Machine Learning for Complex and Unmanned Systems

Auteur

Esteban Tlelo Cuautle received a B.Sc. degree from Instituto Tecnológico de Puebla (ITP) México in 1993. He then received both M.Sc. and Ph.D. degrees from Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), México in 1995 and 2000, respectively. During 1995-2000 he was with the electronics-engineering department at ITP. In 2001 he was appointed as Professor-Researcher at INAOE. He has been Visiting Researcher in the department of Electrical Engineering at University of California Riverside, USA (2009-2010), in the department of Computer Science at CINVESTAV, México City, México (2016-2017), and Visiting Lecturer at University of Electronic Science and Technology of China (UESTC, Chengdu 2014-2019). He has authored 5 books, edited 12 books and more than 300 works published in book chapters, international journals and conferences. He is member in the National System for Researchers (SNI-CONACyT-México). His research interests include integrated circuit design, optimization by metaheuristics, fractional-order chaotic systems, artificial intelligence, security in Internet of Things, and analog/RF and mixed-signal design automation tools.

Jose Martinez-Carranza is a Full-Time Principal Researcher B (equivalent to Associate Professor) in the Computer Science Department at the Instituto Nacional de Astrofisica Optica y Electronica (INAOE). In 2015, he was awarded the Newton Advanced Fellowship granted by the Newton Fund and the Royal Society in the UK. Currently, he holds an Honorary Senior Research Fellowship in the Computer Science Department at the University of Bristol in the UK. He leads a research team that has won international competitions such as 1st Place in the IEEE IROS 2017 Autonomous Drone Racing competition and 1st Place in the Regional Prize of the OpenCV AI Competition 2021. He also served as General Chair of the International Micro Air Vehicle conference, the IMAV 2021. In 2022, he joined the editorial board of the journal "Unmanned Systems". His research focuses on vision-based methods for robotics with applications in autonomous and intelligent drones.

Everardo Inzunza-Gonzalez received his Ph.D. degree in Electrical Sciences from UABC Mexico in 2013, and the M.Sc. degree in Electronics and Telecommunications from the Scientific Research and Advanced Studies Center of Ensenada (CICESE) in 2001, the B.Sc. degree in Electronics Engineering from Culiacan Institute of Technology, in 1999. He is currently a full-time Professor and Researcher of Electronics Engineering at Universidad Autónoma de Baja California (UABC-FIAD) Mexico. He is currently a reviewer for several prestigious journals. His research interest includes the Internet of things, Network Security, Data Science, Artificial Intelligence, Machine-Learning and Deep-Learning, Wireless Communication, Image Processing, WSN, Pattern Recognition, Wearable Devices, Embedded Systems, FPGA, SoC, Microcontrollers, Chaotic encryption, Image encryption, Image enhancement, Image processing, Chaotic oscillators and Applied Cryptography.

Enrique Efren García-Guerrero studied physics engineering at the University Autonomous Metropolitana, Mexico, and received the PhD and M.Sc. degree in optical physics from the Scientic Research and Advanced Studies Center of Ensenada (CICESE) Mexico. He has been with the Facultad de Ingeniería, Arquitectura y Diseño of the Universidad Autónoma de Baja California (UABC-FIAD) Mexico since 2004. His current research interest includes Image enhancement, embedded systems, chaotic cryptography, artificial intelligence, machine-learning, deep-learning, neural networks, digital image processing and optical systems.

Texte du rabat

This book highlights applications that include machine learning methods to enhance new developments in complex and unmanned systems. The main topics covered under this title include: machine learning, artificial intelligence, cryptography, submarines, drones, security in healthcare, Internet of Things and robotics.

Contenu

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