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 modelling 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.
- 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 modelling, simulation, and optimization of complex systems, cryptography, electronics, healthcare, control systems, Internet of Things, security, and unmanned systems such as submarines, drones, and robots.
Section 1: Machine Learning for Complex Systems
1. Echo State Networks to Solve Classification Tasks
Luis Gerardo de la Fraga, Astrid Maritza Gonzalez-Zapata, and Andres Cureno Ramirez
2. Continual Learning for Camera Localisation
Aldrich A. Cabrera-Ponce, Manuel Martin-Ortiz and Jose Martinez-Carranza
3. Classifying Ornamental Fish Using Deep Learning Algorithms and Edge Computing Devices
O. A. Aguirre-Castro, E. Inzunza-Gonz´alez, O. R. L´opez-Bonilla et al.
4. Power Amplifier Modeling Comparison for Highly and Sparse Nonlinear Behavior Based on Regression Tree,Random Forest, and CNN for Wideband Systems
J. A. Galaviz-Aguilar, C. Vargas-Rosales, D. S. Aguila-Torres et al.
5. Models and Methods for Anomaly Detection in Video Surveillance
Ernesto Cruz-Esquivel and Zobeida J. Guzman-Zavaleta
6. Deep Learning to Classify Pulmonary Infectious Diseases
Dora-Luz Flores, Ricardo Perea-Jacobo, Miguel Angel Chevannier-Guerrero et al.
7. Memristor-based Ring Oscillators as Alternatives for Reliable Physical Unclonable Functions
Joseph Herbert Mitchell-Moreno, Guillermo Espinosa Flores-Verdad, and Arturo Sarmiento-Reyes
Section 2: Machine Learning for Unmanned Systems
8. Past and Future Data to Train an Artificial Pilot for Autonomous Drone Racing
L. Oyuki Rojas-Perez, Alejandro Gutierrez-Giles, and Jose Martinez-Carranza
9. Optimization of UAV Flight Controllers for Trajectory Tracking by Metaheuristics
Jonathan Daniel Diaz-Munoz, Oscar Martinez-Fuentes, and Israel Cruz-Vega
10. Development of a Synthetic Dataset Using Aerial Navigation to Validate a Texture Classification Model
J. M. Fortuna-Cervantes, M. T. Ramirez-Torres, M. Mejia-Carlos et al.
11. Coverage Analysis in Air-Ground Communications Under Random Disturbances in an Unmanned Aerial Vehicle
Esteban Tlelo-Coyotecatl, Giselle Monserrat Galvan-Tejada, and Manuel Mauricio Lara-Barron
12. A Review of Noise Production and Mitigation in UAVs
Caleb Rascon and Jose Martinez-Carranza
13. An Overview of NeRF Methods for Aerial Robotics
Luis Fernando Rosas-Ordaz, Leticia Oyuki Rojas-Perez, Cesar Martinez-Torres et al.
14. Warehouse Inspection Using Autonomous Drones and Spatial AI
Jose Martinez-Carranza and Leticia Oyuki Rojas-Perez
15.Cognitive Dynamic Systems for Cyber-Physical Engineering
Cesar Torres Huitzil
16. EEG-Based Motor and Imaginary Movement Classification: ML Approach
Francisco Javier Ram´ırez-Arias, Juan Miguel Colores-Vargas, Jovn Oseas Mrida-Rubio et al.