Machine Learning for Complex and Unmanned Systems [Hardcover]

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? ? ??

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 learnilóˆ