Signal Processing and Machine Learning Theory
eBook - ePub

Signal Processing and Machine Learning Theory

  1. 1,234 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Signal Processing and Machine Learning Theory

Book details
Table of contents
Citations

About This Book

Signal Processing and Machine Learning Theory, authored by world-leading experts, reviews the principles, methods and techniques of essential and advanced signal processing theory. These theories and tools are the driving engines of many current and emerging research topics and technologies, such as machine learning, autonomous vehicles, the internet of things, future wireless communications, medical imaging, etc.

  • Provides quick tutorial reviews of important and emerging topics of research in signal processing-based tools
  • Presents core principles in signal processing theory and shows their applications
  • Discusses some emerging signal processing tools applied in machine learning methods
  • References content on core principles, technologies, algorithms and applications
  • Includes references to journal articles and other literature on which to build further, more specific, and detailed knowledge

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Yes, you can access Signal Processing and Machine Learning Theory by Paulo S.R. Diniz in PDF and/or ePUB format, as well as other popular books in Technik & Maschinenbau & Signale & Signalverarbeitung. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Academic Press
Year
2023
ISBN
9780323972253
Topic
Technik & Maschinenbau
Subtopic
Signale & Signalverarbeitung

Table of contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Dedication
  6. List of contributors
  7. Contributors
  8. Signal processing and machine learning theory
  9. Chapter 1: Introduction to signal processing and machine learning theory
  10. Chapter 2: Continuous-time signals and systems
  11. Chapter 3: Discrete-time signals and systems
  12. Chapter 4: Random signals and stochastic processes
  13. Chapter 5: Sampling and quantization
  14. Chapter 6: Digital filter structures and their implementation
  15. Chapter 7: Multirate signal processing for software radio architectures
  16. Chapter 8: Modern transform design for practical audio/image/video coding applications
  17. Chapter 9: Data representation: from multiscale transforms to neural networks
  18. Chapter 10: Frames in signal processing
  19. Chapter 11: Parametric estimation
  20. Chapter 12: Adaptive filters
  21. Chapter 13: Machine learning
  22. Chapter 14: A primer on graph signal processing
  23. Chapter 15: Tensor methods in deep learning
  24. Chapter 16: Nonconvex graph learning: sparsity, heavy tails, and clustering
  25. Chapter 17: Dictionaries in machine learning
  26. Index