AI for Emerging Verticals Human-robot computing, sensing and networking
By specializing in a vertical market, companies can better understand their customers and bring more insight to clients in order to become an integral part of their businesses. This approach requires dedicated tools, which is where artificial intelligence (AI) and machine learning (ML) will play a m...
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| Main Authors | , |
|---|---|
| Format | eBook |
| Language | English |
| Published |
Stevenage
The Institution of Engineering and Technology
2021
Institution of Engineering and Technology (The IET) Institution of Engineering & Technology Institution of Engineering and Technology |
| Edition | 1 |
| Series | Computing and Networks |
| Subjects | |
| Online Access | Get full text |
| ISBN | 1785619829 9781785619823 |
| DOI | 10.1049/PBPC034E |
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Table of Contents:
- Part I: Human-robot -- Chapter 1: Deep learning techniques for modelling human manipulation and its translation for autonomous robotic grasping with soft end-effectors -- Chapter 2: Artificial intelligence for affective computing: an emotion recognition case study -- Chapter 3: Machine learning-based affect detection within the context of human-horse interaction -- Chapter 4: Robot intelligence for real-world applications -- Chapter 5: Visual object tracking by quadrotor AR.Drone using artificial neural networks and fuzzy logic controller -- -- Part II: Network -- Chapter 6: Predictive mobility management in cellular networks -- Chapter 7: Artificial intelligence and data analytics in 5G and beyond-5G wireless networks -- Chapter 8: Deep -- -network-based coverage hole detection for future wireless networks -- Chapter 9: Artificial intelligence for localization of ultrawide bandwidth (UWB) sensor nodes -- Chapter 10: A Cascaded Machine Learning Approach for indoor classification and localization using adaptive feature selection -- -- Part III: Sensing -- Chapter 11: EEG-based biometrics: effects of template ageing -- Chapter 12: A machine-learning-driven solution to the problem of perceptual video quality metrics -- Chapter 13: Multitask learning for autonomous driving -- Chapter 14: Machine-learning-enabled ECG monitoring for early detection of hyperkalaemia -- Chapter 15: Combining deterministic compressed sensing and machine learning for data reduction in connected health -- Chapter 16: Large-scale distributed and scalable SOM-based architecture for high-dimensional data reduction -- Chapter 17: Surface water pollution monitoring using the Internet of Things (IoT) and machine learning -- Chapter 18: Conclusions --
- Title Page Preface Table of Contents 1. Deep Learning Techniques for Modelling Human Manipulation and its Translation for Autonomous Robotic Grasping with Soft End-Effectors 2. Artificial Intelligence for Affective Computing: An Emotion Recognition Case Study 3. Machine Learning-Based Affect Detection within the Context of Human-Horse Interaction 4. Robot Intelligence for Real-World Applications 5. Visual Object Tracking by Quadrotor AR.Drone Using Artificial Neural Networks and Fuzzy Logic Controller 6. Predictive Mobility Management in Cellular Networks 7. Artificial Intelligence and Data Analytics in 5G and Beyond-5G Wireless Networks 8. Deep Q-Network-Based Coverage Hole Detection for Future Wireless Networks 9. Artificial Intelligence for Localization of Ultrawide Bandwidth (UWB) Sensor Nodes 10. A Cascaded Machine Learning Approach for Indoor Classification and Localization Using Adaptive Feature Selection 11. EEG-Based Biometrics: Effects of Template Ageing 12. A Machine-Learning-Driven Solution to the Problem of Perceptual Video Quality Metrics 13. Multitask Learning for Autonomous Driving 14. Machine-Learning-Enabled ECG Monitoring for Early Detection of Hyperkalaemia 15. Combining Deterministic Compressed Sensing and Machine Learning for Data Reduction in Connected Health 16. Large-Scale Distributed and Scalable SOM-Based Architecture for High-Dimensional Data Reduction 17. Surface Water Pollution Monitoring Using the Internet of Things (IoT) and Machine Learning Conclusions Index
- 5.3 Fuzzy-logic-based identification and target tracking -- 5.4 Artificial neural networks (ANN) for target identification and tracking using a quadrotor -- 5.5 Conclusion -- References -- Part II: Network -- 6. Predictive mobility management in cellular networks | Metin Öztürk, Paulo Valente Klaine, Sajjad Hussain, and Muhammad Ali Imran -- 6.1 Introduction -- 6.2 Mobility management in cellular networks -- 6.3 Predictive mobility management -- 6.4 Advanced Markov-chain-assisted predictive mobility management -- 6.5 Summary -- References -- 7. Artificial intelligence and data analytics in 5G and beyond-5G wireless networks | Maziar Nekovee, Dehao Wu, YueWang and Mehrdad Shariat -- 7.1 Introduction -- 7.2 Case studies of AI in 5G wireless networks -- 7.3 Data analytics in 5G -- 7.4 Industry and standard activities -- 7.5 Challenges and open questions -- 7.6 Conclusions -- References -- 8. Deep Q-network-based coverage hole detection for future wireless networks | Shahriar Abdullah Al-Ahmed, Muhammad Zeeshan Shakir,and Qasim Zeeshan Ahmed -- 8.1 Introduction -- 8.2 Machine learning -- 8.3 System model -- 8.4 DQN-based coverage hole detection -- 8.5 Simulation results and discussion -- 8.6 Conclusions -- References -- 9. Artificial intelligence for localization of ultrawide bandwidth (UWB) sensor nodes | Fuhu Che, Abbas Ahmed, Qasim Zeeshan Ahmed, and Muhammad Zeeshan Shakir -- 9.1 Introduction -- 9.2 Indoor positioning system -- 9.3 UWB ranging accuracy evaluation -- 9.4 Implementation and evaluation -- 9.5 Conclusion -- References -- 10. A Cascaded Machine Learning Approach for indoor classification and localization using adaptive feature selection | Mohamed I. AlHajri, Nazar T. Ali and Raed M. Shubair -- 10.1 Introduction -- 10.2 Indoor radio propagation channel -- 10.3 Data collection phase: practical measurements campaign
- Intro -- Contents -- About the editors -- Preface -- Part I: Human-robot -- 1. Deep learning techniques for modelling human manipulation and its translation for autonomous robotic grasping with soft end-effectors | Visar Arapi, Yujie Zhang, Giuseppe Averta, Cosimo Della Santina, and Matteo Bianchi -- 1.1 Introduction -- 1.2 Investigation of the human example -- 1.3 Autonomous grasping with anthropomorphic soft hands -- 1.4 Discussion and conclusions -- Acknowledgement -- References -- 2. Artificial intelligence for affective computing: an emotion recognition case study | Pablo Arnau-González, Stamos Katsigiannis, Miguel Arevalillo-Herráez, and Naeem Ramzan -- 2.1 Introduction -- 2.2 Models of human affect -- 2.3 Previous work on emotion recognition -- 2.4 Data sets for emotion recognition -- 2.5 Proposed methodology -- 2.6 Experimental results -- 2.7 Conclusions and discussion -- Acknowledgement -- References -- 3. Machine learning-based affect detection within the context of human-horse interaction | Turke Althobaiti, Stamos Katsigiannis, DauneWest, Hassan Rabah, and Naeem Ramzan -- 3.1 Introduction -- 3.2 Background -- 3.3 Experimental protocol -- 3.4 Analysis of captured data -- 3.5 Experimental results -- 3.6 Discussion -- 3.7 Conclusion -- References -- 4. Robot intelligence for real-world applications | Eleftherios Triantafyllidis, Chuanyu Yang, Christopher McGreavy, Wenbin Hu, and Zhibin Li -- 4.1 Introduction -- 4.2 Novel robotic applications in locomotion -- 4.3 Novel robotic applications in human-guided manipulation -- 4.4 Novel robotic applications in fully autonomous manipulation -- 4.5 Conclusion -- References -- 5. Visual object tracking by quadrotor AR.Drone using artificial neural networks and fuzzy logic controller | Kamel Boudjit, Cherif Larbes and Naeem Ramzan -- 5.1 Introduction -- 5.2 System overview
- 10.4 Signatures of indoor environment -- 10.5 Spatial correlation coefficient -- 10.6 Machine learning algorithms -- 10.7 Cascaded Machine Learning Approach -- 10.8 Conclusion -- References -- Part III: Sensing -- 11. EEG-based biometrics: effects of template ageing | Pablo Arnau-González, Stamos Katsigiannis, Miguel Arevalillo-Herraez and Naeem Ramzan -- 11.1 Introduction -- 11.2 Background -- 11.3 Data acquisition and experimental protocol -- 11.4 Experimental results -- 11.5 Conclusions -- References -- 12. A machine-learning-driven solution to the problem of perceptual video quality metrics | Stamos Katsigiannis, Hassan Rabah, and Naeem Ramzan -- 12.1 Introduction -- 12.2 Objective video quality assessment methods -- 12.3 The video multimethod assessment fusion (VMAF) metric -- 12.4 Experimental evaluation -- 12.5 Conclusion -- References -- 13. Multitask learning for autonomous driving | Murtaza Taj and Waseem Abbas -- 13.1 Introduction -- 13.2 Related work -- 13.3 Problem formulation -- 13.4 Driving parameter estimation -- 13.5 Scene understanding -- 13.6 Computational complexity -- 13.7 Summary -- References -- 14 Machine-learning-enabled ECG monitoring for early detection of hyperkalaemia | Constance Farrell and Muhammad Zeeshan Shakir -- 14.1 Introduction -- 14.2 ECG signal analysis -- 14.3 ECG data collection and preprocessing -- 14.4 Machine learning classification models -- 14.5 Results -- 14.6 Conclusions and recommendations -- References -- 15. Combining deterministic compressed sensing and machine learning for data reduction in connected health | Hassan Rabah, Slavisa Jovanovic and Naeem Ramzan -- 15.1 Introduction -- 15.2 Background and related work -- 15.3 Method -- 15.4 Experimental results and discussion -- 15.5 Conclusion -- References
- 16. Large-scale distributed and scalable SOM-based architecture for high-dimensional data reduction | Slavisa Jovanovic, Hassan Rabah, and SergeWeber -- 16.1 Introduction -- 16.2 Related work -- 16.3 Background -- 16.4 Proposed SOM model -- 16.5 Results and discussion -- 16.6 Conclusion -- References -- 17. Surface water pollution monitoring using the Internet of Things (IoT) and machine learning | Hamza Khurshid, Rafia Mumtaz, Noor Alvi, Faisal Shafait, Sheraz Ahmed, Muhammad Imran Malik, Andreas Dengel, and Quanita Kiran -- 17.1 Introduction -- 17.2 Literature review -- 17.3 Methodology -- 17.4 Results and discussion -- 17.5 Conclusion and future work -- Acknowledgment -- References -- 18. Conclusions -- Index