Deep Learning and Its Applications for Vehicle Networks

Auteur

Dr. Fei Hu is a professor in the department of Electrical and Computer Engineering at the University of Alabama. He has published over 10 technical books with CRC press. His research focus includes cyber security and networking. He obtained his Ph.D. degrees at Tongji University (Shanghai, China) in the field of Signal Processing (in 1999), and at Clarkson University (New York, USA) in Electrical and Computer Engineering (in 2002). He has published over 200 journal/conference papers and books. Dr. Hu's research has been supported by U.S. National Science Foundation, Cisco, Sprint, and other sources. He won the school's President's Faculty Research Award (

Texte du rabat

This book is based on the work from world-famous experts on the application of DL for vehicle networks. It consists of the following five parts: 1. DL for vehicle safety and security, 2. DL for effective vehicle communications, 3. DL for vehicle control, 4. DL for information management, 5. Other applications.

Contenu

Part I. Deep Learning for Vehicle Safety and Security

1. Deep Learning for Vehicle Safety. 2. Deep Learning for Driver Drowsiness Classification for a Safe Vehicle Application.
   3. A Deep Learning Perspective on Connected Automated Vehicle (CAV) Cybersecurity and Threat Intelligence..

Part II. Deep Learning for Vehicle Communications

4. Deep Learning for UAV Network Optimization.
   5. State-of-the-Art in PHY Layer Deep Learning for Future Wireless Communication Systems and Networks. 6. Deep Learning-Based Index Modulation Systems for Vehicle Communications.
      7. Deep Reinforcement Learning Applications in Connected-Automated Transportation Systems.

Part III. Deep Learning for Vehicle Control

8. Vehicle Emission Control on Road with Temporal Träc Information using Deep Reinforcement Learning.
   9. Load Prediction of Electric Vehicle Charging Pile.
      10. Deep Learning for Autonomous Vehicles: A Vision-Based Approach to Self-Adapted Robust Control.

Part IV. DL for Information Management

11. A Natural Language Processing Based Approach for Automating IoT Search. 12. Towards Incentive-Compatible Vehicular Crowdsensing: A Reinforcement Learning-Based Approach.
    13. Sub-Signal Detection from Noisy Complex Signals Using Deep Learning and Mathematical Morphology.

Part V. Miscellaneous

14. The Basics of Deep Learning Algorithms and their effect on driving behavior and vehicle communications.
    15. Integrated Simulation of Deep Learning, Computer Vision and Physical Layer of UAV and Ground Vehicle Networks.