This book uses an active learning approach to the topic of digital signal processing (DSP). DSP is a fundamental technology with wide ranging applications as, for example, digital downloads of movies, mobile and broadband communications, digital television, and many other areas. In this book the subject is taught using a "hands-on" experimental approach with MATLAB examples throughout the text to illustrate the mathematical concepts and DSP algorithms developed and explained. Existing books in this area tends to fall into one of two camps - either a highly mathematical treatment with few…mehr
This book uses an active learning approach to the topic of digital signal processing (DSP). DSP is a fundamental technology with wide ranging applications as, for example, digital downloads of movies, mobile and broadband communications, digital television, and many other areas. In this book the subject is taught using a "hands-on" experimental approach with MATLAB examples throughout the text to illustrate the mathematical concepts and DSP algorithms developed and explained. Existing books in this area tends to fall into one of two camps - either a highly mathematical treatment with few practical examples of the everyday uses of DSP, or else very verbose and descriptive treatments with little or no mathematical content to back up the topics. The former type of text is excellent as a standard reference, but poor as a learning vehicle. The latter type of text is good for general knowledge, but fails to meet the needs of University students and practicing industry professionals. Students need a learn-by-doing approach, and industry professionals need to come up to speed as rapidly as possible. The book uses MATLAB throughout, inn an established industry and University programming environment. Furthermore, it requires that the reader only have access to the Student/Educational version of MATLAB, not the full commercial version, which is out of reach of most students.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
John Leis, PhD, is Associate Professor of Electrical and Computer Engineering at the University of Southern Queensland. He has authored many technical papers in areas involving signal processing and networking. Dr. Leis's interests include signal processing for data compression and coding, medical signal processing, and advanced instrumentation using signal processing algorithms. He has collaborated internationally on various research projects. Dr. Leis has taught the subject material extensively in Australia and also Singapore. He is a Senior Member of the Institution of Electrical & Electronic Engineers and a Member of the Association for Computing Machinery.
Inhaltsangabe
Preface xi Chapter 1. What Is Signal Processing? 1 1.1 Chapter Objectives 1 1.2 Introduction 1 1.3 Book Objectives 2 1.4 DSP and ITS Applications 3 1.5 Application Case Studies Using DSP 4 1.6 Overview of Learning Objectives 12 1.7 Conventions Used in This Book 15 1.8 Chapter Summary 16 Chapter 2. Matlab for Signal Processing 19 2.1 Chapter Objectives 19 2.2 Introduction 19 2.3 What Is MATLAB? 19 2.4 Getting Started 20 2.5 Everything Is a Matrix 20 2.6 Interactive Use 21 2.7 Testing and Looping 23 2.8 Functions and Variables 25 2.9 Plotting and Graphing 30 2.10 Loading and Saving Data 31 2.11 Multidimensional Arrays 35 2.12 Bitwise Operators 37 2.13 Vectorizing Code 38 2.14 Using MATLAB for Processing Signals 40 2.15 Chapter Summary 43 Chapter 3. Sampled Signals and Digital Processing 45 3.1 Chapter Objectives 45 3.2 Introduction 45 3.3 Processing Signals Using Computer Algorithms 45 3.4 Digital Representation of Numbers 47 3.5 Sampling 61 3.6 Quantization 64 3.7 Image Display 74 3.8 Aliasing 81 3.9 Reconstruction 84 3.10 Block Diagrams and Difference Equations 88 3.11 Linearity, Superposition, and Time Invariance 92 3.12 Practical Issues and Computational Efficiency 95 3.13 Chapter Summary 98 Chapter 4. Random Signals 103 4.1 Chapter Objectives 103 4.2 Introduction 103 4.3 Random and Deterministic Signals 103 4.4 Random Number Generation 105 4.5 Statistical Parameters 106 4.6 Probability Functions 108 4.7 Common Distributions 112 4.8 Continuous and Discrete Variables 114 4.9 Signal Characterization 116 4.10 Histogram Operators 117 4.11 Median Filters 122 4.12 Chapter Summary 125 Chapter 5. Representing Signals and Systems 127 5.1 Chapter Objectives 127 5.2 Introduction 127 5.3 Discrete-Time Waveform Generation 127 5.4 The z Transform 137 5.5 Polynomial Approach 144 5.6 Poles, Zeros, and Stability 146 5.7 Transfer Functions and Frequency Response 152 5.8 Vector Interpretation of Frequency Response 153 5.9 Convolution 156 5.10 Chapter Summary 160 Chapter 6. Temporal and Spatial Signal Processing 165 6.1 Chapter Objectives 165 6.2 Introduction 165 6.3 Correlation 165 6.4 Linear Prediction 177 6.5 Noise Estimation and Optimal Filtering 183 6.6 Tomography 188 6.7 Chapter Summary 201 Chapter 7. Frequency Analysis of Signals 203 7.1 Chapter Objectives 203 7.2 Introduction 203 7.3 Fourier Series 203 7.4 How Do the Fourier Series Coefficient Equations Come About? 209 7.5 Phase-Shifted Waveforms 211 7.6 The Fourier Transform 212 7.7 Aliasing in Discrete-Time Sampling 231 7.8 The FFT as a Sample Interpolator 233 7.9 Sampling a Signal over a Finite Time Window 236 7.10 Time-Frequency Distributions 240 7.11 Buffering and Windowing 241 7.12 The FFT 243 7.13 The DCT 252 7.14 Chapter Summary 266 Chapter 8. Discrete-Time Filters 271 8.1 Chapter Objectives 271 8.2 Introduction 271 8.3 What Do We Mean by "Filtering"? 272 8.4 Filter Specification, Design, and Implementation 274 8.5 Filter Responses 282 8.6 Nonrecursive Filter Design 285 8.7 Ideal Reconstruction Filter 293 8.8 Filters with Linear Phase 294 8.9 Fast Algorithms for Filtering, Convolution, and Correlation 298 8.10 Chapter Summary 311 Chapter 9. Recursive Filters 315 9.1 Chapter Objectives 315 9.2 Introduction 315 9.3 Essential Analog System Theory 319 9.4 Continuous-Time Recursive Filters 326 9.5 Comparing Continuous-Time Filters 339 9.6 Converting Continuous-Time Filters to Discrete Filters 340 9.7 Scaling and Transformation of Continuous Filters 361 9.8 Summary of Digital Filter Design via Analog Approximation 371 9.9 Chapter Summary 372 Bibliography 375 Index 379
Preface xi Chapter 1. What Is Signal Processing? 1 1.1 Chapter Objectives 1 1.2 Introduction 1 1.3 Book Objectives 2 1.4 DSP and ITS Applications 3 1.5 Application Case Studies Using DSP 4 1.6 Overview of Learning Objectives 12 1.7 Conventions Used in This Book 15 1.8 Chapter Summary 16 Chapter 2. Matlab for Signal Processing 19 2.1 Chapter Objectives 19 2.2 Introduction 19 2.3 What Is MATLAB? 19 2.4 Getting Started 20 2.5 Everything Is a Matrix 20 2.6 Interactive Use 21 2.7 Testing and Looping 23 2.8 Functions and Variables 25 2.9 Plotting and Graphing 30 2.10 Loading and Saving Data 31 2.11 Multidimensional Arrays 35 2.12 Bitwise Operators 37 2.13 Vectorizing Code 38 2.14 Using MATLAB for Processing Signals 40 2.15 Chapter Summary 43 Chapter 3. Sampled Signals and Digital Processing 45 3.1 Chapter Objectives 45 3.2 Introduction 45 3.3 Processing Signals Using Computer Algorithms 45 3.4 Digital Representation of Numbers 47 3.5 Sampling 61 3.6 Quantization 64 3.7 Image Display 74 3.8 Aliasing 81 3.9 Reconstruction 84 3.10 Block Diagrams and Difference Equations 88 3.11 Linearity, Superposition, and Time Invariance 92 3.12 Practical Issues and Computational Efficiency 95 3.13 Chapter Summary 98 Chapter 4. Random Signals 103 4.1 Chapter Objectives 103 4.2 Introduction 103 4.3 Random and Deterministic Signals 103 4.4 Random Number Generation 105 4.5 Statistical Parameters 106 4.6 Probability Functions 108 4.7 Common Distributions 112 4.8 Continuous and Discrete Variables 114 4.9 Signal Characterization 116 4.10 Histogram Operators 117 4.11 Median Filters 122 4.12 Chapter Summary 125 Chapter 5. Representing Signals and Systems 127 5.1 Chapter Objectives 127 5.2 Introduction 127 5.3 Discrete-Time Waveform Generation 127 5.4 The z Transform 137 5.5 Polynomial Approach 144 5.6 Poles, Zeros, and Stability 146 5.7 Transfer Functions and Frequency Response 152 5.8 Vector Interpretation of Frequency Response 153 5.9 Convolution 156 5.10 Chapter Summary 160 Chapter 6. Temporal and Spatial Signal Processing 165 6.1 Chapter Objectives 165 6.2 Introduction 165 6.3 Correlation 165 6.4 Linear Prediction 177 6.5 Noise Estimation and Optimal Filtering 183 6.6 Tomography 188 6.7 Chapter Summary 201 Chapter 7. Frequency Analysis of Signals 203 7.1 Chapter Objectives 203 7.2 Introduction 203 7.3 Fourier Series 203 7.4 How Do the Fourier Series Coefficient Equations Come About? 209 7.5 Phase-Shifted Waveforms 211 7.6 The Fourier Transform 212 7.7 Aliasing in Discrete-Time Sampling 231 7.8 The FFT as a Sample Interpolator 233 7.9 Sampling a Signal over a Finite Time Window 236 7.10 Time-Frequency Distributions 240 7.11 Buffering and Windowing 241 7.12 The FFT 243 7.13 The DCT 252 7.14 Chapter Summary 266 Chapter 8. Discrete-Time Filters 271 8.1 Chapter Objectives 271 8.2 Introduction 271 8.3 What Do We Mean by "Filtering"? 272 8.4 Filter Specification, Design, and Implementation 274 8.5 Filter Responses 282 8.6 Nonrecursive Filter Design 285 8.7 Ideal Reconstruction Filter 293 8.8 Filters with Linear Phase 294 8.9 Fast Algorithms for Filtering, Convolution, and Correlation 298 8.10 Chapter Summary 311 Chapter 9. Recursive Filters 315 9.1 Chapter Objectives 315 9.2 Introduction 315 9.3 Essential Analog System Theory 319 9.4 Continuous-Time Recursive Filters 326 9.5 Comparing Continuous-Time Filters 339 9.6 Converting Continuous-Time Filters to Discrete Filters 340 9.7 Scaling and Transformation of Continuous Filters 361 9.8 Summary of Digital Filter Design via Analog Approximation 371 9.9 Chapter Summary 372 Bibliography 375 Index 379
Rezensionen
"Intended for undergraduate or graduate students in engineering or related disciplines, this introductory volume examines key theories in signal processing and presents this information optimized for use with MATLAB technical computing software." (Book News, 1 October 2011)
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