance across seasons at the 30 m scale of a Landsat pixel. Comparison of night light brightness with land cover composition, stability and variability

  16. Spatio-temporal analysis of brain electrical activity in epilepsy based on cellular nonlinear networks

    NASA Astrophysics Data System (ADS)

    Gollas, Frank; Tetzlaff, Ronald

    2009-05-01

    Epilepsy is the most common chronic disorder of the nervous system. Generally, epileptic seizures appear without foregoing sign or warning. The problem of detecting a possible pre-seizure state in epilepsy from EEG signals has been addressed by many authors over the past decades. Different approaches of time series analysis of brain electrical activity already are providing valuable insights into the underlying complex dynamics. But the main goal the identification of an impending epileptic seizure with a sufficient specificity and reliability, has not been achieved up to now. An algorithm for a reliable, automated prediction of epileptic seizures would enable the realization of implantable seizure warning devices, which could provide valuable information to the patient and time/event specific drug delivery or possibly a direct electrical nerve stimulation. Cellular Nonlinear Networks (CNN) are promising candidates for future seizure warning devices. CNN are characterized by local couplings of comparatively simple dynamical systems. With this property these networks are well suited to be realized as highly parallel, analog computer chips. Today available CNN hardware realizations exhibit a processing speed in the range of TeraOps combined with low power consumption. In this contribution new algorithms based on the spatio-temporal dynamics of CNN are considered in order to analyze intracranial EEG signals and thus taking into account mutual dependencies between neighboring regions of the brain. In an identification procedure Reaction-Diffusion CNN (RD-CNN) are determined for short segments of brain electrical activity, by means of a supervised parameter optimization. RD-CNN are deduced from Reaction-Diffusion Systems, which usually are applied to investigate complex phenomena like nonlinear wave propagation or pattern formation. The Local Activity Theory provides a necessary condition for emergent behavior in RD-CNN. In comparison linear spatio-temporal

  17. Dying like rabbits: general determinants of spatio-temporal variability in survival.

    PubMed

    Tablado, Zulima; Revilla, Eloy; Palomares, Francisco

    2012-01-01

    1. Identifying general patterns of how and why survival rates vary across space and time is necessary to truly understand population dynamics of a species. However, this is not an easy task given the complexity and interactions of processes involved, and the interpopulation differences in main survival determinants. 2. Here, using European rabbits (Oryctolagus cuniculus) as a model and information from local studies, we investigated whether we could make inferences about trends and drivers of survival of a species that are generalizable to large spatio-temporal scales. To do this, we first focused on overall survival and then examined cause-specific mortalities, mainly predation and diseases, which may lead to those patterns. 3. Our results show that within the large-scale variability in rabbit survival, there exist general patterns that are explained by the integration of factors previously known to be important at the local level (i.e. age, climate, diseases, predation or density dependence). We found that both inter- and intrastudy survival rates increased in magnitude and decreased in variability as rabbits grow old, although this tendency was less pronounced in populations with epidemic diseases. Some causes leading to these higher mortalities in young rabbits could be the stronger effect of rainfall at those ages, as well as, other death sources like malnutrition or infanticide. 4. Predation is also greater for newborns and juveniles, especially in population without diseases. Apart from the effect of diseases, predation patterns also depended on factors, such as, density, season, and type and density of predators. Finally, we observed that infectious diseases also showed general relationships with climate, breeding (i.e. new susceptible rabbits) and age, although the association type varied between myxomatosis and rabbit haemorrhagic disease. 5. In conclusion, large-scale patterns of spatio-temporal variability in rabbit survival emerge from the combination

  18. Mapping the spatio-temporal risk of lead exposure in apex species for more effective mitigation.

    PubMed

    Mateo-Tomás, Patricia; Olea, Pedro P; Jiménez-Moreno, María; Camarero, Pablo R; Sánchez-Barbudo, Inés S; Rodríguez Martín-Doimeadios, Rosa C; Mateo, Rafael

    2016-07-27

    Effective mitigation of the risks posed by environmental contaminants for ecosystem integrity and human health requires knowing their sources and spatio-temporal distribution. We analysed the exposure to lead (Pb) in griffon vulture Gyps fulvus-an apex species valuable as biomonitoring sentinel. We determined vultures' lead exposure and its main sources by combining isotope signatures and modelling analyses of 691 bird blood samples collected over 5 years. We made yearlong spatially explicit predictions of the species risk of lead exposure. Our results highlight elevated lead exposure of griffon vultures (i.e. 44.9% of the studied population, approximately 15% of the European, showed lead blood levels more than 200 ng ml(-1)) partly owing to environmental lead (e.g. geological sources). These exposures to environmental lead of geological sources increased in those vultures exposed to point sources (e.g. lead-based ammunition). These spatial models and pollutant risk maps are powerful tools that identify areas of wildlife exposure to potentially harmful sources of lead that could affect ecosystem and human health. PMID:27466455

  19. Minimal Spatio-Temporal Extent of Events, Neutrinos, and the Cosmological Constant Problem

    NASA Astrophysics Data System (ADS)

    Ahluwalia-Khalilova, D. V.

    Chryssomalakos and Okon, through a uniqueness analysis, have strengthened the Vilela Mendes suggestion that the immunity to infinitesimal perturbations in the structure constants of a physically-relevant Lie algebra should be raised to the status of a physical principle. Since the Poincaré-Heisenberg algebra does not carry the indicated immunity, it is suggested that the Lie algebra for the interface of the gravitational and quantum realms (IGQR) is its stabilized form. It carries three additional parameters: a length scale pertaining to the Planck/unification scale, a second length scale associated with cosmos, and a new dimensionless constant. Here, we show that the adoption of the stabilized Poincaré-Heisenberg algebra (SPHA) for the IGQR has the immediate implication that a "point particle" ceases to be a viable physical notion. It must be replaced by objects which carry a well-defined, representation space-dependent, minimal spatio-temporal extent. The ensuing implications have the potential, without spoiling any of the successes of the Standard Model of particle physics, to resolve the cosmological constant problem while concurrently offering a first-principle hint as to why there exists a coincidence between cosmic vacuum energy density and neutrino masses. The main theses which the essay presents is the following: an extension of the present-day physics to a framework which respects SPHA should be seen as the most natural and systematic path towards gaining a deeper understanding of outstanding questions, if not providing answers to them.

  20. Spatio-Temporal Dynamics of Yeast Mitochondrial Biogenesis: Transcriptional and Post-Transcriptional mRNA Oscillatory Modules

    PubMed Central

    Lelandais, Gaëlle; Saint-Georges, Yann; Geneix, Colette; Al-Shikhley, Liza; Dujardin, Geneviève; Jacq, Claude

    2009-01-01

    Examples of metabolic rhythms have recently emerged from studies of budding yeast. High density microarray analyses have produced a remarkably detailed picture of cycling gene expression that could be clustered according to metabolic functions. We developed a model-based approach for the decomposition of expression to analyze these data and to identify functional modules which, expressed sequentially and periodically, contribute to the complex and intricate mitochondrial architecture. This approach revealed that mitochondrial spatio-temporal modules are expressed during periodic spikes and specific cellular localizations, which cover the entire oscillatory period. For instance, assembly factors (32 genes) and translation regulators (47 genes) are expressed earlier than the components of the amino-acid synthesis pathways (31 genes). In addition, we could correlate the expression modules identified with particular post-transcriptional properties. Thus, mRNAs of modules expressed “early” are mostly translated in the vicinity of mitochondria under the control of the Puf3p mRNA-binding protein. This last spatio-temporal module concerns mostly mRNAs coding for basic elements of mitochondrial construction: assembly and regulatory factors. Prediction that unknown genes from this module code for important elements of mitochondrial biogenesis is supported by experimental evidence. More generally, these observations underscore the importance of post-transcriptional processes in mitochondrial biogenesis, highlighting close connections between nuclear transcription and cytoplasmic site-specific translation. PMID:19521515

  1. Modeling self-organized spatio-temporal patterns of PIP3 and PTEN during spontaneous cell polarization

    NASA Astrophysics Data System (ADS)

    Knoch, Fabian; Tarantola, Marco; Bodenschatz, Eberhard; Rappel, Wouter-Jan

    2014-08-01

    During spontaneous cell polarization of Dictyostelium discoideum cells, phosphatidylinositol (3,4,5)-triphoshpate (PIP3) and PTEN (phosphatase tensin homolog) have been identified as key signaling molecules which govern the process of polarization in a self-organized manner. Recent experiments have quantified the spatio-temporal dynamics of these signaling components. Surprisingly, it was found that membrane-bound PTEN can be either in a high or low state, that PIP3 waves were initiated in areas lacking PTEN through an excitable mechanism, and that PIP3 was degraded even though the PTEN concentration remained low. Here we develop a reaction-diffusion model that aims to explain these experimental findings. Our model contains bistable dynamics for PTEN, excitable dynamics for PIP3, and postulates the existence of two species of PTEN with different dephosphorylation rates. We show that our model is able to produce results that are in good qualitative agreement with the experiments, suggesting that our reaction-diffusion model underlies the self-organized spatio-temporal patterns observed in experiments.

  2. New Fast Fall Detection Method Based on Spatio-Temporal Context Tracking of Head by Using Depth Images

    PubMed Central

    Yang, Lei; Ren, Yanyun; Hu, Huosheng; Tian, Bo

    2015-01-01

    In order to deal with the problem of projection occurring in fall detection with two-dimensional (2D) grey or color images, this paper proposed a robust fall detection method based on spatio-temporal context tracking over three-dimensional (3D) depth images that are captured by the Kinect sensor. In the pre-processing procedure, the parameters of the Single-Gauss-Model (SGM) are estimated and the coefficients of the floor plane equation are extracted from the background images. Once human subject appears in the scene, the silhouette is extracted by SGM and the foreground coefficient of ellipses is used to determine the head position. The dense spatio-temporal context (STC) algorithm is then applied to track the head position and the distance from the head to floor plane is calculated in every following frame of the depth image. When the distance is lower than an adaptive threshold, the centroid height of the human will be used as the second judgment criteria to decide whether a fall incident happened. Lastly, four groups of experiments with different falling directions are performed. Experimental results show that the proposed method can detect fall incidents that occurred in different orientations, and they only need a low computation complexity. PMID:26378540

  3. The dynamics of spatio-temporal Rho GTPase signaling: formation of signaling patterns

    PubMed Central

    Fritz, Rafael Dominik; Pertz, Olivier

    2016-01-01

    Rho GTPases are crucial signaling molecules that regulate a plethora of biological functions. Traditional biochemical, cell biological, and genetic approaches have founded the basis of Rho GTPase biology. The development of biosensors then allowed measuring Rho GTPase activity with unprecedented spatio-temporal resolution. This revealed that Rho GTPase activity fluctuates on time and length scales of tens of seconds and micrometers, respectively. In this review, we describe Rho GTPase activity patterns observed in different cell systems. We then discuss the growing body of evidence that upstream regulators such as guanine nucleotide exchange factors and GTPase-activating proteins shape these patterns by precisely controlling the spatio-temporal flux of Rho GTPase activity. Finally, we comment on additional mechanisms that might feed into the regulation of these signaling patterns and on novel technologies required to dissect this spatio-temporal complexity. PMID:27158467

  4. Geovisualization Approaches for Spatio-temporal Crime Scene Analysis - Towards 4D Crime Mapping

    NASA Astrophysics Data System (ADS)

    Wolff, Markus; Asche, Hartmut

    This paper presents a set of methods and techniques for analysis and multidimensional visualisation of crime scenes in a German city. As a first step the approach implies spatio-temporal analysis of crime scenes. Against this background a GIS-based application is developed that facilitates discovering initial trends in spatio-temporal crime scene distributions even for a GIS untrained user. Based on these results further spatio-temporal analysis is conducted to detect variations of certain hotspots in space and time. In a next step these findings of crime scene analysis are integrated into a geovirtual environment. Behind this background the concept of the space-time cube is adopted to allow for visual analysis of repeat burglary victimisation. Since these procedures require incorporating temporal elements into virtual 3D environments, basic methods for 4D crime scene visualisation are outlined in this paper.

  5. Why risk managers need information about spatio-temporal variability of natural hazards. Examples from practice

    NASA Astrophysics Data System (ADS)

    Zischg, Andreas

    2013-04-01

    Integrated risk management consists of risk prevention, early warning, intervention during an event and restoration/re-construction after an event. The prevention phase consists of land use planning measures with a long-term time horizon and of structural measures that sometimes have a lifespan of more than 30-50 years. In this case, it is important to analyse the long-term evolvement of natural risks due to climate changes or land use changes. Besides of this, the spatial and temporal variability of a natural hazard process during the course of an event is also important. The shift from "static" hazard and risk assessment towards a "dynamic" assessment offers benefits for improving the intervention phase in risk management. This contribution describes some examples and points out the benefits of this shift for risk management. One example is the variable disposition of small alpine catchments for runoff and its relevance for early warning. The disposition for runoff depends on the actual status of environmental variables such as soil moisture and the snowpack characteristics. A feasibility study showed how the monitoring of soil moisture and the status of the snowpack can be incorporated into a rule base for describing the temporal variability of the disposition for high runoff in alpine catchments. The study showed that this information about the system state of alpine catchments can be used to improve the assessment of the consequences of a weather forecast for risk management. Another example is the use of snowpack and weather monitoring and traffic intensity measurements for avalanche risk management on alpine roads. Here, the information about the spatio-temporal variability of the snow avalanches and the presence of vehicles can be used for improving the procedures for road closure and re-opening. Another example is the preparation of intervention plans for fire brigades and other relief units during urban floods. The simulation of the temporal evolvement of

  6. Spatio-temporal microseismic analysis of the Woodford Shale, Canadian County, Oklahoma

    NASA Astrophysics Data System (ADS)

    Eppehimer, Jarred

    Microseismicity provides data that can be used to monitor hydraulic fracture stimulation programs as well as to characterize the resulting hydraulic fractures. This is especially important in low permeability gas and oil shales, where the creation of additional permeability through these fracture treatments is essential to production. However, many applications and analyses of these data are either qualitative in nature or include a large element of interpretational bias. This study looks at two microseismic analytical techniques: the radius of gyration (ROG) tensor, as described in Sayers & Le Calvez (2010), and the methods described in Shapiro (2008) that relate spatio-temporal microseismic signatures to hydraulic diffusivity and ultimately hydraulic permeability. The radius of gyration tensor is used to generate a characteristic ellipsoid for any set of microseismic events, and the aspect ratio of this ellipsoid can be related to local in-situ horizontal stress ratios. These methods are applied to surface microseismic data collected for six horizontal wells drilled and completed in the Woodford Shale in Canadian County, Oklahoma. Additionally, an attempt is made to bridge the gap between these methods. Specifically, the characteristic ellipsoid generation from the radius of gyration tensor in Sayers & Le Calvez (2010) is applied to Shapiro's workflow. Shapiro attempts to link the 3D anisotropic triggering front of seismicity, which is an ellipsoid that envelops time-scaled microseismic events, to reservoir permeability. The radius of gyration tensor will generate this ellipsoid and remove interpretational bias that would have been present otherwise. Lastly, an attempt is made at relating the signatures present in the characteristic ellipsoids to zones of natural fracture reactivation. This is done on a stage-by-stage basis. The hypothesis is that an ellipsoid with a significant tilt in its most vertical principal axis and a significant azimuthal rotation away

  7. Spatio-temporal Remodeling of Functional Membrane Microdomains Organizes the Signaling Networks of a Bacterium

    PubMed Central

    Schneider, Johannes; Klein, Teresa; Mielich-Süss, Benjamin; Koch, Gudrun; Franke, Christian; Kuipers, Oscar P.; Kovács, Ákos T.; Sauer, Markus; Lopez, Daniel

    2015-01-01

    Lipid rafts are membrane microdomains specialized in the regulation of numerous cellular processes related to membrane organization, as diverse as signal transduction, protein sorting, membrane trafficking or pathogen invasion. It has been proposed that this functional diversity would require a heterogeneous population of raft domains with varying compositions. However, a mechanism for such diversification is not known. We recently discovered that bacterial membranes organize their signal transduction pathways in functional membrane microdomains (FMMs) that are structurally and functionally similar to the eukaryotic lipid rafts. In this report, we took advantage of the tractability of the prokaryotic model Bacillus subtilis to provide evidence for the coexistence of two distinct families of FMMs in bacterial membranes, displaying a distinctive distribution of proteins specialized in different biological processes. One family of microdomains harbors the scaffolding flotillin protein FloA that selectively tethers proteins specialized in regulating cell envelope turnover and primary metabolism. A second population of microdomains containing the two scaffolding flotillins, FloA and FloT, arises exclusively at later stages of cell growth and specializes in adaptation of cells to stationary phase. Importantly, the diversification of membrane microdomains does not occur arbitrarily. We discovered that bacterial cells control the spatio-temporal remodeling of microdomains by restricting the activation of FloT expression to stationary phase. This regulation ensures a sequential assembly of functionally specialized membrane microdomains to strategically organize signaling networks at the right time during the lifespan of a bacterium. PMID:25909364

  8. Spatio-temporal evolution of shoreline changes along the coast between sousse- Monastir (Eastearn of Tunisia)

    NASA Astrophysics Data System (ADS)

    Fathallah, S.; Ben Amor, R.; Gueddari, M.

    2009-04-01

    Spatio-temporal evolution of shoreline Changes along the coast between Sousse-Monastir (Eastern of Tunisia). Safa Fathallah*, Rim Ben Amor and Moncef Gueddari Unit of Research of Geochemistry and Environmental Geology. Faculty of Science of Tunis, University of Tunis El Manar, 2092. (*) Corresponding author: safa_fathallah@yahoo.fr The coast of Sousse-Monastir in eastern of Tunisia, has undergone great changes, due to natural and anthropic factors. Increasing human use, the construction of two ports and coastal urbanization (hotels and industries) has accelerated the erosion process. The coastal defense structures (breakwaters and enrockment), built to protect the most eroded zone are efficient, but eroded zones appeared in the southern part of breakwaters. Recent and historic aerial photography was used to estimate, observe, and analyze past shoreline and bathymetric positions and trends involving shore evolution for Sousse-Monastir coast. All of the photographs were calibrated and mosaicked by Arc Map Gis 9.1, the years used are 1925, 1962, 1988, 1996, and 2001 for shoreline change analysis and 1884 and 2001 for bathymetric changes. The analyze of this photographs show that the zone located at the south of breakwater are mostly eroded with high speed process (2m/year). Another zone appears as eroded at the south part of Hamdoun River, with 1,5m/year erosion speed . Keywords: Shoreline evolution, defense structures, Sousse-Monastir coast, Tunisia.

  9. Clustering Approach to Quantify Long-Term Spatio-Temporal Interactions in Epileptic Intracranial Electroencephalography

    PubMed Central

    Hegde, Anant; Erdogmus, Deniz; Shiau, Deng S.; Principe, Jose C.; Sackellares, Chris J.

    2007-01-01

    Abnormal dynamical coupling between brain structures is believed to be primarily responsible for the generation of epileptic seizures and their propagation. In this study, we attempt to identify the spatio-temporal interactions of an epileptic brain using a previously proposed nonlinear dependency measure. Using a clustering model, we determine the average spatial mappings in an epileptic brain at different stages of a complex partial seizure. Results involving 8 seizures from 2 epileptic patients suggest that there may be a fixed pattern associated with regional spatio-temporal dynamics during the interictal to pre-post-ictal transition. PMID:18317515

  10. A Kinect based sign language recognition system using spatio-temporal features

    NASA Astrophysics Data System (ADS)

    Memiş, Abbas; Albayrak, Songül

    2013-12-01

    This paper presents a sign language recognition system that uses spatio-temporal features on RGB video images and depth maps for dynamic gestures of Turkish Sign Language. Proposed system uses motion differences and accumulation approach for temporal gesture analysis. Motion accumulation method, which is an effective method for temporal domain analysis of gestures, produces an accumulated motion image by combining differences of successive video frames. Then, 2D Discrete Cosine Transform (DCT) is applied to accumulated motion images and temporal domain features transformed into spatial domain. These processes are performed on both RGB images and depth maps separately. DCT coefficients that represent sign gestures are picked up via zigzag scanning and feature vectors are generated. In order to recognize sign gestures, K-Nearest Neighbor classifier with Manhattan distance is performed. Performance of the proposed sign language recognition system is evaluated on a sign database that contains 1002 isolated dynamic signs belongs to 111 words of Turkish Sign Language (TSL) in three different categories. Proposed sign language recognition system has promising success rates.