Signal Processing and Machine Learning Theory
- 1st Edition - November 20, 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
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
Upper level undergraduates, Graduate students, researchers in electrical and electronic engineering
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 wavelets
1.10 Discrete multiscale and transforms
1.11 Frames
1.12 Parameter estimation
1.13 Adaptive filtering
1.14 Graph Signal Processing
1.15 Tensors
1.16 Non-convex Optimization
1.17 Dictionary Learning
1.18 Closing comments
References
2. Continuous-Time Signals and Systems
Abstract
Nomenclature
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
2.9 Relevant Websites:
2.10 Supplementary data
2.11 Supplementary data
Glossary
References
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 (LTI) systems
3.5 Discrete-time signals and systems with MATLAB
3.6 Conclusion
References
4. Random Signals and Stochastic Processes
Abstract
Acknowledgments
4.1 Introduction
4.2 Probability
4.3 Random variable
4.4 Random process
References
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
5.8 Discrete-time modeling of mixed-signal systems
References
6. Digital Filter Structures and Their Implementation
Abstract
6.1 Introduction
6.2 Digital FIR filters
6.3 The analog approximation problem
6.4 Doubly resistively terminated lossless networks
6.5 Ladder structures
6.6 Lattice structures
6.7 Wave digital filters
6.8 Frequency response masking (FRM) structure
6.9 Computational properties of filter algorithms
6.10 Architecture
6.11 Arithmetic operations
6.12 Sum-of-products (SOP)
6.13 Power reduction techniques
References
7. Multi-rate 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 Closing comments
Glossary
References
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 filters design via spectral factorization
8.7 Higher-order design approach via optimization
8.8 Conclusion
References
9. Discrete Multi-Scale Transforms in Signal Processing
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
A Appendix
References
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
11. Parametric Estimation
Abstract
11.1 Introduction
11.2 Deterministic and stochastic signals
11.3 Parametric models for signals and systems
References
12. Adaptive Filters
Abstract
Acknowledgment
12.1 Introduction
12.2 Optimum filtering
12.3 Stochastic algorithms
12.4 Statistical analysis
12.5 Extensions and current research
12.6 Supplementary data
References
13. Signal Processing over Graphs
Abstract
Acknowledgment
13.1 Introduction
13.6 Supplementary data
References
14. Tensors for Signal Processing and Machine Learning
Abstract
Acknowledgment
14.1 Introduction
15. Non-convex Optimization for Machine Learning
16. Dictionary Learning and Sparse Representation
Abstract
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 wavelets
1.10 Discrete multiscale and transforms
1.11 Frames
1.12 Parameter estimation
1.13 Adaptive filtering
1.14 Graph Signal Processing
1.15 Tensors
1.16 Non-convex Optimization
1.17 Dictionary Learning
1.18 Closing comments
References
2. Continuous-Time Signals and Systems
Abstract
Nomenclature
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
2.9 Relevant Websites:
2.10 Supplementary data
2.11 Supplementary data
Glossary
References
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 (LTI) systems
3.5 Discrete-time signals and systems with MATLAB
3.6 Conclusion
References
4. Random Signals and Stochastic Processes
Abstract
Acknowledgments
4.1 Introduction
4.2 Probability
4.3 Random variable
4.4 Random process
References
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
5.8 Discrete-time modeling of mixed-signal systems
References
6. Digital Filter Structures and Their Implementation
Abstract
6.1 Introduction
6.2 Digital FIR filters
6.3 The analog approximation problem
6.4 Doubly resistively terminated lossless networks
6.5 Ladder structures
6.6 Lattice structures
6.7 Wave digital filters
6.8 Frequency response masking (FRM) structure
6.9 Computational properties of filter algorithms
6.10 Architecture
6.11 Arithmetic operations
6.12 Sum-of-products (SOP)
6.13 Power reduction techniques
References
7. Multi-rate 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 Closing comments
Glossary
References
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 filters design via spectral factorization
8.7 Higher-order design approach via optimization
8.8 Conclusion
References
9. Discrete Multi-Scale Transforms in Signal Processing
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
A Appendix
References
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
11. Parametric Estimation
Abstract
11.1 Introduction
11.2 Deterministic and stochastic signals
11.3 Parametric models for signals and systems
References
12. Adaptive Filters
Abstract
Acknowledgment
12.1 Introduction
12.2 Optimum filtering
12.3 Stochastic algorithms
12.4 Statistical analysis
12.5 Extensions and current research
12.6 Supplementary data
References
13. Signal Processing over Graphs
Abstract
Acknowledgment
13.1 Introduction
13.6 Supplementary data
References
14. Tensors for Signal Processing and Machine Learning
Abstract
Acknowledgment
14.1 Introduction
15. Non-convex Optimization for Machine Learning
16. Dictionary Learning and Sparse Representation
Abstract
- Edition: 1
- Published: November 20, 2023
- Language: English
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