Deep Learning and Its Applications for Vehicle Networks
portes grátis
Deep Learning and Its Applications for Vehicle Networks
Hu, Fei; Rasheed, Iftikhar
Taylor & Francis Ltd
12/2024
342
Mole
9781032041384
Pré-lançamento - envio 15 a 20 dias após a sua edição
Descrição não disponível.
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 Tra?c 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.
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 Tra?c 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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Deep Learning;AI-based vehicular networks;DL for vehicle safety and security;DL for vehicle control;UAV Network Optimization;Deep RL;Deep Learning Model;DNN Model;Deep Learning Algorithms;Vehicular Networks;Deep Learning Methods;Deep CNN;RBMs;Deep Learning Architectures;4G LTE;SVM;EV;IoT Device;EV Load;NLP Model;Polar Codes;IoT Data;Emission Episodes;Spreading Code;Macroscopic Traffic Flow Model;Caching Algorithm;Traffic Flow Forecasting;Query Processor;Drowsiness Detection;Conventional OFDM
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 Tra?c 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.
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 Tra?c 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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Deep Learning;AI-based vehicular networks;DL for vehicle safety and security;DL for vehicle control;UAV Network Optimization;Deep RL;Deep Learning Model;DNN Model;Deep Learning Algorithms;Vehicular Networks;Deep Learning Methods;Deep CNN;RBMs;Deep Learning Architectures;4G LTE;SVM;EV;IoT Device;EV Load;NLP Model;Polar Codes;IoT Data;Emission Episodes;Spreading Code;Macroscopic Traffic Flow Model;Caching Algorithm;Traffic Flow Forecasting;Query Processor;Drowsiness Detection;Conventional OFDM
