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Intelligent Sensors for Human Motion Analysis
| Intelligent Sensors for Human Motion Analysis |
| Autore | Krzeszowski Tomasz |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (382 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology |
| Soggetto non controllato |
gait recognition
biometrics regularized discriminant analysis particle swarm optimization grey wolf optimization whale optimization algorithm FMCW vital sign XGBoost MFCC COVID-19 3D human pose estimation deep learning generalization optical sensing principle modular sensing unit plantar pressure measurement gait parameters 3D human mesh reconstruction deep neural network motion capture neural networks reconstruction gap filling FFNN LSTM BILSTM GRU pose estimation movement tracking computer vision artificial intelligence markerless motion capture assessment kinematics development machine learning human action recognition features fusion features selection recognition fall risk detection balance Berg Balance Scale human tracking elderly telemedicine diagnosis precedence indicator knowledge measure fuzzy inference rule induction posture detection aggregation function markerless human motion analysis gait analysis data augmentation skeletal data time series classification EMG pattern recognition robot cyber-physical systems RGB-D sensors human motion modelling F-Formation Kinect v2 Azure Kinect Zed 2i socially occupied space facial expression recognition facial landmarks action units convolutional neural networks graph convolutional networks artifact classification artifact detection anomaly detection 3D multi-person pose estimation absolute poses camera-centric coordinates deep-learning |
| ISBN | 3-0365-5074-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910619469003321 |
Krzeszowski Tomasz
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| MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Unmanned Aerial Vehicles : Platforms, Applications, Security and Services
| Unmanned Aerial Vehicles : Platforms, Applications, Security and Services |
| Autore | Calafate Carlos Tavares |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (174 p.) |
| Soggetto topico | History of engineering & technology |
| Soggetto non controllato |
computer vision
oil well working condition real-time detection sort unmanned aerial vehicle (UAV) YOLOv3 UAV autonomous landing vision-based ArduSim ArUco marker blind signature security MEC UAVs FANET 5G IoT Mutual authentication Privacy Traceable BAN logic coverage model human mobility model UAVs/drones positioning energy model UAS horizon undistortion FPGA sense-and-avoid LoRaWAN Unmanned Aerial Vehicles topology control virtual spring forces firefighting communications |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Unmanned Aerial Vehicles |
| Record Nr. | UNINA-9910557698003321 |
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Calafate Carlos Tavares
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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