Signal Processing and Machine Learning Theory

1st Edition - July 10, 2023
Editor: Paulo S.R. Diniz
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 1 7 7 2 - 8
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 7 2 2 5 - 3

Signal Processing and Machine Learning Theory, authored by world-leading experts, reviews the principles, methods and techniques of essential and advanced signal processing theory… Read more

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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.

Cover image
Title page
Table of Contents
Copyright
Dedication
List of contributors
Contributors
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
Chapter 14
Chapter 15
Chapter 16
Chapter 17
Signal processing and machine learning theory
Chapter 1: Introduction to signal processing and machine learning theory
Abstract
1.1. Introduction
1.2. Continuous-time signals and systems
1.3. Discrete-time signals and systems
1.4. Random signals and stochastic processes
1.5. Sampling and quantization
1.6. FIR and IIR filter design
1.7. Digital filter structures and implementations
1.8. Multirate signal processing
1.9. Filter banks and transform design
1.10. Discrete multiscale and transforms
1.11. Frames
1.12. Parameter estimation
1.13. Adaptive filtering
1.14. Machine learning: review and trends
1.15. Signal processing over graphs
1.16. Tensor methods in deep learning
1.17. Nonconvex graph learning: sparsity, heavy tails, and clustering
1.18. Dictionaries in machine learning
1.19. Closing comments
References
Chapter 2: Continuous-time signals and systems
Abstract
Glossary
2.1. Introduction
2.2. Continuous-time systems
2.3. Differential equations
2.4. Laplace transform: definition and properties
2.5. Transfer function and stability
2.6. Frequency response
2.7. The Fourier series and the Fourier transform
2.8. Conclusion and future trends
Relevant websites
References
Chapter 3: Discrete-time signals and systems
Abstract
3.1. Introduction
3.2. Discrete-time signals: sequences
3.3. Discrete-time systems
3.4. Linear time-invariant systems
3.5. Discrete-time signals and systems with MATLAB®
3.6. Conclusions
Notation
References
Chapter 4: Random signals and stochastic processes
Abstract
Foreword
Acknowledgment
4.1. Introduction
4.2. Probability
4.3. Random variable
4.4. Random process
4.5. Afterword
References
Chapter 5: Sampling and quantization
Abstract
5.1. Introduction
5.2. Preliminaries
5.3. Sampling of deterministic signals
5.4. Sampling of stochastic processes
5.5. Nonuniform sampling and generalizations
5.6. Quantization
5.7. Oversampling techniques and MIMO systems
5.8. Discrete-time modeling of mixed-signal systems
References
Chapter 6: Digital filter structures and their implementation
Abstract
6.1. Introduction
6.2. Properties of digital filters
6.3. Synthesis of digital FIR filters
6.4. FIR structures
6.5. Frequency response masking filters
6.6. The analog approximation problem
6.7. Doubly resistively terminated lossless networks
6.8. Design of doubly resistively terminated analog filters
6.9. Design and realization of IIR filters
6.10. Wave digital filters
6.11. Ladder wave digital filters
6.12. Design of lattice wave digital filters
6.13. Circulator-tree wave digital filters
6.14. Numerically equivalent state-space realization of wave digital filters
6.15. Computational properties of filter algorithms
6.16. Architecture
6.17. Arithmetic operations
6.18. Composite arithmetic operations
6.19. Power reduction techniques
References
Chapter 7: Multirate signal processing for software radio architectures
Abstract
7.1. Introduction
7.2. The sampling process and the “resampling” process
7.3. Digital filters
7.4. Windowing
7.5. Basics on multirate filters
7.6. From single-channel down converter to standard down converter channelizer
7.7. Modifications of the standard down converter channelizer – M:2 down converter channelizer
7.8. Preliminaries on software-defined radios
7.9. Proposed architectures for software radios
7.10. Case study: enabling automated signal detection, segregation, and classification
7.11. Closing comments
Glossary
References
Chapter 8: Modern transform design for practical audio/image/video coding applications
Abstract
8.1. Introduction
8.2. Background and fundamentals
8.3. Design strategy
8.4. Approximation approach via direct scaling
8.5. Approximation approach via structural design
8.6. Wavelet filter design via spectral factorization
8.7. Higher-order design approach via optimization
8.8. Conclusion
References
Chapter 9: Data representation: from multiscale transforms to neural networks
Abstract
9.1. Introduction
9.2. Wavelets: a multiscale analysis tool
9.3. Curvelets and their applications
9.4. Contourlets and their applications
9.5. Shearlets and their applications
9.6. Incorporating wavelets into neural networks
Appendix 9.A.
References
Chapter 10: Frames in signal processing
Abstract
10.1. Introduction
10.2. Basic concepts
10.3. Relevant definitions
10.4. Some computational remarks
10.5. Construction of frames from a prototype signal
10.6. Some remarks and highlights on applications
10.7. Conclusion
References
Chapter 11: Parametric estimation
Abstract
11.1. Introduction
11.2. Preliminaries
11.3. Parametric models for linear time-invariant systems
11.4. Joint process estimation and sequential modeling
11.5. Model order estimation
References
Chapter 12: Adaptive filters
Abstract
12.1. Introduction
12.2. Optimum filtering
12.3. Stochastic algorithms
12.4. Statistical analysis
12.5. Extensions and current research
References
Chapter 13: Machine learning
Abstract
13.1. Introduction
13.2. Learning concepts
13.3. Unsupervised learning
13.4. Supervised learning
13.5. Ensemble learning
13.6. Deep learning
13.7. CNN visualization
13.8. Deep reinforcement learning
13.9. Current trends
13.10. Concluding remarks
13.11. Appendix: RNN's gradient derivations
References
Chapter 14: A primer on graph signal processing
Abstract
14.1. The case for GSP
14.2. Fundamentals of graph theory
14.3. Graph signals and systems
14.4. Graph Fourier transform
14.5. Graph filtering
14.6. Down- and upsampling graph signals
14.7. Examples and applications
14.8. A short history of GSP
References
Chapter 15: Tensor methods in deep learning
Abstract
15.1. Introduction
15.2. Preliminaries on matrices and tensors
15.3. Tensor decomposition
15.4. Tensor methods in deep learning architectures
15.5. Tensor methods in quantum machine learning
15.6. Software
15.7. Limitations and guidelines
References
Chapter 16: Nonconvex graph learning: sparsity, heavy tails, and clustering
Abstract
Acknowledgements
16.1. Introduction
16.2. Sparse graphs
16.3. Heavy-tail graphs
16.4. Clustering
16.5. Conclusion
References
Chapter 17: Dictionaries in machine learning
Abstract
17.1. Data-driven AI via dictionary learning for sparse signal processing
17.2. Sparsity and sparse representations
17.3. Sparse signal processing
17.4. Dictionary learning I – basic models and algorithms
17.5. Dictionary learning II – the hierarchical/empirical Bayes approach
17.6. Nonnegative matrix factorization in dictionary learning
17.7. Dictionaries, data manifold learning, and geometric multiresolution analysis
17.8. Hard clustering and classification in dictionary learning
17.9. Multilayer dictionary learning and classification
17.10. Kernel dictionary learning
17.11. Conclusion
Appendix 17.A. Derivation and properties of the K-SVD algorithm
Appendix 17.B. Derivation of the SBL EM update equation
Appendix 17.C. SBL dictionary learning algorithm
Appendix 17.D. Mathematical background for kernelizing dictionary learning
References
Index

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Paulo S.R. Diniz