Signal Processing Roadmap: Technologies, Applications, and Future Directions explores cutting-edge and emerging signal-processing techniques across various measurement and monitoring applications, serving as an authoritative reference for engineers, researchers, and technologists. The book critically analyzes key signal processing considerations such as uncertainty modeling that enable more intelligent and reliable next-generation measurement systems, all of which are backed by real-world implementation examples in areas ranging from Internet of Things devices to complex biomedical equipment. In addition, sections provide an overview of the latest research in the hybrid information system modeling field, with a particular emphasis on practical applications in various fields.
The book includes case studies and examples of how these models have been used to solve problems in finance, healthcare, engineering, and other related fields. Finally, the book reviews the theories and concepts related to non-linear optimization, fuzzy sets, and rough sets.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xv 7. Preface - p. xix 8. Chapter 1. Foundations of signal processing: a comprehensive overview - p. 1 9. Chapter 2. Frontiers in digital and analog signal processing circuits and systems in the era of artificial intelligence (AI) - p. 19 10. Chapter 3. Sustainable 6G connectivity through artificial intelligence powered energy efficiency - p. 29 11. Chapter 4. Computing requirements and techniques to support signal processing - p. 43 12. Chapter 5. Signal processing techniques for 6G wireless networks: potential applications, trends, and challenges - p. 53 13. Chapter 6. Signal processing for localization and sensing techniques in next-generation wireless networks - p. 75 14. Chapter 7. Advanced signal processing techniques for nonorthogonal multiple access in 6G networks - p. 91 15. Chapter 8. Signal processing innovations for improved 6 G ubiquitous connectivity: paving the path towards integrated space-ground-underwater networks - p. 107 16. Chapter 9. Advanced real-time signal processing techniques for adaptive beamforming in beyond 5G wireless networkss - p. 123 17. Chapter 10. Signal processing for autonomous systems and robotics: principles, techniques, and future directions - p. 139 18. Chapter 11. Signal processing: a roadmap towards image reconstruction and image enhancement - p. 159 19. Chapter 12. Study of image enhancement techniques for satellite images - p. 173 20. Chapter 13. Intelligent signal processing for physical and medium access control layers: a machine learning perspective - p. 187 21. Chapter 14. Optimization techniques in 6 G communication: from foundations to AI-driven solutions - p. 205 22. Chapter 15. Advanced image restoration via lightweight convolutional networks and graph-based contextual learning - p. 223 23. Chapter 16. Emerging trends in edge-AIoriented signal processing for IoT devices - p. 233 24. Chapter 17. Quantum-enhanced fuzzy convolutional neural networks for high-performance analog signal processing systems - p. 245 25. Chapter 18. Resource optimization, error mitigation, and algorithmic partitioning in multinode quantum systems: a practical perspective - p. 257 26. Chapter 19. Artificial intelligence-driven heterogeneous wireless network optimization: composite fading, mobility modeling, and resource allocation - p. 275 27. Chapter 20. Intelligent traffic management system with machine learning for congestion prediction and control - p. 299 28. Index - p. 319
Signal Processing Roadmap: Technologies, Applications, and Future Directions explores cutting-edge and emerging signal-processing techniques across various measurement and monitoring applications, serving as an authoritative reference for engineers, researchers, and technologists. The book critically analyzes key signal processing considerations such as uncertainty modeling that enable more intelligent and reliable next-generation measurement systems, all of which are backed by real-world implementation examples in areas ranging from Internet of Things devices to complex biomedical equipment. In addition, sections provide an overview of the latest research in the hybrid information system modeling field, with a particular emphasis on practical applications in various fields.
The book includes case studies and examples of how these models have been used to solve problems in finance, healthcare, engineering, and other related fields. Finally, the book reviews the theories and concepts related to non-linear optimization, fuzzy sets, and rough sets.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xv 7. Preface - p. xix 8. Chapter 1. Foundations of signal processing: a comprehensive overview - p. 1 9. Chapter 2. Frontiers in digital and analog signal processing circuits and systems in the era of artificial intelligence (AI) - p. 19 10. Chapter 3. Sustainable 6G connectivity through artificial intelligence powered energy efficiency - p. 29 11. Chapter 4. Computing requirements and techniques to support signal processing - p. 43 12. Chapter 5. Signal processing techniques for 6G wireless networks: potential applications, trends, and challenges - p. 53 13. Chapter 6. Signal processing for localization and sensing techniques in next-generation wireless networks - p. 75 14. Chapter 7. Advanced signal processing techniques for nonorthogonal multiple access in 6G networks - p. 91 15. Chapter 8. Signal processing innovations for improved 6 G ubiquitous connectivity: paving the path towards integrated space-ground-underwater networks - p. 107 16. Chapter 9. Advanced real-time signal processing techniques for adaptive beamforming in beyond 5G wireless networkss - p. 123 17. Chapter 10. Signal processing for autonomous systems and robotics: principles, techniques, and future directions - p. 139 18. Chapter 11. Signal processing: a roadmap towards image reconstruction and image enhancement - p. 159 19. Chapter 12. Study of image enhancement techniques for satellite images - p. 173 20. Chapter 13. Intelligent signal processing for physical and medium access control layers: a machine learning perspective - p. 187 21. Chapter 14. Optimization techniques in 6 G communication: from foundations to AI-driven solutions - p. 205 22. Chapter 15. Advanced image restoration via lightweight convolutional networks and graph-based contextual learning - p. 223 23. Chapter 16. Emerging trends in edge-AIoriented signal processing for IoT devices - p. 233 24. Chapter 17. Quantum-enhanced fuzzy convolutional neural networks for high-performance analog signal processing systems - p. 245 25. Chapter 18. Resource optimization, error mitigation, and algorithmic partitioning in multinode quantum systems: a practical perspective - p. 257 26. Chapter 19. Artificial intelligence-driven heterogeneous wireless network optimization: composite fading, mobility modeling, and resource allocation - p. 275 27. Chapter 20. Intelligent traffic management system with machine learning for congestion prediction and control - p. 299 28. Index - p. 319
