Signal Processing and Machine Learning with Applications / by Michael M. Richter, Sheuli Paul, Veton Këpuska, Marius Silaghi.

Format:

Book

Author/Creator:

Richter, Michael M., Author.

Paul, Sheuli., Author.

Këpuska, Veton., Author.

Silaghi, Marius., Author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Language:

English

Subjects (All):

Artificial intelligence.

Signal processing.

Data mining.

Artificial Intelligence.

Digital and Analog Signal Processing.

Data Mining and Knowledge Discovery.

Local Subjects:

Artificial Intelligence.

Digital and Analog Signal Processing.

Data Mining and Knowledge Discovery.

Physical Description:

1 online resource (XLI, 607 pages) : 300 illustrations, 237 illustrations in color.

Edition:

1st ed. 2022.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2022.

System Details:

text file PDF

Summary:

Signal processing captures, interprets, describes and manipulates physical phenomena. Mathematics, statistics, probability, and stochastic processes are among the signal processing languages we use to interpret real-world phenomena, model them, and extract useful information. This book presents different kinds of signals humans use and applies them for human machine interaction to communicate. Signal Processing and Machine Learning with Applications presents methods that are used to perform various Machine Learning and Artificial Intelligence tasks in conjunction with their applications. It is organized in three parts: Realms of Signal Processing; Machine Learning and Recognition; and Advanced Applications and Artificial Intelligence. The comprehensive coverage is accompanied by numerous examples, questions with solutions, with historical notes. The book is intended for advanced undergraduate and postgraduate students, researchers and practitioners who are engaged with signal processing, machine learning and the applications. .

Contents:

Part I Realms of Signal Processing

1 Digital Signal Representation

1.1 Introduction

1.2 Numbers

1.2.1 Numbers and Numerals

1.2.2 Types of Numbers

1.2.3 Positional Number Systems

1.3 Sampling and Reconstruction of Signals

1.3.1 Scalar Quantization

1.3.2 Quantization Noise

1.3.3 Signal-To-Noise Ratio

1.3.4 Transmission Rate

1.3.5 Nonuniform Quantizer

1.3.6 Companding

1.4 Data Representations

1.4.1 Fixed-Point Number Representations

1.4.2 Sign-Magnitude Format

1.4.3 One's-Complement Format

1.4.4 Two's-Complement Format

1.5 Fix-Point DSP's

1.6 Fixed-Point Representations Based on Radix-Point

1.7 Dynamic Range

1.8 Precision

1.9 Background Information

1.10 Exercises

2 Signal Processing Background

2.1 Basic Concepts

2.2 Signals and Information

2.3 Signal Processing

ix

x Contents

2.4 Discrete Signal Representations

2.5 Delta and Impulse Function

2.6 Parseval's Theorem

2.7 Gibbs Phenomenon

2.8 Wold Decomposition

2.9 State Space Signal Processing

2.10 Common Measurements

2.10.1 Convolution

2.10.2 Correlation

2.10.3 Auto Covariance

2.10.4 Coherence

2.10.5 Power Spectral Density (PSD)

2.10.6 Estimation and Detection

2.10.7 Central Limit Theorem

2.10.8 Signal Information Processing Types

2.10.9 Machine Learning

2.10.10Exercises

3 Fundamentals of Signal Transformations

3.1 Transformation Methods

3.1.1 Laplace Transform

3.1.2 Z-Transform

3.1.3 Fourier Series

3.1.4 Fourier Transform

3.1.5 Discrete Fourier Transform and Fast Fourier Transform

3.1.6 Zero Padding

3.1.7 Overlap-Add and Overlap-Save Convolution

Algorithms

3.1.8 Short Time Fourier Transform (STFT)

3.1.9 Wavelet Transform

3.1.10 Windowing Signal and the DCT Transforms

3.2 Analysis and Comparison of Transformations

3.3 Background Information

3.4 Exercises

3.5 References

4 Digital Filters

4.1 Introduction

4.1.1 FIR and IIR Filters

4.1.2 Bilinear Transform

4.2 Windowing for Filtering

4.3 Allpass Filters

4.4 Lattice Filters

4.5 All-Zero Lattice Filter

4.6 Lattice Ladder Filters

Contents xi

4.7 Comb Filter

4.8 Notch Filter

4.9 Background Information

4.10 Exercises

5 Estimation and Detection

5.1 Introduction

5.2 Hypothesis Testing

5.2.1 Bayesian Hypothesis Testing

5.2.2 MAP Hypothesis Testing

5.3 Maximum Likelihood (ML) Hypothesis Testing

5.4 Standard Analysis Techniques

5.4.1 Best Linear Unbiased Estimator (BLUE)

5.4.2 Maximum Likelihood Estimator (MLE)

5.4.3 Least Squares Estimator (LSE)

5.4.4 Linear Minimum Mean Square Error Estimator

(LMMSE)

5.5 Exercises

6 Adaptive Signal Processing

6.1 Introduction

6.2 Parametric Signal Modeling

6.2.1 Parametric Estimation

6.3 Wiener Filtering

6.4 Kalman Filter

6.4.1 Smoothing

6.5 Particle Filter

6.6 Fundamentals of Monte Carl

6.6.1 Importance Sampling (IS)

6.7 Non-Parametric Signal Modeling

6.8 Non-Parametric Estimation

6.8.1 Correlogram

6.8.2 Periodogram

6.9 Filter Bank Method

6.10 Quadrature Mirror Filter Bank (QMF)

6.11 Background Information

6.12 Exercises

7 Spectral Analysis

7.1 Introduction

7.2 Adaptive Spectral Analysis

7.3 Multivariate Signal Processing

7.3.1 Sub-band Coding and Subspace Analysis

7.4 Wavelet Analysis

7.5 Adaptive Beam Forming

xii Contents

7.6 Independent Component Analysis (ICA)

7.7 Principal Component Analysis (PCA)

7.8 Best Basis Algorithms

7.9 Background Information

7.10 Exercises

Part II Machine Learning and Recognition

8 General Learning

8.1 Introduction to Learning

8.2 The Learning Phases

8.2.1 Search and Utility

8.3 Search

8.3.1 General Search Model

8.3.2 Preference relations

8.3.3 Different learning methods

8.3.4 Similarities

8.3.5 Learning to Recognize

8.3.6 Learning again

8.4 Background Information

8.5 Exercises

9 Signal Processes, Learning, and Recognition

9.1 Learning

9.2 Bayesian Formalism

9.2.1 Dynamic Bayesian Theory

9.2.2 Recognition and Search

9.2.3 Influences

9.3 Subjectivity

9.4 Background Information

9.5 Exercises

10 Stochastic Processes

10.1 Preliminaries on Probabilities

10.2 Basic Concepts of Stochastic Processes

10.2.1 Markov Processes

10.2.2 Hidden Stochastic Models (HSM)

10.2.3 HSM Topology

10.2.4 Learning Probabilities

10.2.5 Re-estimation

10.2.6 Redundancy

10.2.7 Data Preparation

10.2.8 Proper Redundancy Removal

10.3 Envelope Detection

10.3.1 Silence Threshold Selection

10.3.2 Pre-emphasis

Other Format:

Printed edition:

ISBN:

978-3-319-45372-9

9783319453729

Access Restriction:

Restricted for use by site license.