KI-Beratung Beta
Vor Ort
Merkzettel Warenkorb
Produktbild: Machine Learning for Complex and Unmanned Systems

Machine Learning for Complex and Unmanned Systems

135,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

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...

Beschreibung

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.

Produktdetails

Einband

Taschenbuch
Erscheinungsdatum

29.09.2025
Abbildungen

schwarz-weiss Illustrationen, farbige Illustrationen, Raster,schwarz-weiss, Raster, farbig, Zeichnungen, schwarz-weiss, Zeichnungen, farbig, Tabellen, schwarz-weiss
Herausgeber Jose Martinez-Carranza + weitere

Produktdetails

Einband

Taschenbuch
Erscheinungsdatum

29.09.2025
Abbildungen

schwarz-weiss Illustrationen, farbige Illustrationen, Raster,schwarz-weiss, Raster, farbig, Zeichnungen, schwarz-weiss, Zeichnungen, farbig, Tabellen, schwarz-weiss
Herausgeber
Verlag Taylor and Francis
Seitenzahl

386
Maße (L/B/H)

23,4/15,6/2,1 cm
Gewicht

585 g
Sprache

Englisch
ISBN

978-1-03-247330-7

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)
Die Leseprobe wird geladen.
  • Produktbild: Machine Learning for Complex and Unmanned Systems

  • 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