This edited book explores novel concepts and cutting-edge research and developments towards designing fully automated advanced digital systems. Fostered by technological advances in artificial intelligence and machine learning, such systems potentially have a wide range of applications in robotics, human computing, sensing and networking.
This edited book explores novel concepts and cutting-edge research and developments towards designing fully automated advanced digital systems. Fostered by technological advances in artificial intelligence and machine learning, such systems potentially have a wide range of applications in robotics, human computing, sensing and networking.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
1. Part I: Human-robot * Chapter 1: Deep learning techniques for modelling human manipulation and its translation for autonomous robotic grasping with soft end-effectors * Chapter 2: Artificial intelligence for affective computing: an emotion recognition case study * Chapter 3: Machine learning-based affect detection within the context of human-horse interaction * Chapter 4: Robot intelligence for real-world applications * Chapter 5: Visual object tracking by quadrotor AR.Drone using artificial neural networks and fuzzy logic controller 2. Part II: Network * Chapter 6: Predictive mobility management in cellular networks * Chapter 7: Artificial intelligence and data analytics in 5G and beyond-5G wireless networks * Chapter 8: Deep Q-network-based coverage hole detection for future wireless networks * Chapter 9: Artificial intelligence for localization of ultrawide bandwidth (UWB) sensor nodes * Chapter 10: A Cascaded Machine Learning Approach for indoor classification and localization using adaptive feature selection 3. Part III: Sensing * Chapter 11: EEG-based biometrics: effects of template ageing * Chapter 12: A machine-learning-driven solution to the problem of perceptual video quality metrics * Chapter 13: Multitask learning for autonomous driving * Chapter 14: Machine-learning-enabled ECG monitoring for early detection of hyperkalaemia * Chapter 15: Combining deterministic compressed sensing and machine learning for data reduction in connected health * Chapter 16: Large-scale distributed and scalable SOM-based architecture for high-dimensional data reduction * Chapter 17: Surface water pollution monitoring using the Internet of Things (IoT) and machine learning * Chapter 18: Conclusions
1. Part I: Human-robot * Chapter 1: Deep learning techniques for modelling human manipulation and its translation for autonomous robotic grasping with soft end-effectors * Chapter 2: Artificial intelligence for affective computing: an emotion recognition case study * Chapter 3: Machine learning-based affect detection within the context of human-horse interaction * Chapter 4: Robot intelligence for real-world applications * Chapter 5: Visual object tracking by quadrotor AR.Drone using artificial neural networks and fuzzy logic controller 2. Part II: Network * Chapter 6: Predictive mobility management in cellular networks * Chapter 7: Artificial intelligence and data analytics in 5G and beyond-5G wireless networks * Chapter 8: Deep Q-network-based coverage hole detection for future wireless networks * Chapter 9: Artificial intelligence for localization of ultrawide bandwidth (UWB) sensor nodes * Chapter 10: A Cascaded Machine Learning Approach for indoor classification and localization using adaptive feature selection 3. Part III: Sensing * Chapter 11: EEG-based biometrics: effects of template ageing * Chapter 12: A machine-learning-driven solution to the problem of perceptual video quality metrics * Chapter 13: Multitask learning for autonomous driving * Chapter 14: Machine-learning-enabled ECG monitoring for early detection of hyperkalaemia * Chapter 15: Combining deterministic compressed sensing and machine learning for data reduction in connected health * Chapter 16: Large-scale distributed and scalable SOM-based architecture for high-dimensional data reduction * Chapter 17: Surface water pollution monitoring using the Internet of Things (IoT) and machine learning * Chapter 18: Conclusions
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