Academic Press Library in Signal Processing
Signal Processing Theory and Machine Learning
- 1st Edition, Volume 1 - September 21, 2013
- Latest edition
- Editors: Paulo S.R. Diniz, Sergios Theodoridis, Patrick A. Naylor, Rama Chellappa, Johan Suykens
- Language: English
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 te… Read more
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
- 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
PhD students, Post Docs, R&D engineers in signal processing and wireless and mobile communications, Consultants
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
- Edition: 1
- Latest edition
- Volume: 1
- Published: September 21, 2013
- 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, BrazilST
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, GreeceRC
Rama Chellappa
Prof. Rama Chellappa received the B.E. (Hons.) degree from the University of Madras, India, in 1975 and the M.E. (Distinction) degree from Indian Institute of Science, Bangalore, in 1977. He received M.S.E.E. and Ph.D. Degrees in Electrical Engineering from Purdue University, West Lafayette, IN, in 1978 and 1981 respectively. Since 1991, he has been a Professor of Electrical Engineering and an affiliate Professor of Computer Science at University of Maryland, College Park. He is also affiliated with the Center for Automation Research (Director) and the Institute for Advanced Computer Studies (Permanent Member). In 2005, he was named a Minta Martin Professor of Engineering. Prior to joining the University of Maryland, he was an Assistant (1981-1986) and Associate Professor (1986-1991) and Director of the Signal and Image Processing Institute (1988-1990) at University of Southern California, Los Angeles.
Over the last 29 years, he has published numerous book chapters, peer-reviewed journal and conference papers. He has co-authored and edited books on MRFs, face and gait recognition and collected works on image processing and analysis. His current research interests are face and gait analysis, markerless motion capture, 3D modeling from video, image and video-based recognition and exploitation and hyper spectral processing.
Affiliations and expertise
University of Maryland, College Park, MD, USARead Academic Press Library in Signal Processing on ScienceDirect