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.

• 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
  
  
  
  
  
• 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
  
  
  
  
  
• 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