
Signal Processing and Machine Learning Theory
- 1st Edition - July 10, 2023
- Imprint: Academic Press
- 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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Request a sales quoteSignal 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
Upper level undergraduates, Graduate students, researchers in electrical and electronic engineering
- 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
- Edition: 1
- Published: July 10, 2023
- Imprint: Academic Press
- No. of pages: 1234
- Language: English
- Paperback ISBN: 9780323917728
- eBook ISBN: 9780323972253
PD
Paulo S.R. Diniz
Paulo S. R. Diniz’s teaching and research interests are in analog and digital signal processing, adaptive signal processing, digital communications, wireless communications, multirate systems, stochastic processes, and electronic circuits. He has published over 300 refereed papers in some of these areas and wrote two textbooks and a research book. He has received awards for best papers and technical achievements
Affiliations and expertise
Department of Electronics and Computer Engineering (DEL/Poli), Program of Electrical Engineering (COPPE), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, BrazilRead Signal Processing and Machine Learning Theory on ScienceDirect