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Intelligent sensors for positioning, tracking, monitoring, navigation and smart sensing in smart cities / Li Tiancheng (2021)  

Public Titre : Intelligent sensors for positioning, tracking, monitoring, navigation and smart sensing in smart cities Type de document : Monographie Auteurs : Li Tiancheng, Éditeur scientifique ; Jan Junkun, Éditeur scientifique ; Cao Yue, Éditeur scientifique ; et al., Auteur Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 266 p. Format : 17 x 25 cm ISBN/ISSN/EAN : 978-3-0365-0123-9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage automatique [Termes IGN] capteur (télédétection) [Termes IGN] cartographie et localisation simultanées [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] détection de cible [Termes IGN] exploration de données [Termes IGN] Extreme Gradient Machine [Termes IGN] fusion de données [Termes IGN] Inférence floue [Termes IGN] internet des objets [Termes IGN] logique floue [Termes IGN] navigation autonome [Termes IGN] odomètre [Termes IGN] positionnement en intérieur [Termes IGN] réseau de capteurs [Termes IGN] simulation de signal [Termes IGN] ville intelligente Résumé : (éditeur) The rapid development of advanced, arguably, intelligent sensors and their massive deployment provide a foundation for new paradigms to combat the challenges that arise in significant tasks such as positioning, tracking, navigation, and smart sensing in various environments. Relevant advances in artificial intelligence (AI) and machine learning (ML) are also finding rapid adoption by industry and fan the fire. Consequently, research on intelligent sensing systems and technologies has attracted considerable attention during the past decade, leading to a variety of effective applications related to intelligent transportation, autonomous vehicles, wearable computing, wireless sensor networks (WSN), and the internet of things (IoT). In particular, the sensors community has a great interest in novel, intelligent information fusion, and data mining methods coupling AI and ML for substantial performance enhancement, especially for the challenging scenarios that make traditional approaches inappropriate. This reprint book has collected 14 excellent papers that represent state-of-the-art achievements in the relevant topics and provides cutting-edge coverage of recent advances in sensor signal and data mining techniques, algorithms, and approaches, particularly applied for positioning, tracking, navigation, and smart sensing. Note de contenu : 1- MIMU/odometer fusion with state constraints for vehicle positioning during BeiDou signal outage: Testing and results 2- Autonomous road roundabout detection and navigation system for smart vehicles and cities using laser simulator–fuzzy logic algorithms and sensor fusion 3- An elaborated signal model for simultaneous range and vector velocity estimation in FMCW radar 4- Hybrid solution combining Kalman filtering with Takagi–Sugeno fuzzy inference system for online car-following model calibration 5- Computationally efficient cooperative dynamic range-only SLAM based on sum of Gaussian filter 6- LoRaWAN geo-tracking using map matching and compass sensor fusion 7- A robust multi-sensor data fusion clustering algorithm based on density peaks 8- Extended target marginal distribution Poisson multi-Bernoulli mixture filter 9- A multi-core object detection coprocessor for multi-scale/type classification applicable to IoT devices 10- Leveraging uncertainties in softmax decision-making models for low-power IoT devices 11- Implementing deep learning techniques in 5G IoT networks for 3D indoor positioning: DELTA (DeEp Learning-Based Co-operaTive Architecture) 12- A novel hybrid algorithm based on Grey Wolf optimizer and fireworks algorithm 13- Passenger flow forecasting in metro transfer station based on the combination of singular spectrum analysis and AdaBoost-weighted extreme learning machine 14- A unified fourth-order tensor-based smart community system Numéro de notice : 28609 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE/POSITIONNEMENT Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-0365-0123-9 En ligne : https://doi.org/10.3390/books978-3-0365-0123-9 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99453