Machine Learning for Complex and Unmanned Systems

This book highlights applications that include machine learning methods to enhance new developments in complex and unmanned systems. The contents are organized from the applications requiring few methods to the ones combining different methods and discussing their development and hardware/software implementation. The book includes two parts: the first one collects machine learning applications in complex systems, mainly discussing developments highlighting their modeling and simulation, and hardware implementation. The second part collects applications of machine learning in unmanned systems including optimization and case studies in submarines, drones, and robots. The chapters discuss miscellaneous applications required by both complex and unmanned systems, in the areas of artificial intelligence, cryptography, embedded hardware, electronics, the Internet of Things, and healthcare. Each chapter provides guidelines and details of different methods that can be reproduced in hardware/software and discusses future research. Features Provides details of applications using machine learning methods to solve real problems in engineering Discusses new developments in the areas of complex and unmanned systems Includes details of hardware/software implementation of machine learning methods Includes examples of applications of different machine learning methods for future lines for research in the hot topic areas of submarines, drones, robots, cryptography, electronics, healthcare, and the Internet of Things This book can be used by graduate students, industrial and academic professionals to examine real case studies in applying machine learning in the areas of modeling, simulation, and optimization of complex systems, cryptography, electronics, healthcare, control systems, Internet of Things, security, and unmanned systems such as submarines, drones, and robots.

Table of Contents:
Section 1: Machine Learning for Complex Systems 1. Echo State Networks to Solve Classification Tasks 2. Continual Learning for Camera Localisation 3. Classifying Ornamental Fish Using Deep Learning Algorithms and Edge Computing Devices 4. Power Amplifier Modeling Comparison for Highly and Sparse Nonlinear Behavior Based on Regression Tree,Random Forest, and CNN for Wideband Systems 5. Models and Methods for Anomaly Detection in Video Surveillance 6. Deep Learning to Classify Pulmonary Infectious Diseases 7. Memristor-based Ring Oscillators as Alternatives for Reliable Physical Unclonable Functions Section 2: Machine Learning for Unmanned Systems 8. Past and Future Data to Train an Artificial Pilot for Autonomous Drone Racing 9. Optimization of UAV Flight Controllers for Trajectory Tracking by Metaheuristics 10. Development of a Synthetic Dataset Using Aerial Navigation to Validate a Texture Classification Model 11. Coverage Analysis in Air-Ground Communications Under Random Disturbances in an Unmanned Aerial Vehicle 12. A Review of Noise Production and Mitigation in UAVs 13. An Overview of NeRF Methods for Aerial Robotics 14. Warehouse Inspection Using Autonomous Drones and Spatial AI 15.Cognitive Dynamic Systems for Cyber-Physical Engineering 16. EEG-Based Motor and Imaginary Movement Classification: ML Approach