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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Description

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

Readership

Upper-level undergraduates, Graduate students, researchers in electrical and electronic engineering

Table of Contents

  • 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

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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