Academic Press Library in Signal Processing

Academic Press Library in Signal Processing

Signal Processing Theory and Machine Learning

1st Edition - September 5, 2013

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  • Editors-in-Chief: Sergios Theodoridis, Rama Chellappa
  • eBook ISBN: 9780123972262

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Description

This first volume, edited and authored by world leading experts, gives a review of the principles, methods and techniques of important and emerging research topics and technologies in machine learning and advanced signal processing theory. With this reference source you will: Quickly grasp a new area of research  Understand the underlying principles of a topic and its application Ascertain how a topic relates to other areas and learn of the research issues yet to be resolved

Key Features

  • Quick tutorial reviews of important and emerging topics of research in machine learning
  • Presents core principles in signal processing theory and shows their applications
  • Reference content on core principles, technologies, algorithms and applications
  • Comprehensive references to journal articles and other literature on which to build further, more specific and detailed knowledge
  • Edited by leading people in the field who, through their reputation, have been able to commission experts to write on a particular topic

Readership

PhD students, Post Docs, R&D engineers in signal processing and wireless and mobile communications, Consultants

Table of Contents

  • Introduction

    Signal Processing at Your Fingertips!

    About the Editors

    Section Editors

    Section 1

    Section 2

    Authors Biography

    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 14

    Chapter 15

    Chapter 16

    Chapter 17

    Chapter 18

    Chapter 19

    Chapter 20

    Chapter 21

    Chapter 22

    Chapter 24

    Chapter 25

    Chapter 26

    Section 1: SIGNAL PROCESSING THEORY

    Chapter 1. Introduction to Signal Processing Theory

    Abstract

    1.01.1 Introduction

    1.01.2 Continuous-time signals and systems

    1.01.3 Discrete-time signals and systems

    1.01.4 Random signals and stochastic processes

    1.01.5 Sampling and quantization

    1.01.6 FIR and IIR filter design

    1.01.7 Digital filter structures and implementations

    1.01.8 Multirate signal processing

    1.01.9 Filter banks and wavelets

    1.01.10 Discrete multiscale and transforms

    1.01.11 Frames

    1.01.12 Parameter estimation

    1.01.13 Adaptive filtering

    1.01.14 Closing comments

    References

    Chapter 2. Continuous-Time Signals and Systems

    Abstract

    Nomenclature

    1.02.1 Introduction

    1.02.2 Continuous-time systems

    1.02.3 Differential equations

    1.02.4 Laplace transform: definition and properties

    1.02.5 Transfer function and stability

    1.02.6 Frequency response

    1.02.7 The Fourier series and the Fourier transform

    1.02.8 Conclusion and future trends

    1.02.9 Relevant Websites:

    1.02.10 Supplementary data

    1.02.11 Supplementary data

    Glossary

    References

    Chapter 3. Discrete-Time Signals and Systems

    Abstract

    1.03.1 Introduction

    1.03.2 Discrete-time signals: sequences

    1.03.3 Discrete-time systems

    1.03.4 Linear time-invariant (LTI) systems

    1.03.5 Discrete-time signals and systems with MATLAB

    1.03.6 Conclusion

    References

    Chapter 4. Random Signals and Stochastic Processes

    Abstract

    Acknowledgements

    1.04.1 Introduction

    1.04.2 Probability

    1.04.3 Random variable

    1.04.4 Random process

    References

    Chapter 5. Sampling and Quantization

    Abstract

    1.05.1 Introduction

    1.05.2 Preliminaries

    1.05.3 Sampling of deterministic signals

    1.05.4 Sampling of stochastic processes

    1.05.5 Nonuniform sampling and generalizations

    1.05.6 Quantization

    1.05.7 Oversampling techniques

    1.05.8 Discrete-time modeling of mixed-signal systems

    References

    Chapter 6. Digital Filter Structures and Their Implementation

    Abstract

    1.06.1 Introduction

    1.06.2 Digital FIR filters

    1.06.3 The analog approximation problem

    1.06.4 Doubly resistively terminated lossless networks

    1.06.5 Ladder structures

    1.06.6 Lattice structures

    1.06.7 Wave digital filters

    1.06.8 Frequency response masking (FRM) structure

    1.06.9 Computational properties of filter algorithms

    1.06.10 Architecture

    1.06.11 Arithmetic operations

    1.06.12 Sum-of-products (SOP)

    1.06.13 Power reduction techniques

    References

    Chapter 7. Multirate Signal Processing for Software Radio Architectures

    Abstract

    1.07.1 Introduction

    1.07.2 The Sampling process and the “Resampling” process

    1.07.3 Digital filters

    1.07.4 Windowing

    1.07.5 Basics on multirate filters

    1.07.6 From single channel down converter to standard down converter channelizer

    1.07.7 Modifications of the standard down converter channelizer—M:2 down converter channelizer

    1.07.8 Preliminaries on software defined radios

    1.07.9 Proposed architectures for software radios

    1.07.10 Closing comments

    Glossary

    References

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

    Abstract

    1.8.1 Introduction

    1.8.2 Background and fundamentals

    1.8.3 Design strategy

    1.8.4 Approximation approach via direct scaling

    1.8.5 Approximation approach via structural design

    1.8.6 Wavelet filters design via spectral factorization

    1.8.7 Higher-order design approach via optimization

    1.8.8 Conclusion

    References

    Chapter 9. Discrete Multi-Scale Transforms in Signal Processing

    Abstract

    1.09.1 Introduction

    1.09.2 Wavelets: a multiscale analysis tool

    1.09.3 Curvelets and their applications

    1.09.4 Contourlets and their applications

    1.09.5 Shearlets and their applications

    A Appendix

    References

    Chapter 10. Frames in Signal Processing

    Abstract

    1.10.1 Introduction

    1.10.2 Basic concepts

    1.10.3 Relevant definitions

    1.10.4 Some computational remarks

    1.10.5 Construction of frames from a prototype signal

    1.10.6 Some remarks and highlights on applications

    1.10.7 Conclusion

    References

    Chapter 11. Parametric Estimation

    Abstract

    1.11.1 Introduction

    1.11.2 Deterministic and stochastic signals

    1.11.3 Parametric models for signals and systems

    References

    Chapter 12. Adaptive Filters

    Abstract

    Acknowledgment

    1.12.1 Introduction

    1.12.2 Optimum filtering

    1.12.3 Stochastic algorithms

    1.12.4 Statistical analysis

    1.12.5 Extensions and current research

    1.12.6 Supplementary data

    References

    Section 2: MACHINE LEARNING

    Chapter 13. Introduction to Machine Learning

    Abstract

    Acknowledgments

    1.13.1 Scope and context

    1.13.2 Contributions

    References

    Chapter 14. Learning Theory

    Abstract

    1.14.1 Introduction

    1.14.2 Probabilistic formulation of learning problems

    1.14.3 Uniform convergence of empirical means

    1.14.4 Model selection

    1.14.5 Alternatives to uniform convergence

    1.14.6 Computational aspects

    1.14.7 Beyond the basic probabilistic framework

    1.14.8 Conclusions and future trends

    Glossary

    Relevant websites

    References

    Chapter 15. Neural Networks

    Abstract

    1.15.1 Introduction

    1.15.2 Learning with single neurons

    1.15.3 Recurrent neural networks

    1.15.4 Learning by focussing on the generalization ability

    1.15.5 Unsupervised learning

    1.15.6 Applications

    1.15.7 Open issues and problems

    1.15.8 Implementation, code, and data sets

    1.15.9 Conclusions and future trends

    Glossary

    References

    Chapter 16. Kernel Methods and Support Vector Machines

    Abstract

    Nomenclature

    Acknowledgment

    1.16.1 Introduction

    1.16.2 Foundations of kernel methods

    1.16.3 Fundamental kernel methods

    1.16.4 Computational issues of kernel methods

    1.16.5 Multiple kernel learning

    1.16.6 Applications

    1.16.7 Open issues and problems

    Glossary

    References

    Chapter 17. Online Learning in Reproducing Kernel Hilbert Spaces

    Abstract

    Nomenclature

    1.17.1 Introduction

    1.17.2 Parameter estimation: The regression and classification tasks

    1.17.3 Overfitting and regularization

    1.17.4 Mapping a nonlinear to a linear task

    1.17.5 Reproducing Kernel Hilbert spaces

    1.17.6 Least squares learning algorithms

    1.17.7 A convex analytic toolbox for online learning

    1.17.8 Related work and applications

    1.17.9 Conclusions

    Appendices

    B Proof of Proposition 60

    C Proof of convergence for Algorithm 61

    References

    Chapter 18. Introduction to Probabilistic Graphical Models

    Abstract

    Nomenclature

    Acknowledgments

    1.18.1 Introduction

    1.18.2 Preliminaries

    1.18.3 Representations

    1.18.4 Learning

    1.18.5 Inference

    1.18.6 Applications

    1.18.7 Implementation/code

    1.18.8 Data sets

    1.18.9 Conclusion

    Glossary

    References

    Chapter 19. A Tutorial Introduction to Monte Carlo Methods, Markov Chain Monte Carlo and Particle Filtering

    Abstract

    1.19.1 Introduction

    1.19.2 The Monte Carlo principle

    1.19.3 Basic techniques for simulating random variables

    1.19.4 Markov Chain Monte Carlo

    1.19.5 Sequential Monte Carlo

    1.19.6 Advanced Monte Carlo methods

    1.19.7 Open issues and problems

    1.19.8 Further reading

    Glossary

    References

    Chapter 20. Clustering

    Abstract

    1.20.1 Introduction

    1.20.2 Clustering algorithms

    1.20.3 Clustering validation

    1.20.4 Applications

    1.20.5 Open issues and problems

    1.20.6 Conclusion

    Glossary

    References

    Chapter 21. Unsupervised Learning Algorithms and Latent Variable Models: PCA/SVD, CCA/PLS, ICA, NMF, etc.

    Abstract

    1.21.1 Introduction and of problems statement

    1.21.2 PCA/SVD and related problems

    1.21.3 ICA and related problems

    1.21.4 NMF and related problems

    1.21.5 Future directions: constrained multi-block tensor factorizations and multilinear blind source separation

    1.21.6 Summary

    References

    Chapter 22. Semi-Supervised Learning

    Abstract

    1.22.1 Introduction

    1.22.2 Semi-supervised learning algorithms

    1.22.3 Semi-supervised learning for structured outputs

    1.22.4 Large scale semi-supervised learning

    1.22.5 Theoretical analysis overview

    1.22.6 Challenges

    Glossary

    References

    Relevant websites

    Chapter 23. Sparsity-Aware Learning and Compressed Sensing: An Overview

    1.23.1 Introduction

    1.23.2 Parameter estimation

    1.23.3 Searching for a norm

    1.23.4 The least absolute shrinkage and selection operator (LASSO)

    1.23.5 Sparse signal representation

    1.23.6 In quest for the sparsest solution

    1.23.7 Uniqueness of the minimizer

    1.23.8 Equivalence of and minimizers: sufficiency conditions

    1.23.9 Robust sparse signal recovery from noisy measurements

    1.23.10 Compressed sensing: the glory of randomness

    1.23.11 Sparsity-promoting algorithms

    1.23.12 Variations on the sparsity-aware theme

    1.23.13 Online time-adaptive sparsity-promoting algorithms

    1.23.14 Learning sparse analysis models

    1.23.15 A case study: time-frequency analysis

    1.23.16 From sparse vectors to low rank matrices: a highlight

    1.23.17 Conclusions

    Appendix

    References

    Chapter 24. Information Based Learning

    1.24.1 Introduction

    1.24.2 Information theoretic descriptors

    1.24.3 Unifying information theoretic framework for machine learning

    1.24.4 Nonparametric information estimators

    1.24.5 Reproducing kernel Hilbert space framework for ITL

    1.24.6 Information particle interaction for learning from samples

    1.24.7 Illustrative examples

    1.24.8 Conclusions and future trends

    References

    Chapter 25. A Tutorial on Model Selection

    Abstract

    1.25.1 Introduction

    1.25.2 Minimum distance estimation criteria

    1.25.3 Bayesian approaches to model selection

    1.25.4 Model selection by compression

    1.25.5 Simulation

    References

    Chapter 26. Music Mining

    Abstract

    Acknowledgments

    1.26.1 Introduction

    1.26.2 Ground truth acquisition and evaluation

    1.26.3 Audio feature extraction

    1.26.4 Extracting context information about music

    1.26.5 Similarity search

    1.26.6 Classification

    1.26.7 Tag annotation

    1.26.8 Visualization

    1.26.9 Advanced music mining

    1.26.10 Software and datasets

    1.26.11 Open problems and future trends

    1.26.12 Further reading

    Glossary

    References

    Index

Product details

  • No. of pages: 1480
  • Language: English
  • Copyright: © Academic Press 2013
  • Published: September 5, 2013
  • Imprint: Academic Press
  • eBook ISBN: 9780123972262

About the Editors in Chief

Sergios Theodoridis

Sergios Theodoridis
Sergios Theodoridis acquired a Physics degree with honors from the University of Athens, Greece in 1973 and a MSc and a Ph.D. degree in Signal Processing and Communications from the University of Birmingham, UK in 1975 and 1978 respectively. Since 1995 he has been a Professor with the Department of Informatics and Communications at the University of Athens.

Affiliations and Expertise

Department of Informatics and Telecommunications, University of Athens, Greece

Rama Chellappa

Affiliations and Expertise

University of Maryland, College Park, MD, USA

About the Editors

Paulo 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

Patrick Naylor

Johan Suykens

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