Digital Signal Processing by Rawat, Tarun Kumar;

10% KEDVEZMÉNY?
A kedvezmény csak az 'Értesítés a kedvenc témákról' hírlevelünk címzettjeinek rendeléseire érvényes.

Kiadói listaár GBP 31.99
Az ár azért becsült, mert a rendelés pillanatában nem lehet pontosan tudni, hogy a beérkezéskor milyen lesz a forint árfolyama az adott termék eredeti devizájához képest. Ha a forint romlana, kissé többet, ha javulna, kissé kevesebbet kell majd fizetnie.

14 443 Ft (13 755 Ft + 5% áfa)
Kedvezmény(ek) 10% (cc. 1 444 Ft off)
Kedvezményes ár 12 998 Ft (12 380 Ft + 5% áfa)

14 443 Ft

Beszerezhetőség

Csak rendelésre kapható a kiadónál, kissé időigényes.

A termék adatai:

Kiadó OUP India
Megjelenés dátuma 2015. december 3.

ISBN 9780198081937
Kötéstípus Puhakötés
Terjedelem1100 oldal
Méret 247x188x45 mm
Súly 1514 g
Nyelv angol
Illusztrációk 678 illustrations

Kategóriák

Villamosmérnöki tudományok, híradástechnika, műszeripar

Rövid leírás:

Digital Signal Processing is a comprehensive textbook designed for undergraduate and postgraduate students of engineering for a course on digital signal processing. Divided into 17 Chapters, this text covers basic topics to the advanced topics relevant to the UG curricula. Following the book's step-by-step approach, students can quickly master the fundamental concepts and applications of DSP.

Több

Hosszú leírás:

Digital Signal Processing is a comprehensive textbook designed for undergraduate and post-graduate students of enginee01ring for a course on digital signal processing. Following the book's step-by-step approach, students can quickly master the fundamental concepts and applications of DSP. Each topic is explained lucidly through illustrations and solved examples.

Divided into 17 Chapters, this text presents the introductory topics such as discrete-time signals and systems, sampling and quantization, convolution, discrete-time Fourier series, discrete-time Fourier transform, and z-transform in a rigorous fashion. Further, topics such as DFT, FFT, filter concepts, filter structures, FIR filter design and IIR filter design are dealt in detail. It also covers the advanced topics such as finite word length effects, multirate DSP, optimum linear filters, and spectrum estimation techniques.

The chapters are packed with numerous illustrations, solved examples, multiple choice questions, numerical exercises and MATLAB programs. Additional solved examples at the end of the book will provide some more practice to students.

Több

Tartalomjegyzék:

Dedication iii
Features of the Book iv
Preface vi
Brief Content s x
Discrete-Time Signals and Systems
1.1 Introduction 1
1.2 Signals, Systems, and Signal Processing 1.2.1 Basic Elements of a Digital Signal Processing Systems 1.2.2 Advantages of Digital Signal Processing (DSP) over Analog Signal Processing (ASP) 1.2.3 DSP Applications 1.3 Classification of Signals 1.3.1 Continuous-Time and Discrete-Time Signals
1.3.2 Continuous-Valued and Discrete-Valued Signals 1.3.3 Multichannel andMultidimensional Signals
1.3.4 Deterministic and RandomSignals
1.4 Discrete-Time Signal or Sequence
1.4.1 Finite-Length and Infinite-Length Signal 8
1.4.2 Right-Sided, Causal, Left-Sided, and Anticausal Signals
1.5 Basic Operations on Discrete-Time Signals
1.5.1 Signal Addition Operation
1.5.2 Scalar Addition Operation
1.5.3 SignalMultiplication Operation
1.5.4 ScalarMultiplication Operation
1.6 Transformations of the Independent Variable (Time)
1.6.1 Time-Shifting
1.6.2 Time-Scaling (Decimation and Interpolation
1.6.3 Time-Reversal
1.6.4 Combined Operations
1.7 Some Basic Discrete-Time Signals
1.7.1 Unit Step Signal
1.7.2 Unit Impulse Signal (or Unit Sample Signal)
1.7.3 Unit Ramp Signal 24
1.7.4 Discrete-Time Real Exponential Signal 25
1.7.5 Discrete-Time Sinusoidal Signal 26
1.7.6 Discrete-Time Complex Exponential Signal 27
1.8 Periodic and Aperiodic Signals 28
1.8.1 Properties of Periodic Signals 28
1.8.2 Periodicity of Discrete-Time Sinusoidal Signals 31
1.9 Energy and Power Signals 41
1.10 Even and Odd Signals 48
1.10.1 Even and Odd Components of a Signal 49
1.10.2 Properties of Discrete-Time Even and Odd Signals 49
1.10.3 Conjugate-Symmetric and Conjugate-Antisymmetric Signals 54
1.11 Bounded Signal, Absolutely Summable Signal, and Square-summable Signal 56
1.12 Discrete-Time Systems 56
1.13 Basic SystemProperties 57
1.13.1 Linear and Nonlinear Systems 57
1.13.2 Time-Varying and Time-Invariant Systems (or Shift-Invariant Systems) 63
1.13.3 Causal Systems 67
1.13.4 Stable Systems 68
1.13.5 Systems with andWithoutMemory 70
1.13.6 Invertibility and Inverse Systems 71
1.13.7 Passive and Lossless Systems 72
1.14 Examples 72
1.15 MATLAB Programs 96
1.16 Summary 102
1.17 Multiple Choice Questions 103
1.18 Problems 105
1.19 Answers toMultiple Choice Questions 107
2 Sampling and Quantization
2.1 Introduction 2
2.2 Sampling 2
2.3 Sampling Theorem for Low-Pass Signals 3
2.3.1 Aliasing or SpectrumFolding 6
2.4 Sampling Techniques 14
2.5 Impulse Sampling or Ideal Sampling or Instantaneous Sampling 15
2.6 Natural Sampling or Chopper Sampling 16
2.7 Flat-Top Sampling 18
2.7.1 Aperture Effect 21
2.8 Reconstruction of a signal fromits Samples using Interpolation 22
2.8.1 Zero-Order-Hold Interpolation 24
2.8.2 First-Order-Hold Interpolation (or Linear Interpolation) 26
2.9 Sampling of Sinusoidal Signals 27
2.10 Sampling Theorem for Band-Pass Signals 32
2.10.1 Reconstruction of Bandpass Signal 35
2.11 Quantization 39
2.11.1 UniformQuantizers 39
2.11.2 Quantization Error and Quantization Noise 40
2.11.3 Signal-to-Quantization Noise Ratio (SQNR) 42
2.11.4 SQNR for Sinusoidal Signals 42
2.11.5 NonuniformQuantizer (Lloyd-Max Quantizers) 43
2.12 Sampling of Discrete-Time Signals 46
2.12.1 Decimation or Down-Sampling 49
2.12.2 Interpolation or Up-Sampling 51
2.12.3 Fractional Delays 54
2.13 Relationship Between DTFT and CTFT 57
2.14 Examples 59
2.15 MATLAB Programs 63
2.16 Summary 67
2.17 Multiple Choice Questions 69
2.18 Problems 70
2.19 Answers to Multiple Choice Questions 71
3 Convolution and Correlation
3.1 Introduction 3
3.2 The Discrete-Time LTI systems: The Convolution Sum 4
3.2.1 The Impulse Response (or Unit Sample Response) 4
3.2.2 The Convolution Sum 4
3.2.3 GraphicalMethod for the Convolution Sum 8
3.2.4 Analytical Method (using convolution sumexpression) 12
3.3 Properties of Convolution Sum 16
3.3.1 Commutative Property 16
3.3.2 Associative Property 17
3.3.3 Distributive Property 18
3.3.4 Shift Property 19
3.3.5 Convolution with an Impulse 20
3.3.6 Width Property 20
3.3.7 SumProperty 22
3.4 Convolution of Finite-Length Signals 24
3.4.1 TabulationMethod 25
3.4.2 Multiplication Method 27
3.5 System Response to Periodic Inputs 28
3.6 Relations between LTI system Properties and the Impulse Response 31
3.6.1 LTI Systems with and withoutMemory 31
3.6.2 Causality for LTI Systems 32
3.6.3 Stability for LTI Systems 33
3.6.4 Invertibility for LTI Systems 34
3.6.5 The Unit Step Response of an LTI Systems 35
3.7 Correlation of Signals 40
3.7.1 Crosscorrelation Sequence of Discrete-Time Energy Signals 40
3.7.2 Crosscorrelation Sequence of Power Signals 41
3.7.3 Autocorrelation Sequence of Discrete-time Signals 42
3.7.4 Properties of Crosscorrelation and Autocorrelation Sequences 44
3.8 Examples 46
3.9 MATLAB Programs 56
3.10 Summary 63
3.11 Multiple Choice Questions 64
3.12 Problems 65
3.13 Answers toMultiple Choice Questions 67
4 Discrete-Time Fourier Series
4.1 Introduction 4
4.2 Discrete-Time Fourier Series (DTFS) 5
4.2.1 Evaluation of DTFS Coefficient 6
4.2.2 Magnitude and Phase Spectrum of Discrete-Time Periodic Signals (Fourier
Spectra)
4.3 Properties of DTFS 14
4.3.1 Linearity 15
4.3.2 Time Shifting 15
4.3.3 Frequency Shifting 16
4.3.4 Time Reversal 16
4.3.5 Time Scaling or Time Expansion 17
4.3.6 Periodic Convolution 18
4.3.7 Multiplication 19
4.3.8 First Difference 20
4.3.9 Running Sum or Accumulation 20
4.3.10 Conjugation and Conjugate Symmetry 21
4.3.11 Parseval's Relation 24
4.4 Systems with Periodic Inputs
4.5 Examples 26
4.6 MATLAB Programs 37
4.7 Summary 41
4.8 Multiple Choice Questions 42
4.9 Problems 43
4.10 Answers toMultiple Choice Questions 44
5 Discrete-Time Fourier Transform
5.1 Introduction 5
5.2 Fourier Transform Representation of Aperiodic Discrete-Time Signals 6
5.3 Periodicity of the DTFT 9
5.4 Convergence of DTFT 9
5.4.1 Gibbs phenomenon 11
5.5 Properties of Discrete-Time Fourier Transform 24
5.5.1 Linearity 25
5.5.2 Time Shifting 25
5.5.3 Frequency Shifting 27
5.5.4 Time Reversal 28
5.5.5 Time Expansion 29
5.5.6 Differencing in Time Domain 29
5.5.7 Differentiation in Frequency Domain 30
5.5.8 Convolution Property 32
5.5.9 Accumulation Property 34
5.5.10 Multiplication (orModulation orWindowing) Property 35
5.5.11 Conjugation and Conjugate Symmetry 36
5.5.12 Parseval's Relation 41
5.6 Some Important Results 42
5.7 Fourier Transformof Periodic Signals 47
5.8 Signal Transmission Through LTI Systems 50
5.8.1 Response to Complex Exponentials 51
5.8.2 Response to Sinusoidal Signals 53
5.8.3 Response to a Causal Exponential Sequence 55
5.8.4 Linear and Nonlinear Phase 62
5.8.5 Phase Delay and Group Delay 63
5.9 Ideal and Practical Filters 70
5.9.1 Paley-Wiener Criterion 73
5.10 Energy Spectral Density (ESD) 78
5.10.1 Relationship Between Input and Output Energy Spectral Densities of an
LTI System 79
5.10.2 Relation of ESD to Autocorrelation 79
5.11 Power Spectral Density (PSD) 79
5.11.1 Relationship Between Input and Output Power Spectral Densities of an
LTI System 80
5.11.2 Relation of PSD to Autocorrelation 81
5.12 Examples 81
5.13 MATLAB Programs 98
5.14 Summary 106
5.15 Multiple Choice Questions 107
5.16 Problems 108
5.17 Answers to Multiple Choice Questions 110
6 The z-Transform
6.1 Introduction 6
6.2 Bilateral (Two-sided) z-Transform 7
6.2.1 Inverse z-Transform 7
6.3 Relationship Between z-Transform and Discrete-Time Fourier Transform 8
6.4 z-plane 9
6.4.1 Poles and Zeros 10
6.5 Region-of-Convergence for z-Transforms 11
6.6 Properties of ROC 15
6.7 Relationship Between Laplace Transform and z-transform (s to z-plane Mapping) 26
6.8 Relationship Between z-transformand DTFS 29
6.9 Properties of the z-Transform 29
6.9.1 Linearity 29
6.9.2 Time Shifting 31
6.9.3 Scaling in the z-Domain 32
6.9.4 Time Reversal 34
6.9.5 Differentiation in the z-Domain 35
6.9.6 Time Expansion 40
6.9.7 Convolution Property 43
6.9.8 Correlation Property 45
6.9.9 Accumulation Property 47
6.9.10 First Difference 49
6.9.11 Conjugation and Conjugate Symmetry 50
6.10 z-Transformof Causal Periodic Signals 51
6.11 Inversion of the z-Transform53
6.11.1 Contour Integration Method (or ResidueMethod) 53
6.11.2 Power Series Expansion Method (or Long Division Method) 57
6.11.3 Partial Fraction ExpansionMethod62
6.12 Analysis and Characterization of LTI Systems using the z-Transform 71
6.12.1 The Transfer Function and Difference-Equation System Description 72
6.12.2 Impulse Response and Step response 72
6.12.3 Causality 77
6.12.4 Stability 79
6.12.5 Stability of a Causal LTI System 80
6.13 The Unilateral (One-Sided) z-Transform 85
6.14 Properties of unilateral Z-Transform 89
6.14.1 Linearity 89
6.14.2 Scaling in the z-Domain 89
6.14.3 Differentiation in the z-Domain 90
6.14.4 Time Expansion 90
6.14.5 Conjugation Property 90
6.14.6 Convolution Property 90
6.14.7 Accumulation Property 91
6.14.8 Time-Delay (Right-Shift) Property 92
6.14.9 Time-Advance (Left-Shift) Property 95
6.14.10 First Difference 97
6.14.11 Initial-Value Theorem 97
6.14.12 Final-Value Theorem 99
6.14.13 Solving Difference Equations using the Unilateral z-Transform 102
6.14.14 Zero-Input Response and Zero-State Response 106
6.14.15Natural Response and Forced Response 107
6.14.16Transient Response and Steady-State Response 108
6.15 Block Diagrams Representation 114
6.15.1 Cascade Interconnection 114
6.15.2 Parallel Interconnection 115
6.15.3 Feedback Interconnection 116
6.16 Some Application of z-Transformin Signal Processing 116
6.16.1 Pole-Zero Description of Discrete-Time Systems 116
6.16.2 Frequency Response Estimation 117
6.16.3 Frequency Used in Discrete-Time Systems 117
6.16.4 Causality and Stability Considerations 119
6.16.5 Difference Equations 119
6.16.6 Applications in Digital Filter Design 120
6.16.7 Realization Structures for Digital Filters 120
6.17 Examples 120
6.18 MATLAB Programs 137
6.19 Summary 140
6.20 Multiple Choice Questions 140
6.21 Problems 142
6.22 Answers toMultiple Choice Questions 144
7 Filter Concepts
7.1 Introduction 7
7.2 Frequency Response and Filter Characteristics 8
7.2.1 Phase Delay and Group delay 9
7.2.2 Geometric Evaluation of Frequency Response 9
7.3 Zero-Phase Filter 12
7.3.1 Zero Locations of Zero-Phase FIR Transfer Functions 13
7.4 Linear-Phase Filter 14
7.5 Simple FIR Digital Filters 14
7.5.1 Lowpass FIR Digital Filter 14
7.5.2 Highpass FIR Digital Filter 16
7.5.3 Bandpass FIR Digital Filter 18
7.5.4 Bandstop (Notch) FIR Digital Filter 20
7.6 Simple IIR Digital Filters 22
7.6.1 Lowpass IIR Digital Filter 23
7.6.2 Highpass IIR Digital Filter 24
7.6.3 Bandpass IIR Digital Filter 26
7.6.4 Bandstop IIR Digital Filter 27
7.7 Allpass Filters 28
7.7.1 Properties of an allpass filter 30
7.8 Minimum-Phase, Maximum-Phase and Non-minimum ( or Mixed) Phase Systems 32
7.8.1 Invertibility and Inverse System 36
7.8.2 Minimum-phase and Allpass Decomposition 37
7.9 System Identification and Deconvolution 38
7.9.1 The Cepstrumand Homomorphic Deconvolution 39
7.10 Averaging Filters 41
7.10.1 Zeros of Averaging Filters 42
7.11 Comb Filters 42
7.12 Digital Resonators 45
7.13 Notch Filters 47
7.14 Digital Sinusoidal Oscillators (Sinusoid Generators) 50
7.14.1 Digital Sine-Cosine Generators 52
7.15 Digital Differentiator 54
7.16 Digital Hilbert Transformer 57
7.17 Examples 59
7.18 MATLAB Programs 72
7.19 Summary 82
7.20 Multiple Choice Questions 83
7.21 Problems 85
7.22 Answers toMultiple Choice Questions 86
8 Discrete Fourier Transform (DFT)
8.1 Introduction 8
8.2 Frequency Domain Sampling (Sampling of DTFT) 9
8.3 The Discrete Fourier Transform(DFT) and its Inverse 13
8.3.1 Derivation of Inverse DFT (IDFT) 16
8.3.2 Magnitude and Phase of DFT 17
8.3.3 Zero Padding 24
8.4 DFT as a Linear Transformation (Matrix Formulation) 28
8.4.1 Twiddle factor (WN) and its Properties 30
8.5 Properties of the DFT 33
8.5.1 Periodicity 34
8.5.2 Linearity 34
8.5.3 Circular Time Shifting 35
8.5.4 Circular Frequency Shifting 40
8.5.5 Circular Time Reversal (Circular Folding or Circular Flipping) 43
8.5.6 Conjugation and Conjugate Symmetry (Symmetry Properties) 47
8.5.7 Duality 51
8.5.8 Circular Convolution (Multiplication of Two DFTs) 54
8.5.9 Circular Correlation 62
8.5.10 Multiplication (orModulation) Property 63
8.5.11 Parseval's Relation 63
8.6 Some Important Results 64
8.7 Linear Convolution Using the DFT (Linear Convolution Using Circular Convolution) 68
8.7.1 Circular Convolution as Linear Convolution with Aliasing 71
8.8 Filtering of Long Data Sequences Using DFT (Linear Convolution of a Finite Length Sequence with an Infinite length Sequence) (or Fast Convolution) (or Block Convolution) 72
8.8.1 Overlap-Save Method 73
8.8.2 Overlap-AddMethod 75
8.9 The Discrete Cosine Transform(DCT) 79
8.9.1 Relationship between the DFT and DCT 81
8.10 DiscreteWalsh Transform(DWT) 83
8.10.1 Discrete Hadamard Transform(DHT) 84
8.11 Relationship of the DFT to Other Transforms 85
8.11.1 Relationship to Discrete-Time Fourier Series (DTFS) 85
8.11.2 Relationship to Discrete-Time Fourier Transform(DTFT) 86
8.11.3 Relationship to z-Transform86
8.12 SpectrumAnalysis Using DFT 87
8.12.1 Relationship Between DFT and Continuous-Time Fourier Transform (CTFT) 87
8.12.2 Relationship Between the Frequency Bin k and its Associated Analog Frequency O or f 88
8.12.3 Selection of Parameters for Signal Processing with the DFT 90
8.12.4 High Density Spectrum(Zero-Padding) 91
8.12.5 Spectral Leakage 92
8.12.6 Spectral Estimation UsingWindow Functions 93
8.13 Spectral Analysis of Nonstationary Signals 95
8.13.1 Short-Time Fourier Transform(STFT) 97
8.14 Examples 97
8.15 MATLAB Programs 109
8.16 Summary 119
8.17 Multiple Choice Questions 120
8.18 Problems 121
8.19 Answers toMultiple Choice Questions 122
9 Fast Fourier Transform (FFT)
9.1 Introduction 9
9.2 Computational Complexity of the Direct Computation of the DFT 9
9.2.1 Symmetry and Periodicity Properties of the Twiddle Factor (WN) 10
9.2.2 Radix-2 FFT Algorithms 11
9.3 Decimation-In-Time (DIT) FFT Algorithm 11
9.3.1 Computational Advantage of the DIT-FFT 16
9.3.2 In-Place Computation 17
9.3.3 Bit-Reversal 17
9.4 Decimation-In-Frequency (DIF) FFT Algorithm 21
9.4.1 Computational Cost 26
9.5 Comparison Between DIT and DIF Algorithms 27
9.6 Inverse DFT Using FFT Algorithms 34
9.7 A Linear Filtering Approach to Computation of the DFT 37
9.7.1 The Goertzel Algorithm 37
9.7.2 The Chirp-z TransformAlgorithm 43
9.7.3 Dual-Tome Multi-Frequency (DTMF) Tone Detection Using the Goertzel Algorithm 46
9.8 Examples 48
9.9 MATLAB Programs 55
9.10 Summary 58
9.11 Multiple Choice Questions 59
9.12 Problems 60
9.13 Answers toMultiple Choice Questions 61
10 Realization of Digital Filters
10.1 Introduction 10
10.1.1 FIR Filter or All Zero (AZ) or Moving Average (MA) system: 11
10.1.2 IIR Filter or All Pole (AP) or Autoregressive (AR) system 11
10.1.3 IIR Filter or Pole-Zero (PZ) or Autoregressive, Moving average (ARMA) system 12
10.2 Nonrecursive and Recursive Structures 13
10.3 Factors that Influence the Choice of Structure 13
10.4 Block Diagram Representation and Signal Flow Graph 14
10.4.1 Basic Building Blocks 14
10.4.2 Advantages in Representing the Digital Filter in Block Diagram Form 14
10.4.3 Canonic and Noncanonic Structures 15
10.4.4 Equivalent Structures (Transposed Structure) 15
10.5 FIR Filter Structures 15
10.5.1 Direct Form (Transversal or Tapped-Delay Line) Structure 16
10.5.2 Cascade-Form Structure 17
10.5.3 Linear-Phase Structure 18
10.5.4 Polyphase Structure 20
10.5.5 Conversion of Nonrecursive Structure into Recursive Structure 24
10.5.6 Frequency Sampling Structure 26
10.6 Basic Structures for IIR Systems 31
10.6.1 Direct Form I Structure 32
10.6.2 Direct-Form II Structure 34
10.6.3 Cascade Form Structure 40
10.6.4 Parallel From Structure 42
10.6.5 Polyphase Structure 47
10.7 Lattice Structures 51
10.7.1 Advantages of Lattice Structures 53
10.8 Lattice Structures for FIR Systems (All-Zero Systems) 53
10.8.1 Conversion of Lattice Coefficients to Direct-Form Filter Coefficients 57
10.8.2 Conversion of Direct-Form Filter Coefficients to Lattice Coefficients 59
10.9 Lattice Structures for All-Pole (AP) IIR Systems 65
10.9.1 Stability of an All-Pole System (Schur-Cohn Stability Test) 68
10.10Lattice Structures for Pole-Zero (PZ) IIR Systems (or Lattice-Ladder Structure) 70
10.11 Examples 75
10.12 MATLAB Programs 83
10.13Summary 86
10.14 Multiple Choice Questions 86
11 Finite Impulse Response (FIR) Digital Filter
11.1 Introduction to Digital Filters 11
11.1.1 Advantages and Disadvantages of Digital Filters 11
11.1.2 Types of Digital Filters: FIR and IIR Filters 12
11.1.3 Difference Between FIR and IIR Filters 13
11.2 Desirability of Linear-Phase Filters 14
11.2.1 Effect of Phase Distortion 15
11.2.2 Condition for a Filter to have a Linear-Phase Response 16
11.3 Frequency Response of Linear-Phase FIR Filters 21
11.3.1 Type 1: Symmetric Impulse Response with Odd Length (M odd) 24
11.3.2 Type 2: Symmetric Impulse Response with Even Length (M Even) 26
11.3.3 Type 3: Antisymmetric Impulse Response with Odd Length (M Odd) 28
11.3.4 Type 4: Antisymmetric Impulse Response with Even Length (M Even) 31
11.3.5 Location of Zeros of Linear Phase FIR Transfer Functions 34
11.4 Filter Specifications 38
11.4.1 Absolute Specifications 39
11.4.2 Relative Specifications 40
11.4.3 Continuous-time (Analog) Filter Specifications 42
11.4.4 Estimation of FIR Filter Order 43
11.5 Impulse Responses of Ideal Filters 45
11.5.1 Impulse Response of an Ideal Lowpass Filter 45
11.5.2 Impulse Response of an Ideal Highpass Filter 46
11.5.3 Impulse Response of an Ideal Bandpass Filter 48
11.5.4 Impulse Response of an Ideal Bandstop Filter 48
11.6 Design Techniques for Linear-Phase FIR Filters 49
11.7 Fourier Series Method 50
11.7.1 Gibbs Phenomenon 52
11.8 Windowing Method 62
11.8.1 Rectangular Window 63
11.8.2 Triangular (or Bartlett) Window 76
11.8.3 Hann (or Hanning) Window 81
11.8.4 Hamming Window 86
11.8.5 Blackman Window 97
11.8.6 Kaiser Window 118
11.8.7 Advantages and Disadvantages of the Window Method 123
11.9 Half-Band FIR Filters 124
11.10 Design of FIR Digital Differentiators 127
11.11 Design of FIR Hilbert Transformers 135
11.12 Frequency Sampling Method 142
11.12.1 Type-I Design 143
11.12.2 Type-II Design 151
11.12.3 Transition-band Optimization 155
11.13 The Optimal Method 159
11.13.1 Minimax Criterion 160
11.13.2 Alternation Theorem 166
11.13.3 Parks-McClellan Algorithm 168
11.13.4 Disadvantages of Optimal Method 172
11.14 Comparison of Design Methods for Linear-Phase FIR Filters 172
11.15 Examples 172
11.16 MATLAB Programs 175
11.17 Summary 193
11.18 Multiple Choice Questions 195
11.19 Problems 197
11.20Answers to Multiple Choice Questions 198
12 Infinite Impulse Response (IIR) Digital Filter
12.1 Introduction 12
12.2 Design of IIR Filters from Analog Filters 13
12.2.1 Filter Design Steps 13
12.3 IIR Filter Design by Approximation of Derivatives 14
12.3.1 Backward Difference Algorithm 14
12.4 Impulse-Invariant Method 16
12.4.1 Relationship Between Analog and Digital Filter Poles 18
12.4.2 Relationship Between Analog and Digital Frequency 22
12.4.3 Advantages and Disadvantages of Impulse-Invariant Method 22
12.5 The Matched z-Transformation 33
12.6 Step -Invariant Method 35
12.7 Bilinear Transformation Method 37
12.7.1 Relationship Between Analog and Digital frequency 39
12.7.2 Effect of Warping on the Magnitude and Phase Response 40
12.7.3 Prewarping 40
12.7.4 Advantages and Disadvantages of Bilinear Transformation Method 41
12.7.5 Comparison of Impulse-Invariant and Bilinear Transformations 41
12.7.6 Relationship Between Impulse-Invariant and Bilinear Transformations 41
12.8 IIR Filter Specifications 50
12.9 Specifications of the Lowpass Filter 51
12.9.1 Properties of Magnitude-squared Response jHa(j-)j2 51
12.10Analog Butterworth Filter 52
12.10.1 Determination of Filter Parameters (Order N and Cutoff Frequency -c) of
Butterworth Filter 60
12.10.2 Design Procedure for Lowpass Digital Butterworth Filters 63
12.11Analog Chebyshev Filter 77
12.11.1Type I Chebyshev Filter 77
12.11.2 Determination of Filter Parameters (Order N and Cut-off Frequency -c) of Chebyshev Filter 81
12.11.3 Design Procedure for Lowpass Digital Chebyshev Filters 85
12.11.4Type II Chebyshev Filter (Inverse Chebyshev Filter) 92
12.11.5 Difference Between Butterworth and Chebyshev Filter 93
12.12Frequency (or Spectral) Transformation in the Analog Domain 94
12.12.1 Lowpass to Lowpass Transformation 94
12.12.2 Lowpass to Highpass Transformation 95
12.12.3 Lowpass to Bandpass Transformation 96
12.12.4 Lowpass to Bandstop Transformation 97
12.13Frequency (or Spectral) Transformation in the Digital Domain 106
12.13.1 Lowpass to Lowpass Transformation 107
12.13.2 Lowpass to Highpass Transformation 108
12.13.3 Lowpass to Bandpass Transformation 108
12.13.4 Lowpass to Bandstop Transformation 109
12.14Applications 113
12.14.1 Digital Audio Equalizer 113
12.14.2 Generation and Detection of Dual-Tone Multifrequency Tones Using Goertzel Algorithm 114
12.14.3 60-Hz Hum Eliminator and Heart Rate Detection Using Electrocardiography (ECG) 117
12.14.4 Timing (or Clock) Recovery for Synchronous Digital Communication System121
12.15 Examples 122
12.16 MATLAB Programs 126
12.17Summary 133
12.18 Multiple Choice Questions 134
12.19 Problems 136
12.20Answers to Multiple Choice Questions 138
13 Analysis of Finite Word Length Effects
13.1 Introduction 13
13.2 Representation of Numbers 14
13.2.1 Fixed-Point Representation of Numbers 15
13.2.2 Binary Floating-Point Representation of Numbers 18
13.3 Quantization 21
13.3.1 Truncation 22
13.3.2 Rounding 22
13.4 Quantization of Fixed-Point Numbers 26
13.4.1 Effect of Truncation 26
13.4.2 Effect of Rounding 29
13.5 Quantization of Floating-Point Numbers 30
13.5.1 Effect of Truncation 31
13.5.2 Effect of Rounding 33
13.6 Coefficient Quantization Error 34
13.6.1 Coefficient Sensitivity Analysis of a Second-order Direct Form II Structure 37
13.6.2 Analysis of Coefficient Quantization Effects in FIR Filters 43
13.7 Quantization in Sampling Analog Signals (A/D Conversion Noise Analysis) 44
13.7.1 Quantization NoiseModel 46
13.7.2 Signal-to-Quantization Noise Ratio 47
13.7.3 Effect of Input Scaling on SNR 48
13.7.4 Propagation of Input Quantization Noise to Digital Filter Output 48
13.8 Product Quantization Error 57
13.8.1 Direct Form I Structure 58
13.8.2 Direct Form II Structure 61
13.8.3 Product Quantization using Double-Length Accumulator 78
13.9 Limit Cycles in IIR Digital Filters 82
13.9.1 Zero-Input Limit Cycle 83
13.9.2 Dead Band 84
13.9.3 Overflow Limit Cycle 88
13.9.4 Saturation to Avoid Overflow 89
13.9.5 Scaling to Prevent Overflow 90
13.10Quantization Effects in Floating-Point Realizations of IIR Digital Filters 101
13.10.1First Order IIR Digital Filter 102
13.11Quantization Effects in Realizations of FIR Digital Filters 103
13.11.1 Quantization Effects in Fixed-Point Realizations of FIR Digital Filters 104
13.11.2 Quantization Effects in Floating-Point Realizations of FIR Digital Filters 105
13.12Quantization Effects in Discrete Fourier Transform (DFT) Computations 107
13.12.1 Quantization Effects in Direct Computation of the DFT 107
13.12.2 Quantization Effects in Fixed-Point FFT Algorithms 110
13.13 Examples 112
13.14 MATLAB Programs 116
13.15Summary 117
13.16 Multiple Choice Questions 119
13.17 Problems 121
13.18Answers toMultiple Choice Questions 123
14Multirate Digital Signal Processing
14.1 Introduction 14
14.1.1 Advantages ofMultirate DSP 14
14.2 Decimation 15
14.2.1 Time-Domain Characterization 15
14.2.2 Frequency-Domain Characterization 16
14.2.3 Aliasing Effect 19
14.2.4 Anti-aliasing Filter Specifications 20
14.3 Interpolation 22
14.3.1 Time-Domain Characterization 22
14.3.2 Frequency-Domain Characterization 23
14.3.3 Anti-imaging Filter Specifications 25
14.4 Sampling Rate Conversion by a Rational factor LM 28
14.5 Identities (Cascaded Equivalences) 33
14.5.1 Identities for the Downsampling 33
14.5.2 Identities for the Upsampling 35
14.5.3 Upsampler and Downsampler Cascade 37
14.6 Computational Requirements 41
14.6.1 Efficient Direct Form Structure of a Decimator 42
14.6.2 Efficient Direct Form Structure of an Interpolator 43
14.7 The Polyphase Decomposition 44
14.7.1 FIR Filter Structures Based on Polyphase Decomposition 44
14.7.2 Decimation with Polyphase Filters 46
14.7.3 Interpolation with Polyphase Filters 49
14.7.4 Efficient Rational Sampling Rate Converter with Polyphase Filters 50
14.7.5 The Polyphase Identity 53
14.8 Application ofMultirate DSP: Design of Narrowband Filters 54
14.8.1 Interpolated FIR (IFIR) Filters 55
14.9 Multistage Implementation of Sampling Rate Conversion 59
14.9.1 Multistage Implementation of Decimator 60
14.9.2 Multistage Implementation of Interpolator 67
14.9.3 Multistage Implementation of Rational Sampling Rate Converter 67
14.9.4 The IFIR Approach forMultistage Implementation 68
14.10Nyquist Filters (or Lth-Band Filters) 74
14.10.1Half-Band Filters 74
14.10.2Lth-Band Filters 76
14.11Digital Filter Banks 79
14.11.1UniformDFT Filter Bank 80
14.11.2Polyphase Implementations of UniformDFT Filter Bank 81
14.11.3Application of Multirate DSP: Subband Coding of Speech and Audio Signals 83
14.12Two-Channel Quadrature-Mirror Filter (QMF) Bank 85
14.12.1Analysis of Two-Channel QMF Bank 86
14.13Application ofMultirate DSP: Transmultiplexer 92
14.14 Examples 93
14.15 MATLAB Programs 96
14.16Summary 102
14.17 Multiple Choice Questions 102
14.18 Problems 104
14.19Answers toMultiple Choice Questions 105
15 Optimum Linear Filters (Wiener Filters)
15.1 Introduction 15
15.1.1 Mean-Square Error Criterion 15
15.2 The FIRWiener Filter 17
15.2.1 Filtering 20
15.2.2 Linear Prediction 27
15.3 Noncausal IIRWiener Filter 32
15.3.1 Filtering 35
15.4 Innovations Representation 37
15.4.1 Spectral Factorization 38
15.5 Causal IIRWiener Filter 39
15.6 Deconvolution (or Deblurring) 44
15.7 Examples 47
15.8 MATLAB Programs 49
15.9 Summary 51
15.10 Multiple Choice Questions 52
15.11 Problems 52
15.12Answers toMultiple Choice Questions 53
16 Power Spectrum Estimation
16.1 Introduction 16
16.1.1 Parseval's Theorem 17
16.1.2 Performance of Estimators 19
16.2 Nonparametric (or Classical) Methods 20
16.2.1 The Periodogram 21
16.2.2 TheModified Periodogram 24
16.2.3 Bartlett's Method: Periodogram Averaging 25
16.2.4 Welch's Method: AveragingModified Periodograms 28
16.2.5 Blackman-Tukey Approach: Periodogram Smoothing 30
16.3 Parametric (or Nonclassical) Methods 34
16.3.1 Autoregressive SpectrumEstimation 36
16.3.2 Computation of Model Parameters Yule-Walker Equations 37
16.3.3 Least-squares (LS)Method and Linear Prediction 39
16.3.4 Moving Average SpectrumEstimation 41
16.3.5 AutoregressiveMoving Average SpectrumEstimation 42
16.4 Eigenvalues and Eigenvectors of the Autocorrelation Matrix 42
16.4.1 Properties of Eigenvalues and Eigenvectors 43
16.5 Eigenanalysis Algorithms for SpectrumEstimation 47
16.5.1 HarmonicModel 47
16.5.2 Eigen-Decomposition of the Autocorrelation Matrix 48
16.5.3 Pisarenko Harmonic Decomposition Method 59
16.5.4 MUSIC Algorithm 62
16.6 Examples 64
16.7 MATLAB Programs 66
16.8 Summary 72
16.9 Multiple Choice Questions 73
16.10 Problems 74
16.11Answers toMultiple Choice Questions 74
17 Introduction to Digital Signal Processors (DSPs)
17.1 Introduction 17
17.2 Evolution of Digital Signal Processors 18
17.2.1 DSP Algorithms mold DSP Architectures 18
17.2.2 FastMultipliers 18
17.2.3 Multiple Execution Units 18
17.2.4 Efficient memory Accesses 19
17.2.5 Data Format 20
17.2.6 Zero-Overhead Looping 20
17.2.7 Streamlined I/O 21
17.2.8 Specialized Instruction Set 21
17.3 Digital Signal Processor Architecture 21
17.3.1 Von Neumann Architecture 22
17.3.2 Harvard Architecture 23
17.3.3 Super Harvard Architecture (SHARC) 24
17.4 Digital Signal Processor Hardware Units 24
17.4.1 Multiplier and Accumulator (MAC) Unit 24
17.4.2 Shifters 24
17.4.3 Address Generators 26
17.5 Fixed-Point and Floating-Point Format 26
17.6 Fixed-Point Digital Signal Processor 27
17.7 Floating-Point Digital Signal Processor 27
17.8 Pipelining 28
17.9 Memory Access schemes in DSPs 29
17.9.1 Multiple Access Memory 29
17.9.2 Multiport memory 29
17.10 Very Long InstructionWord (VLIW) Architecture 29
17.10.1Advantages 30
17.10.2Disadvantages 31
17.11AddressingModes 31
17.11.1Implied Addressing 31
17.11.2Immediate Addressing 31
17.11.3Memory-Direct Addressing 32
17.11.4Register-Direct Addressing 32
17.11.5Register-Indirect Addressing 32
17.11.6Bit-Reversed Addressing 32
17.11.7Circular Addressing 32
17.12The TMS320 Family 33
17.12.1 TMS320 C2X Generation 33
17.12.2 TMS320 C3X Generation 34
17.12.3 TMS320 C4X Generation 34
17.12.4 TMS320 C5X Generation 35
17.12.5Overview of TMS 320 C6713 DSP 35
17.13 Interfacing
17.13.1 External Memory Interfacing
17.13.2 Serial-port Interfacing
17.13.3 Parallel-port Interfacing
17.13.4 Host-port Interfacing 1063
Summary
Multiple Choice Questions
Questions 36
Answers to Multiple Choice Questions
Bibliography
Index

Több

Digital Signal Processing by Rawat, Tarun Kumar;

10% KEDVEZMÉNY?

14 443 Ft

Beszerezhetőség

Why don't you give exact delivery time?

A termék adatai:

Kategóriák

Rövid leírás:

Hosszú leírás:

Tartalomjegyzék: