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Design and implementation of a model predictive observer for AHRS / Jafar Keighobadi in GPS solutions, vol 22 n° 1 (January 2018)
[article]
Titre : Design and implementation of a model predictive observer for AHRS Type de document : Article/Communication Auteurs : Jafar Keighobadi, Auteur ; Hamid Vosoughi, Auteur ; Javad Faraji, Auteur Année de publication : 2018 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] angle d'Euler
[Termes IGN] attitude and heading reference system AHRS
[Termes IGN] erreur instrumentale
[Termes IGN] erreur systématique
[Termes IGN] estimateur
[Termes IGN] filtre de Kalman
[Termes IGN] GPS-INS
[Termes IGN] microsystème électromécanique
[Termes IGN] véhiculeRésumé : (auteur) A GPS-aided Inertial Navigation System (GAINS) is used to determine the orientation‚ position and velocity of ground and aerial vehicles. The data measured by Inertial Navigation System (INS) and GPS are commonly integrated through an Extended Kalman Filter (EKF). Since the EKF requires linearized models and complete knowledge of predefined stochastic noises‚ the estimation performance of this filter is attenuated by unmodeled nonlinearity and bias uncertainties of MEMS inertial sensors. The Attitude Heading Reference System (AHRS) is applied based on the quaternion and Euler angles methods. A moving horizon-based estimator such as Model Predictive Observer (MPO) enables us to approximate and estimate linear systems affected by unknown uncertainties. The main objective of this research is to present a new MPO method based on the duality principle between controller and observer of dynamic systems and its implementation in AHRS mode of a low-cost INS aided by a GPS. Asymptotic stability of the proposed MPO is proven by applying Lyapunov’s direct method. The field test of a GAINS is performed by a ground vehicle to assess the long-time performance of the MPO method compared with the EKF. Both the EKF and MPO estimators are applied in AHRS mode of the MEMS GAINS for the purpose of real-time performance comparison. Furthermore‚ we use flight test data of the GAINS for evaluation of the estimation filters. The proposed MPO based on both the Euler angles and quaternion methods yields better estimation performances compared to the classic EKF. Numéro de notice : A2018-017 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-017-0696-4 En ligne : https://doi.org/10.1007/s10291-017-0696-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89114
in GPS solutions > vol 22 n° 1 (January 2018)[article]From Google Maps to a fine-grained catalog of street trees / Steve Branson in ISPRS Journal of photogrammetry and remote sensing, vol 135 (January 2018)
[article]
Titre : From Google Maps to a fine-grained catalog of street trees Type de document : Article/Communication Auteurs : Steve Branson, Auteur ; Jan Dirk Wegner, Auteur ; David Hall, Auteur ; Nico Lang, Auteur ; Konrad Schindler, Auteur ; Pietro Perona, Auteur Année de publication : 2018 Article en page(s) : pp 13 - 30 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] arbre urbain
[Termes IGN] architecture pipeline (processeur)
[Termes IGN] classification dirigée
[Termes IGN] détection de changement
[Termes IGN] Google Maps
[Termes IGN] inventaire de la végétation
[Termes IGN] photo-interprétation assistée par ordinateur
[Termes IGN] réseau neuronal convolutif
[Termes IGN] villeRésumé : (Auteur) Up-to-date catalogs of the urban tree population are of importance for municipalities to monitor and improve quality of life in cities. Despite much research on automation of tree mapping, mainly relying on dedicated airborne LiDAR or hyperspectral campaigns, tree detection and species recognition is still mostly done manually in practice. We present a fully automated tree detection and species recognition pipeline that can process thousands of trees within a few hours using publicly available aerial and street view images of Google MapsTM. These data provide rich information from different viewpoints and at different scales from global tree shapes to bark textures. Our work-flow is built around a supervised classification that automatically learns the most discriminative features from thousands of trees and corresponding, publicly available tree inventory data. In addition, we introduce a change tracker that recognizes changes of individual trees at city-scale, which is essential to keep an urban tree inventory up-to-date. The system takes street-level images of the same tree location at two different times and classifies the type of change (e.g., tree has been removed). Drawing on recent advances in computer vision and machine learning, we apply convolutional neural networks (CNN) for all classification tasks. We propose the following pipeline: download all available panoramas and overhead images of an area of interest, detect trees per image and combine multi-view detections in a probabilistic framework, adding prior knowledge; recognize fine-grained species of detected trees. In a later, separate module, track trees over time, detect significant changes and classify the type of change. We believe this is the first work to exploit publicly available image data for city-scale street tree detection, species recognition and change tracking, exhaustively over several square kilometers, respectively many thousands of trees. Experiments in the city of Pasadena, California, USA show that we can detect >70% of the street trees, assign correct species to >80% for 40 different species, and correctly detect and classify changes in >90% of the cases. Numéro de notice : A2018-068 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.11.008 Date de publication en ligne : 20/11/2017 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.11.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89426
in ISPRS Journal of photogrammetry and remote sensing > vol 135 (January 2018) . - pp 13 - 30[article]Réservation
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Titre : Machine learning - advanced techniques and emerging applications Type de document : Monographie Auteurs : Hamed Farhadi, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2018 Importance : 230 p. Format : 19 x 27 cm ISBN/ISSN/EAN : 9781789237528 9781789237535 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage automatique
[Termes IGN] données massives
[Termes IGN] informatique en nuage
[Termes IGN] processeur graphique
[Termes IGN] statistiquesRésumé : (éditeur) The volume of data that is generated, stored, and communicated across different industrial sections, business units, and scientific research communities has been rapidly expanding. The recent developments in cellular telecommunications and distributed/parallel computation technology have enabled real-time collection and processing of the generated data across different sections. On the one hand, the internet of things (IoT) enabled by cellular telecommunication industry connects various types of sensors that can collect heterogeneous data. On the other hand, the recent advances in computational capabilities such as parallel processing in graphical processing units (GPUs) and distributed processing over cloud computing clusters enabled the processing of a vast amount of data. There has been a vital need to discover important patterns and infer trends from a large volume of data (so-called Big Data) to empower data-driven decision-making processes. Tools and techniques have been developed in machine learning to draw insightful conclusions from available data in a structured and automated fashion. Machine learning algorithms are based on concepts and tools developed in several fields including statistics, artificial intelligence, information theory, cognitive science, and control theory. The recent advances in machine learning have had a broad range of applications in different scientific disciplines. This book covers recent advances of machine learning techniques in a broad range of applications in smart cities, automated industry, and emerging businesses. Note de contenu : 1- Hardware accelerator design for machine learning
2- Regression models to predict air pollution from affordable data collections
3- Multiple kernel-based multimedia fusion for automated event detection from tweets
4- Using sentiment analysis and machine learning algorithms to determine citizens’ perceptions
5- Overcoming challenges in predictive modeling of Laser-plasma interaction scenarios. The sinuous route from advanced machine learning to deep learning
6- Machine learning approaches for spectrum management in cognitive radio networks
7- Machine learning algorithm for wireless indoor localization
8- classification of malaria-infected cells using deep convolutional neuronal networks
9- Machine learning in educational technology
10- Sentiment-based semantic rule learning for improved product recommandations
11- A multilevel evolutionary algorithm applied to the maximum satisfiability problemsNuméro de notice : 25952 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.69783 En ligne : https://doi.org/10.5772/intechopen.69783 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96406 An efficient data organization and scheduling strategy for accelerating large vector data rendering / Mingqiang Guo in Transactions in GIS, vol 21 n° 6 (December 2017)
[article]
Titre : An efficient data organization and scheduling strategy for accelerating large vector data rendering Type de document : Article/Communication Auteurs : Mingqiang Guo, Auteur ; Ying Huang, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 1217 - 1236 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] données vectorielles
[Termes IGN] processeur graphique
[Termes IGN] processeur multicoeur
[Termes IGN] rendu (géovisualisation)
[Termes IGN] traitement parallèle
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Rendering large volumes of vector data is computationally intensive and therefore time consuming, leading to lower efficiency and poorer interactive experience. Graphics processing units (GPUs) are powerful tools in data parallel processing but lie idle most of the time. In this study, we propose an approach to improve the performance of vector data rendering by using the parallel computing capability of many‐core GPUs. Vertex transformation, largely a mathematical calculation that does not require communication with the host storage device, is a time‐consuming procedure because all coordinates of each vector feature need to be transformed to screen vertices. Use of a GPU enables optimization of a general‐purpose mathematical calculation, enabling the procedure to be executed in parallel on a many‐core GPU and optimized effectively. This study mainly focuses on: (1) an organization and storage strategy for vector data based on equal pitch alignment, which can adapt to the GPU's calculating characteristics; (2) a paging‐coalescing transfer and memory access strategy for vector data between the CPU and the GPU; and (3) a balancing allocation strategy to take full advantage of all processing cores of the GPU. Experimental results demonstrate that the approach proposed can significantly improve the efficiency of vector data rendering. Numéro de notice : A2017-837 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12275 Date de publication en ligne : 23/05/2017 En ligne : https://doi.org/10.1111/tgis.12275 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89373
in Transactions in GIS > vol 21 n° 6 (December 2017) . - pp 1217 - 1236[article]Concept of AHRS algorithm designed for platform independent IMU attitude alignment / Dariusz Tomaszewski in Reports on geodesy and geoinformatics, vol 104 n° 1 (December 2017)
[article]
Titre : Concept of AHRS algorithm designed for platform independent IMU attitude alignment Type de document : Article/Communication Auteurs : Dariusz Tomaszewski, Auteur ; Jacek Rapinski, Auteur ; Renata Pelc-Mieczkowska, Auteur Année de publication : 2017 Article en page(s) : pp 33 - 47 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] accélération
[Termes IGN] angle d'Euler
[Termes IGN] attitude and heading reference system AHRS
[Termes IGN] centrale inertielle
[Termes IGN] instabilité du vecteur (télédétection)
[Termes IGN] système de coordonnées
[Termes IGN] téléphone intelligentRésumé : (Auteur) Nowadays, along with the advancement of technology one can notice the rapid development of various types of navigation systems. So far the most popular satellite navigation, is now supported by positioning results calculated with use of other measurement system. The method and manner of integration will depend directly on the destination of system being developed. To increase the frequency of readings and improve the operation of outdoor navigation systems, one will support satellite navigation systems (GPS, GLONASS ect.) with inertial navigation. Such method of navigation consists of several steps. The first stage is the determination of initial orientation of inertial measurement unit, called INS alignment. During this process, on the basis of acceleration and the angular velocity readings, values of Euler angles (pitch, roll, yaw) are calculated allowing for unambiguous orientation of the sensor coordinate system relative to external coordinate system. The following study presents the concept of AHRS (Attitude and heading reference system) algorithm, allowing to define the Euler angles. The study were conducted with the use of readings from low-cost MEMS cell phone sensors. Subsequently the results of the study were analyzed to determine the accuracy of featured algorithm. On the basis of performed experiments the legitimacy of developed algorithm was stated. Numéro de notice : A2017-794 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1515/rgg-2017-0013 En ligne : https://doi.org/10.1515/rgg-2017-0013 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89071
in Reports on geodesy and geoinformatics > vol 104 n° 1 (December 2017) . - pp 33 - 47[article]Low-cost warning system for the monitoring of the Corinth Canal / George Hloupis in Applied geomatics, vol 9 n° 4 (December 2017)PermalinkExperiences with the QDaedalus system for astrogeodetic determination of deflections of the vertical / Markus Hauk in Survey review, vol 49 n° 355 (October 2017)PermalinkA GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data / Guiming Zhang in International journal of geographical information science IJGIS, vol 31 n° 9-10 (September - October 2017)PermalinkA new GPU bundle adjustment method for large-scale data / Zhou Shunping ; Xiong Xiaodong ; Junfeng Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 9 (September 2017)PermalinkImplementation of an IMU aided image stacking algorithm in a digital camera for Unmanned Aerial Vehicles / Ahmad Audi in Sensors, Vol 17 n°7 (july 2017)PermalinkAutomatic GPS ionospheric amplitude and phase scintillation detectors using a machine learning algorithm / Yu Jiao in Inside GNSS, vol 12 n° 3 (May - June 2017)PermalinkChange detection of linear features in temporally spaced remotely sensed images using edge-based grid analysis / Arati Paul in Geocarto international, vol 32 n° 6 (June 2017)PermalinkA parallel scheme for large-scale polygon rasterization on CUDA-enabled GPUs / Chen Zhou in Transactions in GIS, vol 21 n° 3 (June 2017)PermalinkImplantation dans le matériel de fonctionnalités temps-réel dans une caméra intelligente ultralégère spécialisée pour la prise de vue aérienne / Ahmad Audi (2017)PermalinkRéalisation d'une caméra photogrammétrique ultralégère et de haute résolution / Olivier Martin in Revue Française de Photogrammétrie et de Télédétection, n° 213 - 214 (janvier - avril 2017)PermalinkThe index array approach and the dual tiled similarity algorithm for UAS hyper-spatial image processing / Lihong Su in Geoinformatica, vol 20 n° 4 (October - December 2016)PermalinkDistance measure based change detectors for polarimetric SAR imagery / Yonghong Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 9 (September 2016)PermalinkA simulated annealing algorithm for zoning in planning using parallel computing / Inès Santé in Computers, Environment and Urban Systems, vol 59 (September 2016)PermalinkSpaceborne synthetic aperture radar data focusing on multicore-based architectures / Pasquale Imperatore in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)PermalinkA bootstrap test for constant coefficients in geographically weighted regression models / Chang-Lin Mei in International journal of geographical information science IJGIS, vol 30 n° 7- 8 (July - August 2016)PermalinkA study on the dependency of GNSS pseudorange biases on correlator spacing / André Hauschild in GPS solutions, vol 20 n° 2 (April 2016)PermalinkNull-steering antennas: Assessing the performance of multi-antenna interference-rejection techniques / James T. Curran in GPS world, vol 27 n° 2 (February 2016)PermalinkImproving MEMS-IMU/GPS integrated systems for land vehicle navigation applications / S. Sasani in GPS solutions, vol 20 n° 1 (January 2016)PermalinkVers la prise en compte de la dépendance spatio temporelle des séries de position GNSS dans leur analyse / Clément Benoist (2016)PermalinkEfficient visualization of urban simulation data using modern GPUs / Aleksandr Zagarskikh in Procedia Computer Science, vol 51 (2015)Permalink