Relevant bibliographies by topics / Spatiotemporal identification

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Spatiotemporal identification'

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Published: 14 April 2023

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Journal articles on the topic "Spatiotemporal identification"

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Lakumarapu, Srikanth, and Rashmi Agarwal. "Cramming Identification through Spatiotemporal Data." International Journal of Computer Sciences and Engineering 6, no. 6 (June 30, 2018): 693–701. http://dx.doi.org/10.26438/ijcse/v6i6.693701.

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Voss, H., M. Bünner, and M. Abel. "Identification of continuous, spatiotemporal systems." Physical Review E 57, no. 3 (March 1998): 2820–23. http://dx.doi.org/10.1103/physreve.57.2820.

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PAN, Y., and S. A. BILLINGS. "THE IDENTIFICATION OF COMPLEX SPATIOTEMPORAL PATTERNS USING COUPLED MAP LATTICE MODELS." International Journal of Bifurcation and Chaos 18, no. 04 (April 2008): 997–1013. http://dx.doi.org/10.1142/s021812740802080x.

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Many complex and interesting spatiotemporal patterns have been observed in a wide range of scientific areas. In this paper, two kinds of spatiotemporal patterns including spot replication and Turing systems are investigated and new identification methods are proposed to obtain Coupled Map Lattice (CML) models for this class of systems. Initially, a new correlation analysis method is introduced to determine an appropriate temporal and spatial data sampling procedure for the identification of spatiotemporal systems. A new combined Orthogonal Forward Regression and Bayesian Learning algorithm with Laplace priors is introduced to identify sparse and robust CML models for complex spatiotemporal patterns. The final identified CML models are validated using correlation-based model validation tests for spatiotemporal systems. Numerical results illustrate the identification procedure and demonstrate the validity of the identified models.
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Pan, J. B., S. C. Hu, H. Wang, Q. Zou, and Z. L. Ji. "PaGeFinder: quantitative identification of spatiotemporal pattern genes." Bioinformatics 28, no. 11 (April 6, 2012): 1544–45. http://dx.doi.org/10.1093/bioinformatics/bts169.

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Conkling, Tara J., James A. Martin, Jerrold L. Belant, and Travis L. DeVault. "Spatiotemporal Dynamics in Identification of Aircraft–Bird Strikes." Transportation Research Record: Journal of the Transportation Research Board 2471, no. 1 (January 2015): 19–25. http://dx.doi.org/10.3141/2471-03.

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Pan, Y., and S. A. Billings. "Neighborhood Detection for the Identification of Spatiotemporal Systems." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38, no. 3 (June 2008): 846–54. http://dx.doi.org/10.1109/tsmcb.2008.918571.

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Ning, Hanwen, Xingjian Jing, and Li Cheng. "Identification of non-linear stochastic spatiotemporal dynamical systems." IET Control Theory & Applications 7, no. 17 (November 21, 2013): 2069–83. http://dx.doi.org/10.1049/iet-cta.2013.0150.

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Krakover, Shaul. "Identification of Spatiotemporal Paths of Spread and Backwash." Geographical Analysis 15, no. 4 (September 3, 2010): 318–29. http://dx.doi.org/10.1111/j.1538-4632.1983.tb00790.x.

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Ellison, Adrian B., Richard B. Ellison, Asif Ahmed, Dean Rance, and Stephen P. Greaves. "Spatiotemporal Identification of Trip Stops from Smartphone Data." Applied Spatial Analysis and Policy 12, no. 1 (May 4, 2016): 27–43. http://dx.doi.org/10.1007/s12061-016-9188-0.

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Dong, Xunde, and Cong Wang. "Identification of the Gray–Scott Model via Deterministic Learning." International Journal of Bifurcation and Chaos 31, no. 04 (March 30, 2021): 2150051. http://dx.doi.org/10.1142/s0218127421500516.

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Gray–Scott model is one of the most well-known reaction–diffusion models which has a wealth of spatiotemporal chaos behavior. It is commonly used to study spatiotemporal chaos. In the paper, a novel method is proposed for the identification of the Gray–Scott model via deterministic learning and interpolation. The method mainly consists of two phases: the local identification phase and the global identification phase. Local identification is achieved using the finite difference method and deterministic learning. Based on the local identification results, the interpolation method is employed to obtain global identification. Numerical experiments show the feasibility and effectiveness of the proposed method.
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Dissertations / Theses on the topic "Spatiotemporal identification"

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Townsend, Rory George. "Spatiotemporal patterns in neural population activity: Identification, dynamics, and function." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/18039.

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In the brain, electrical signals measured from large populations of neurons exhibit dynamical patterns of activity that are highly structured in both space and time. These spatiotemporal patterns have been observed at many scales throughout different brain regions and species, but their dynamics and relation to brain function remain largely unknown. This thesis provides a methodological framework to identify different classes of spatiotemporal patterns in neural activity by adapting and developing methods from the fields of computer vision and turbulence. This framework is used initially to examine local field potential recordings taken from monkey visual cortex in the absence of relevant stimuli. A variety of spatiotemporal patterns are identified in low-frequency oscillations, including wide-spread propagating and synchronous activity; and expanding, contracting, and rotating waves organised around a central point. It is then established that these patterns evolve along preferred temporal motifs instead of occurring randomly, and that they are linked to neural spiking activity and low-frequency oscillatory power. Further, examination of recordings taken during the presentation of visual stimuli demonstrates that stimulus features can modify the direction of propagating activity patterns, providing a neural mechanism for representing stimulus information beyond that of synchronised activity. This work determines that averaging signals across many trials obscures these patterns, which are present in the phase of ongoing oscillations, and instead chiefly captures the slow spread of cortical activation represented by changes in oscillation amplitude. Finally, this thesis introduces a computational framework to implement the pattern detection methods, and novel techniques to extract the dominant patterns in large-scale recordings. A MATLAB implementation of the framework is freely available at https://github.com/BrainDynamicsUSYD/NeuroPattToolbox.
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Pan, Yi. "The identification and analysis of spatiotemporal systems using coupled map lattice." Thesis, University of Sheffield, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.500125.

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Liu, Gang. "Spatiotemporal Sensing and Informatics for Complex Systems Monitoring, Fault Identification and Root Cause Diagnostics." Scholar Commons, 2015. https://scholarcommons.usf.edu/etd/5727.

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In order to cope with system complexity and dynamic environments, modern industries are investing in a variety of sensor networks and data acquisition systems to increase information visibility. Multi-sensor systems bring the proliferation of high-dimensional functional Big Data that capture rich information on the evolving dynamics of natural and engineered processes. With spatially and temporally dense data readily available, there is an urgent need to develop advanced methodologies and associated tools that will enable and assist (i) the handling of the big data communicated by the contemporary complex systems, (ii) the extraction and identification of pertinent knowledge about the environmental and operational dynamics driving these systems, and (iii) the exploitation of the acquired knowledge for more enhanced design, analysis, monitoring, diagnostics and control. My methodological and theoretical research as well as a considerable portion of my applied and collaborative work in this dissertation aims at addressing high-dimensional functional big data communicated by the systems. An innovative contribution of my work is the establishment of a series of systematic methodologies to investigate the complex system informatics including multi-dimensional modeling, feature extraction and selection, model-based monitoring and root cause diagnostics. This study presents systematic methodologies to investigate spatiotemporal informatics of complex systems from multi-dimensional modeling and feature extraction to model-driven monitoring, fault identification and root cause diagnostics. In particular, we developed a multiscale adaptive basis function model to represent and characterize the high-dimensional nonlinear functional profiles, thereby reducing the large amount of data to a parsimonious set of variables (i.e., model parameters) while preserving the information. Furthermore, the complex interdependence structure among variables is identified by a novel self-organizing network algorithm, in which the homogeneous variables are clustered into sub-network communities. Then we minimize the redundancy of variables in each cluster and integrate the new set of clustered variables with predictive models to identify a sparse set of sensitive variables for process monitoring and fault diagnostics. We evaluated and validated our methodologies using real-world case studies that extract parameters from representation models of vectorcardiogram (VCG) signals for the diagnosis of myocardial infarctions. The proposed systematic methodologies are generally applicable for modeling, monitoring and diagnosis in many disciplines that involve a large number of highly-redundant variables extracted from the big data. The self-organizing approach was also innovatively developed to derive the steady geometric structure of a network from the recurrence-based adjacency matrix. As such, novel network-theoretic measures can be achieved based on actual node-to-node distances in the self-organized network topology.
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Wimberly, Brent. "Identification of spatiotemporal nutrient patterns and associated ecohydrological trends in the tampa bay coastal region." Honors in the Major Thesis, University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/642.

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Improvements for environmental monitoring and assessment were achieved to advance our understanding of sea-land interactions and nutrient cycling in a coastal bay.; The comprehensive assessment techniques for monitoring of water quality of a coastal bay can be diversified via an extensive investigation of the spatiotemporal nutrient patterns and the associated eco-hydrological trends in a coastal urban region. With this work, it is intended to thoroughly investigate the spatiotemporal nutrient patterns and associated eco-hydrological trends via a two part inquiry of the watershed and its adjacent coastal bay. The findings show that the onset of drought lags the crest of the evapotranspiration and precipitation curve during each year of drought. During the transition year, ET and precipitation appears to start to shift back into the analogous temporal pattern as the 2005 wet year. NDVI shows a flat receding tail for the September crest in 2005 due to the hurricane impact signifying that the hurricane event in October dampening the severity of the winter dry season in which alludes to relative system memory. The k-means model with 8 clusters is the optimal choice, in which cluster 2 at Lower Tampa Bay had the minimum values of total nitrogen (TN) concentrations, chlorophyll a (Chl-a) concentrations, and ocean color values in every season as well as the minimum concentration of total phosphorus (TP) in three consecutive seasons in 2008. Cluster 5, located in Middle Tampa Bay, displayed elevated TN concentrations, ocean color values, and Chl-a concentrations, suggesting that high colored dissolved organic matter values are linked with some nutrient sources. The data presented by the gravity modeling analysis indicate that the Alafia River Basin is the major contributor of nutrients in terms of both TP and TN values in all seasons. Such ecohydrological evaluation can be applied for supporting the LULC management of climatic vulnerable regions as well as further enrich the comprehensive assessment techniques for estimating and examining the multi-temporal impacts and dynamic influence of urban land use and land cover.
B.S.C.E.
Bachelors
Engineering and Computer Science
Civil Engineering
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Alford, Lea Marie. "Identification and Spatiotemporal Control of the Asymmetrical Membrane Cortex in Cleavage Stage Sea Urchin Embryos." Thesis, Boston College, 2009. http://hdl.handle.net/2345/978.

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Thesis advisor: David R. Burgess
Polarity established by the first cleavages in sea urchin embryos was investigated in this thesis revealing precocious embryonic polarity. Studies of embryonic polarity have focused on protostomes such as C. elegans, and those on deuterostomes have focused on later developmental stages. I find asymmetries in the sea urchin membrane cell cortex as early as the first division after fertilization as a result of new membrane addition in the cleavage furrow. Membrane domains and the polarity determinants Par6, aPKC, and Cdc42 are polarized to the apical, or free, cell surface, while the cell-cell contact site remains distinct. Using immunofluorescence, fluorescence recovery after photobleaching (FRAP), and specific inhibitor treatments, myosin filaments were identified as the major regulator of membrane cortex polarity. However, membrane domains and cortical polarity determinants are differentially regulated with respect to blastomere dissociation. These asymmetries are required for proper spindle alignment and cleavage plane determination and are responsible for polarized fluid phase endocytosis. The work in this thesis and future studies addressing the connection between the membrane cortex and myosin filaments has and will lead to a greater understanding of the maintenance of embryonic polarity in cleavage stage sea urchin embryos
Thesis (PhD) — Boston College, 2009
Submitted to: Boston College. Graduate School of Arts and Sciences
Discipline: Biology
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Al, Jord Adel. "Centriole amplification in brain multiciliated cells : high resolution spatiotemporal dynamics and identification of regulatory mechanisms." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066706/document.

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Les cellules multiciliées jouent un rôle essentiel dans la propulsion des fluides physiologiques. Leur dysfonctionnement provoque des maladies chroniques. Contrairement à la plupart des cellules de mammifères qui possèdent un centrosome composé de deux centrioles, les cellules multiciliées possèdent une centaine de centrioles qui servent de base à la nucléation des cils motiles. Les mécanismes d'amplification de centrioles ou de régulation du nombre de centrioles dans ce type cellulaire étaient jusque-là inconnus. Les centrioles nouvellement formés étaient considérés comme apparaissant " de novo ". Une approche de vidéomicroscopie et de microscopie de super-résolution corrélative nous a d'abord permis de déterminer que tous les procentrioles sont générés à partir du centrosome préexistant. Nous démontrons que le centriole fils du centrosome est le site principal de nucléation de 95% de centrioles nouvellement formés dans les cellules multiciliées. Ces résultats réfutent par conséquent l'origine " de novo " des centrioles dans ce type cellulaire. Puis, nous montrons que la machinerie mitotique orchestre la progression spatio-temporelle de la dynamique centriolaire dans ces cellules post-mitotiques et en phase terminale de différentiation. L'amortissement de l'activité de Cdk1 empêche la rentrée en mitose tout en permettant la coordination du nombre de centrioles, leur croissance, et leur désengagement par des transitions phasiques nécessaires à la nucléation de cils motiles. Cette thèse aide à mieux comprendre la différentiation des cellules multiciliées, les ciliopathies, ainsi que l'amplification centriolaire pathologique associée avec le cancer et la microcéphalie
Multiciliated mammalian cells play a crucial role in the propulsion of physiological fluids. Their dysfunction causes severe chronic diseases. In contrast to the strict centriole number control in cycling cells, multiciliated cell differentiation is marked by the production of up to several hundred centrioles, each nucleating a motile cilium. The mechanisms of centriole amplification or centriole number control in these cells were unknown and new centrioles were thought to appear de novo in the cytoplasm. First, videomicroscopy combined with correlative super-resolution and electron microscopy has enabled us to determine that all procentrioles are generated via runs of nucleation from the pre-existing progenitor cell centrosome. We show that the daughter centriole of the centrosome is the primary nucleation site for 95% of the new centrioles in multiciliated cells and thus refute the de novo hypothesis. Then, we provide evidence of an activation of the mitosis regulatory network during the centriole dynamic. With single cell live imaging and pharmacological modulation of mitosis regulators, we show that the mitosis machinery orchestrates the spatiotemporal progression of centriole amplification in terminally differentiating multiciliated cell progenitors. The fine-tuning of Cdk1 activity prevents mitosis while allowing the timely coordination of centriole number, growth, and disengagement through checkpoint-like phase transitions necessary for subsequent functional motile ciliation. This PhD provides a new paradigm for studying multiciliated cell differentiation, cilia-related diseases and pathological centriole amplification associated with cancer and microcephaly
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Pruzinsky, Nina. "Identification and spatiotemporal dynamics of tuna (Family: Scombridae; Tribe: Thunnini) early life stages in the oceanic Gulf of Mexico." Thesis, NSUWorks, 2018. https://nsuworks.nova.edu/occ_stuetd/472.

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Fishes within the family Scombridae (i.e. tunas, mackerels and bonitos) are of high ecological and economic value, as they are heavily targeted by commercial and recreational fisheries. In coastal and open-ocean environments, adults are high-level predators, while larvae and juveniles serve as prey for numerous species. Much is known about the distribution and abundance of adult tunas, but high taxonomic uncertainty and limited knowledge regarding the distributional patterns of larval and juvenile tunas have led to an “operational taxonomic unit” gap in our understanding of tuna ecology. Scombrids were collected across the Gulf of Mexico (GoM, hereafter) during seven research cruises from 2010-2011, as part of the NOAA-supported Offshore Nekton Sampling and Analysis Program, and during five research cruises from 2015-2017, as a part of the GOMRI-supported Deep Pelagic Nekton Dynamics of the Gulf of Mexico Consortium. In this thesis, species composition, distribution, and abundance of tunas collected from the surface to 1500 m depth are characterized in relation to depth, time of year, and physical oceanographic features. A synthesis of the morphological characteristics used to identify the taxonomically challenging larval and juvenile stages of tunas is presented, along with length-weight regressions to fill the data gap on the growth patterns of these early life stages. A total of 945 scombrid specimens were collected, representing 11 of the 16 species that occur in the GoM. The dominant species included: Euthynnus alletteratus (Little Tunny), Thunnus atlanticus (Blackfin Tuna), Auxis thazard (Frigate Mackerel), and Katsuwonus pelamis (Skipjack Tuna). Evidence of sampling gear selectivity was observed, with a MOCNESS (rectangular, research-sized trawl) collecting larvae predominantly, and a large, high-speed rope trawl catching only juveniles. Scombrids were collected primarily in the upper 200 m of the water column. Species-specific environmental preferences and seasonality were identified as the main drivers of tuna spatial distributions across the epipelagic GoM. Integrating aspects of scombrid ecology in neritic and oceanic environments improves management and conservation efforts for this highly important taxon.
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Berro, Soumaya. "Identification of muscle activation schemes by inverse methods applied on HD-sEMG signals." Electronic Thesis or Diss., Compiègne, 2022. http://www.theses.fr/2022COMP2708.

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L'identification rapide ou en temps réel de l'activation spatio-temporelle des unités motrices (UM) qui représentent les unités fonctionnelles du système neuromusculaire est fondamentale dans les applications de contrôle des prothèses et en réhabilitation fonctionnelle. Cependant, cette procédure demande un temps de calcul énorme. Par conséquent, le travail de cette thèse a été consacré à fournir un algorithme permettant l'identification en temps réel des stratégies d'activation spatiale et temporelle des UMs en appliquant des méthodes inverses sur les signaux HD-sEMG (électromyogramme de surface à haute densité) à partir d'une grille placée sur le Biceps Brachial (BB). À cette fin, nous proposons une approche innovante, qui implique l'utilisation de la méthode inverse classique de minimisation de norme et une interpolation de courbe en 3D, à savoir l'approche est nommée CFB-MNE. Cette méthode, fondée sur l'identification inverse (estimation de la norme minimale) couplée à un dictionnaire des potentiels d'action des unités motrices simulées (MUAP) d'un modèle récent et testée sur des simulations, a permis la localisation en temps réel des unités motrices individuelles simulées. Une analyse de robustesse (modifications anatomiques, physiologiques et instrumentales) a ensuite été effectuée pour vérifier l'efficacité de l'algorithme proposé. Enfin, l'algorithme proposé a été testé sur des UMs avec des schémas de recrutement réalistes donnant des résultats prometteurs et encourageants en identification spatiale et temporelle sur trois scenarios. Pour conclure, en perspectives, les résultats prometteurs obtenus suggèrent l'utilisation de l'apprentissage automatique et de l'intelligence artificielle (IA) pour améliorer encore les performances de l'algorithme proposé
Fast or real-time identification of the spatiotemporal activation of Motor Units (MUs), functional units of the neuromuscular system, is fundamental in applications as prosthetic control and rehabilitation guidance but often dictates expensive computational times. Therefore, the thesis work was devoted to providing an algorithm that enables the real-time identification of MU spatial and temporal activation strategies by applying inverse methods on HD-sEMG (high-density surface electromyogram) signals from a grid placed over the Biceps Brachii (BB). For this purpose, we propose an innovative approach, that involves the use of the classical minimum norm inverse method and a 3D fitting curve interpolation, namely CFB-MNE approach. This method, based on inverse identification (minimum norm estimation) coupled to simulated motor unit action potential (MUAP) dictionary from a recent model and tested on simulations, allowed the real time localization of simulated individual motor units. A robustness analysis (anatomical, physiological, and instrumental modifications) was then performed to verify the efficiency of the proposed algorithm. Finally, the proposed algorithm was tested on MUs with realistic recruitment patterns giving promising results in both spatial and temporal identification. To conclude, a door to future perspectives was opened, according to the obtained promising results, suggesting the use of machine learning and artificial intelligence (AI) to further boost the performance of the proposed algorithm
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Ndione, Méry. "Dynamique et identification des sources de contamination fécale dans un espace littoral connaissant des pratiques de tourisme et de loisirs : l’exemple de la baie d’Aytré." Thesis, La Rochelle, 2022. http://www.theses.fr/2022LAROS006.

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La qualité microbiologique des eaux de baignade diminue progressivement d’années en années, et peut être un problème majeur de santé publique. Ainsi, des surveillances sanitaires de la qualité microbiologique des eaux de baignade sont effectuées conformément à la directive européenne (2006/7/EC) pour assurer la sécurité sanitaire des baigneurs et préserver l’image de ces écosystèmes. Depuis de nombreuses années, la baie d’Aytré (Charente Maritime, France), a été classée en « qualité insuffisante » et cette plage est interdite à la baignade depuis 2018. Les enjeux sanitaires et le rôle prépondérant de cette plage sur le développement touristique et l’économie locale ont conduit à rechercher l’origine et le déterminisme spatiotemporel de cette contamination fécale. Cette thèse présente une approche intégrée de l’analyse de la contamination fécale de la baie d’Aytré par une étude pluridisciplinaire de différentes hypothèses analysées depuis le début des années 2000 par les collectivités locales. Le niveau de contamination fécale des eaux de baignade durant une année était relativement faible avec une variation saisonnière notable de l’abondance des indicateurs de contamination fécale Escherichia coli et entérocoques. Les dépassements des seuils réglementaires sur 24% et 32% des échantillons d’eau du Platin Nord et du Platin Sud, les deux sites de baignade de la baie d’Aytré étaient principalement dus aux entérocoques. La qualité microbiologique du sédiment au cours du temps a montré que le sédiment n’était pas une source diffuse de contamination dans l’eau. L’analyse combinée des marqueurs protéiques, génétiques et chimiques a révélé la présence d’une part d’espèces d’entérocoques d’origine environnementale et d’autre part une contamination d’origine animale principalement et une faible contribution de l’origine humaine. La stratégie d’analyse élaborée et les outils développés au cours de cette étude permettront d’améliorer les méthodes de surveillance sanitaire des eaux de baignade
The microbiological quality of bathing water is progressively decreasing from year to year, and can constitute a major public health problem. Thus, sanitary monitoring of the microbiological quality of bathing waters is carried out in accordance with the European directive (2006/7/EC) to ensure the sanitary safety of bathers and preserve the image of these recreational ecosystems. For many years, the bay of Aytré (Charente Maritime, France), has been classified as "poor quality" and this beach is prohibited for bathing since 2018. The health issues and the preponderant role of this beach on the tourism development and the local economy led to investigate the origin and the spatiotemporal determinism of this fecal contamination. This thesis presents an integrated approach to the analysis of the fecal contamination of Aytré Bay through a multidisciplinary study of different hypotheses analysed since the beginning of the 2000s by the local authorities. The level of fecal contamination of the bathing water during a year was relatively low with a notable seasonal variation in the abundance of the fecal contamination indicators Escherichia coli and enterococci. Exceedances of the regulatory thresholds on 24% and 32% of the water samples from Platin Nord and Platin Sud, the two bathing sites in Aytré Bay, were mainly due to enterococci. The microbiological quality of the sediment over time showed that the sediment was not a diffuse source of contamination in the water. The combined analysis of protein, genetic and chemical markers revealed the presence of enterococci species of environmental origin on the one hand, and on the other hand, a contamination of mainly animal origin and a small contribution from human origin. The analytical strategy and tools developed during this study will help to improve the sanitary surveillance methods of bathing waters
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Ghrissi, Amina. "Ablation par catheter de fibrillation atriale persistante guidée par dispersion spatiotemporelle d’électrogrammes : Identification automatique basée sur l’apprentissage statistique." Thesis, Université Côte d'Azur, 2021. http://www.theses.fr/2021COAZ4026.

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La fibrillation atriale (FA) est l’arythmie cardiaque soutenue la plus fréquemment rencontrée dans la pratique clinique. Pour la traiter, l’ablation par cathéter de zones cardiaques jugées responsables de soutenir l’arythmie est devenue la thérapie la plus utilisée. Un nouveau protocole d’ablation se base sur l’identification des zones atriales où les électrogrammes (EGM) enregistrés à l’aide d’un cathéter à électrodes multiples, appelé PentaRay, manifestent des décalages spatiotemporels significatifs sur plusieurs voies adjacentes. Ce phénomène est appelé dispersion spatio-temporelle (DST). L’intervention devient ainsi plus adaptée aux spécificités de chaque patient et elle atteint un taux de succès procédural de 95%. Cependant, à l’heure actuelle les zones de DST sont identifiées de manière visuelle par le spécialiste pratiquant l’ablation. Cette thèse vise à identifier automatiquement les sites potentiels d’ablation basée sur la DST à l’aide de techniques d’apprentissage statistique et notamment d’apprentissage profond adaptées. Dans la première partie, les enregistrements EGM sont classés par catégorie en DST vs. non-DST. Cependant, le rapport très déséquilibré entre les données issues des deux classes dégrade les résultats de classification. Nous abordons ce problème en utilisant des techniques d’augmentation de données adaptées à la problématique médicale et qui permettent d’obtenir de bons taux de classification. La performance globale s’élève ainsi atteignant des valeurs de précision et d’aire sous la courbe ROC autour de 90%. Deux approches sont ensuite comparées, l’ingénierie des caractéristiques et l’extraction automatique de ces caractéristiques par apprentissage statistique à partir d’une série temporelle, appelée valeur absolue de tension maximale aux branches du PentRay (VAVp). Les résultats montrent que la classification supervisée de VAVp est prometteuse avec des valeurs de précision, sensibilité et spécificité autour de 90%. Ensuite, la classification des enregistrements EGM bruts est effectuée à l’aide de plusieurs outils d’apprentissage statistique. Une première approche consiste à étudier les circuits arithmétiques à convolution pour leur intérêt théorique prometteur, mais les expériences sur des données synthétiques sont infructueuses. Enfin, nous investiguons des outils d’apprentissage supervisé plus conventionnels comme les réseaux de neurones convolutifs (RNC). Nous concevons une sélection de représentation des données adaptées à différents algorithmes de classification. Ces modèles sont ensuite évalués en termes de performance et coût de calcul. L’apprentissage profond par transfert est aussi étudié. La meilleure performance est obtenue avec un RNC peu profond pour la classification des matrices EGM brutes, atteignant 94% de précision et d’aire sous la courbe ROC en plus d’un score F1 de 60%. Dans la deuxième partie, les enregistrements EGM acquis pendant la cartographie sont étiquetés ablatés vs. non-ablatés en fonction de leur proximité par rapport aux sites d’ablation, puis classés dans les mêmes catégories. Les annotations de dispersion sont aussi prises en compte comme une probabilité à priori dans la classification. La meilleure performance représente un score F1 de 76%. L’agrégation de l’étiquette DST ne permet pas d’améliorer les performances du modèle. Globalement, ce travail fait partie des premières tentatives d’application de l’analyse statistique et d’outils d’apprentissage pour l’identification automatique et réussie des zones d’ablation en se basant sur la DST. En fournissant aux cardiologues interventionnels un outil intelligent, objectif et déployé en temps réel qui permet la caractérisation de la dispersion spatiotemporelle, notre solution permet d’améliorer potentiellement l’efficacité de la thérapie personnalisée d’ablation par cathéter de la FA persistante
Catheter ablation is increasingly used to treat atrial fibrillation (AF), the most common sustained cardiac arrhythmia encountered in clinical practice. A recent patient-tailored AF ablation therapy, giving 95% of procedural success rate, is based on the use of a multipolar mapping catheter called PentaRay. It targets areas of spatiotemporal dispersion (STD) in the atria as potential AF drivers. STD stands for a delay of the cardiac activation observed in intracardiac electrograms (EGMs) across contiguous leads.In practice, interventional cardiologists localize STD sites visually using the PentaRay multipolar mapping catheter. This thesis aims to automatically characterize and identify ablation sites in STD-based ablation of persistent AF using machine learning (ML) including deep learning (DL) techniques. In the first part, EGM recordings are classified into STD vs. non-STD groups. However, highly imbalanced dataset ratio hampers the classification performance. We tackle this issue by using adapted data augmentation techniques that help achieve good classification. The overall performance is high with values of accuracy and AUC around 90%. First, two approaches are benchmarked, feature engineering and automatic feature extraction from a time series, called maximal voltage absolute values at any of the bipoles (VAVp). Statistical features are extracted and fed to ML classifiers but no important dissimilarity is obtained between STD and non-STD categories. Results show that the supervised classification of raw VAVp time series itself into the same categories is promising with values of accuracy, AUC, sensi-tivity and specificity around 90%. Second, the classification of raw multichannel EGM recordings is performed. Shallow convolutional arithmetic circuits are investigated for their promising theoretical interest but experimental results on synthetic data are unsuccessful. Then, we move forward to more conventional supervised ML tools. We design a selection of data representations adapted to different ML and DL models, and benchmark their performance in terms of classification and computational cost. Transfer learning is also assessed. The best performance is achieved with a convolutional neural network (CNN) model for classifying raw EGM matrices. The average performance over cross-validation reaches 94% of accuracy and AUC added to an F1-score of 60%. In the second part, EGM recordings acquired during mapping are labeled ablated vs. non-ablated according to their proximity to the ablation sites then classified into the same categories. STD labels, previously defined by interventional cardiologists at the ablation procedure, are also aggregated as a prior probability in the classification task.Classification results on the test set show that a shallow CNN gives the best performance with an F1-score of 76%. Aggregating STD label does not help improve the model’s performance. Overall, this work is among the first attempts at the application of statistical analysis and ML tools to automatically identify successful ablation areas in STD-based ablation. By providing interventional cardiologists with a real-time objective measure of STD, the proposed solution offers the potential to improve the efficiency and effectiveness of this fully patient-tailored catheter ablation approach for treating persistent AF
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Books on the topic "Spatiotemporal identification"

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Dicecco, Nico. The Aura of Againness. Edited by Thomas Leitch. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199331000.013.35.

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By examining the complexities of aura, authenticity, materiality, and reception in the context of adaptation studies, Chapter 35 argues against the idea that adaptations are a specific kind of text and in favor of the idea that adaptations are actively constituted as such through performance: through live and embodied acts of identification that have significant material consequences. Drawing on several foundational concepts in adaptation studies and performance theory, Chapter 35 articulates a reception model of adaptation that is relevant not only to theatrical adaptations but across media and genres by showing the ways the aura of adaptation is generated as much through the unique spatiotemporal presence of an artwork as through its momentary disappearance from its place and time.
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Book chapters on the topic "Spatiotemporal identification"

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Zhao, Guoying, and Matti Pietikäinen. "Visual Speaker Identification with Spatiotemporal Directional Features." In Lecture Notes in Computer Science, 1–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39094-4_1.

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Li, Ye, Guangqiang Yin, Shaoqi Hou, Jianhai Cui, and Zicheng Huang. "Spatiotemporal Feature Extraction for Pedestrian Re-identification." In Wireless Algorithms, Systems, and Applications, 188–200. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23597-0_15.

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Zhong, Xian, Meng Feng, Wenxin Huang, Zheng Wang, and Shin’ichi Satoh. "Poses Guide Spatiotemporal Model for Vehicle Re-identification." In MultiMedia Modeling, 426–39. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05716-9_35.

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Fang, Zhixiang, Shih-Lung Shaw, Wei Tu, and Qingquan Li. "Spatiotemporal Critical Opportunity and Link Identification for Joint Participation Scheduling." In Space-Time Integration in Geography and GIScience, 109–26. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-9205-9_7.

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Chen, Wei, and Wangchongyu Peng. "Spatiotemporal Quantification and Identification of Urban Development and Its Characteristics." In Digital Analysis of Urban Structure and Its Environment Implication, 49–80. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6641-5_3.

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Huo, Yonghua, Hongwu Ge, Libin Jiao, Bowen Gao, and Yang Yang. "Encrypted Traffic Identification Method Based on Multi-scale Spatiotemporal Feature Fusion Model with Attention Mechanism." In Proceedings of the 11th International Conference on Computer Engineering and Networks, 857–66. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6554-7_92.

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Spurney, Ryan, Michael Schwartz, Mariah Gobble, Rosangela Sozzani, and Lisa Van den Broeck. "Spatiotemporal Gene Expression Profiling and Network Inference: A Roadmap for Analysis, Visualization, and Key Gene Identification." In Modeling Transcriptional Regulation, 47–65. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1534-8_4.

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Eshel, Gidon. "Regression and Least Squares." In Spatiotemporal Data Analysis. Princeton University Press, 2011. http://dx.doi.org/10.23943/princeton/9780691128917.003.0009.

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This chapter focuses on linear regression, the process of identifying the unique model that best explains a set of observed data among a specified class of general models. Regression thus occupies a uniquely important position at the very interface of modeling and data analysis. Regression arises very often, in various guises, in handling and analyzing data. Since it is one of the most basic, useful, and frequently employed data analysis tools, and since some understanding of regression is needed in later sections, regression will be discussed in some detail. Topics covered include setting up the problem; the linear system Ax = b, least squares, special problems giving rise to linear systems, statistical issues in regression analysis, and multidimensional regression and linear model identification.
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Bhowmick, Sutanu, and Satish Nagarajaiah. "Structural System Identification Using Vision-Based Full-Field Spatiotemporal Measurements." In Recent Developments in Structural Health Monitoring and Assessment — Opportunities and Challenges, 375–405. WORLD SCIENTIFIC, 2022. http://dx.doi.org/10.1142/9789811243011_0013.

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Betancourt, Ramón J., Ramón Daniel Rodríguez-Soto, Antonio Concha Sánchez, and Emilio Barocio Espejo. "Identification of source harmonics in electrical networks using spatiotemporal approaches." In Monitoring and Control of Electrical Power Systems Using Machine Learning Techniques, 163–89. Elsevier, 2023. http://dx.doi.org/10.1016/b978-0-32-399904-5.00013-2.

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Conference papers on the topic "Spatiotemporal identification"

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Khan, Muhammad Hassan, Muhammad Shahid Farid, and Marcin Grzegorzek. "Person identification using spatiotemporal motion characteristics." In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296264.

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Brandstrom, Gary, Dennis W. Ruck, Steven K. Rogers, and Bruce E. Stribling. "Space object identification using spatiotemporal pattern recognition." In Aerospace/Defense Sensing and Controls, edited by Steven K. Rogers and Dennis W. Ruck. SPIE, 1996. http://dx.doi.org/10.1117/12.235937.

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Gkentsidis, K., T. Pistola, N. Mitianoudis, and N. V. Boulgouris. "Deep Person Identification Using Spatiotemporal Facial Motion Amplification." In 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020. http://dx.doi.org/10.1109/icip40778.2020.9191281.

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Li, Shuang, Slawomir Bak, Peter Carr, and Xiaogang Wang. "Diversity Regularized Spatiotemporal Attention for Video-Based Person Re-identification." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00046.

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Li, Weihong, and Yeben Chen. "Risk factor identification and spatiotemporal diffusion path during the dengue outbreak." In 2016 4th International Workshop on Earth Observation and Remote Sensing Applications (EORSA). IEEE, 2016. http://dx.doi.org/10.1109/eorsa.2016.7552827.

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Yang, Xu, Bin Zhang, Yuan Dong, Fengye Xiong, and Hongliang Bai. "Spatiotemporal Attention on Sliced Parts for Video-based Person Re-identification." In 2018 IEEE Visual Communications and Image Processing (VCIP). IEEE, 2018. http://dx.doi.org/10.1109/vcip.2018.8698653.

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Zheng, Chong, Ping Wei, and Nanning Zheng. "A Duplex Spatiotemporal Filtering Network for Video-based Person Re-identification." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412371.

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Mohseni, Hamid R., and Saeid Sanei. "A new method for spatiotemporal identification of event-related potential subcomponents." In 2009 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2009. http://dx.doi.org/10.1109/iembs.2009.5332547.

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Castells, F., R. Ruiz, J. J. Rieta, and J. Millet. "An integral atrial wave identification based on spatiotemporal source separation: clinical validation." In Computers in Cardiology, 2003. IEEE, 2003. http://dx.doi.org/10.1109/cic.2003.1291256.

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Wang, Liwei, Xuedong Yan, Deqi Chen, Xiaobing Liu, and Tong Liu. "Identification and Classification of Spatiotemporal Traffic Congestion Based on Floating Car Data." In 20th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2020. http://dx.doi.org/10.1061/9780784482933.009.

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