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Статті в журналах з теми "Data localisation"
Komaitis, Konstantinos. "The ‘wicked problem’ of data localisation." Journal of Cyber Policy 2, no. 3 (September 2, 2017): 355–65. http://dx.doi.org/10.1080/23738871.2017.1402942.
Повний текст джерелаEtheridge, Thomas J., Antony M. Carr, and Alex D. Herbert. "GDSC SMLM: Single-molecule localisation microscopy software for ImageJ." Wellcome Open Research 7 (September 29, 2022): 241. http://dx.doi.org/10.12688/wellcomeopenres.18327.1.
Повний текст джерелаTissier, Ann-Sophie, Jean-Michel Brankart, Charles-Emmanuel Testut, Giovanni Ruggiero, Emmanuel Cosme, and Pierre Brasseur. "A multiscale ocean data assimilation approach combining spatial and spectral localisation." Ocean Science 15, no. 2 (April 26, 2019): 443–57. http://dx.doi.org/10.5194/os-15-443-2019.
Повний текст джерелаSamizadeh Nikoo, Mohammad, and Fereidoon Behnia. "Single‐site source localisation using scattering data." IET Radar, Sonar & Navigation 12, no. 2 (February 2018): 250–59. http://dx.doi.org/10.1049/iet-rsn.2017.0348.
Повний текст джерелаMuroň, Mikuláš, and David Procházka. "Wi‑Fi Indoor Localisation: A Deeper Insight Into Patterns in the Fingerprint Map Data." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 66, no. 6 (2018): 1565–71. http://dx.doi.org/10.11118/actaun201866061565.
Повний текст джерелаFraser, Erica. "Data Localisation and the Balkanisation of the Internet." SCRIPTed 13, no. 3 (December 16, 2016): 359–73. http://dx.doi.org/10.2966/scrip.130316.359.
Повний текст джерелаSchwendner, Jakob, and Frank Kirchner. "eSLAM—Self Localisation and Mapping Using Embodied Data." KI - Künstliche Intelligenz 24, no. 3 (June 29, 2010): 241–44. http://dx.doi.org/10.1007/s13218-010-0033-3.
Повний текст джерелаPetrie, Ruth E., and Sarah L. Dance. "Ensemble-based data assimilation and the localisation problem." Weather 65, no. 3 (March 2010): 65–69. http://dx.doi.org/10.1002/wea.505.
Повний текст джерелаYun Cho, Seong. "Implementation Technology for Localising a Group of Mobile Nodes in a Mobile Wireless Sensor Network." Journal of Navigation 67, no. 6 (July 30, 2014): 1089–108. http://dx.doi.org/10.1017/s0373463314000460.
Повний текст джерелаBreckels, Lisa M., Claire M. Mulvey, Kathryn S. Lilley, and Laurent Gatto. "A Bioconductor workflow for processing and analysing spatial proteomics data." F1000Research 5 (December 28, 2016): 2926. http://dx.doi.org/10.12688/f1000research.10411.1.
Повний текст джерелаДисертації з теми "Data localisation"
Romero, Marcelo. "Landmark localisation in 3D face data." Thesis, University of York, 2010. http://etheses.whiterose.ac.uk/1147/.
Повний текст джерелаNelson, Peter. "Driven by data : city-scale localisation at night." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:d67f91ea-5a9c-47b3-be07-7a46875ed503.
Повний текст джерелаFarchi, Alban. "On the localisation of ensemble data assimilation methods." Thesis, Paris Est, 2019. http://www.theses.fr/2019PESC1034.
Повний текст джерелаData assimilation is the mathematical discipline which gathers all the methods designed to improve the knowledge of the state of a dynamical system using both observations and modelling results of this system. In the geosciences, data assimilation it mainly applied to numerical weather prediction. It has been used in operational centres for several decades, and it has significantly contributed to the increase in quality of the forecasts.Ensemble methods are powerful tools to reduce the dimension of the data assimilation systems. Currently, the two most widespread classes of ensemble data assimilation methods are the ensemble Kalman filter (EnKF) and the particle filter (PF). The success of the EnKF in high-dimensional geophysical systems is largely due to the use of localisation. Localisation is based on the assumption that correlations between state variables in a dynamical system decrease at a fast rate with the distance. In this thesis, we have studied and improved localisation methods for ensemble data assimilation.The first part is dedicated to the implementation of localisation in the PF. The recent developments in local particle filtering are reviewed, and a generic and theoretical classification of local PF algorithms is introduced, with an emphasis on the advantages and drawbacks of each category. Alongside the classification, practical solutions to the difficulties of local particle filtering are suggested. The local PF algorithms are tested and compared using twin experiments with low- to medium-order systems. Finally, we consider the case study of the prediction of the tropospheric ozone using concentration measurements. Several data assimilation algorithms, including local PF algorithms, are applied to this problem and their performances are compared.The second part is dedicated to the implementation of covariance localisation in the EnKF. We show how covariance localisation can be efficiently implemented in the deterministic EnKF using an augmented ensemble. The proposed algorithm is tested using twin experiments with a medium-order model and satellite-like observations. Finally, the consistency of the deterministic EnKF with covariance localisation is studied in details. A new implementation is proposed and compared to the original one using twin experiments with low-order models
Engin, Zeynep. "Biologically plausible pattern localisation and parameter estimation on visual data." Thesis, Imperial College London, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.511987.
Повний текст джерелаZaman, Munir uz. "Mobile robot localisation : error modelling, data synchronisation and vision techniques." Thesis, University of Surrey, 2006. http://epubs.surrey.ac.uk/844082/.
Повний текст джерелаStrumia, Maddalena [Verfasser], and Thomas [Akademischer Betreuer] Brox. "Localisation and characterisation of brain pathology from structural MRI data." Freiburg : Universität, 2016. http://d-nb.info/1122594186/34.
Повний текст джерелаJagbrant, Gustav. "Autonomous Crop Segmentation, Characterisation and Localisation." Thesis, Linköpings universitet, Institutionen för systemteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-97374.
Повний текст джерелаEftersom fruktodlingar kräver stora markområden är de ofta belägna långt från större befolkningscentra. Detta gör det svårt att finna tillräckligt med arbetskraft och begränsar expansionsmöjligheterna. Genom att integrera autonoma robotar i drivandet av odlingarna skulle arbetet kunna effektiviseras och behovet av arbetskraft minska. Ett nyckelproblem för alla autonoma robotar är lokalisering; hur vet roboten var den är? I jordbruksrobotar är standardlösningen att använda GPS-positionering. Detta är dock problematiskt i fruktodlingar, då den höga och täta vegetationen begränsar användandet till större robotar som når ovanför omgivningen. För att möjliggöra användandet av mindre robotar är det istället nödvändigt att använda ett GPS-oberoende lokaliseringssystem. Detta problematiseras dock av den likartade omgivningen och bristen på distinkta riktpunkter, varför det framstår som osannolikt att existerande standardlösningar kommer fungera i denna omgivning. Därför presenterar vi ett GPS-oberoende lokaliseringssystem, speciellt riktat mot fruktodlingar, som utnyttjar den naturliga strukturen hos omgivningen.Därutöver undersöker vi och utvärderar tre relaterade delproblem. Det föreslagna systemet använder ett 3D-punktmoln skapat av en 2D-LIDAR och robotens rörelse. Först visas hur en dold semi-markovmodell kan användas för att segmentera datasetet i enskilda träd. Därefter introducerar vi ett antal deskriptorer för att beskriva trädens geometriska form. Vi visar därefter hur detta kan kombineras med en dold markovmodell för att skapa ett robust lokaliseringssystem.Slutligen föreslår vi en metod för att detektera segmenteringsfel när nya mätningar av träd associeras med tidigare uppmätta träd. De föreslagna metoderna utvärderas individuellt och visar på goda resultat. Den föreslagna segmenteringsmetoden visas vara noggrann och ge upphov till få segmenteringsfel. Därutöver visas att de introducerade deskriptorerna är tillräckligt konsistenta och informativa för att möjliggöra lokalisering. Ytterligare visas att den presenterade lokaliseringsmetoden är robust både mot brus och segmenteringsfel. Slutligen visas att en signifikant majoritet av alla segmenteringsfel kan detekteras utan att felaktigt beteckna korrekta segmenteringar som inkorrekta.
Graham, Caroline C. "Scale model seismicity : a detailed study of deformation localisation from laboratory acoustic emission data." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/4654.
Повний текст джерелаDanielsson, Simon, and Jakob Flygare. "A Multi-Target Graph-Constrained HMM Localisation Approach using Sparse Wi-Fi Sensor Data." Thesis, KTH, Optimeringslära och systemteori, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231090.
Повний текст джерелаI det här examensarbetet har lokalisering av gångtrafikanter med hjälp av Hidden Markov Models utförts. Lokaliseringen är byggd på data från Wi-Fi sensorer i ett område i Stockholm. Området är modellerat som ett graf-baserat nätverk där linjerna mellan noderna representerar möjliga vägar för en person att befinna sig på. Resultatet för varje individ är aggregerat för att visa förväntat antal personer på varje segment över en hel dag. Två metoder för att analysera hur event påverkar området introduceras och beskrivs. Den första är baserad på tidsserieanalys och den andra är en maskinlärningsmetod som bygger på Baum-Welch algoritmen. Båda metoderna visar vilka segment som drabbas mest av en snabb ökning av trafik i området och var trängsel är troligt att förekomma.
Ahmed, Bacha Adda Redouane. "Localisation multi-hypothèses pour l'aide à la conduite : conception d'un filtre "réactif-coopératif"." Thesis, Evry-Val d'Essonne, 2014. http://www.theses.fr/2014EVRY0051/document.
Повний текст джерела“ When we use information from one source,it's plagiarism;Wen we use information from many,it's information fusion ”This work presents an innovative collaborative data fusion approach for ego-vehicle localization. This approach called the Optimized Kalman Particle Swarm (OKPS) is a data fusion and an optimized filtering method. Data fusion is made using data from a low cost GPS, INS, Odometer and a Steering wheel angle encoder. This work proved that this approach is both more appropriate and more efficient for vehicle ego-localization in degraded sensors performance and highly nonlinear situations. The most widely used vehicle localization methods are the Bayesian approaches represented by the EKF and its variants (UKF, DD1, DD2). The Bayesian methods suffer from sensitivity to noises and instability for the highly non-linear cases. Proposed for covering the Bayesian methods limitations, the Multi-hypothesis (particle based) approaches are used for ego-vehicle localization. Inspired from monte-carlo simulation methods, the Particle Filter (PF) performances are strongly dependent on computational resources. Taking advantages of existing localization techniques and integrating metaheuristic optimization benefits, the OKPS is designed to deal with vehicles high nonlinear dynamic, data noises and real time requirement. For ego-vehicle localization, especially for highly dynamic on-road maneuvers, a filter needs to be robust and reactive at the same time. The OKPS filter is a new cooperative-reactive localization algorithm inspired by dynamic Particle Swarm Optimization (PSO) metaheuristic methods. It combines advantages of the PSO and two other filters: The Particle Filter (PF) and the Extended Kalman filter (EKF). The OKPS is tested using real data collected using a vehicle equipped with embedded sensors. Its performances are tested in comparison with the EKF, the PF and the Swarm Particle Filter (SPF). The SPF is an interesting particle based hybrid filter combining PSO and particle filtering advantages; It represents the first step of the OKPS development. The results show the efficiency of the OKPS for a high dynamic driving scenario with damaged and low quality GPS data
Книги з теми "Data localisation"
Boer, A. de. Coorelation, error-localisation and updating of the second problem defined in GARTEUR AG11. Amsterdam: National Aerospace Laboratory, 1990.
Знайти повний текст джерелаYuste, Rodrigo Elia, ed. Topics in language resources for translation and localisation. Philadelphia: John Benjamins Pub. Company, 2008.
Знайти повний текст джерелаЧастини книг з теми "Data localisation"
Lewis, David, Alexander O’Connor, Sebastien Molines, Leroy Finn, Dominic Jones, Stephen Curran, and Séamus Lawless. "Linking Localisation and Language Resources." In Linked Data in Linguistics, 45–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28249-2_5.
Повний текст джерелаGrognard, R. J.-M., and A. D. Seagar. "Source Localisation from Somatosensory Neuromagnetic Data." In Advances in Biomagnetism, 149–52. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4613-0581-1_24.
Повний текст джерелаOwen, Dylan, George Ashdown, Juliette Griffié, and Michael Shannon. "Co-Localisation and Correlation in Fluorescence Microscopy Data." In Standard and Super-Resolution Bioimaging Data Analysis, 143–71. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781119096948.ch6.
Повний текст джерелаAnastasiadis, Aristoklis D., George D. Magoulas, and Xiaohui Liu. "Classification of Protein Localisation Patterns via Supervised Neural Network Learning." In Advances in Intelligent Data Analysis V, 430–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45231-7_40.
Повний текст джерелаNoppen, Ivo F. R., Desislava C. Dimitrova, and Torsten Braun. "Data Filtering and Aggregation in a Localisation WSN Testbed." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 210–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35576-9_19.
Повний текст джерелаGarimella, Sai Ramani, and B. Parthiban. "Ringfencing Data?—Perspectives on Sovereignty and Localisation from India." In Blurry Boundaries of Public and Private International Law, 261–81. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8480-7_14.
Повний текст джерелаMorlier, Joseph, F. Bos, and P. Castera. "Benchmark of Damage Localisation Algorithms Using Mode Shape Data." In Damage Assessment of Structures VI, 305–12. Stafa: Trans Tech Publications Ltd., 2005. http://dx.doi.org/10.4028/0-87849-976-8.305.
Повний текст джерелаCeleste, Edoardo, and Federico Fabbrini. "Competing Jurisdictions: Data Privacy Across the Borders." In Palgrave Studies in Digital Business & Enabling Technologies, 43–58. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54660-1_3.
Повний текст джерелаCruz-Lara, Samuel, Nadia Bellalem, Julien Ducret, and Isabelle Kramer. "10. Standardising the management and the representation of multilingual data: The Multi Lingual Information Framework." In Topics in Language Resources for Translation and Localisation, 151–72. Amsterdam: John Benjamins Publishing Company, 2008. http://dx.doi.org/10.1075/btl.79.11cru.
Повний текст джерелаDjenouri, Youcef, Jon Hjelmervik, Elias Bjorne, and Milad Mobarhan. "How Image Retrieval and Matching Can Improve Object Localisation on Offshore Platforms." In Intelligent Data Engineering and Automated Learning – IDEAL 2022, 262–70. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21753-1_26.
Повний текст джерелаТези доповідей конференцій з теми "Data localisation"
Romero, Marcelo, and Nick Pears. "Landmark Localisation in 3D Face Data." In 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2009. http://dx.doi.org/10.1109/avss.2009.90.
Повний текст джерелаMercaldo, Francesco, Fabio Martinelli, Antonella Santone, and Mario Cesarelli. "Blood Cells Counting and Localisation through Deep Learning Object Detection." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020952.
Повний текст джерелаGupta, Ankush, Andrea Vedaldi, and Andrew Zisserman. "Synthetic Data for Text Localisation in Natural Images." In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.254.
Повний текст джерелаFinger, Jean Sebastien, and Aurelien Francillon. "Unprotected geo-localisation data through ARGOS satellite signals." In WiSec '20: 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3395351.3401706.
Повний текст джерелаGkovedarou, M., and I. Brilakis. "Road Drainage System Localisation and Condition Data Capture." In International Conference on Smart Infrastructure and Construction 2019 (ICSIC). ICE Publishing, 2019. http://dx.doi.org/10.1680/icsic.64669.043.
Повний текст джерелаHajjami, Amine, Abbad Khalid, and Zarghili Arsalane. "Iris Localisation and segmentation using Convolutional neural network." In 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS). IEEE, 2019. http://dx.doi.org/10.1109/icds47004.2019.8942341.
Повний текст джерелаLuo, Shixin, Yibin Ng, Terence Zheng Wei Lim, Cliff Choon Hua Tan, Nannan He, Giuseppe Manai, and Ying Li. "Improved Localisation Using Spatio-Temporal Data from Cellular Network." In 2018 19th IEEE International Conference on Mobile Data Management (MDM). IEEE, 2018. http://dx.doi.org/10.1109/mdm.2018.00022.
Повний текст джерелаFranken, Dietrich. "An approximate maximum-likelihood estimator for localisation using bistatic measurements." In 2018 Sensor Data Fusion: Trends, Solutions, Applications (SDF). IEEE, 2018. http://dx.doi.org/10.1109/sdf.2018.8547074.
Повний текст джерелаKozlowski, Michal, Dallan Byrne, Raul Santos-Rodriguez, and Robert Piechocki. "Data fusion for robust indoor localisation in digital health." In 2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW). IEEE, 2018. http://dx.doi.org/10.1109/wcncw.2018.8369009.
Повний текст джерелаGunatilaka, Ajith, Branko Ristic, Champake Mendis, Shanika Karunasekera, and Alex Skvortsov. "The effect of data collection geometry on radiological source localisation." In 2009 International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 2009. http://dx.doi.org/10.1109/issnip.2009.5416850.
Повний текст джерелаЗвіти організацій з теми "Data localisation"
Kira, Beatriz, Rutendo Tavengerwei, and Valary Mumbo. Points à examiner à l'approche des négociations de Phase II de la ZLECAf: enjeux de la politique commerciale numérique dans quatre pays d'Afrique subsaharienne. Digital Pathways at Oxford, March 2022. http://dx.doi.org/10.35489/bsg-dp-wp_2022/01.
Повний текст джерелаHakeem, Luqman, and Riaz Hussain. Key Considerations: Localisation of Polio Vaccination Efforts in the Newly Merged Districts (Tribal Areas) of Pakistan. SSHAP, September 2022. http://dx.doi.org/10.19088/sshap.2022.035.
Повний текст джерела