Signal Processing and Machine Learning Theory

Signal Processing and Machine Learning Theory

1st Edition - May 1, 2023

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  • Editor: Paulo S.R. Diniz
  • Paperback ISBN: 9780323917728

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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.

Key Features

  • 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

Table of Contents

  • 1. Introduction to Signal Processing and Machine Learning Theory
    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

    2. Continuous-Time Signals and Systems
    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

    3. Discrete-Time Signals and Systems
    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

    4. Random Signals and Stochastic Processes
    4.1 Introduction
    4.2 Probability
    4.3 Random variable
    4.4 Random process

    5. Sampling and Quantization
    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

    6. Digital Filter Structures and Their Implementation
    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

    7. Multi-rate Signal Processing for Software Radio Architectures
    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

    8. Modern Transform Design for Practical Audio/Image/Video Coding Applications
    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

    9. Discrete Multi-Scale Transforms in Signal Processing
    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

    10. Frames in Signal Processing
    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

    11. Parametric Estimation
    11.1 Introduction
    11.2 Deterministic and stochastic signals
    11.3 Parametric models for signals and systems

    12. Adaptive Filters
    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

    13. Signal Processing over Graphs
    13.1 Introduction
    13.6 Supplementary data

    14. Tensors for Signal Processing and Machine Learning
    14.1 Introduction

    15. Non-convex Optimization for Machine Learning

    16. Dictionary Learning and Sparse Representation

Product details

  • Language: English
  • Copyright: © Academic Press 2023
  • Published: May 1, 2023
  • Imprint: Academic Press
  • Paperback ISBN: 9780323917728

About the Editor

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, Brazil

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