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Notes on Digital Signal Processing: Practical Recipes for Design, Analysis and Implementation, Portable Documents

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Notes on Digital Signal Processing: Practical Recipes for Design, Analysis and Implementation, Portable Documents

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  • Copyright 2011
  • Dimensions: 8" x 10"
  • Edition: 1st
  • eBook (Watermarked)
  • ISBN-10: 0-13-259876-0
  • ISBN-13: 978-0-13-259876-7

The Most Complete, Modern, and Useful Collection of DSP Recipes: More Than 50 Practical Solutions and More than 30 Summaries of Pertinent Mathematical Concepts for Working Engineers

Notes on Digital Signal Processing is a comprehensive, easy-to-use collection of step-by-step procedures for designing and implementing modern DSP solutions. Leading DSP expert and IEEE Signal Processing Magazine associate editor C. Britton Rorabaugh goes far beyond the basic procedures found in other books while providing the supporting explanations and mathematical materials needed for a deeper understanding.

Rorabaugh covers the full spectrum of challenges working engineers are likely to encounter and delves into crucial DSP nuances discussed nowhere else. Readers will find valuable, tested recipes for working with multiple sampling techniques; Fourier analysis and fast Fourier transforms; window functions; classical spectrum analysis; FIR and IIR filter design; analog prototype filters; z-transform analysis; multirate and statistical signal processing; bandpass and quadrature techniques; and much more.

Notes on Digital Signal Processing begins with mapping diagrams that illuminate the relationships between all topics covered in the book. Many recipes include examples demonstrating actual applications, and most sections rely on widely used MATLAB tools.

  • DSP fundamentals: ideal, natural, and instantaneous sampling; delta functions; physical signal reconstruction; and more
  • Fourier Analysis: Fourier series and transforms; discrete-time and discrete Fourier transforms; signal truncation; DFT leakage and resolution
  • Fast Fourier transforms: decimation in time and frequency; prime factor algorithms; and fast convolution
  • Window techniques: sinusoidal analysis; window characteristics and choices; Kaiser windows; and more
  • Classical spectrum analysis: unmodified and modified periodograms; Bartlett’s and Welch’s periodograms; and periodogram performance
  • FIR filters: design options; linear-phase FIR filters; periodicities; basic and Kaiser window methods; and the Parks-McClellan algorithm
  • Analog prototype filters: Laplace transforms; characterization; and Butterworth, Chebyshev, elliptic, and Bessel filters
  • z-Transform analysis: computation and transforms using partial fraction expansion
  • IIR filters: design options; impulse invariance methods; and bilinear transformation
  • Multirate signal processing: decimation and interpolation fundamentals; multistage and polyphase decimators and interpolation
  • Bandpass and quadrature techniques: bandpass sampling; wedge diagrams; complex and analytic signals; and advanced signal generation techniques
  • Statistical signal processing: parametric modeling of discrete-time signals; autoregressive signal models; fitting AR and All-Pole models; and more

Sample Content

Table of Contents

Preface         xi

About the Author         xiii

Part I: DSP Fundamentals

Note 1: Navigating the DSP Landscape       1-1

Note 2: Overview of Sampling Techniques        2-1

Note 3: Ideal Sampling       3-1

Note 4: Practical Application of Ideal Sampling       4-1

Note 5: Delta Functions and the Sampling Theorem       5-1

Note 6: Natural Sampling       6-1

Note 7: Instantaneous Sampling       7-1

Note 8: Reconstructing Physical Signals       8-1

Part II: Fourier Analysis

Note 9: Overview of Fourier Analysis       9-1

Note 10: Fourier Series       10-1

Note 11: Fourier Transform      11-1

Note 12: Discrete-Time Fourier Transform       12-1

Note 13: Discrete Fourier Transform       13-1

Note 14: Analyzing Signal Truncation       14-1

Note 15: Exploring DFT Leakage       15-1

Note 16: Exploring DFT Resolution       16-1

Part III: Fast Fourier Transform Techniques

Note 17: FFT: Decimation-in-Time Algorithms          17-1

Note 18 FFT: Decimation-in-Frequency Algorithms         18-1

Note 19: FFT: Prime Factor Algorithm         19-1

Note 20: Fast Convolution Using the FFT       20-1

Part IV: Window Techniques

Note 21: Using Window Functions: Some Fundamental Concepts        21-1

Note 22: Assessing Window Functions: Sinusoidal Analysis Techniques        22-1

Note 23: Window Characteristics        23-1

Note 24: Window Choices        24-1

Note 25: Kaiser Windows         25-1

Part V: Classical Spectrum Analysis

Note 26: Unmodified Periodogram         26-1

Note 27: Exploring Periodogram Performance: Sinusoids in Additive White Gaussian Noise        27-1

Note 28: Exploring Periodogram Performance: Modulated Communications Signals       28-1

Note 29: Modified Periodogram        29-1

Note 30: Bartlett’s Periodogram         30-1

Note 31: Welch’s Periodogram         31-1

Part VI: FIR Filter Design

Note 32: Designing FIR Filters: Background and Options       32-1

Note 33: Linear-Phase FIR Filters        33-1

Note 34: Periodicities in Linear-Phase FIR Responses       34-1

Note 35: Designing FIR Filters: Basic Window Method        35-1

Note 36: Designing FIR Filters: Kaiser Window Method        36-1

Note 37: Designing FIR Filters: Parks-McClellan Algorithm       37-1

Part V: Analog Prototype Filters

Note 38: Laplace Transform       38-1

Note 39: Characterizing Analog Filters       39-1

Note 40: Butterworth        40-1

Note 41: Chebyshev Filters       41-1

Note 42: Elliptic Filters       42-1

Note 43: Bessel Filters       43-1

Part VI: z-Transform Analysis

Note 44: The z Transform        44-1

Note 45: Computing the Inverse z Transform Using the Partial Fraction Expansion        45-1

Note 46: Inverse z Transform via Partial Fraction Expansion

            Case 1: All Poles Distinct with M < N in System Function        46-1

Note 47: Inverse z Transform via Partial Fraction Expansion

            Case 2: All Poles Distinct with M ≥ N in System Function (Explicit Approach)       47-1

Note 48: Inverse z Transform via Partial Fraction Expansion

            Case 3: All Poles Distinct with M ≥ N in System Function (Implicit Approach)        48-1

Part VII: IIR Filter Design

Note 49: Designing IIR Filters: Background and Options       49-1

Note 50: Designing IIR Filters: Impulse Invariance Method       50-1

Note 51: Designing IIR Filters: Bilinear Transformation        51-1

Part VIII: Multirate Signal Processing

Note 52: Decimation: The Fundamentals       52-1

Note 53: Multistage Decimators       53-1

Note 54: Polyphase Decimators       54-1

Note 55: Interpolation Fundamentals        55-1

Note 56: Multistage Interpolation        56-1

Note 57: Polyphase Interpolators       57-1

Part IX: Bandpass and Quadrature Techniques

Note 58: Sampling Bandpass Signals        58-1

Note 59: Bandpass Sampling: Wedge Diagrams        59-1

Note 60: Complex and Analytic Signals        60-1

Note 61: Generating Analytic Signals with FIR Hilbert Transformers        61-1

Note 62: Generating Analytic Signals with Frequency-Shifted FIR Lowpass Filters        62-1

Note 63: IIR Phase-Splitting Networks for Generating Analytic Signals          63-1

Note 64: Generating Analytic Signals with Complex Equiripple FIR Filters         64-1

Note 65: Generating I and Q Channels Digitally: Rader’s Approach       65-1

Note 66: Generating I and Q Channels Digitally: Generalization of Rader’s Approach        66-1

Part X: Statistical Signal Processing

Note 67: Parametric Modeling of Discrete-Time Signals         67-1

Note 68: Autoregressive Signal Models          68-1

Note 69: Fitting AR Models to Stochastic Signals: Yule-Walker Method       69-1

Note 70: Fitting All-Pole Models to Deterministic Signals: Autocorrelation Method       70-1

Note 71: Fitting All-Pole Models to Deterministic Signals: Covariance Method       71-1

Note 72: Autoregressive Processes and Linear Prediction Analysis       72-1

Note 73: Estimating Coefficients for Autoregressive Models: Burg Algorithm        73-1

Index        I-1


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