Signal processing and machine learning theory /

"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, s...

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Bibliographic Details
Corporate Author:	ScienceDirect (Online service)
Other Authors:	Diniz, Paulo Sergio Ramirez, 1956- (Editor)
Format:	eBook
Language:	English
Published:	London ; Cambridge, MA : Academic Press, [2024]
Subjects:	Signal processing. Machine learning. Traitement du signal. Apprentissage automatique. Machine learning Signal processing Aprenentatge automàtic. Programari d'aplicació. Electronic books. Llibres electrònics.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Front Cover
Signal Processing and Machine Learning Theory
Copyright
Contents
List of contributors
Contributors
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 13
Chapter 14
Chapter 15
Chapter 16
Chapter 17
Signal processing and machine learning theory
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 transform design
1.10 Discrete multiscale and transforms
1.11 Frames
1.12 Parameter estimation
1.13 Adaptive filtering
1.14 Machine learning: review and trends
1.15 Signal processing over graphs
1.16 Tensor methods in deep learning
1.17 Nonconvex graph learning: sparsity, heavy tails, and clustering
1.18 Dictionaries in machine learning
1.19 Closing comments
References
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
Relevant websites
Glossary
Nomenclature
References
3 Discrete-time signals and systems
3.1 Introduction
3.2 Discrete-time signals: sequences
3.3 Discrete-time systems
3.3.1 Classification
Memoryless systems
Dynamic systems
Linear systems
Nonlinear systems
Time-invariant systems
Time-variant systems.
Causal systems
3.4 Linear time-invariant systems
3.4.1 State-space description
3.4.2 Transfer function of a discrete-time LTI system
3.4.3 Finite duration impulse response systems
3.4.4 Infinite duration impulse response systems
3.4.5 Observability and controllability
3.4.6 Stability
3.5 Discrete-time signals and systems with MATLAB®
3.5.1 Discrete-time signals
Unit impulse
Sinusoid
White Gaussian noise
Elaborated signal model
3.5.2 Discrete-time systems representation and implementation
3.6 Conclusions
Notation
References
4 Random signals and stochastic processes
Foreword
Acknowledgment
4.1 Introduction
4.2 Probability
4.2.1 Joint, conditional, and total probability
Bayes' rule
4.2.2 Probabilistic independence
4.2.3 Combined experiments
Bernoulli trials
4.3 Random variable
4.3.1 Probability distributions
4.3.2 Usual distributions
4.3.3 Conditional distribution
4.3.4 Statistical moments
4.3.5 Transformation of random variables
4.3.6 Multiple random variable distributions
4.3.7 Statistically independent random variables
4.3.8 Joint statistical moments
4.3.9 Central limit theorem
4.3.10 Multivariate Gaussian distribution
4.3.11 Transformation of vector random variables
4.3.12 Complex random variables
4.3.13 Application: estimators
4.3.14 Application: feature selection
4.4 Random process
4.4.1 Distributions of random processes and sequences
4.4.2 Statistical independence
4.4.3 First- and second-order moments for random processes and sequences
4.4.4 Stationarity
4.4.5 Properties of correlation functions for WSS processes and sequences
4.4.6 Time averages of random processes and sequences
4.4.7 Ergodicity
4.4.8 An encompassing example
4.4.9 Gaussian processes and sequences.
4.4.10 Poisson random process
4.4.11 Complex random processes and sequences
4.4.12 Markov chains
4.4.13 Spectral description of random processes and sequences
4.4.14 White and colored noise
4.4.15 Applications: modulation, ``bandpass,'' and band-limited processes, and sampling
4.4.16 Processing of random processes and sequences
4.4.17 Application: characterization of LTI systems
4.4.18 Modeling of bandpass WSS random processes
4.4.19 Statistical modeling of signals: random sequence as the output of an LTI system
4.5 Afterword
References
5 Sampling and quantization
5.1 Introduction
5.1.1 Scope and prerequisites
5.1.2 Chapter outline
5.1.3 Recent and current trends
5.2 Preliminaries
5.2.1 Classification of signals
5.2.2 Discrete-time signals
sequences
5.2.3 Sampling of continuous-time signals
5.2.4 Classification of systems
5.2.5 Digital signal processing of analog signals
5.2.6 Digital communication
analog signal processing of digital signals
5.3 Sampling of deterministic signals
5.3.1 Uniform sampling
5.3.2 Poisson's summation formula
5.3.2.1 Example 1: illustration of Poisson's summation formula
5.3.3 The sampling theorem
5.3.3.1 Example 2: illustration of aliasing
5.3.4 Antialiasing filter
5.3.5 Reconstruction
5.3.5.1 Ideal reconstruction
5.3.5.2 Reconstruction using a D/A converter and an analog reconstruction filter
5.3.6 Distortion caused by undersampling
5.3.6.1 Example 3: illustration of distortion due to undersampling
5.3.7 Distortion measure for energy signals
5.3.7.1 Example 4: illustration of distortion measure
5.3.8 Bandpass sampling
5.4 Sampling of stochastic processes
5.4.1 Uniform sampling
5.4.2 Reconstruction of stochastic processes
5.4.2.1 Example 5: computation of reconstruction error power.
6.3.6 Miscellaneous FIR filters
6.4 FIR structures
6.4.1 Direct-form FIR structures
6.4.1.1 Adder tree
6.4.2 Transposed direct form
6.4.3 Linear-phase FIR structures
6.4.4 Half-band FIR filters
6.4.5 Delay-complementary FIR structure
6.4.6 Cascade-form FIR structures
6.4.7 Lattice FIR structures
6.4.8 Recursive frequency-sampling structures
6.4.9 Multirate FIR filters
6.5 Frequency response masking filters
6.5.1 Basic FRM structure
6.5.1.1 Case A
6.5.1.2 Case B
6.5.1.3 Conditions for feasible FRM solutions
6.5.1.4 Selection of L
6.5.1.5 Computational complexity
6.5.1.6 Related structure
6.5.1.7 Design procedure
6.5.1.8 Design example
6.5.2 Multistage FRM structure
6.5.2.1 Exercise
6.5.3 Half-band filter
6.5.3.1 Structure
6.5.3.2 Design procedure
6.5.3.3 Design example
6.5.4 Hilbert transformer
6.5.4.1 Exercise
6.5.5 Decimator and interpolator
6.5.5.1 Difficulties of FRM multirate filters
6.5.5.2 A variant of the FRM structure
6.5.5.3 Design example
6.5.5.4 Exercise
6.5.5.5 Two more variants of FRM structures
6.5.5.6 An alternative recursive structure
6.5.6 Filter banks
6.5.7 Cosine-modulated transmultiplexer
6.5.8 FRM structure for recursive filters
6.5.8.1 Design example
6.5.9 2D FRM structure
6.5.10 Summary
6.6 The analog approximation problem
6.6.1 Typical requirements
6.6.2 Standard low-pass approximations
6.6.3 Comparison of the standard approximations
6.6.3.1 Example
6.6.4 Filters with constant pole radius
6.6.5 Frequency transformations
6.7 Doubly resistively terminated lossless networks
6.7.1 Maximal power transfer
6.7.2 Reflection function
6.7.3 Element sensitivity
6.7.4 Errors in the elements in doubly terminated filters
6.7.4.1 Example
6.7.5 Filters with diminishing ripple.