Relevant bibliographies by topics / Data localisation

Academic literature on the topic 'Data localisation'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Data localisation"

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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.

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Etheridge, 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.

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Single-molecule localisation microscopy (SMLM) uses software to extract super-resolved positions from microscope images of fluorescent molecules. These localisations can then be used to render super-resolution images or analysed to extract information about molecular behaviour. The GDSC SMLM software provides a set of tools for analysing SMLM data in a single cross-platform environment. The software identifies fluorescent molecules in raw microscope images and localises their positions using stages of spot detection, spot fitting and spot rejection. The resulting localisation data set can then be visualised, cropped and filtered. A suite of downstream analysis tools enable the user to perform single-particle tracking, cluster analysis and drift correction. In addition, GDSC SMLM also provides utility tools that enable modelling of EM-CCD and sCMOS cameras as well as point spread functions (PSFs) for data simulation. The software is written in Java and runs as a collection of plugins for the ImageJ software.
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Tissier, 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.

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Abstract. Ocean data assimilation systems encompass a wide range of scales that are difficult to control simultaneously using partial observation networks. All scales are not observable by all observation systems, which is not easily taken into account in current ocean operational systems. The main reason for this difficulty is that the error covariance matrices are usually assumed to be local (e.g. using a localisation algorithm in ensemble data assimilation systems), so that the large-scale patterns are removed from the error statistics. To better exploit the observational information available for all scales in the assimilation systems of the Copernicus Marine Environment Monitoring Service, we investigate a new method to introduce scale separation in the assimilation scheme. The method is based on a spectral transformation of the assimilation problem and consists in carrying out the analysis with spectral localisation for the large scales and spatial localisation for the residual scales. The target is to improve the observational update of the large-scale components of the signal by an explicit observational constraint applied directly on the large scales and to restrict the use of spatial localisation to the small-scale components of the signal. To evaluate our method, twin experiments are carried out with synthetic altimetry observations (simulating the Jason tracks), assimilated in a 1/4∘ model configuration of the North Atlantic and the Nordic Seas. Results show that the transformation to the spectral domain and the spectral localisation provides consistent ensemble estimates of the state of the system (in the spectral domain or after backward transformation to the spatial domain). Combined with spatial localisation for the residual scales, the new scheme is able to provide a reliable ensemble update for all scales, with improved accuracy for the large scale; and the performance of the system can be checked explicitly and separately for all scales in the assimilation system.
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Samizadeh 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.

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Muroň, 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.

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Localisation via Wi‑Fi networks is one of the possible techniques which can be used for positioning inside buildings or in other places without the GPS signal. The accurate indoor positioning system can help users with localisation or navigation within unfamiliar places. Almost all buildings are covered with the Wi‑Fi signal. Using the currently existing infrastructure will minimise cost for construction other types of indoor positioning systems. Among other reasons, usage of Wi‑Fi for positioning is also convenient because almost every mobile device has a Wi‑Fi capability and therefore the system can be easily used by everyone. However, an important factor is the precision of such a solution. The article is focused on the evaluation of Wi‑Fi localisation precision within the university grounds.
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Fraser, 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.

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Schwendner, 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.

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Petrie, 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.

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Yun 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.

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This paper addresses a simple implementation technology for localising a group of mobile nodes using the Peer-To-Peer (P2P) ranging measurements obtained from a Mobile Wireless Sensor Network (MWSN). In recent years, MWSN-based multi-target localisation technologies have been investigated with alacrity. However, it is difficult to implement P2P ranging and data transformation for multi-target localisation iteratively using single channel MWSN. Also, there are critical problems in the conventional localisation algorithms associated with the estimation errors owing to the erroneous linearization of a nonlinear range equation and bad relative locations of the reference nodes and target node. In this paper, very simple P2P ranging and connection data sharing procedures that can be easily implemented in single channel MWSN are established. Moreover, an adaptive localisation algorithm is presented to reduce the estimation errors that may occur in the conventional localisation algorithms. To verify the performance of the presented implementation technology, a localisation system prototype is developed and the presented procedures and algorithm are embedded in the prototype. Through actual experiments, the validity of the localisation system containing the proposed procedures and algorithm is demonstrated.
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Breckels, 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.

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Spatial proteomics is the systematic study of protein sub-cellular localisation. In this workflow, we describe the analysis of a typical quantitative mass spectrometry-based spatial proteomics experiment using the MSnbase and pRoloc Bioconductor package suite. To walk the user through the computational pipeline, we use a recently published experiment predicting protein sub-cellular localisation in pluripotent embryonic mouse stem cells. We describe the software infrastructure at hand, importing and processing data, quality control, sub-cellular marker definition, visualisation and interactive exploration. We then demonstrate the application and interpretation of statistical learning methods, including novelty detection using semi-supervised learning, classification, clustering and transfer learning and conclude the pipeline with data export. The workflow is aimed at beginners who are familiar with proteomics in general and spatial proteomics in particular.
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Dissertations / Theses on the topic "Data localisation"

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Romero, Marcelo. "Landmark localisation in 3D face data." Thesis, University of York, 2010. http://etheses.whiterose.ac.uk/1147/.

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Accurate landmark localisation is an essential precursor to many 3D face processing algorithms but, as yet, there is a lack of convincing solutions that work well over a wide range of head poses. In this thesis, an investigation to localise facial landmarks from 3D images is presented, without using any assumption concerning facial pose. In particular, this research devises new surface descriptors, which are derived from either unstructured face data, or a radial basis function (RBF) model of the facial surface. A ground–truth of eleven facial landmarks is collected over well–registered facial images in the Face Recognition Grand Challenge (FRGC) database. Then, a range of feature descriptors of varying complexity are investigated to illustrate repeatability and accuracy when computed for the full set of eleven facial landmarks. At this stage, the nose–tip and two inner–eye corners are observed as the most distinctive facial landmarks as a trade–off among repeatability, accuracy, and complexity. Thus, this investigation focuses on the localisation of these three facial landmarks, which is the minimum number of landmarks necessary for pose normalisation. Two new families of descriptors are introduced, namely point–pair and point–triplet descriptors, which require two and three vertices respectively for their computation. Also, two facial landmark localisation methods are investigated; in the first, a binary decision tree is used to implement a cascade filter, in the second, graph matching is implemented via relaxation by elimination. Then, using all of these descriptors and algorithms, a number of systems are designed to localise the nose–tip and two inner–eye corners. Above all, 99.92% of nose–tip landmarks within an accuracy of 12mm is the best localisation performance, which is achieved by one cascade filter system. Finally, landmark localisation performance is reported by using a novel cumulative error curve. Localisation results are gathered by computing errors of estimated landmark locations against respective ground–truth data.
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Nelson, 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.

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This thesis is about facilitating the use of vast quantities of data for robotics applications, particularly for the tasks of mapping and localisation in the context of lifelong learning. Further, we tackle the specific problem of localising an autonomous vehicle at night, in urban environments, using vision. In general, robotics applications have some unique and unusual data requirements. The curation and management of such data are often overlooked. An emerging theme is the use of large corpora of spatiotemporally indexed sensor data which must be searched and leveraged both offline and online. Without careful thought to their organisation, however, this leads to vast quantities of data which quickly become unmanageable. The current paradigm of collecting data for specific purposes and storing them in ad-hoc ways will not scale to meet this challenge. In the first part of this thesis, we present the design and implementation of a data management framework that is capable of dealing with large datasets, and provides functionality required by many offline and online robotics applications. We systematically identify the data requirements of these applications and design a relational database that is capable of meeting their demands. This database framework is then used to facilitate the storage and retrieval of maps in our visual nighttime navigation system, a problem which has its own unique set of challenges. Despite it being dark exactly half of the time, surprisingly little work has addressed these challenges. A defining aspect of nighttime urban scenes is the presence and effect of artificial lighting. By building a model of the environment which includes a representation of the spatial location of every light source, localisation becomes possible using monocular cameras. One major challenge we face is the gross change in light appearance as a function of distance, due to flare, saturation, and bleeding. To overcome this, we model the appearance of each light as a function of vehicle location, using this to inform our data association decisions and to regularise the cost function which is used to infer vehicle pose. We demonstrate that our system is able to localise successfully at night, over 40km, in situations where a traditional point feature based system fails.
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Farchi, Alban. "On the localisation of ensemble data assimilation methods." Thesis, Paris Est, 2019. http://www.theses.fr/2019PESC1034.

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L’assimilation de données est la discipline permettant de combiner des observations d’un système dynamique avec un modèle numérique simulant ce système, l'objectif étant d'améliorer la connaissance de l'état du système. Le principal domaine d'application de l'assimilation de données est la prévision numérique du temps. Les techniques d'assimilation sont implémentées dans les centres opérationnels depuis plusieurs décennies et elles ont largement contribué à améliorer la qualité des prédictions. Une manière efficace de réduire la dimension des systèmes d'assimilation de données est d'utiliser des méthodes ensemblistes. La plupart de ces méthodes peuvent être regroupées en deux classes~: le filtre de Kalman d'ensemble (EnKF) et le filtre particulaire (PF). Le succès de l'EnKF pour des problèmes géophysiques de grande dimension est largement dû à la localisation. La localisation repose sur l'hypothèse que les corrélations entre variables d'un système dynamique décroissent très rapidement avec la distance. Dans cette thèse, nous avons étudié et amélioré les méthodes de localisation pour l'assimilation de données ensembliste. La première partie est dédiée à l'implémentation de la localisation dans le PF. Nous passons en revue les récents développements concernant la localisation dans le PF et nous proposons une classification théorique des algorithmes de type PF local. Nous insistons sur les avantages et les inconvénients de chaque catégorie puis nous proposons des solutions pratiques aux problèmes que posent les PF localisés. Les PF locaux sont testés et comparés en utilisant des expériences jumelles avec des modèles de petite et moyenne dimension. Finalement, nous considérons le cas de la prédiction de l'ozone troposphérique en utilisant des mesures de concentration. Plusieurs algorithmes, dont des PF locaux, sont implémentés et appliqués à ce problème et leurs performances sont comparées.La deuxième partie est dédiée à l'implémentation de la localisation des covariances dans l'EnKF. Nous montrons comment la localisation des covariances peut être efficacement implémentée dans l'EnKF déterministe en utilisant un ensemble augmenté. L'algorithme obtenu est testé au moyen d'expériences jumelles avec un modèle de moyenne dimension et des observations satellitaires. Finalement, nous étudions en détail la cohérence de l'EnKF déterministe avec localisation des covariances. Une nouvelle méthode est proposée puis comparée à la méthode traditionnelle en utilisant des simulation jumelles avec des modèles de petite dimension
Data 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
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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.

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Zaman, Munir uz. "Mobile robot localisation : error modelling, data synchronisation and vision techniques." Thesis, University of Surrey, 2006. http://epubs.surrey.ac.uk/844082/.

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Mobile robot localisation has been, and continues to be, a very active research area. Estimating the position of a mobile robot is fundamental for its navigation and map-building. This thesis addresses some of the problems associated with mobile robot localisation. Three distinct items of research presented in this thesis are (i) A systematic odometry error model for a synchronous drive robot; and (ii) A novel method to synchronise two independent sensor data streams, and (iii) A proposal for an exteroceptive truly odometric sensor system - 'Visiodometry'. Cyclops is a synchronous drive mobile robot. The kinematics causes the path of the robot to curve, with the degree of curvature affected by the orientation of the wheels. A systematic odometry error model is proposed to correct for this. The proposed model is supported both experimentally, and theoretically from modelling the kinematics. Combining sensor data from different sensor data streams is commonly done to improve the accuracy of estimated variables. However, in some cases the sensors are not networked making it impossible to synchronise the data streams. The second item of research proposes a novel method to estimate the time difference in the local clocks of the discrete sensor data from their time-stamps alone. A proposed enhancement to the method improves both the rate of convergence and the precision of the estimate. Results show that the method is more optimum and robust than one based on known methods, including those based on Gaussian assumptions. Wheel odometry is a common method for mobile robot localisation. However, wheel odometry is unreliable if there is wheel slip. In these environments visual odometry has been used. However, the method does not work well on planar surfaces or surfaces with fine texture. It is also unable to accurately detect small motions less than a few centimetres. The third area of research proposes an exteroceptive odometric sensor called 'visiodometry' which is independent of the kinematics and therefore robust to wheel odometry errors. Two methods are proposed (i) a dual camera 'shift vector' method and (ii) a monocular 'roto-translation' method. The results demonstrate that the proposed system can provide odometric localisation data in planar environments to a high precision. The method is based upon extracting global motion estimates of affine transformed images of the ground using the phase correlation method. Experimental results demonstrate that, as a proof-of-concept, this type of sensor input is an alternative genuinely odometric input which has the potential to be comparable in accuracy and precision to wheel odometry in environments where wheel odometry and visual odometry methods are unreliable.
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Strumia, 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.

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Jagbrant, 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.

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Orchards demand large areas of land, thus they are often situated far from major population centres. As a result it is often difficult to obtain the necessary personnel, limiting both growth and productivity. However, if autonomous robots could be integrated into the operation of the orchard, the manpower demand could be reduced. A key problem for any autonomous robot is localisation; how does the robot know where it is? In agriculture robots, the most common approach is to use GPS positioning. However, in an orchard environment, the dense and tall vegetation restricts the usage to large robots that reach above the surroundings. In order to enable the use of smaller robots, it is instead necessary to use a GPS independent system. However, due to the similarity of the environment and the lack of strong recognisable features, it appears unlikely that typical non-GPS solutions will prove successful. Therefore we present a GPS independent localisation system, specifically aimed for orchards, that utilises the inherent structure of the surroundings. Furthermore, we examine and individually evaluate three related sub-problems. The proposed system utilises a 3D point cloud created from a 2D LIDAR and the robot’s movement. First, we show how the data can be segmented into individual trees using a Hidden Semi-Markov Model. Second, we introduce a set of descriptors for describing the geometric characteristics of the individual trees. Third, we present a robust localisation method based on Hidden Markov Models. Finally, we propose a method for detecting segmentation errors when associating new tree measurements with previously measured trees. Evaluation shows that the proposed segmentation method is accurate and yields very few segmentation errors. Furthermore, the introduced descriptors are determined to be consistent and informative enough to allow localisation. Third, we show that the presented localisation method is robust both to noise and segmentation errors. Finally it is shown that a significant majority of all segmentation errors can be detected without falsely labeling correct segmentations as incorrect.
Eftersom 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.
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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.

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Acoustic emissions (AE) can provide information relating to the internal state of a deforming rock sample during laboratory testing and have been utilised to quantify damage progression for time-dependent failure modeling. However, the underlying physical mechanisms that produce AE in different materials and their evolution during the process of damage localisation are not fully understood, particularly in porous media. In order to investigate the sources of laboratory acoustic emissions, a moment tensor inversion was applied to data from triaxial compression experiments on Aue granite and Clashach sandstone. The moment tensor inversion was verified for granite, by comparison with results obtained using a more simplistic source analysis technique. In the non-porous Aue granite, AE sources exhibited a predominantly tensile behaviour in the early stages of AE activity. However, shear sources become dominant in the vicinity of the peak stress. In contrast, during deformation of the Clashach sandstone, which has a significant pre-existing porosity, AE sources are dominated by a collapse signature and generally involve a notable shear component. AE that have a predominantly shear mechanism are also a major contributor to the microscale deformation imaged by the technique, and dominate during shear localisation. A combination of correlation analysis and source analysis was used to elucidate the temporal and spatial evolution of the AE source mechanisms involved in the localisation process, as well as during a temporary hiatus in the progression to failure. The results support the concept that the cascade to failure requires the simultaneous involvement of a range of micromechanical behaviours to maintain the progression of localised damage, and eventual formation of a fault. Localisation of collapse mechanisms was not observed until the final approach to failure. Finally, AE sources produced during brittle deformation of the Clashach sandstone were characterised in detail and compared to microstructural observations representing the integrated effect of all times up to the end of the test, and including smaller structures that may have been formed insufficiently dynamically to produce AE. Equivalent focal mechanisms for these events are presented and the relative proportions of their volumetric and shear components considered. The results indicate that AE sources display a wide spectrum of micromechanical behaviour that is consistent with microstructural observations, indicating that AE mechanisms are representative of ongoing deformation processes within the sandstone. It is argued that moment tensor inversion of acoustic emissions is a powerful tool for elucidating the micromechanical evolution of damage, during the brittle deformation of rock.
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Danielsson, 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.

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This thesis explored the possibilities of using a Hidden Markov Model approach for multi-target localisation in an urban environment, with observations generated from Wi-Fi sensors. The area is modelled as a network of nodes and arcs, where the arcs represent sidewalks in the area and constitutes the hidden states in the model. The output of the model is the expected amount of people at each road segment throughout the day. In addition to this, two methods for analyzing the impact of events in the area are proposed. The first method is based on a time series analysis, and the second one is based on the updated transition matrix using the Baum-Welch algorithm. Both methods reveal which road segments are most heavily affected by a surge of traffic in the area, as well as potential bottleneck areas where congestion is likely to have occurred.
I 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.
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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.

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“ Lorsqu'on utilise des données provenant d'une seule source,C'est du plagiat;Lorsqu'on utilise plusieurs sources,C'est de la fusion de données ”Ces travaux présentent une approche de fusion de données collaborative innovante pour l'égo-localisation de véhicules routiers. Cette approche appelée filtre de Kalman optimisé à essaim de particules (Optimized Kalman Particle Swarm) est une méthode de fusion de données et de filtrage optimisé. La fusion de données est faite en utilisant les données d'un GPS à faible coût, une centrale inertielle, un compteur odométrique et un codeur d'angle au volant. Ce travail montre que cette approche est à la fois plus robuste et plus appropriée que les méthodes plus classiques d'égo-localisation aux situations de conduite urbaine. Cette constatation apparait clairement dans le cas de dégradations des signaux capteurs ou des situations à fortes non linéarités. Les méthodes d'égo-localisation de véhicules les plus utilisées sont les approches bayésiennes représentées par le filtre de Kalman étendu (Extended Kalman Filter) et ses variantes (UKF, DD1, DD2). Les méthodes bayésiennes souffrent de sensibilité aux bruits et d'instabilité pour les cas fortement non linéaires. Proposées pour couvrir les limitations des méthodes bayésiennes, les approches multi-hypothèses (à base de particules) sont aussi utilisées pour la localisation égo-véhiculaire. Inspiré des méthodes de simulation de Monte-Carlo, les performances du filtre à particules (Particle Filter) sont fortement dépendantes des ressources en matière de calcul. Tirant avantage des techniques de localisation existantes et en intégrant les avantages de l'optimisation méta heuristique, l'OKPS est conçu pour faire face aux bruits, aux fortes dynamiques, aux données non linéaires et aux besoins d'exécution en temps réel. Pour l'égo-localisation d'un véhicule, en particulier pour les manœuvres très dynamiques sur route, un filtre doit être robuste et réactif en même temps. Le filtre OKPS est conçu sur un nouvel algorithme de localisation coopérative-réactive et dynamique inspirée par l'Optimisation par Essaim de Particules (Particle Swarm Optimization) qui est une méthode méta heuristique. Cette nouvelle approche combine les avantages de la PSO et des deux autres filtres: Le filtre à particules (PF) et le filtre de Kalman étendu (EKF). L'OKPS est testé en utilisant des données réelles recueillies à l'aide d'un véhicule équipé de capteurs embarqués. Ses performances sont testées en comparaison avec l'EKF, le PF et le filtre par essaim de particules (Swarm Particle Filter). Le filtre SPF est un filtre à particules hybride intéressant combinant les avantages de la PSO et du filtrage à particules; Il représente la première étape de la conception de l'OKPS. Les résultats montrent l'efficacité de l'OKPS pour un scénario de conduite à dynamique élevée avec des données GPS endommagés et/ou de qualité faible
“ 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
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Books on the topic "Data localisation"

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Boer, A. de. Coorelation, error-localisation and updating of the second problem defined in GARTEUR AG11. Amsterdam: National Aerospace Laboratory, 1990.

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Yuste, Rodrigo Elia, ed. Topics in language resources for translation and localisation. Philadelphia: John Benjamins Pub. Company, 2008.

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Book chapters on the topic "Data localisation"

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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.

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Grognard, 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.

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Owen, 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.

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Anastasiadis, 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.

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Noppen, 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.

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Garimella, 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.

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Morlier, 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.

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Celeste, 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.

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Abstract Borderless cloud computing technologies are exacerbating tensions between European and other existing regulatory models for data privacy. On the one hand, in the European Union (EU), a series of data localisation initiatives are emerging with the objective of preserving Europe’s digital sovereignty, guaranteeing the respect of EU fundamental rights and preventing foreign law enforcement and intelligence agencies from accessing personal data. On the other hand, foreign countries are unilaterally adopting legislation requiring national corporations to disclose data stored in Europe, in this way bypassing jurisdictional boundaries grounded on physical data location. The chapter investigates this twofold dynamic by focusing particularly on the current friction between the EU data protection approach and the data privacy model of the United States (US) in the field of cloud computing.
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Cruz-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.

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Djenouri, 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.

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Conference papers on the topic "Data localisation"

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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.

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Mercaldo, 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.

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Gupta, 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.

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Finger, 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.

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Gkovedarou, 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.

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Hajjami, 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.

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Luo, 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.

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Franken, 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.

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Kozlowski, 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.

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Gunatilaka, 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.

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Reports on the topic "Data localisation"

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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.

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Realities such as the COVID-19 pandemic have expedited the move to online operations, highlighting the undeniable fact that the world is continuing to go digital. This emphasises the need for policymakers to regulate in a manner that allows them to harness digital trade benefits while also avoiding associated risk. However, given that digital trade remains unco-ordinated globally, with countries adopting different approaches to policy issues, national regulatory divergence on the matter continues, placing limits on the benefits that countries can obtain from digital trade. Given these disparities, ahead of the African Continental Free Trade Area (AfCFTA) Phase II Negotiations, African countries have been considering the best way to harmonise regulations on issues related to digital trade. To do this effectively, AfCFTA members need to identify where divergencies exist in their domestic regulatory systems. This will allow AfCFTA members to determine where harmonisation is possible, as well as what is needed to achieve such harmonisation. This report analyses the domestic regulations and policies of four focus countries – South Africa, Nigeria, Kenya and Senegal – comparing their regulatory approaches to five policy issues: i) regulation of online transactions; ii) cross-border data flows, data localisation, and personal data protection; iii) access to source code and technology transfer; iv) intermediary liability; and v) customs duties on electronic transmissions. The study highlights where divergencies exist in adopted approaches, indicating the need for the four countries – and AfCFTA members in general – to carefully consider the implications of the divergences, and determine where it is possible and beneficial to harmonise approaches. This was intended to encourage AfCFTA member states to take ownership of these issues and reflect on the reforms needed. As seen in Table 1 below, the study shows that the four countries diverge on most of the five policy issues. There are differences in how all four countries regulate online transactions – that is, e-signatures and online consumer protection. Nigeria was the only country out of the four to recognise all types of e-signatures as legally equivalent. Kenya and Senegal only recognise specific e-signatures, which are either issued or validated by a recognised institution, while South Africa adopts a mixed approach, where it recognises all e-signatures as legally valid, but provides higher evidentiary weight to certain types of e-signatures. Only South Africa and Senegal have specific regulations relating to online consumer protection, while Nigeria and Kenya do not have any clear rules. With regards to cross border data flows, data localisation, and personal data protection, the study shows that all four focus countries have regulations that consist of elements borrowed from the European Union (EU) General Data Protection Regulation (GDPR). In particular, this was regarding the need for the data subject's consent, and also the adequacy requirement. Interestingly, the study also shows that South Africa, Kenya and Nigeria also adopt data localisation measures, although at different levels of strictness. South Africa’s data localisation laws are mostly imposed on data that is considered critical – which is then required to be processed within South African borders – while Nigeria requires all data to be processed and stored locally, using local servers. Kenya imposes data localisation measures that are mostly linked to its priority for data privacy. Out of the four focus countries, Senegal is the only country that does not impose any data localisation laws. Although the study shows that all four countries share a position on customs duties on electronic transmissions, it is also interesting to note that none of the four countries currently have domestic regulations or policies on the subject. The report concludes by highlighting that, as the AfCFTA Phase II Negotiations aim to arrive at harmonisation and to improve intra-African trade and international trade, AfCFTA members should reflect on their national policies and domestic regulations to determine where harmonisation is needed, and whether AfCFTA is the right platform for achieving this efficiently.
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Hakeem, 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.

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Poliomyelitis (polio) remains a vital global public health challenge, particularly in countries where eradication efforts are ongoing. For almost three decades, polio programme and frontline workers in Pakistan have suffered human and financial losses due to complex political and bureaucratic management, local resistance to programme efforts, and the context of cross-border insurgency and insecurity.1 Many stakeholders in Pakistan continue to have low confidence in frontline workers and polio vaccination campaigns. In this environment, it is essential that vaccination programmes localise – by taking careful account of the local context, improving local ownership of the programmes, understanding and mitigating the issues at a grassroots level, and tailoring efforts to achieve polio eradication goals. This brief draws on evidence from academic and grey literature, data on polio vaccine uptake, consultations with partners working on polio eradication in Pakistan, and the authors’ own programme implementation experience in the country. The brief reviews the social, cultural, and contextual considerations relevant to increasing polio vaccine uptake amongst vulnerable groups in Pakistan’s tribal areas. It focuses on the current country context, in the aftermath of the 2018 merger of the former Federally Administered Tribal Areas (FATA) into Khyber Pakhtunkhwa province (KPK). This brief is part of a series authored by participants from the SSHAP Fellowship, and was written by Luqman Hakeem and Riaz Hussain from Cohort 2. Contributions were provided by response partners in Pakistan including health communication and delivery staff and local administrative authorities. This brief was reviewed by Muhammad Sufyan (University of Swabi) and Ilyas Sharif (Quaid-e-Azam College of Commerce, University of Peshawar). The brief was supported by Megan Schmidt-Sane and Santiago Ripoll at the Institute of Development Studies and is the responsibility of SSHAP.
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