Section 1: SIGNAL PROCESSING THEORY
by Rama Chellappa, Sergios Theodoridis
Academic Press Library in Signal Processing
- Cover image
- Title page
- Table of Contents
- Copyright
- Introduction
- About the Editors
- Section Editors
- Authors Biography
- 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
- Glossary
- 1.02.9 Relevant Websites:
- 1.02.10 Supplementary data
- 1.02.11 Supplementary data
- References
- Chapter 3. Discrete-Time Signals and Systems
- Chapter 4. Random Signals and Stochastic Processes
- Chapter 5. Sampling and Quantization
- Chapter 1. Introduction to Signal Processing Theory
- 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
- Chapter 10. Frames in Signal Processing
- Chapter 12. Adaptive Filters
- Section 2: MACHINE LEARNING
- Chapter 13. Introduction to Machine Learning
- 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
- 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
- Chapter 19. A Tutorial Introduction to Monte Carlo Methods, Markov Chain Monte Carlo and Particle Filtering
- Chapter 20. Clustering
- Chapter 21. Unsupervised Learning Algorithms and Latent Variable Models: PCA/SVD, CCA/PLS, ICA, NMF, etc.
- Chapter 22. Semi-Supervised Learning
- 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 25. A Tutorial on Model Selection
- 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
Section 1: SIGNAL PROCESSING THEORY
Outline
Chapter 1 Introduction to Signal Processing Theory
Chapter 2 Continuous-Time Signals and Systems
Chapter 3 Discrete-Time Signals and Systems
Chapter 4 Random Signals and Stochastic Processes
Chapter 5 Sampling and Quantization
Chapter 6 Digital Filter Structures and Their Implementation
Chapter 7 Multirate Signal Processing for Software Radio Architectures
Chapter 8 Modern Transform Design for Practical Audio/Image/Video Coding Applications
Chapter 9 Discrete Multi-Scale Transforms in Signal Processing
Chapter 10 Frames in Signal Processing
-
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click here login for view all page.