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Deep learning based vehicular mobility models for intelligent transportation systems / Jian Zhang (2018)  

Public Titre : Deep learning based vehicular mobility models for intelligent transportation systems Type de document : Thèse/HDR Auteurs : Jian Zhang, Auteur ; Abdelkader El Kamel, Directeur de thèse Editeur : Lille [France] : Ecole Centrale de Lille Année de publication : 2018 Importance : 175 p. Format : 21 x 30 cm Note générale : bibliographie Thèse présentée en vue d’obtenir le grade de Docteur, Spécialité : Automatique, génie informatique, traitement du signal et des images, Doctorat délivré par Centrale Lille Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] chaîne de Markov [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] données de flux [Termes descripteurs IGN] itération [Termes descripteurs IGN] mobilité [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] sécurité routière [Termes descripteurs IGN] système de transport intelligent [Termes descripteurs IGN] trafic routier [Termes descripteurs IGN] transport [Termes descripteurs IGN] UML [Termes descripteurs IGN] véhicule sans pilote Résumé : (auteur) The intelligent transportation systems gain great research interests in recent years. Although the realistic traffic simulation plays an important role, it has not received enough attention. This thesis is devoted to studying the traffic simulation in microscopic level, and proposes corresponding vehicular mobility models. Using deep learning methods, these mobility models have been proven with a promising credibility to represent the vehicles in real-world. Firstly, a data-driven neural network based mobility model is proposed. This model comes from real-world trajectory data and allows mimicking local vehicle behaviors. By analyzing the performance of this basic learning based mobility model, we indicate that an improvement is possible and we propose its specification. An HMM is then introduced. The preparation of this integration is necessary, which includes an examination of traditional dynamics based mobility models and the adaptation method of “classical” models to our situation. At last, the enhanced model is presented, and a sophisticated scenario simulation is built with it to validate the theoretical results. The performance of our mobility model is promising and implementation issues have also been discussed. Note de contenu : 1- Introduction 2- Neural network based data-driven mobility model 3- Enhanced Mobility Model with HMM 4- Experiment platform and scenario simulation Conclusions and Perspectives Numéro de notice : 25873 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Automatique, génie informatique, traitement du signal et des images : École Centrale Lille : 2018 Organisme de stage : CRIStAL (laboratoire) DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-02136219/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95562

Probability and statistics for computer science / David Forsyth (2018)  

Public Titre : Probability and statistics for computer science Type de document : Guide/Manuel Auteurs : David Forsyth, Auteur Editeur : Springer International Publishing Année de publication : 2018 Importance : 367 p. ISBN/ISSN/EAN : 978-3-319-64410-3 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes descripteurs IGN] chaîne de Markov [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] probabilité conditionnelle [Termes descripteurs IGN] régression [Termes descripteurs IGN] variable aléatoire Résumé : (Editeur) This textbook is aimed at computer science undergraduates late in sophomore or early in junior year, supplying a comprehensive background in qualitative and quantitative data analysis, probability, random variables, and statistical methods, including machine learning. Note de contenu : 1. Describing Datasets 2. Probability 3. Inference 4. Tools 5. Mathematical Bits and Pieces Numéro de notice : 26282 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Manuel DOI : 10.1007/978-3-319-64410-3 En ligne : https://doi.org/10.1007/978-3-319-64410-3 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94935

Robust hand pose recognition from stereoscopic capture / Rilwan Remilekun Basaru (2018)  

Public Titre : Robust hand pose recognition from stereoscopic capture Type de document : Thèse/HDR Auteurs : Rilwan Remilekun Basaru, Auteur Editeur : Londres : University of London Press Année de publication : 2018 Importance : 200 p. Format : 21 x 30 cm Note générale : bibliographie A thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy, Department of Computer Science, City, University of London Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] estimation de pose [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] méthode de Monte-Carlo par chaînes de Markov [Termes descripteurs IGN] régression [Termes descripteurs IGN] réseau neuronal convolutif Résumé : (auteur) Hand pose is emerging as an important interface for human-computer interaction. The problem of hand pose estimation from passive stereo inputs has received less attention in the literature compared to active depth sensors. This thesis seeks to address this gap by presenting a data-driven method to estimate a hand pose from a stereoscopic camera input, with experimental results comparable to more expensive active depth sensors. The frameworks presented in this thesis are based on a two camera stereo rig capture as it yields a simpler and cheaper set-up and calibration. Three frameworks are presented, describing the sequential steps taken to solve the problem of depth and pose estimation of hands. The first is a data-driven method to estimate a high quality depth map of a hand from a stereoscopic camera input by introducing a novel regression framework. The method first computes disparity using a robust stereo matching technique. Then, it applies a machine learning technique based on Random Forest to learn the mapping between the estimated disparity and depth given ground truth data. We introduce Eigen Leaf Node Features (ELNFs) that perform feature selection at the leaf nodes in each tree to identify features that are most discriminative for depth regression. The system provides a robust method for generating a depth image with an inexpensive stereo camera. The second framework improves on the task of hand depth estimation from stereo capture by introducing a novel superpixel-based regression framework that takes advantage of the smoothness of the depth surface of the hand. To this end, it introduces Conditional Regressive Random Forest (CRRF), a method that combines a Conditional Random Field (CRF) and a Regressive Random Forest (RRF) to model the mapping from a stereo RGB image pair to a depth image. The RRF provides a unary term that adaptively selects different stereo-matching measures as it implicitly determines matching pixels in a coarse-to-fine manner. While the RRF makes depth prediction for each super-pixel independently, the CRF unifies the prediction of depth by modeling pair-wise interactions between adjacent superpixels. The final framework introduces a stochastic approach to propose potential depth solutions to the observed stereo capture and evaluate these proposals using two convolutional neural networks (CNNs). The first CNN, configured in a Siamese network architecture, evaluates how consistent the proposed depth solution is to the observed stereo capture. The second CNN estimates a hand pose given the proposed depth. Unlike sequential approaches that reconstruct pose from a known depth, this method jointly optimizes the hand pose and depth estimation through Markov-chain Monte Carlo (MCMC) sampling. This way, pose estimation can correct for errors in depth estimation, and vice versa. Experimental results using an inexpensive stereo camera show that the proposed system measures pose more accurately than competing methods. More importantly, it presents the possibility of pose recovery from stereo capture that is on par with depth based pose recovery. Numéro de notice : 17505 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère En ligne : https://openaccess.city.ac.uk/19938/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90396

Vehicle dynamic model based navigation for small UAVs / Mehran Khaghani (2018)  

Public Titre : Vehicle dynamic model based navigation for small UAVs Type de document : Thèse/HDR Auteurs : Mehran Khaghani, Auteur ; Jan Skaloud, Directeur de thèse Editeur : Zurich : Schweizerischen Geodatischen Kommission / Commission Géodésique Suisse Année de publication : 2018 Autre Editeur : Lausanne : Ecole Polytechnique Fédérale de Lausanne EPFL Collection : Geodätisch-Geophysikalische Arbeiten in der Schweiz, ISSN 0257-1722 num. 101 Importance : 138 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-3-908440-47-5 Note générale : bibliography Thèse de Doctorat, EPFL, Lausanne, 2018 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes descripteurs IGN] analyse de sensibilité [Termes descripteurs IGN] centrale inertielle [Termes descripteurs IGN] démonstration de faisabilité [Termes descripteurs IGN] drone [Termes descripteurs IGN] filtre de Kalman [Termes descripteurs IGN] GPS-INS [Termes descripteurs IGN] méthode de Monte-Carlo [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] navigation autonome [Termes descripteurs IGN] positionnement par GNSS [Termes descripteurs IGN] processus stochastique [Termes descripteurs IGN] vent Index. décimale : 30.70 Navigation et positionnement Résumé : (auteur) The dominant navigation system for small civilian UAVs today is based on integration of inertial navigation system (INS) and global navigation satellite system (GNSS). This strategy works well to navigate the UAV, as long as proper reception of GNSS signal is maintained. However, when GNSS outage occurs, the INS-based navigation solution drifts very quickly, considering the limited quality of IMU(s) employed in INS for small UAVs. In beyond visual line of sight (BVLOS) flights, this poses the serious danger of losing the UAV and its eventual falling down. Limited payload capacity and cost for small UAVs, as well as the need for operating in different conditions, with limited visibility for example, make it challenging to find a solution to reach higher levels of navigation autonomy based on conventional approaches. This thesis aims to improve the accuracy of autonomous navigation for small UAVs by at least one order of magnitude. The proposed novel approach employs vehicle dynamic model (VDM) as process model within navigation system, and treats data from other sensors such as IMU, barometric altimeter, and GNSS receiver, whenever available, as observations within the system. Such improvement comes with extra effort required to determine the VDM parameters for any specific UAV. This work investigates the internal capability of the proposed system for estimating VDM parameters as part of the augmented state vector within an extended Kalman filter (EKF) as the estimator. This reduces the efforts required to setup such navigation system that is platform dependent. Multiple experimental flights using two custom made fixed-wing UAVs are presented together with Monte-Carlo simulations. The results reveal improvements of 1 to 2 orders of magnitude in navigation accuracy during GNSS outages of a few minutes' duration. Computational cost for the proposed VDM-based navigation does not exceed 3~times that of conventional INS-based systems, which establishes its applicability for online application. A global sensitivity analysis is presented, spotting the VDM parameters with higher influence on navigation performance. This provides insight for design of calibration procedures. The proposed VDM-based navigation system can be interesting for professional UAVs from at least two points of view. Firstly, it adds little to no extra hardware and cost to the UAV. Secondly and more importantly, it might be currently the only way to reach such significant improvement in navigation autonomy for small UAVs regardless of visibility conditions and electromagnetic signals reception. Possibly, such environmental condition independence for navigation system may be needed to obtain certifications from legal authorities to expand UAV applications to new types of mission. Note de contenu : 1- Preliminaries 2- VDM-based navigation framework 3- Results and analyses 4- Conclusion remarks Numéro de notice : 21988 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Thèse étrangère Note de thèse : Thèse Doctorat : : EPFL : 2018 nature-HAL : Thèse DOI : 10.5075/epfl-thesis-8494 En ligne : https://www.sgc.ethz.ch/publications.html Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91986

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A Markov chain model for simulating wood supply from any-aged forest management based on national forest inventory (NFI) data / Jari Vauhkonen in Forests, vol 8 n° 9 (September 2017)  

Public [article]  inForests > vol 8 n° 9 (September 2017) . - pp 307 - Titre : A Markov chain model for simulating wood supply from any-aged forest management based on national forest inventory (NFI) data Type de document : Article/Communication Auteurs : Jari Vauhkonen, Auteur ; Tuula Packalen, Auteur Année de publication : 2017 Article en page(s) : pp 307 - Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] chaîne de Markov [Termes descripteurs IGN] Finlande [Termes descripteurs IGN] gestion forestière durable [Termes descripteurs IGN] inventaire forestier étranger (données) [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] modélisation de la forêt [Termes descripteurs IGN] ressources forestières [Vedettes matières IGN] Economie forestière Résumé : (auteur) Markov chain models have been applied for a long time to simulate forest dynamics based on transitions in matrices of tree diameter classes or areas of forest size and structure types. To date, area-based matrix models have been applied assuming either even-aged or uneven-aged forest management. However, both management systems may be applied simultaneously due to land-use constraints or the rationality of combining the systems, which is called any-aged management. We integrated two different Markov chain models, one for even-aged and another for uneven-aged forest management, in an area-based approach to analyze wood supply from any-aged forest management. We evaluate the impacts of parameterizing the model based on available data sets, namely permanent and temporary Finnish National Forest Inventory (NFI) sample plots and a plot-level simulator to determine transitions due to different types of thinning treatments, and present recommendations for the related methodological choices. Our overall observation is that the combined modelling chain simulated the development of both the even- and uneven-aged forest structures realistically. Due to the flexibility of the implementation, the approach is very well suited for situations where scenario assumptions need to be varied according to expected changes in silvicultural practices or land-use constraints, for example. Numéro de notice : A2017-636 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f8090307 En ligne : http://doi.org/10.3390/f8090307 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86986 [article]

An evaluation of sampling and full enumeration strategies for Fisher Jenks classification in big data settings / Sergio J. Rey in Transactions in GIS, vol 21 n° 4 (August 2017)  

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Modeling canopy reflectance over sloping terrain based on path length correction / Gaofei Yin in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)  

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Uncertainty assessment in geodetic network adjustment by combining GUM and Monte-Carlo-simulations / Wolfgang Niemeier in Journal of applied geodesy, vol 11 n° 2 (June 2017)  

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Inverting Glacial Isostatic Adjustment signal using Bayesian framework and two linearly relaxing rheologies / Lambert Caron in Geophysical journal international, vol 209 n° 2 (May 2017)  

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Hyperspectral band selection from statistical wavelet models / Siwei Feng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)  

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Integrating cellular automata and Markov techniques to generate urban development potential surface : a study on Kolkata agglomeration / Biswajit Mondal in Geocarto international, vol 32 n° 4 (April 2017)  

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Integrating uncertainty propagation in GNSS radio occultation retrieval: From bending angle to dry-air atmospheric profiles / Jakob Schwarz in Earth and space science, vol 4 n° 4 (April 2017)  

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Minimizing construction emissions using Building Information Modeling and Decision-Making techniques / Mohamed Marzouk in International journal of 3-D information modeling, vol 6 n° 2 (April-June 2017)  

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Determining the appropriate timing of the next forest inventory: incorporating forest owner risk preferences and the uncertainty of forest data quality / Kyle J. Eyvindson in Annals of Forest Science [en ligne], vol 74 n° 1 (March 2017)  

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Bayesian inference / Javier Prieto Tejedor (2017)  

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