Dissertations / Theses: 'Spatio temporal networks'

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Moradi, Mohammad Mehdi. "Spatial and spatio-temporal point patterns on linear networks." Doctoral thesis, Universitat Jaume I, 2018. http://hdl.handle.net/10803/664140.

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A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information Systems
The last decade witnessed an extraordinary increase in interest in the analysis of network related data and trajectories. This pervasive interest is partly caused by a strongly expanded availability of such datasets. In the spatial statistics field, there are numerous real examples such as the locations of traffic accidents and geo-coded locations of crimes in the streets of cities that need to restrict the support of the underlying process over such linear networks to set and define a more realistic scenario. Examples of trajectories are the path taken by moving objects such as taxis, human beings, animals, etc. Intensity estimation on a network of lines, such as a road network, seems to be a surprisingly complicated task. Several techniques published in the literature, in geography and computer science, have turned out to be erroneous. We propose several adaptive and non-adaptive intensity estimators, based on kernel smoothing and Voronoi tessellation. Theoretical properties such as bias, variance, asymptotics, bandwidth selection, variance estimation, relative risk estimation, and adaptive smoothing are discussed. Moreover, their statistical performance is studied through simulation studies and is compared with existing methods. Adding the temporal component, we also consider spatio-temporal point patterns with spatial locations restricted to a linear network. We present a nonparametric kernel-based intensity estimator and develop second-order characteristics of spatio-temporal point processes on linear networks such as K-function and pair correlation function to analyse the type of interaction between points. In terms of trajectories, we introduce the R package trajectories that contains different classes and methods to handle, summarise and analyse trajectory data. Simulation and model fitting, intensity estimation, distance analysis, movement smoothing, Chi maps and second-order summary statistics are discussed. Moreover, we analyse different real datasets such as a crime data from Chicago (US), anti-social behaviour in Castell´on (Spain), traffic accidents in Medell´ın (Colombia), traffic accidents in Western Australia, motor vehicle traffic accidents in an area of Houston (US), locations of pine saplings in a Finnish forest, traffic accidents in Eastbourne (UK) and one week taxi movements in Beijing (China).

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O'Donnell, David. "Spatial prediction and spatio-temporal modelling on river networks." Thesis, University of Glasgow, 2012. http://theses.gla.ac.uk/3161/.

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The application of existing geostatistical theory to the context of stream networks provides a number of interesting and challenging problems. The most important of these is how to adapt existing theory to allow for stream, as opposed to Euclidean, distance to be used. Valid stream distance based models for the covariance structure have been denied in the literature, and this thesis explores the use of such models using data from the River Tweed. The data span a period of twenty-one years, beginning in 1986. During this time period, up to eighty-three stations are monitored for a variety of chemical and biological determinands. This thesis will focus on nitrogen, a key nutrient in determining water quality, especially given the Nitrates Directive (adopted in 1991) and the Water Framework Directive(adopted in 2002). These are European Union legislations that have set legally enforcable guidelines for controlling pollution which national bodies must comply with. The focus of analysis is on several choices that must be made in order to carry out spatial prediction on a river network. The role of spatial trend, whether it be based on stream or Euclidean distance, is discussed and the impact of the bandwidth of the estimate of nonparametric trend is explored. The stream distance based "tail-up" covariance model structure of Ver Hoef and Peterson (2010) is assessed and combined with a standard Euclidean distance based structure to form a mixture model. This is then evaluated using crossvalidation studies in order to determine the optimum mixture of the two covariance models for the data. Finally, the covariance models used for each of the elements of the mixture model are explored to determine the impact they have on the lowest root mean squared error, and the mixing proportion at which it is found. Using the predicted values at unobserved locations on the River Tweed, the distribution of yearly averaged nitrate levels around the river network is predicted and evaluated. Changes through the 21 years of data are noted and areas exceeding the limits set by the Nitrates Directive are highlighted. The differences in fitted values caused by using stream or Euclidean distance are evident in these predictions. The data is then modelled through space and time using additive models. A novel smoothing function for the spatial trend is defined. It is adapted from the tail-up model in order to retain its core features of flow connectivity and flow volume based weightings, in addition to being based on stream distance. This is then used to model all of the River Tweed data through space and time and identify temporal trends and seasonal patterns at different locations on the river.

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Sutherland, Connie. "Spatio-temporal feedback in stochastic neural networks." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27559.

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The mechanisms by which groups of neurons interact is an important facet to understanding how the brain functions. Here we study stochastic neural networks with delayed feedback. The first part of our study looks at how feedback and noise affect the mean firing rate of the network. Secondly we look at how the spatial profile of the feedback affects the behavior of the network. Our numerical and theoretical results show that negative (inhibitory) feedback linearizes the frequency vs input current (f-I) curve via the divisive gain effect it has on the network. The interaction of the inhibitory feedback and the input bias is what produces the divisive decrease in the slope (known as the gain) of the f-I curve. Our work predicts that an increase in noise is required along with increase in inhibitory feedback to attain a divisive and subtractive shift of the gain as seen in experiments [1]. Our results also show that, although the spatial profile of the feedback does not effect the mean activity of the network, it does influence the overall dynamics of the network. Local feedback generates a network oscillation, which is more robust against disruption by noise or uncorrelated input or network heterogeneity, than that for the global feedback (all-to-all coupling) case. For example uncorrelated input completely disrupts the network oscillation generated by global feedback, but only diminishes the network oscillation due to local feedback. This is characterized by 1st and 2nd order spike train statistics. Further, our theory agrees well with numerical simulations of network dynamics.

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Mitchell, Elaine Irwin. "Spatio-temporal modelling of gene regulatory networks." Thesis, University of Dundee, 2018. https://discovery.dundee.ac.uk/en/studentTheses/259d76f6-76cf-474d-a26a-2802808b126e.

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Akbarzadeh, Vahab. "Spatio-temporal coverage optimization of sensor networks." Doctoral thesis, Université Laval, 2016. http://hdl.handle.net/20.500.11794/27065.

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Les réseaux de capteurs sont formés d’un ensemble de dispositifs capables de prendre individuellement des mesures d’un environnement particulier et d’échanger de l’information afin d’obtenir une représentation de haut niveau sur les activités en cours dans la zone d’intérêt. Une telle détection distribuée, avec de nombreux appareils situés à proximité des phénomènes d’intérêt, est pertinente dans des domaines tels que la surveillance, l’agriculture, l’observation environnementale, la surveillance industrielle, etc. Nous proposons dans cette thèse plusieurs approches pour effectuer l’optimisation des opérations spatio-temporelles de ces dispositifs, en déterminant où les placer dans l’environnement et comment les contrôler au fil du temps afin de détecter les cibles mobiles d’intérêt. La première nouveauté consiste en un modèle de détection réaliste représentant la couverture d’un réseau de capteurs dans son environnement. Nous proposons pour cela un modèle 3D probabiliste de la capacité de détection d’un capteur sur ses abords. Ce modèle inègre également de l’information sur l’environnement grâce à l’évaluation de la visibilité selon le champ de vision. À partir de ce modèle de détection, l’optimisation spatiale est effectuée par la recherche du meilleur emplacement et l’orientation de chaque capteur du réseau. Pour ce faire, nous proposons un nouvel algorithme basé sur la descente du gradient qui a été favorablement comparée avec d’autres méthodes génériques d’optimisation «boites noires» sous l’aspect de la couverture du terrain, tout en étant plus efficace en terme de calculs. Une fois que les capteurs placés dans l’environnement, l’optimisation temporelle consiste à bien couvrir un groupe de cibles mobiles dans l’environnement. D’abord, on effectue la prédiction de la position future des cibles mobiles détectées par les capteurs. La prédiction se fait soit à l’aide de l’historique des autres cibles qui ont traversé le même environnement (prédiction à long terme), ou seulement en utilisant les déplacements précédents de la même cible (prédiction à court terme). Nous proposons de nouveaux algorithmes dans chaque catégorie qui performent mieux ou produits des résultats comparables par rapport aux méthodes existantes. Une fois que les futurs emplacements de cibles sont prédits, les paramètres des capteurs sont optimisés afin que les cibles soient correctement couvertes pendant un certain temps, selon les prédictions. À cet effet, nous proposons une méthode heuristique pour faire un contrôle de capteurs, qui se base sur les prévisions probabilistes de trajectoire des cibles et également sur la couverture probabiliste des capteurs des cibles. Et pour terminer, les méthodes d’optimisation spatiales et temporelles proposées ont été intégrées et appliquées avec succès, ce qui démontre une approche complète et efficace pour l’optimisation spatio-temporelle des réseaux de capteurs.
Sensor networks consist in a set of devices able to individually capture information on a given environment and to exchange information in order to obtain a higher level representation on the activities going on in the area of interest. Such a distributed sensing with many devices close to the phenomena of interest is of great interest in domains such as surveillance, agriculture, environmental monitoring, industrial monitoring, etc. We are proposing in this thesis several approaches to achieve spatiotemporal optimization of the operations of these devices, by determining where to place them in the environment and how to control them over time in order to sense the moving targets of interest. The first novelty consists in a realistic sensing model representing the coverage of a sensor network in its environment. We are proposing for that a probabilistic 3D model of sensing capacity of a sensor over its surrounding area. This model also includes information on the environment through the evaluation of line-of-sight visibility. From this sensing model, spatial optimization is conducted by searching for the best location and direction of each sensor making a network. For that purpose, we are proposing a new algorithm based on gradient descent, which has been favourably compared to other generic black box optimization methods in term of performance, while being more effective when considering processing requirements. Once the sensors are placed in the environment, the temporal optimization consists in covering well a group of moving targets in the environment. That starts by predicting the future location of the mobile targets detected by the sensors. The prediction is done either by using the history of other targets who traversed the same environment (long term prediction), or only by using the previous displacements of the same target (short term prediction). We are proposing new algorithms under each category which outperformed or produced comparable results when compared to existing methods. Once future locations of targets are predicted, the parameters of the sensors are optimized so that targets are properly covered in some future time according to the predictions. For that purpose, we are proposing a heuristics for making such sensor control, which deals with both the probabilistic targets trajectory predictions and probabilistic coverage of sensors over the targets. In the final stage, both spatial and temporal optimization method have been successfully integrated and applied, demonstrating a complete and effective pipeline for spatiotemporal optimization of sensor networks.

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Dondo, C. "Bayesian networks for spatio-temporal integrated catchment assessment." Doctoral thesis, University of Cape Town, 2010. http://hdl.handle.net/11427/10327.

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Includes abstract.
Includes bibliographical references (leaves 181-203).
In this thesis, a methodology for integrated catchment water resources assessment using Bayesian Networks was developed. A custom made software application that combines Bayesian Networks with GIS was used to facilitate data pre-processing and spatial modelling. Dynamic Bayesian Networks were implemented in the software for time-series modelling.

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YEGHIKYAN, Gevorg. "Urban Structure and Mobility as Spatio-temporal complex Networks." Doctoral thesis, Scuola Normale Superiore, 2020. http://hdl.handle.net/11384/94477.

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Contemporary urban life and functioning have become increasingly dependent on mobility. Having become an inherent constituent of urban dynamics, the role of urban moblity in influencing urban processes and morphology has increased dramat- ically. However, the relationship between urban mobility and spatial socio-economic structure has still not been thoroughly understood. This work will attempt to take a complex network theoretical approach to studying this intricate relationship through • the spatio-temporal evolution of ad-hoc developed network centralities based on the Google PageRank, • multilayer network regression with statistical random graphs respecting net- work structures for explaining urban mobility flows from urban socio-economic attributes, • and Graph Neural Networks for predicting mobility flows to or from a specific location in the city. Making both practical and theoretical contributions to urban science by offering methods for describing, monitoring, explaining, and predicting urban dynamics, this work will thus be aimed at providing a network theoretical framework for developing tools to facilitate better decision-making in urban planning and policy making.

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Su, Jionglong. "Online predictions for spatio-temporal systems using time-varying RBF networks." Thesis, University of Sheffield, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.578701.

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In this work. we propose a unified framework called Kalman filter based Radial Basis Functions (KF-RBF) for online functional prediction based on the Radial Basis Functions and the Kalman Filter. The data are nonstationary spatio-ternporal observations irregularly sampled in the spatial domain. We shall assume that a Functional Auto-Regressive (FAR) model is generating the system dynamics. Therefore. to account for the spatial variation. a Radial Basis Function (RBF) network is fitted to the spatial data at every time step. To capture the temporal variation, the regression surfaces arc allowed to change with time. This is achieved by proposing a linear state space model for the RBF weight vectors to evolve temporally. With a fixed functional basis in expressing all regressions. the FAR model call then he re-formulated as a Vector Auto-Regressive (VAR) model embedded in a Kalman Filter. Therefore functional predictions. normally taken place in the Hilbert space. can now be easily implemented 011 a computer. The advantages of our approach are as follows. First it is computationally simple: using the KF. we can obtain the posterior and predictive distributions in closed form. This allows for quick implementation of the model. and provides for full probabilistic inference for the forecasts. Second, the model requires no restrictive assumptions such as stationarity. isotropy or separability of the space/time correlation functions. Third. the method applies to non-lattice data. in which the number and location of sensors can change over time. This framework proposed is further extended by generalizing the real-valued. scalar weights in the functional autoregressive model to operators ill the Reproducing Kernel Hilbert Space (RKHS). This essentially implies that a larger. more intricate class of functions can be represented by this functional autoregressive approach. In other words. the unknown function is expressed as a sum of transformed functions mapped from the past functions in the RKHS. This bigger class of functions can potentially yield a better candidate that is "closer". in the norm sense. to the unknown function. In our research. the KF is used despite the system and observational noise covariance are both unknown. These uncertainties may significantly impact the filter performance. resulting in sub- optimality or divergence. A multiple-model strategy is proposed in view of this. This is motivated by the Interactive Multiple Model (IMM) algorithm in which a collection of filters with different noise characteristics is run in parallel. This strategy avoids the problems associated with the estimation of the noise covariance matrices. Furthermore. it also allows future measurements to be predicted without the assumption of time stationarity of the disturbance terms.

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Sturrock, Marc. "Spatio-temporal modelling of gene regulatory networks containing negative feedback loops." Thesis, University of Dundee, 2013. https://discovery.dundee.ac.uk/en/studentTheses/b824506e-d515-442a-b9dc-ff82568f3c09.

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Holm, Noah, and Emil Plynning. "Spatio-temporal prediction of residential burglaries using convolutional LSTM neural networks." Thesis, KTH, Geoinformatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229952.

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The low amount solved residential burglary crimes calls for new and innovative methods in the prevention and investigation of the cases. There were 22 600 reported residential burglaries in Sweden 2017 but only four to five percent of these will ever be solved. There are many initiatives in both Sweden and abroad for decreasing the amount of occurring residential burglaries and one of the areas that are being tested is the use of prediction methods for more efficient preventive actions. This thesis is an investigation of a potential method of prediction by using neural networks to identify areas that have a higher risk of burglaries on a daily basis. The model use reported burglaries to learn patterns in both space and time. The rationale for the existence of patterns is based on near repeat theories in criminology which states that after a burglary both the burgled victim and an area around that victim has an increased risk of additional burglaries. The work has been conducted in cooperation with the Swedish Police authority. The machine learning is implemented with convolutional long short-term memory (LSTM) neural networks with max pooling in three dimensions that learn from ten years of residential burglary data (2007-2016) in a study area in Stockholm, Sweden. The model's accuracy is measured by performing predictions of burglaries during 2017 on a daily basis. It classifies cells in a 36x36 grid with 600 meter square grid cells as areas with elevated risk or not. By classifying 4% of all grid cells during the year as risk areas, 43% of all burglaries are correctly predicted. The performance of the model could potentially be improved by further configuration of the parameters of the neural network, along with a use of more data with factors that are correlated to burglaries, for instance weather. Consequently, further work in these areas could increase the accuracy. The conclusion is that neural networks or machine learning in general could be a powerful and innovative tool for the Swedish Police authority to predict and moreover prevent certain crime. This thesis serves as a first prototype of how such a system could be implemented and used.

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Krishnan, Shankar. "Spatio-Temporal Correlation in the Performance of Cache-Enabled Cellular Networks." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/71809.

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Exact characterization and performance analysis of wireless networks should incorporate dependencies or correlations in space and time, i.e., study how the network performance varies spatially and temporally while having prior information about the performance at previous locations and time slots. This spatio-temporal correlation in wireless networks is usually characterized by studying metrics such as joint coverage probability at two spatial locations/time slots or spatio-temporal correlation coefficient. While developing models and analytical expressions for studying the two extreme cases of spatio-temoral correlation - i) uncorrelated scenario and ii) fully correlated scenario are easier, studying the intermediate case is non-trivial. In this thesis, we develop realistic and tractable analytical frameworks based on random spatial models (using tools from stochastic geometry) for modeling and analysis of correlation in cellular networks. With an ever increasing data demand, caching popular content in the storage of small cells (small cell caching) or the memory of user devices (device caching) is seen as a good alternative to offload demand from macro base stations and reduce backhaul loads. After providing generic results for traditional cellular networks, we study two applications exploiting spatio-temporal correlation in cache-enabled cellular networks. First, we determine the optimal cache content to be stored in the cache of a small cell network that maximizes the hit probability and minimizes the reception energy for the two extreme cases of correlation. Our results concretely demonstrate that the optimal cache contents are significantly different for the two correlation scenarios, thereby indicating the need of correlation-aware caching strategies. Second, we look at a distributed caching scenario in user devices and show that spatio-temporal correlation (user mobility) can be exploited to improve the network performance (in terms of coverage probability and local delay) significantly.
Master of Science

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Padirac, Adrien. "Tailoring spatio-temporal dynamics with DNA circuits." Phd thesis, Université Claude Bernard - Lyon I, 2012. http://tel.archives-ouvertes.fr/tel-00992096.

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Biological organisms process information through the use of complex reaction networks. These can bea great source of inspiration for the tailoring of dynamic chemical systems. Using basic DNA biochemistry-the DNA-toolbox- modeled after the cell regulatory processes, we explore the construction ofspatio-temporal dynamics from the bottom-up.First, we design a monitoring technique of DNA hybridization by harnessing a usually neglectedinteraction between the nucleobases and an attached fluorophore. This fluorescence technique -calledN-quenching- proves to be an essential tool to monitor and troubleshoot our dynamic reaction circuits.We then go on a journey to the roots of the DNA-toolbox, aiming at defining the best design rulesat the sequence level. With this experience behind us, we tackle the construction of reaction circuitsdisplaying bistability. We link the bistable behavior to a topology of circuit, which asks for specificDNA sequence parameters. This leads to a robust bistable circuit that we further use to explore themodularity of the DNA-toolbox. By wiring additional modules to the bistable function, we make twolarger circuits that can be flipped between states: a two-input switchable memory, and a single-inputpush-push memory. Because all the chemical parameters of the DNA-toolbox are easily accessible,these circuits can be very well described by quantitative mathematical modeling. By iterating thismodular approach, it should be possible to construct even larger, more complex reaction circuits: eachsuccess along this line will prove our good understanding of the underlying design rules, and eachfailure may hide some still unknown rules to unveil.Finally, we propose a simple method to bring DNA-toolbox made reaction circuits from zerodimensional,well-mixed conditions, to a two-dimensional environment allowing both reaction anddiffusion. We run an oscillating reaction circuit in two-dimensions and, by locally perturbing it, areable to provoke the emergence of traveling and spiral waves. This opens up the way to the building ofcomplex, tailor-made spatiotemporal patterns.

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Chandan, Shridhar. "Discrete Event Simulation of Mobility and Spatio-Temporal Spectrum Demand." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/25331.

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Realistic mobility and cellular traffic modeling is key to various wireless networking applications and have a significant impact on network performance. Planning and design, network resource allocation and performance evaluation in cellular networks require realistic traffic modeling. We propose a Discrete Event Simulation framework, Diamond - (Discrete Event Simulation of Mobility and Spatio-Temporal Spectrum Demand) to model and analyze realistic activity based mobility and spectrum demand patterns. The framework can be used for spatio-temporal estimation of load, in deciding location of a new base station, contingency planning, and estimating the resilience of the existing infrastructure. The novelty of this framework lies in its ability to capture a variety of complex, realistic and dynamically changing events effectively. Our initial results show that the framework can be instrumental in contingency planning and dynamic spectrum allocation.
Master of Science

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Orlinski, Matthew. "Neighbour discovery and distributed spatio-temporal cluster detection in pocket switched networks." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/neighbour-discovery-and-distributed-spatiotemporal-cluster-detection-in-pocket-switched-networks(3b1f86f5-f3de-4c8e-921b-a57429c35152).html.

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Pocket Switched Networks (PSNs) offer a means of infrastructureless inter-human communication by utilising Delay and Disruption Tolerant Networking (DTN) technology. However, creating PSNs involves solving challenges which were not encountered in the Deep Space Internet for which DTN technology was originally intended.End-to-end communication over multiple hops in PSNs is a product of short range opportunistic wireless communication between personal mobile wireless devices carried by humans. Opportunistic data delivery in PSNs is far less predictable than in the Deep Space Internet because human movement patterns are harder to predict than the orbital motion of satellites. Furthermore, PSNs require some scheme for efficient neighbour discovery in order to save energy and because mobile devices in PSNs may be unaware of when their next encounter will take place.This thesis offers novel solutions for neighbour discovery and opportunistic data delivery in PSNs that make practical use of dynamic inter-human encounter patterns.The first contribution is a novel neighbour discovery algorithm for PSNs called PISTONS which relies on a new inter-probe time calculation (IPC) and the bursty encounter patterns of humans to set the time between neighbour discovery scans. The IPC equations and PISTONS also give participants the ability to easily specify their required level of connectivity and energy saving with a single variable.This thesis also contains novel distributed spatio-temporal clustering and opportunistic data delivery algorithms for PSNs which can be used to deliver data over multiple hops. The spatio-temporal clustering algorimths are also used to analyse the social networks and transient groups which are formed when humans interact.

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Martirosyan, Anahit. "Towards Design of Lightweight Spatio-Temporal Context Algorithms for Wireless Sensor Networks." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/19857.

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Context represents any knowledge obtained from Wireless Sensor Networks (WSNs) about the object being monitored (such as time and location of the sensed events). Time and location are important constituents of context as the information about the events sensed in WSNs is comprehensive when it includes spatio-temporal knowledge. In this thesis, we first concentrate on the development of a suite of lightweight algorithms on temporal event ordering and time synchronization as well as localization for WSNs. Then, we propose an energy-efficient clustering routing protocol for WSNs that is used for message delivery in the former algorithm. The two problems - temporal event ordering and synchronization - are dealt with together as both are concerned with preserving temporal relationships of events in WSNs. The messages needed for synchronization are piggybacked onto the messages exchanged in underlying algorithms. The synchronization algorithm is tailored to the clustered topology in order to reduce the overhead of keeping WSNs synchronized. The proposed localization algorithm has an objective of lowering the overhead of DV-hop based algorithms by reducing the number of floods in the initial position estimation phase. It also randomizes iterative refinement phase to overcome the synchronicity of DV-hop based algorithms. The position estimates with higher confidences are emphasized to reduce the impact of erroneous estimates on the neighbouring nodes. The proposed clustering routing protocol is used for message delivery in the proposed temporal algorithm. Nearest neighbour nodes are employed for inter-cluster communication. The algorithm provides Quality of Service by forwarding high priority messages via the paths with the least cost. The algorithm is also extended for multiple Sink scenario. The suite of algorithms proposed in this thesis provides the necessary tool for providing spatio-temporal context for context-aware WSNs. The algorithms are lightweight as they aim at satisfying WSN's requirements primarily in terms of energy-efficiency, low latency and fault tolerance. This makes them suitable for emergency response applications and ubiquitous computing.

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Kandukuri, Somasekhar Reddy. "Spatio-Temporal Adaptive Sampling Techniques for Energy Conservation in Wireless Sensor Networks." Thesis, La Réunion, 2016. http://www.theses.fr/2016LARE0021/document.

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La technologie des réseaux de capteurs sans fil démontre qu'elle peut être très utile dans de nombreuses applications. Ainsi chaque jour voit émerger de nouvelles réalisations dans la surveillance de notre environnement comme la détection des feux de forêt, l'approvisionnement en eau. Les champs d'applications couvrent aussi des domaines émergents et sensibles pour la population avec les soins aux personnes âgées ou les patients récemment opérés dans le cadre. L'indépendance des architectures RCSFs par rapport aux infrastructures existantes permet aux d'être déployées dans presque tous les sites afin de fournir des informations temporelles et spatiales. Dans les déploiements opérationnels le bon fonctionnement de l'architecture des réseaux de capteurs sans fil ne peut être garanti que si certains défis sont surmontés. La minisation de l'énergie consommée en fait partie. La limitation de la durée de vie des nœuds de capteurs est fortement couplée à l'autonomie de la batterie et donc à l'optimisation énergétique des nœuds du réseau. Nous présenterons plusieurs propositions à ces problèmes dans le cadre de cette thèse. En résumé, les contributions qui ont été présentées dans cette thèse, abordent la durée de vie globale du réseau, l'exploitation des messages de données redondantes et corrélées et enfin le fonctionnement nœud lui-même. Les travaux ont conduit à la réalisation d'algorithmes de routage hiérarchiques et de filtrage permettant la suppression des redondances. Ils s'appuient sur les corrélations spatio-temporelles des données mesurées. Enfin, une implémentation de ce réseau de capteurs multi-sauts intégrant ces nouvelles fonctionnalités est proposée
Wireless sensor networks (WSNs) technology have been demonstrated to be a usefulmeasurement system for numerous bath indoor and outdoor applications. There is avast amount of applications that are operating with WSN technology, such asenvironmental monitoring, for forest fire detection, weather forecasting, water supplies, etc. The independence nature of WSNs from the existing infrastructure. Virtually, the WSNs can be deployed in any sort of location, and provide the sensor samples accordingly in bath time and space. On the contrast, the manual deployments can only be achievable at a high cost-effective nature and involve significant work. ln real-world applications, the operation of wireless sensor networks can only be maintained, if certain challenges are overcome. The lifetime limitation of the distributed sensor nodes is amongst these challenges, in order to achieve the energy optimization. The propositions to the solution of these challenges have been an objective of this thesis. ln summary, the contributions which have been presented in this thesis, address the system lifetime, exploitation of redundant and correlated data messages, and then the sensor node in terms of usability. The considerations have led to the simple data redundancy and correlated algorithms based on hierarchical based clustering, yet efficient to tolerate bath the spatio-temporal redundancies and their correlations. Furthermore, a multihop sensor network for the implementation of propositions with more features, bath the analytical proofs and at the software level, have been proposed

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Candeago, Lorenzo. "Modeling human and cities' behaviors: from communication synchronization to spatio-temporal networks." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/267995.

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Recent years have seen a huge increase in the amount of data collected from multiple sources: mobile phones are ubiquitous, social networks are widely used, cities are more and more connected and the mobility of people and goods has risen to a global scale. The Big Data Era has opened the doors to new kinds of studies that were unthinkable with previous qualitative methods: human behavior can now be analyzed with a fine-grained resolution, patterns of mobility and behavior can be extracted from the incredible amount of data collected every day. Modern large cities are becoming more and more interconnected and this phenomenon leads to an increasing communication and activities’ synchronization. Due to the amount of data available or for anonymization reasons, it is often necessary to aggregate data spatially and temporally. A natural representation of clustered mobility data is the temporal network representation. In this thesis we focus on these two aspects of spatial distance in human mobility: (i) we study the synchronization of 76 Italian cities, using mobile phone data, showing that both distance between cities and city size determine the synchronization in communication rhythms. Moreover, we show that the effect of the distance in synchronization decreases when the size of the city increases; (ii) we investigate how clustering continuous spatio-temporal data affects spatio-temporal network measures for real-life and synthetic datasets and analyze how spatio-temporal networks’ measures vary at different aggregation levels.

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Candeago, Lorenzo. "Modeling human and cities' behaviors: from communication synchronization to spatio-temporal networks." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/267995.

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Recent years have seen a huge increase in the amount of data collected from multiple sources: mobile phones are ubiquitous, social networks are widely used, cities are more and more connected and the mobility of people and goods has risen to a global scale. The Big Data Era has opened the doors to new kinds of studies that were unthinkable with previous qualitative methods: human behavior can now be analyzed with a fine-grained resolution, patterns of mobility and behavior can be extracted from the incredible amount of data collected every day. Modern large cities are becoming more and more interconnected and this phenomenon leads to an increasing communication and activities’ synchronization. Due to the amount of data available or for anonymization reasons, it is often necessary to aggregate data spatially and temporally. A natural representation of clustered mobility data is the temporal network representation. In this thesis we focus on these two aspects of spatial distance in human mobility: (i) we study the synchronization of 76 Italian cities, using mobile phone data, showing that both distance between cities and city size determine the synchronization in communication rhythms. Moreover, we show that the effect of the distance in synchronization decreases when the size of the city increases; (ii) we investigate how clustering continuous spatio-temporal data affects spatio-temporal network measures for real-life and synthetic datasets and analyze how spatio-temporal networks’ measures vary at different aggregation levels.

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Rex, David Bruce. "Object Parallel Spatio-Temporal Analysis and Modeling System." PDXScholar, 1993. https://pdxscholar.library.pdx.edu/open_access_etds/1278.

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The dissertation will outline an object-oriented model from which a next-generation GIS can be derived. The requirements for a spatial information analysis and modeling system can be broken into three primary functional classes: data management (data classification and access), analysis (modeling, optimization, and simulation) and visualization (display of data). These three functional classes can be considered as the primary colors of the spectrum from which the different shades of spatial analysis are composed. Object classes will be developed which will be designed to manipulate the three primary functions as required by the user and the data.

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Osorio, Cañadas Sergio. "Spatio-temporal variability of bee/wasp communities and their host-parasitoid interaction networks." Doctoral thesis, Universitat Autònoma de Barcelona, 2017. http://hdl.handle.net/10803/457746.

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Uno de los principales objetivos de la ecología es comprender cómo la biodiversidad está estructurada espacial y temporalmente, y cuáles son los mecanismos subyacentes a los gradientes de biodiversidad en diferentes escalas espaciales y temporales. En esta tesis, analizo la variabilidad espacio-temporal de comunidades de abejas/avispas (huéspedes) y de sus parasitoides, y de las redes de interacción huésped-parasitoide que se establecen entre ellas. Las especies de abejas y avispas muestran notables diferencias temporales en su fenología, y, por otro lado, las especies de abejas muestran diferentes capacidades termorreguladoras en relación con su tamaño corporal (cuanto más grandes es una, mayor es su capacidad termoreguladora). Por tanto, se podría hipotetizar una relación entre el tamaño corporal (~’grado de endotermia’) y la temperatura ambiente durante el período de vuelo del adulto. Las comunidades de abejas y avispas también muestran una considerable heterogeneidad espacial en respuesta a sus recursos alimentarios y de nidificación. Estos cambios espacio-temporales en las comunidades de abejas/avispas podrían conllevar cambios en sus ‘rasgos funcionales’, y podrían tener un impacto en sus comunidades de parasitoides y, en consecuencia, esto podría reflejarse en cambios en la estructura de sus redes de interacción y en las funciones ecosistémicas asociadas. En el capítulo 1 se analizó la relación entre el tamaño corporal y la temperatura a lo largo de un gradiente de temperatura ambiental intra-anual, utilizando una fauna regional de abejas mediterráneas. Esperábamos encontrar especies más grandes (más endotérmicas) en las estaciones más frías, y especies progresivamente más pequeñas hacia estaciones más cálidas. Esto se puede considerar un test a la ‘norma de Bergmann’ a lo largo de un gradiente de temperatura temporal (en lugar de su formulación clásica a lo largo de gradientes geográficos). Encontramos una relación diferente entre el tamaño corporal y la temperatura ambiente de las especies para las abejas grandes ('endotérmicas') y para las pequeñas (ectotérmicas): las especies mayores que 27,81 mg (peso seco) siguieron la norma de Bergmann, mientras que las especies por debajo de este umbral no mostraban ningún patrón. Nuestros resultados extienden la norma de Bergmann a un gradiente temporal y son coherentes con el mecanismo fisiológico propuesto originalmente por el propio Bergmann ("hipótesis termorreguladora"). Para estudiar las redes de interacción huésped-parasitode se utilizaron comunidades de abejas y avispas nidificantes en cavidades preestablecidas (AANCP), que actúan como 'huéspedes', y sus comunidades de parasitoides, en una zona templada (Capítulos 2 y 3). En el capítulo 2 se estudiaron los efectos de la estacionalidad (primavera vs verano) sobre la estructura y composición taxonómica y funcional de las comunidades de AANCP y de sus parasitoides, y sobre sus redes de interacción. Se encontraron notables cambios estacionales en la estructura taxonómica y funcional, y en la composición tanto de la comunidad de AANCP como de parasitoides. Sin embargo, no encontramos cambios estacionales en el porcentaje de parasitismo, y los pocos cambios estacionales en la estructura de la red de interacción parecían principalmente motivados por cambios en el tamaño de la red. Por último, en el capítulo 3 se estudiaron los efectos de los factores espaciales locales (ambiente de nidificación: granjas vs agrupaciones de árboles) y paisajísticos (gradiente de cobertura agrícola) sobre la estructura taxonómica y la composición de las comunidades de AANCP y de sus parasitoides, y sobre sus redes de interacción. La estructura y composición de la comunidad AANCP, así como la estructura de la red, fueron mucho más dependientes de los factores locales que de los factores del paisaje. Los hábitats abiertos asociados con explotaciones extensivas favorecen la diversidad local de AANCP (especialmente abejas) lo que origina redes de interacción huésped-parasitoide más complejas en comparación con áreas boscosas.
One of the main goals in ecology is to understand how biodiversity is spatial and temporally structured, and which are the mechanisms underlying biodiversity gradients at different spatial and temporal scales. In this thesis, I analyze spatial and temporal variability in bee/wasp (hosts) and their parasitoid communities, and in the antagonistic interaction networks between them. Bees, wasps and their parasitoids are related to key ecosystem functions (e.g., pollination or herbivore populations control). Bee and wasp species show notably seasonal differences in their phenology. Bee species also show different thermoregulatory capabilities in relation with their body size (the bigger the bee species, the more ‘endothermic’ the species are). So, it could be hypothesized a relationship between body size (~endothermic capabilities) and ambient temperature in the period of adult flying activity. Bee and wasp communities also have been shown to be spatially heterogeneous in response to food and nesting resources. Temporal and spatial changes in bee/wasp communities are expected to impact in their parasitoid communities, as they depend on their host communities. Moreover, if host and parasitoid community structure and composition change over space and time, their functional traits, interaction patterns, network structure and ecosystem functionality are also expected to change spatio-temporally. In Chapter 1 we tested the body size-temperature relationship along an intra-annual, seasonal environmental temperature gradient using a Mediterranean regional bee fauna. We expected to find larger bee species (i.e. more endothermic species) in colder seasons, and progressively smaller bee species towards warmer seasons. This approaches to the Bergmann’s rule along a temporal temperature gradient (instead of their classical formulation along geographical gradients). We found a different relationship between body size and ambient temperature for large (‘endothermic’) and small (ectothermic) bee species: species larger than 27.81 mg (dry weight) followed Bergmann’s rule, whereas species below this threshold did not (no relationship at all). Our results extend Bergmann’s rule to a temporal gradient and are coherent with the physiological mechanism proposed originally by Bergmann himself (“thermoregulatory hypothesis”). In order to analyze spatial and temporal variability in antagonistic interaction networks, we used cavity-nesting bees and wasp communities (‘CNBW’, acting as ‘hosts’), and their interacting ‘parasitoid’ communities in a temperate zone (Chapters 2 and 3). In Chapter 2, we studied the effects of seasonality (spring vs. summer) on taxonomic and functional structure and composition of CNBW and their parasitoid communities, and on their interaction networks. We found strong seasonal changes in taxonomic and functional structure and composition of both the CNBW host and their parasitoid communities. However, we did not find seasonal shifts in percent parasitism, and the few seasonal changes in the structure of the host-parasitoid interaction network appeared to be mostly driven by changes in network size. Our results underscore the need to consider functional traits and to incorporate a temporal component into network analysis if we are to understand the global relationship between network structure and ecosystem function. Finally, in Chapter 3 we studied the effects of local (nesting environment: farms vs tree stands) and landscape (forest-cropland gradient) spatial factors on taxonomic structure and composition of CNBW hos and their parasitoid communities, and on their interaction networks. CNBW host community structure and composition, as well as network structure, were much more dependent on local than on landscape factors. Open habitats associated with extensively farmed exploitations favor local CNBW diversity (especially bees) and result in more complex host–parasitoid interaction networks in comparison to forested areas. This study highlights the conservation value of this kind of open habitat in view of the progressive abandonment of extensively cultivated farmland in favor of agricultural intensification and reforestation taking place in Europe.

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McLean, Marnie Isla. "Spatio-temporal models for the analysis and optimisation of groundwater quality monitoring networks." Thesis, University of Glasgow, 2018. http://theses.gla.ac.uk/38975/.

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Commonly groundwater quality data are modelled using temporally independent spatial models. However, primarily due to cost constraints, data of this type can be sparse resulting in some sampling events only recording a few observations. With data of this nature, spatial models struggle to capture the true underlying state of the groundwater and building models with such small spatial datasets can result in unreliable predictions. This highlights the need for spatio-temporal models which `borrow strength' from earlier sampling events and which allow interpolations of groundwater concentrations between sampling points. To compare the relative merits of analysing groundwater quality data using spatial compared to spatio-temporal statistical models, a comparison study is presented using data from a hypothetical contaminant plume along with a real life dataset. In this study, the estimation accuracy of spatial p-spline and Kriging models are compared with spatio-temporal p-spline models. The results show that spatio-temporal methods can increase prediction efficiency markedly so that, in comparison with repeated spatial analysis, spatio-temporal methods can achieve the same level of performance but with smaller sample sizes. For the comparison study, in the spatio-temporal p-splines model, differing levels of variability over space and time were controlled using different numbers of basis functions rather than separate smoothing parameters due to the computational expense of their optimisation. However, deciding on the number of basis functions for each dimension is subjective due to space and time being measured on different scales, and thus methodology is developed to efficiently tune two smoothing parameters. The proposed methodology exploits lower resolution models to determine starting points for the optimisation procedure allowing for each parameter to be tuned separately. Working with spatio-temporal models can, however, pose their own problems. Due to the sporadic layout of many monitoring well networks, due to built-up urban areas and transport infrastructure, ballooning can be experienced in the predictions of these models. `Ballooning' is a term used to describe the event where high or low predictions are made in regions with little data support. To determine when this has occurred a measure is developed to highlight when ballooning may be present in the models predictions. In addition to the measure, to try to eliminate ballooning from happening in the first place, a penalty based on the idea that the total contaminant mass should not change significantly over time is proposed. However, the preliminary results presented here indicate that further work is needed to make this effective. It is shown that by adopting a spatio-temporal modelling framework a smoother, clearer and more accurate prediction through time can be achieved, compared to spatial modelling of individual time steps, whilst using fewer samples. This was shown using existing sampling schemes where the choice of sampling locations was made by someone with little knowledge or experience in sampling design. Sampling designs on fixed monitoring well networks are then explored and optimised through the minimisation two objective functions; the variance of the predicted plume mass (VM) and the integrated prediction variance (IV). Sampling design optimisations, using spatial and spatio-temporal p-spline models, are carried out, using a variety of numbers of wells and at various future sampling time points. The effects of well-specific sampling frequency are also investigated and it is found that both objective functions tend to propose wells for the next sampling design which have not been sampled recently. Often, an existing monitoring well network will need to be changed, either by adding new wells or by down-scaling and removing wells. The decision to add wells to the network comes at a financial expense, so it is of paramount importance that wells are added into areas where the gain in knowledge of the region is maximised. The decision to remove a well from the network is equally important and involves a trade-off between costs saved and information lost. The design objective functions suggest a well should be added in an area where the distance to the nearest neighbouring wells is greatest. Finally, consideration is given to optimal sampling designs when it is assumed the recorded data has multiplicative error - a common assumption in groundwater quality data. When modelling with this type of data, the response is normally log transformed prior to modelling and the predictions are then transformed back onto the original scale for interpretation. Assuming a log transformed response, the objective functions, initially presented, can be used if computation of the objective function is also on the log scale. However, if the desired scale of interpretation of the objective functions is the original scale but modelling was performed on the log scale, the resulting objective function values cannot simply be exponentiated to give an interpretation on the original scale. Modelling on the log scale while interpreting the objective function on the original scale can be achieved by adopting a lognormal distribution for the predicted response and subsequently numerically integrating its variance to compute the IV objective function. The results indicate that the designs do differ depending on which scale interpretation of the objective function is to be made. When interpreting on the original scale the objective function favours sampling from wells where higher values were previously estimated. Unfortunately, computation of the VM objective function when assuming a lognormal distribution has not been achieved so far.

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Anbaroglu, B. "Spatio-temporal clustering for non-recurrent traffic congestion detection on urban road networks." Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/1408826/.

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Non-Recurrent Congestion events (NRCs) frustrate commuters, companies and traffic operators because they cause unexpected delays. Most existing studies consider NRCs to be an outcome of incidents on motorways. The differences between motorways and urban road networks, and the fact that incidents are not the only cause of NRCs, limit the usefulness of existing automatic incident detection methods for identifying NRCs on an urban road network. This thesis contributes to the literature by developing an NRC detection methodology to support the accurate detection of NRCs on large urban road networks. To achieve this, substantially high Link Journey Time estimates (LJTs) on adjacent links that occur at the same time are clustered. Substantially high LJTs are defined in two different ways: (i) those LJTs that are greater than a threshold, (ii) those LJTs that belong to a statistically significant Space-Time Region (STR). These two different ways of defining the term ‘substantially high LJT’ lead to different NRC detection methods. To evaluate these methods, two novel criteria are proposed. The first criterion, high-confidence episodes, assesses to what extent substantially high LJTs that last for a minimum duration are detected. The second criterion, the Localisation Index, assesses to what extent detected NRCs could be related to incidents. The proposed NRC detection methodology is tested for London’s urban road network, which consists of 424 links. Different levels of travel demand are analysed in order to establish a complete understanding of the developed methodology. Optimum parameter settings of the two proposed NRC detection methods are determined by sensitivity analysis. Related to the first method, LJTs that are at least 40% higher than their expected values are found to maintain the best balance between the proposed evaluation criteria for detecting NRCs. Related to the second method, it is found that constructing STRs by considering temporal adjacencies rather than spatial adjacencies improves the performance of the method. These findings are applied in real life situations to demonstrate the advantages and limitations of the proposed NRC detection methods. Traffic operation centres could readily start using the proposed NRC detection methodology. In this way, traffic operators could be able to quantify the impact of incidents and develop effective NRC reduction strategies.

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ElSaadani, Mohamed. "A spatio-temporal dynamical evaluation of satellite rainfall products in hydrologic applications." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5749.

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In February of 2014 NASA has launched the core observatory of The Global Precipitation Measurement Mission (GPM). Since then, the mission has been providing a wealth of observation data collected by the core observatory along with other satellites belonging to the mission space constellation. One of the most important data products that GPM provides is the Level 4 (L4) rainfall data product called Integrated Multi-satellitE Retrievals for GPM (IMERG). IMERG is constructed using the raw data collected by the Microwave (MW) sensors on board the constellation satellites along with the Infrared (IR) sensors on board geostationary satellites and the advance Dual-frequency Precipitation Radar (DPR) on board the GPM core satellite. The IMERG product is available globally for all interested researchers to use. In this dissertation, I focus on the applicability of IMERG in hydrologic applications, and specifically in flood peak modeling. In order to conduct a comprehensive evaluation of IMERG that is oriented towards hydrologic modeling. I have explored multiple hydrologic models which can be used to produce stream flow estimates using IMERG without the need of parameter calibration based on the model’s inputs. The calibration free capability is essential since model parameter calibration obscures the effect of the errors associated with the rainfall input on the estimated discharges, which in turn will limit our understanding about the distribution of the errors in IMERG over space and time. The two hydrologic models we used in this study are both physically based distributed models and were setup over the domain of the state of Iowa which is located in the United States’ Midwest. I also explored the performance of one of the hydrologic models’ component, which is the runoff-routing component, in order to estimate an additional portion of the errors in the discharge estimates that is not attributed to the model’s input but rather to the hydrologic model itself. A significant portion of my dissertation is concerned with identifying and using accurate methods to evaluate both IMERG and the hydrologic models’ outputs in a hydrologic context that is useful for flood modeling. Several studies have evaluated other satellite rainfall products using methods that vary in complexity. Some studies used the simplest methods of evaluation, such as, mean aerial differences and standard deviation of the differences (additive or multiplicative) compared to a benchmark rainfall product. This is done without taking the spatial dependency of the errors in space into consideration. Other studies modeled the spatial dependency (correlation) between the errors in the rainfall product, however, using Euclidean distance based approaches that do not account for the hydrologic basins’ shape and size. Nevertheless, it is important to realize that hydrologic models will eventually aggregate the rainfall values, along with the errors associated with them, through a stream network that is dichotomous in nature and does not comply with Euclidean distance. Thus, we employed a stream based evaluation framework, called the Spatial Stream Network (SSN) approaches, to characterize the errors in IMERG taking into account the stream distances and the stream connectivity information between evaluation sites. Although previously used in applications such as modeling water temperatures and pollutant transport, to the best of my knowledge this approach has not been used in rainfall product evaluation before this study. The SSN analysis of IMERG allowed me to answer the question, “What is the proper basin scale which is capable of filtering out the correlated errors in IMERG by accumulating the rainfall values through the stream network?” Finally, in order to add value to the current methods of evaluating model simulated stream flows. I proposed a time based evaluation that is capable of detecting peaks in both the observed and simulated flows and estimating the lag time of the simulated peaks. Typically, previous studies have used simple skill scores such as Root Mean Squared Errors (RMSE), correlation coefficient, and Nash-Sutcliff Efficiency (NSE) to evaluate hydrograph performance as a whole, or the difference in time to peak which involves primitive peak detection method (e.g., a moving or a defined time window). In this dissertation I propose a Continuous Wavelet Transform (CWT) based method to evaluate the peak times and shapes produced by the hydrologic model. The method is based on filtering the frequencies in the hydrograph by treating it as a signal and detecting sharp features in both the observed and time series and the phase difference between them. We also emphasized on the importance of the choice of wavelet shape used in the evaluation, and how different wavelet shapes can affect the inference about the time series.

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Agarwal, Ankit [Verfasser], Jürgen [Akademischer Betreuer] Kurths, Bruno [Akademischer Betreuer] Merz, and Norbert [Akademischer Betreuer] Marwan. "Unraveling spatio-temporal climatic patterns via multi-scale complex networks / Ankit Agarwal ; Jürgen Kurths, Bruno Merz, Norbert Marwan." Potsdam : Universität Potsdam, 2018. http://d-nb.info/1218404396/34.

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Abiven, Claude. "A Hybrid Dynamically Adaptive, Super-Spatio Temporal Resolution Digital Particle Image Velocimetry for Multi-Phase Flows." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/34014.

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A unique, super spatio-temporal resolution Digital Particle Image Velocimetry (DPIV) system with capability of resolving velocities in a multi-phase flow field, using a very sophisticated novel Dynamically Adaptive Hybrid velocity evaluation algorithm has been developed The unique methodology of this powerful system is presented, its specific distinctions are enlightened, confirming its flexibility, and its superior performance is established by comparing it to the most established best DPIV software implementations currently available. Taking advantage of the most recent advances in imaging technology coupled with state of the art image processing tools, high-performing validation schemes including neural networks, as well as a hybrid digital particle tracking velocimeter (DPTV), the foundation for a unique system was developed. The presented software enables one to effectively resolve tremendously demanding flow-fields. The resolution of challenging test cases including high speed cavitating underwater projectiles as well as high pressure spray demonstrate the power of the developed device.
Master of Science

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

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

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Pinto, Rafael Coimbra. "Online incremental one-shot learning of temporal sequences." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2011. http://hdl.handle.net/10183/49063.

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Este trabalho introduz novos algoritmos de redes neurais para o processamento online de padrões espaço-temporais, estendendo o algoritmo Incremental Gaussian Mixture Network (IGMN). O algoritmo IGMN é uma rede neural online incremental que aprende a partir de uma única passada através de dados por meio de uma versão incremental do algoritmo Expectation-Maximization (EM) combinado com regressão localmente ponderada (Locally Weighted Regression, LWR). Quatro abordagens diferentes são usadas para dar capacidade de processamento temporal para o algoritmo IGMN: linhas de atraso (Time-Delay IGMN), uma camada de reservoir (Echo-State IGMN), média móvel exponencial do vetor de entrada reconstruído (Merge IGMN) e auto-referência (Recursive IGMN). Isso resulta em algoritmos que são online, incrementais, agressivos e têm capacidades temporais e, portanto, são adequados para tarefas com memória ou estados internos desconhecidos, caracterizados por fluxo contínuo ininterrupto de dados, e que exigem operação perpétua provendo previsões sem etapas separadas para aprendizado e execução. Os algoritmos propostos são comparados a outras redes neurais espaço-temporais em 8 tarefas de previsão de séries temporais. Dois deles mostram desempenhos satisfatórios, em geral, superando as abordagens existentes. Uma melhoria geral para o algoritmo IGMN também é descrita, eliminando um dos parâmetros ajustáveis manualmente e provendo melhores resultados.
This work introduces novel neural networks algorithms for online spatio-temporal pattern processing by extending the Incremental Gaussian Mixture Network (IGMN). The IGMN algorithm is an online incremental neural network that learns from a single scan through data by means of an incremental version of the Expectation-Maximization (EM) algorithm combined with locally weighted regression (LWR). Four different approaches are used to give temporal processing capabilities to the IGMN algorithm: time-delay lines (Time-Delay IGMN), a reservoir layer (Echo-State IGMN), exponential moving average of reconstructed input vector (Merge IGMN) and self-referencing (Recursive IGMN). This results in algorithms that are online, incremental, aggressive and have temporal capabilities, and therefore are suitable for tasks with memory or unknown internal states, characterized by continuous non-stopping data-flows, and that require life-long learning while operating and giving predictions without separated stages. The proposed algorithms are compared to other spatio-temporal neural networks in 8 time-series prediction tasks. Two of them show satisfactory performances, generally improving upon existing approaches. A general enhancement for the IGMN algorithm is also described, eliminating one of the algorithm’s manually tunable parameters and giving better results.

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Cortés, Rudyar. "Scalable location-temporal range query processing for structured peer-to-peer networks." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066106/document.

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La recherche et l'indexation de données en fonction d'une date ou d'une zone géographique permettent le partage et la découverte d'informations géolocalisées telles que l'on en trouve sur les réseaux sociaux comme Facebook, Flickr, ou Twitter. Cette réseau social connue sous le nom de Location Based Social Network (LBSN) s'applique à des millions d'utilisateurs qui partagent et envoient des requêtes ciblant des zones spatio-temporelles, permettant d'accéder à des données géolocalisées générées dans une zone géographique et dans un intervalle de temps donné. Un des principaux défis pour de telles applications est de fournir une architecture capable de traiter la multitude d'insertions et de requêtes spatio-temporelles générées par une grande quantité d'utilisateurs. A ces fins, les Tables de Hachage Distribué (DHT) et le paradigme Pair-à-Pair (P2P) sont autant de primitives qui forment la base pour les applications de grande envergure. Cependant, les DHTs sont mal adaptées aux requêtes ciblant des intervalles donnés; en effet, l'utilisation de fonctions de hachage sacrifie la localité des données au profit d'un meilleur équilibrage de la charge. Plusieurs solutions ajoutent le support de requêtes ciblant des ensembles aux DHTs. En revanche ces solutions ont tendance à générer un nombre de messages et une latence élevée pour des requêtes qui ciblent des intervalles. Cette thèse propose deux solutions à large échelle pour l'indexation des données géolocalisées
Indexing and retrieving data by location and time allows people to share and explore massive geotagged datasets observed on social networks such as Facebook, Flickr, and Twitter. This scenario known as a Location Based Social Network (LBSN) is composed of millions of users, sharing and performing location-temporal range queries in order to retrieve geotagged data generated inside a given geographic area and time interval. A key challenge is to provide a scalable architecture that allow to perform insertions and location-temporal range queries from a high number of users. In order to achieve this, Distributed Hash Tables (DHTs) and the Peer-to-Peer (P2P) computing paradigms provide a powerful building block for implementing large scale applications. However, DHTs are ill-suited for supporting range queries because the use of hash functions destroy data locality for the sake of load balance. Existing solutions that use a DHT as a building block allow to perform range queries. Nonetheless, they do not target location-temporal range queries and they exhibit poor performance in terms of query response time and message traffic. This thesis proposes two scalable solutions for indexing and retrieving geotagged data based on location and time

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Schürholz, Anne-Kathrin [Verfasser], and Jan [Akademischer Betreuer] Lohmann. "Spatio-temporal control of cell wall propterties and signalling networks in Arabidopsis meristems / Anne-Kathrin Schürholz ; Betreuer: Jan Lohmann." Heidelberg : Universitätsbibliothek Heidelberg, 2019. http://d-nb.info/119237312X/34.

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Schürholz, Anne-Kathrin [Verfasser], and Jan U. [Akademischer Betreuer] Lohmann. "Spatio-temporal control of cell wall propterties and signalling networks in Arabidopsis meristems / Anne-Kathrin Schürholz ; Betreuer: Jan Lohmann." Heidelberg : Universitätsbibliothek Heidelberg, 2019. http://nbn-resolving.de/urn:nbn:de:bsz:16-heidok-269083.

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Galvan, Boris [Verfasser]. "Modeling the spatio-temporal evolution of fracture networks and fluid-rock interactions in GPU : Applications to lithospheric geodynamics / Boris Galvan." Bonn : Universitäts- und Landesbibliothek Bonn, 2013. http://d-nb.info/1044870109/34.

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Ozturk, Ibrahim. "Learning spatio-temporal spike train encodings with ReSuMe, DelReSuMe, and Reward-modulated Spike-timing Dependent Plasticity in Spiking Neural Networks." Thesis, University of York, 2017. http://etheses.whiterose.ac.uk/21978/.

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SNNs are referred to as the third generation of ANNs. Inspired from biological observations and recent advances in neuroscience, proposed methods increase the power of SNNs. Today, the main challenge is to discover efficient plasticity rules for SNNs. Our research aims are to explore/extend computational models of plasticity. We make various achievements using ReSuMe, DelReSuMe, and R-STDP based on the fundamental plasticity of STDP. The information in SNNs is encoded in the patterns of firing activities. For biological plausibility, it is necessary to use multi-spike learning instead of single-spike. Therefore, we focus on encoding inputs/outputs using multiple spikes. ReSuMe is capable of generating desired patterns with multiple spikes. The trained neuron in ReSuMe can fire at desired times in response to spatio-temporal inputs. We propose alternative architecture for ReSuMe dealing with heterogeneous synapses. It is demonstrated that the proposed topology exactly mimic the ReSuMe. A novel extension of ReSuMe, called DelReSuMe, has better accuracy using less iteration by using multi-delay plasticity in addition to weight learning under noiseless and noisy conditions. The proposed heterogeneous topology is also used for DelReSuMe. Another plasticity extension based on STDP takes into account reward to modulate synaptic strength named R-STDP. We use dopamine-inspired STDP in SNNs to demonstrate improvements in mapping spatio-temporal patterns of spike trains with the multi-delay mechanism versus single connection. From the viewpoint of Machine Learning, Reinforcement Learning is outlined through a maze task in order to investigate the mechanisms of reward and eligibility trace which are the fundamental in R-STDP. To develop the approach we implement Temporal-Difference learning and novel knowledge-based RL techniques on the maze task. We develop rule extractions which are combined with RL and wall follower algorithms. We demonstrate the improvements on the exploration efficiency of TD learning for maze navigation tasks.

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

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Quiles, Marcos Gonçalves. "Redes com dinâmica espaço-temporal e aplicações computacionais." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-27052009-145639/.

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Nas últimas décadas, testemunhou-se um crescente interesse no estudo de sistemas complexos. Tais sistemas são compostos por pelo menos dois componentes fundamentais: elementos dinâmicos individuais e uma estrutura de organização definindo a forma de interação entre estes. Devido a dinâmica de cada elemento e a complexidade de acoplamento, uma grande variedade de fenômenos espaço-temporais podem ser observados. Esta tese tem como objetivo principal explorar o uso da dinâmica espaço-temporal em redes visando a solução de alguns problemas computacionais. Com relação aos mecanismos dinâmicos, a sincronização entre osciladores acoplados, a caminhada aleatória-determinística e a competição entre elementos na rede foram considerados. Referente à parte estrutural da rede, tanto estruturas regulares baseadas em reticulados quanto redes com estruturas mais gerais, denominadas redes complexas, foram abordadas. Este estudo é concretizado com o desenvolvimento de modelos aplicados a dois domínios específicos. O primeiro refere-se à utilização de redes de osciladores acoplados para construção de modelos de atenção visual. Dentre as principais características desses modelos estão: a seleção baseada em objetos, a utilização da sincronização/ dessincronização entre osciladores neurais como forma de organização perceptual, a competição entre objetos para aquisição da atenção. Além disso, ao comparar com outros modelos de seleção de objetos baseados em redes osciladores, um número maior de atributos visuais é utilizado para definir a saliência dos objetos. O segundo domínio está relacionado ao desenvolvimento de modelos para detecção de comunidades em redes complexas. Os dois modelos desenvolvidos, um baseado em competição de partículas e outro baseado em sincronização de osciladores, apresentam alta precisão de detecção e ao mesmo tempo uma baixa complexidade computacional. Além disso, o modelo baseado em competição de partículas não só oferece uma nova técnica de detecção de comunidades, mas também apresenta uma abordagem alternativa para realização de aprendizado competitivo. Os estudos realizados nesta tese mostram que a abordagem unificada de dinâmica e estrutura é uma ferramenta promissora para resolver diversos problemas computacionais
In the last decades, an increasing interest in complex system study has been witnessed. Such systems have at least two integrated fundamental components: individual dynamical elements and an organizational structure which defines the form of interaction among those elements. Due to the dynamics of each element and the coupling complexity, various spatial-temporal phenomena can be observed. The main objective of this thesis is to explore spatial-temporal dynamics in networks for solving some computational problems. Regarding the dynamical mechanisms, the synchronization among coupled oscillators, deterministic-random walk and competition between dynamical elements are taken into consideration. Referring to the organizational structure, both regular network based on lattice and more general network, called complex networks, are studied. The study of coupled dynamical elements is concretized by developing computational models applied to two specific domains. The first refers to the using of coupled neural oscillators for visual attention. The main features of the developed models in this thesis are: object-based visual selection, realization of visual perceptual organization by using synchronization / desynchronization among neural oscillators, competition among objects to achieve attention. Moreover, in comparison to other object-based selection models, more visual attributes are employed to define salience of objects. The second domain is related to the development of computational models applied to community detection in complex networks. Two developed models, one based on particle competition and another based on synchronization of Integrate-Fire oscillators, present high detection rate and at the same time low computational complexity. Moreover, the model based on particle competition not only offers a new community detection technique, but also presents an alternative way to realize artificial competitive learning. The study realized in this thesis shows that the unified scheme of dynamics and structure is a powerful tool to solve various computational problems

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Kurka, David Burth 1988. "Online social networks = knowledge extraction from information diffusion and analysis of spatio-temporal phenomena = Redes sociais online: extração de conhecimento e análise espaço-temporal de eventos de difusão de informação." [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259074.

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Orientador: Fernando José Von Zuben
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
Made available in DSpace on 2018-08-27T03:14:35Z (GMT). No. of bitstreams: 1 Kurka_DavidBurth_M.pdf: 1660677 bytes, checksum: 7258daf8129b4dac9d1f647195775d3c (MD5) Previous issue date: 2015
Resumo: Com o surgimento e a popularização de Redes Sociais Online e de Serviços de Redes Sociais, pesquisadores da área de computação têm encontrado um campo fértil para o desenvolvimento de trabalhos com grande volume de dados, modelos envolvendo múltiplos agentes e dinâmicas espaço-temporais. Entretanto, mesmo com significativo elenco de pesquisas já publicadas no assunto, ainda existem aspectos das redes sociais cuja explicação é incipiente. Visando o aprofundamento do conhecimento da área, este trabalho investiga fenômenos de compartilhamento coletivo na rede, que caracterizam eventos de difusão de informação. A partir da observação de dados reais oriundos do serviço online Twitter, tais eventos são modelados, caracterizados e analisados. Com o uso de técnicas de aprendizado de máquina, são encontrados padrões nos processos espaço-temporais da rede, tornando possível a construção de classificadores de mensagens baseados em comportamento e a caracterização de comportamentos individuais, a partir de conexões sociais
Abstract: With the advent and popularization of Online Social Networks and Social Networking Services, computer science researchers have found fertile field for the development of studies using large volumes of data, multiple agents models and spatio-temporal dynamics. However, even with a significant amount of published research on the subject, there are still aspects of social networks whose explanation is incipient. In order to deepen the knowledge of the area, this work investigates phenomena of collective sharing on the network, characterizing information diffusion events. From the observation of real data obtained from the online service Twitter, we collect, model and characterize such events. Finally, using machine learning and computational data analysis, patterns are found on the network's spatio-temporal processes, making it possible to classify a message's topic from users behaviour and the characterization of individual behaviour, from social connections
Mestrado
Engenharia de Computação
Mestre em Engenharia Elétrica

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Ali, Azad [Verfasser], Neeraj [Akademischer Betreuer] Suri, Christian [Akademischer Betreuer] Becker, Stefan [Akademischer Betreuer] Katzenbeisser, Andy [Akademischer Betreuer] Schürr, and Marc [Akademischer Betreuer] Fischlin. "Fault-Tolerant Spatio-Temporal Compression Scheme for Wireless Sensor Networks / Azad Ali ; Neeraj Suri, Christian Becker, Stefan Katzenbeisser, Andy Schürr, Marc Fischlin." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2017. http://d-nb.info/1127225405/34.

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

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

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Vadapalli, Hima Bindu. "Recognition of facial action units from video streams with recurrent neural networks : a new paradigm for facial expression recognition." University of the Western Cape, 2011. http://hdl.handle.net/11394/5415.

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Philosophiae Doctor - PhD
This research investigated the application of recurrent neural networks (RNNs) for recognition of facial expressions based on facial action coding system (FACS). Support vector machines (SVMs) were used to validate the results obtained by RNNs. In this approach, instead of recognizing whole facial expressions, the focus was on the recognition of action units (AUs) that are defined in FACS. Recurrent neural networks are capable of gaining knowledge from temporal data while SVMs, which are time invariant, are known to be very good classifiers. Thus, the research consists of four important components: comparison of the use of image sequences against single static images, benchmarking feature selection and network optimization approaches, study of inter-AU correlations by implementing multiple output RNNs, and study of difference images as an approach for performance improvement. In the comparative studies, image sequences were classified using a combination of Gabor filters and RNNs, while single static images were classified using Gabor filters and SVMs. Sets of 11 FACS AUs were classified by both approaches, where a single RNN/SVM classifier was used for classifying each AU. Results indicated that classifying FACS AUs using image sequences yielded better results than using static images. The average recognition rate (RR) and false alarm rate (FAR) using image sequences was 82.75% and 7.61%, respectively, while the classification using single static images yielded a RR and FAR of 79.47% and 9.22%, respectively. The better performance by the use of image sequences can be at- tributed to RNNs ability, as stated above, to extract knowledge from time-series data. Subsequent research then investigated benchmarking dimensionality reduction, feature selection and network optimization techniques, in order to improve the performance provided by the use of image sequences. Results showed that an optimized network, using weight decay, gave best RR and FAR of 85.38% and 6.24%, respectively. The next study was of the inter-AU correlations existing in the Cohn-Kanade database and their effect on classification models. To accomplish this, a model was developed for the classification of a set of AUs by a single multiple output RNN. Results indicated that high inter-AU correlations do in fact aid classification models to gain more knowledge and, thus, perform better. However, this was limited to AUs that start and reach apex at almost the same time. This suggests the need for availability of a larger database of AUs, which could provide both individual and AU combinations for further investigation. The final part of this research investigated use of difference images to track the motion of image pixels. Difference images provide both noise and feature reduction, an aspect that was studied. Results showed that the use of difference image sequences provided the best results, with RR and FAR of 87.95% and 3.45%, respectively, which is shown to be significant when compared to use of normal image sequences classified using RNNs. In conclusion, the research demonstrates that use of RNNs for classification of image sequences is a new and improved paradigm for facial expression recognition.

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Wambua, Raphael Muli [Verfasser]. "Spatio-Temporal Drought Characterization and Forecasting Using Indices and Artificial Neural Networks. A Case of the Upper Tana River Basin, Kenya / Raphael Muli Wambua." München : GRIN Verlag, 2019. http://d-nb.info/118299475X/34.

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Cebecauer, Matej. "Short-Term Traffic Prediction in Large-Scale Urban Networks." Licentiate thesis, KTH, Transportplanering, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-250650.

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City-wide travel time prediction in real-time is an important enabler for efficient use of the road network. It can be used in traveler information to enable more efficient routing of individual vehicles as well as decision support for traffic management applications such as directed information campaigns or incident management. 3D speed maps have been shown to be a promising methodology for revealing day-to-day regularities of city-level travel times and possibly also for short-term prediction. In this paper, we aim to further evaluate and benchmark the use of 3D speed maps for short-term travel time prediction and to enable scenario-based evaluation of traffic management actions we also evaluate the framework for traffic flow prediction. The 3D speed map methodology is adapted to short-term prediction and benchmarked against historical mean as well as against Probabilistic Principal Component Analysis (PPCA). The benchmarking and analysis are made using one year of travel time and traffic flow data for the city of Stockholm, Sweden. The result of the case study shows very promising results of the 3D speed map methodology for short-term prediction of both travel times and traffic flows. The modified version of the 3D speed map prediction outperforms the historical mean prediction as well as the PPCA method. Further work includes an extended evaluation of the method for different conditions in terms of underlying sensor infrastructure, preprocessing and spatio-temporal aggregation as well as benchmarking against other prediction methods.

QC 20190531

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Huang, Yanqiu [Verfasser], Alberto [Akademischer Betreuer] [Gutachter] García-Ortiz, and Anna [Gutachter] Förster. "Transmission Rate Compression Based on Kalman Filter Using Spatio-temporal Correlation for Wireless Sensor Networks / Yanqiu Huang ; Gutachter: Alberto Garcia-Ortiz, Anna Förster ; Betreuer: Alberto Garcia-Ortiz." Bremen : Staats- und Universitätsbibliothek Bremen, 2017. http://d-nb.info/1124975799/34.

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Vuran, Mehmet Can. "Correlation-based Cross-layer Communication in Wireless Sensor Networks." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/16135.

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Wireless sensor networks (WSN) are event based systems that rely on the collective effort of densely deployed sensor nodes continuously observing a physical phenomenon. The spatio-temporal correlation between the sensor observations and the cross-layer design advantages are significant and unique to the design of WSN. Due to the high density in the network topology, sensor observations are highly correlated in the space domain. Furthermore, the nature of the energy-radiating physical phenomenon constitutes the temporal correlation between each consecutive observation of a sensor node. This unique characteristic of WSN can be exploited through a cross-layer design of communication functionalities to improve energy efficiency of the network. In this thesis, several key elements are investigated to capture and exploit the correlation in the WSN for the realization of advanced efficient communication protocols. A theoretical framework is developed to capture the spatial and temporal correlations in WSN and to enable the development of efficient communication protocols. Based on this framework, spatial Correlation-based Collaborative Medium Access Control (CC-MAC) protocol is described, which exploits the spatial correlation in the WSN in order to achieve efficient medium access. Furthermore, the cross-layer module (XLM), which melts common protocol layer functionalities into a cross-layer module for resource-constrained sensor nodes, is developed. The cross-layer analysis of error control in WSN is then presented to enable a comprehensive comparison of error control schemes for WSN. Finally, the cross-layer packet size optimization framework is described.

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Cespedes, Marcela I. "Detection of longitudinal brain atrophy patterns consistent with progression towards Alzheimer's disease." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/118289/1/Marcela_Cespedes_Thesis.pdf.

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This thesis develops and applies statistical methodologies to model brain atrophy in humans among multiple brain regions and how this may change over time. Throughout this work, Bayesian multilevel models are progressively developed for single and multiple regions at a given time point as well as modelling how connectivity between multiple regions evolves over time in conjunction with region level estimates. The application of these models provide insight into the detection of longitudinal brain atrophy patterns consistent with healthy ageing or progression towards Alzheimer's disease, and should be of interest to biostatisticians and researchers who deal with neurological spatial data.

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Hadachi, Amnir. "Travel Time Estimation Using Sparsely Sampled Probe GPS Data in Urban Road Networks Context." Phd thesis, INSA de Rouen, 2013. http://tel.archives-ouvertes.fr/tel-00800203.

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This dissertation is concerned with the problem of estimating travel time per links in urban context using sparsely sampled GPS data. One of the challenges in this thesis is use the sparsely sampled data. A part of this research work, i developed a digital map with its new geographic information system (GIS), dealing with map-matching problem, where we come out with an enhancement tecnique, and also the shortest path problem.The thesis research work was conduct within the project PUMAS, which is an avantage for our research regarding the collection process of our data from the real world field and also in making our tests. The project PUMAS (Plate-forme Urbaine de Mobilité Avancée et Soutenable / Urban Platform for Sustainable and Advanced Mobility) is a preindustrial project that has the objective to inform about the traffic situation and also to develop an implement a platform for sustainable mobility in order to evaluate it in the region, specifically Rouen, France. The result is a framework for any traffic controller or manager and also estimation researcher to access vast stores of data about the traffic estimation, forecasting and status.

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Legrand, Jonathan. "Toward a multi-scale understanding of flower development - from auxin networks to dynamic cellular patterns." Thesis, Lyon, École normale supérieure, 2014. http://www.theses.fr/2014ENSL0947/document.

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Dans le domaine de la biologie développementale, un des principaux défis est de comprendre comment des tissus multicellulaires, à l'origine indifférenciés, peuvent engendrer des formes aussi complexes que celles d'une fleur. De part son implication dans l'organogenèse florale, l'auxine est une phytohormone majeure. Nous avons donc déterminé son réseau binaire potentiel, puis y avons appliqué des modèles de clustering de graphes s'appuyant sur les profils de connexion présentés par ces 52 facteurs de transcription (FT). Nous avons ainsi pu identifier trois groupes, proches des groupes biologiques putatifs: les facteurs de réponse à l'auxine activateurs (ARF+), répresseurs (ARF-) et les Aux/IAAs. Nous avons détecté l'auto-interaction des ARF+ et des Aux/IAA, ainsi que leur interaction, alors que les ARF- en présentent un nombre restreint. Ainsi, nous proposons un mode de compétition auxine indépendent entre ARF+ et ARF- pour la régulation transcriptionelle. Deuxièmement, nous avons modélisé l'influence des séquences de dimérisation des FT sur la structure de l'interactome en utilisant des modèles de mélange Gaussien pour graphes aléatoires. Les groupes obtenus sont proches des précédents, et les paramètres estimés nous on conduit à conclure que chaque sous-domaine peut jouer un rôle différent en fonction de leur proximité phylogénétique.Enfin, nous sommes passés à l'échelle multi-cellulaire ou, par un graphe spatio-temporel, nous avons modélisé les premiers stades du développement floral d'A. thaliana. Nous avons pu extraire des caractéristiques cellulaires (3D+t) de reconstruction d'imagerie confocale, et avons démontré la possibilité de caractériser l'identité cellulaire en utilisant des méthodes de classification hiérarchique et des arbres de Markov cachés
A striking aspect of flowering plants is that, although they seem to display a great diversity of size and shape, they are made of the same basics constituents, that is the cells. The major challenge is then to understand how multicellular tissues, originally undifferentiated, can give rise to such complex shapes. We first investigated the uncharacterised signalling network of auxin since it is a major phytohormone involved in flower organogenesis.We started by determining the potential binary network, then applied model-based graph clustering methods relying on connectivity profiles. We demonstrated that it could be summarise in three groups, closely related to putative biological groups. The characterisation of the network function was made using ordinary differential equation modelling, which was later confirmed by experimental observations.In a second time, we modelled the influence of the protein dimerisation sequences on the auxin interactome structure using mixture of linear models for random graphs. This model lead us to conclude that these groups behave differently, depending on their dimerisation sequence similarities, and that each dimerisation domains might play different roles.Finally, we changed scale to represent the observed early stages of A. thaliana flower development as a spatio-temporal property graph. Using recent improvements in imaging techniques, we could extract 3D+t cellular features, and demonstrated the possibility of identifying and characterising cellular identity on this basis. In that respect, hierarchical clustering methods and hidden Markov tree have proven successful in grouping cell depending on their feature similarities

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Polo, Lucas. "Redes Bayesianas aplicadas a estimação da taxa de prêmio de seguro agrícola de produtividade." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/11/11132/tde-10082016-132524/.

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Informações que caracterizam o risco quebra de produção agrícola são necessárias para a precificação de prêmio do seguro agrícola de produção e de renda. A distribuição de probabilidade da variável rendimento agrícola é uma dessas informações, em especial aquela que descreve a variável aleatória rendimento agrícola condicionada aos fatores de risco climáticos. Este trabalho objetiva aplicar redes Bayesianas (grafo acíclico direcionado, ou modelo hierárquico Bayesiano) a estimação da distribuição de probabilidade de rendimento da soja em alguns municípios do Paraná, com foco na analise comparativa de riscos. Dados meteorológicos (ANA e INMET, período de 1970 a 2011) e de sensoriamento remoto (MODIS, período de 2000 a 2011) são usados conjuntamente para descrever espacialmente o risco climático de quebra de produção. Os dados de rendimento usados no estudo (COAMO, período de 2001 a 2011) requerem agrupamento de todos os dados ao nível municipal e, para tanto, a seleção de dados foi realizada nas dimensões espacial e temporal por meio de um mapa da cultura da soja (estimado por SVM - support vector machine) e os resultados de um algoritmo de identificação de ciclo de culturas. A interpolação requerida para os dados de temperatura utilizou uma componente de tendência estimada por dados de sensoriamento remoto, para descrever variações espaciais da variável que são ofuscadas pelos métodos tradicionais de interpolação. Como resultados, identificou-se relação significativa entre a temperatura observada por estações meteorológicas e os dados de sensoriamento remoto, apoiando seu uso conjunto nas estimativas. O classificador que estima o mapa da cultura da soja apresenta sobre-ajuste para safras das quais as amostras usadas no treinamento foram coletadas. Além da seleção de dados, a identificação de ciclo também permitiu obtenção de distribuições de datas de plantio da cultura da soja para o estado do Paraná. As redes bayesianas apresentam grande potencial e algumas vantagens quando aplicadas na modelagem de risco agrícola. A representação da distribuição de probabilidade por um grafo facilita o entendimento de problemas complexos, por suposições de causalidade, e facilita o ajuste, estruturação e aplicação do modelo probabilístico. A distribuição log-normal demonstrou-se a mais adequada para a modelagem das variáveis de ambiente (soma térmica, chuva acumulada e maior período sem chuva), e a distribuição beta para produtividade relativa e índices de estado (amplitude de NDVI e de EVI). No caso da regressão beta, o parâmetro de precisão também foi modelado com dependência das variáveis explicativas melhorando o ajuste da distribuição. O modelo probabilístico se demonstrou pouco representativo subestimando bastante as taxas de prêmio de seguro em relação a taxas praticadas no mercado, mas ainda assim apresenta contribui para o entendimento comparativo de situações de risco de quebra de produção da cultura da soja.
Information that characterize the risk of crop losses are necessary to crop and revenue insurance underwriting. The probability distribution of yield is one of this information. This research applies Bayesian networks (direct acyclic graph, or hierarchical Bayesian model) to estimate the probability distribution of soybean yield for some counties in Paraná state (Brazil) with focus on risk comparative analysis. Meteorological data (ANA and INMET, from 1970 to 2011) and remote sensing data (MODIS, from 2001 to 2011) were used to describe spatially the climate risk of production loss. The yield data used in this study (COAMO, from 2001 to 2011) required grouping to county level and, for that, a process of data selection was performed on spatial and temporal dimensions by a crop map (estimated by SVM - support vector machine) and by the results of a crop cycle identification algorithm. The interpolation required to spatialize temperature required a trend component which was estimated by remote sensing data, to describe the spatial variations of the variable obfuscated by traditional interpolation methods. As results, a significant relation between temperature from meteorological stations and remote sensing data was found, sustaining the use of the supposed relation between the two variables. The soybean map classifier shown over-fitting for the crop seasons for which the training samples were collected. Besides the data collection, a seeding dates distribution of soybean in Paraná state was obtained from the crop cycle identification process. The Bayesian networks showed big potential and some advantages when applied to agronomic risk modeling. The representation of the probability distribution by graphs helps the understanding of complex problems, with causality suppositions, and also helps the fitting, structuring and application of the probabilistic model. The log-normal probability distribution showed to be the best to model environment variables (thermal sum, accumulated precipitation and biggest period without rain), and the beta distribution to be the best to model relative yield and state indexes (NDVI and EVI ranges). In the case of beta regression, the precision parameter was also modeled with explanation variables as dependencies increasing the quality of the distribution fitting. In the overall, the probabilistic model had low representativity underestimating the premium rates, however it contributes to understand scenarios with risk of yield loss for the soybean crop.

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JERÔNIMO, Caio Libânio Melo. "Analisando padrões de mobilidade a partir de redes sociais e de dados sócio demográficos abertos." Universidade Federal de Campina Grande, 2017. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/1606.

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Capes
A demanda constante por melhorias na qualidade de vida dos habitantes das grandes cidades, somado à crescente urbanização desses centros, torna imprescindível a utilização de meios tecnológicos para um melhor entendimento da dinâmica dos centros urbanos e como seus habitantes interagem nesses ambientes. Nesse sentido, o aumento na utilização de dispositivos eletrônicos equipados com sistemas GPS e o constante anseio da humanidade por comunicação e, mais atualmente, por conexão à internet, vem criando novas oportunidades de estudo e também grandes desafios, especialmente no que tange a grande quantidade de dados gerados pelas redes sociais. Diversas pesquisas vêm utilizando esses dados para realizar estudos que buscam compreender traços do comportamento humano, especialmente no que diz respeito à mobilidade urbana e trajetórias. Porém, grande parte das pesquisas que utilizam dados georreferenciados se restringem às dimensões espaciais e temporais, desconsiderando outros aspectos que podem influenciar na mobilidade humana. Este trabalho propõe um método computacional capaz de extrair padrões de mobilidade oriundos de mensagens georreferenciadas de redes sociais e correlacioná-los com indicadores sociais, econômicos e demográficos fornecidos por órgãos governamentais, buscando assim, analisar quais possíveis fatores poderiam exercer alguma influência sobre a mobilidade dos moradores de uma grande cidade. Para validar o método proposto, foram utilizadas mensagens postadas no Twitter e um conjunto de indicadores sociais, ambos oriundos da cidade de Londres. Os resultados mostraram a existência de correlações entre padrões de mobilidade e indicadores sociais, especialmente os relacionados com condições de emprego e renda, como também com características étnico-religiosas dos indivíduos em estudo.
The constant need for improvements in life quality of inhabitants of big cities, together with the increasing urbanization of these centers, demands the use of technological means for a better understanding of the dynamics of urban centers and how their inhabitants interact in these environments. In this sense, the adoption of electronic devices equipped with GPS systems, the human need for communication and, more recently, for Internet connection, have brought new research opportunities and great challenges, especially due to the huge amount of data generated by social networks. Several studies have used this data to carry out research that seek to understand traces of human behavior, especially with respect to urban mobility and trajectories. However, much of the research that uses georeferenced data are restricted to spatial and temporal dimensions, disregarding other aspects that may influence human mobility. This work proposes a model capable of extracting mobility patterns from georeferenced messages of social networks and correlating them with social, economic and demographic indicators provided by government agencies, seeking to analyze which factors may impact in urban mobility. To evaluate the model, we used messages posted on Twitter and a set of social indicators, both related to the city of London. The results revealed the existence of correlations between mobility patterns and social indicators, especially those related to employment and income conditions, as well as ethnic and religious characteristics of the individuals under study.

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48

Haworth, J. "Spatio-temporal forecasting of network data." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1446923/.

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In the digital age, data are collected in unprecedented volumes on a plethora of networks. These data provide opportunities to develop our understanding of network processes by allowing data to drive method, revealing new and often unexpected insights. To date, there has been extensive research into the structure and function of complex networks, but there is scope for improvement in modelling the spatio-temporal evolution of network processes in order to forecast future conditions. This thesis focusses on forecasting using data collected on road networks. Road traffic congestion is a serious and persistent problem in most major cities around the world, and it is the task of researchers and traffic engineers to make use of voluminous traffic data to help alleviate congestion. Recently, spatio-temporal models have been applied to traffic data, showing improvements over time series methods. Although progress has been made, challenges remain. Firstly, most existing methods perform well under typical conditions, but less well under atypical conditions. Secondly, existing spatio-temporal models have been applied to traffic data with high spatial resolution, and there has been little research into how to incorporate spatial information on spatially sparse sensor networks, where the dependency relationships between locations are uncertain. Thirdly, traffic data is characterised by high missing rates, and existing methods are generally poorly equipped to deal with this in a real time setting. In this thesis, a local online kernel ridge regression model is developed that addresses these three issues, with application to forecasting of travel times collected by automatic number plate recognition on London’s road network. The model parameters can vary spatially and temporally, allowing it to better model the time varying characteristics of traffic data, and to deal with abnormal traffic situations. Methods are defined for linking the spatially sparse sensor network to the physical road network, providing an improved representation of the spatial relationship between sensor locations. The incorporation of the spatio-temporal neighbourhood enables the model to forecast effectively under missing data. The proposed model outperforms a range of benchmark models at forecasting under normal conditions, and under various missing data scenarios.

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49

Tupikina, Liubov. "Temporal and spatial aspects of correlation networks and dynamical network models." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2017. http://dx.doi.org/10.18452/17746.

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In der vorliegenden Arbeit untersuchte ich die komplexen Strukturen von Netzwerken, deren zeitliche Entwicklung, die Interpretationen von verschieden Netzwerk-Massen und die Klassen der Prozesse darauf. Als Erstes leitete ich Masse für die Charakterisierung der zeitlichen Entwicklung der Netzwerke her, um räumlich Veränderungsmuster zu erkennen. Als Nächstes führe ich eine neue Methode zur Konstruktion komplexer Netzwerke von Flussfeldern ein, bei welcher man das Set-up auch rein unter Berufung Berufung auf das Geschwindigkeitsfeld ändern kann. Diese Verfahren wurden für die Korrelationen skalarer Grössen, z. B. Temperatur, entwickelt, welche eine Advektions-Diﬀusions-Dynamik in der Gegenwart von Zwingen und Dissipation. Die Flussnetzwerk-Methode zur Zeitreihenanalyse konstruiert die Korrelationsmatrizen und komplexen Netzwerke. Dies ermöglicht die Charakterisierung von Transport in Flüssigkeiten, die Identiﬁkation verschiedene Misch-Regimes in dem Fluss und die Anwendung auf die Advektions-DiﬀusionsDynamik, Klimadaten und anderen Systemen, in denen Teilchentransport eine entscheidende Rolle spielen. Als Letztes, entwickelte ich ein neuartiges Heterogener Opinion Status Modell (HOpS) und Analysetechnik basiert auf Random Walks und Netzwerktopologie Theorien, um dynamischen Prozesse in Netzwerken zu studieren, wie die Verbreitung von Meinungen in sozialen Netzwerken oder Krankheiten in der Gesellschaft. Ein neues Modell heterogener Verbreitung auf einem Netzwerk wird als Beispielssystem für HOpS verwendent, um die vergleichsweise Einfachheit zu nutzen. Die Analyse eines diskreten Phasenraums des HOPS-Modells hat überraschende Eigenschaften, welches sensibel auf die Netzwerktopologie reagieren. Sie können verallgemeinert werden, um verschiedene Klassen von komplexen Netzwerken zu quantiﬁzieren, Transportphänomene zu charakterisieren und verschiedene Zeitreihen zu analysieren.
In the thesis I studied the complex architectures of networks, the network evolution in time, the interpretation of the networks measures and a particular class of processes taking place on complex networks. Firstly, I derived the measures to characterize temporal networks evolution in order to detect spatial variability patterns in evolving systems. Secondly, I introduced a novel ﬂow-network method to construct networks from ﬂows, that also allows to modify the set-up from purely relying on the velocity ﬁeld. The ﬂow-network method is developed for correlations of a scalar quantity (temperature, for example), which satisﬁes advection-diﬀusion dynamics in the presence of forcing and dissipation. This allows to characterize transport in the ﬂuids, to identify various mixing regimes in the ﬂow and to apply this method to advection-diﬀusion dynamics, data from climate and other systems, where particles transport plays a crucial role. Thirdly, I developed a novel Heterogeneous Opinion-Status model (HOpS) and analytical technique to study dynamical processes on networks. All in all, methods, derived in the thesis, allow to quantify evolution of various classes of complex systems, to get insight into physical meaning of correlation networks and analytically to analyze processes, taking place on networks.

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Ibrahim, Marwa. "Toward efficient data collection and decision-making strategies for resource-constrained sensor networks." Thesis, Brest, École nationale supérieure de techniques avancées Bretagne, 2021. http://www.theses.fr/2021ENTA0016.

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Bien que les avantages potentiels de la technologie de capteurs soient réels et importants, deux défis majeurs restent à relever pour réaliser pleinement ce potentiel : les ressources limitées de capteurs, en particulier la puissance de la batterie, et la prise de décision dans les applications de temps réel. Dans cette thèse, nous proposons plusieurs mécanismes de collecte et d'analyse de données qui permettent de surmonter les ressources limitées de capteurs et les défis de collecte de données volumineux imposés par les réseaux de capteurs, en se basant sur l’architecture clustering de réseaux. Principalement, les mécanismes proposés fonctionnent à trois niveaux de réseau (par exemple, capteur, CH et puits), et ils visent à réduire la quantité de données disséminées dans le réseau tout en préservant l'intégrité des informations au niveau du puits. Au niveau du capteur, nous proposons des méthodes de prédiction, d'agrégation et de compression de données basées respectivement sur des algorithmes de Newton Forward Difference, de divide-and-conquer et d'élimination de similarité dans le but de réduire les données brutes collectées par chaque capteur. Au niveau de CH, nous proposons de nouvelles techniques de clustering, de fusion, d'agrégation intermédiaire et d'ordonnancement qui visent à rechercher la corrélation entre les noeuds voisins puis à éliminer les redondances de données existantes avant d'envoyer les données vers le puits. Au niveau du puits, nous introduisons des modèles de prise de décision efficaces basés sur un tableau de score qui permet aux utilisateurs finaux d'analyser les données et de prendre une décision convenable. Nous avons évalué les performances de nos mécanismes en se basant sur de simulations et d'expérimentations. Les résultats obtenus ont montré l'efficacité de nos mécanismes en terme de la consommation d'énergie, de la précision des données et de la zone de couverture tout en améliorant les performances des réseaux de capteurs
While the potential benefits of sensingbased technology is real and significant, two major challenges remain in front of fully realizing this potential: resource-constrained sensors, especially the battery power, and decision making in real-time applications. In this thesis, we propose several data collection and analysis mechanisms that allow overcoming the limited sensor resources and the big data collection challenges imposed by sensing-based networks, under the clustering-based network architecture. Mainly, the proposed mechanisms work on three network levels (e.g. sensor, CH and sink), and they aim to reduce the amount of data routed in the network while preserving the information integrityat the sink. At the sensor level, we propose data prediction, aggregation and compression methods based respectively on Newton forward difference, divide-and-conquer and elimination similarity algorithms with the aim to reduce the raw data collected by each sensor. At the CH level, we propose new data clustering, fusion, in-network aggregation and scheduling techniques that aim to search the correlation among neighbouring nodes then to eliminate the existing data redundancies before sending the data toward the sink. At the sink level, we introduce efficient decision-making models based on customizable user-defined tables that allow end users to analyse the data and make an early decision. Weanalysed the performance of our mechanisms based on a set of simulation and experimentations. The obtained results have shown the efficiency of our mechanisms according to energy consumption, data accuracy, and coverage area while improving the performance of sensing-based networks

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Dissertations / Theses on the topic 'Spatio temporal networks'

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