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Part IX. Bandpass and Quadrature Techniques
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Part IX. Bandpass and Quadrature Techniques
by C. Britton Rorabaugh
Notes on Digital Signal Processing: Practical Recipes for Design, Analysis and Implementation
Title Page
Copyright Page
Dedication Page
Contents
Preface
About the Author
Part I. DSP Fundamentals
Note 1. Navigating the DSP Landscape
Note 2. Overview of Sampling Techniques
Note 3. Ideal Sampling
Note 4. Practical Application of Ideal Sampling
Note 5. Delta Functions and the Sampling Theorem
Note 6. Natural Sampling
Note 7. Instantaneous Sampling
Note 8. Reconstructing Physical Signals
Part II. Fourier Analysis
Note 9. Overview of Fourier Analysis
Note 10. Fourier Series
Note 11. Fourier Transform
Note 12. Discrete-Time Fourier Transform
Note 13. Discrete Fourier Transform
Note 14. Analyzing Signal Truncation
Note 15. Exploring DFT Leakage
Note 16. Exploring DFT Resolution
Part III. Fast Fourier Transform Techniques
Note 17. FFT: Decimation-in-Time Algorithms
Note 18. FFT: Decimation-in-Frequency Algorithms
Note 19. FFT: Prime Factor Algorithm
Note 20. Fast Convolution Using the FFT
Part IV. Window Techniques
Note 21. Using Window Functions: Some Fundamental Concepts
Note 22. Assessing Window Functions: Sinusoidal Analysis Techniques
Note 23. Window Characteristics
Note 24. Window Choices
Note 25. Kaiser Windows
Part V. Classical Spectrum Analysis
Note 26. Unmodified Periodogram
Note 27. Exploring Periodogram Performance: Sinusoids in Additive White Gaussian Noise
Note 28. Exploring Periodogram Performance: Modulated Communications Signals
Note 29. Modified Periodogram
Note 30. Bartlett’s Periodogram
Note 31. Welch’s Periodogram
Part VI. FIR Filter Design
Note 32. Designing FIR Filters: Background and Options
Note 33. Linear-Phase FIR Filters
Note 34. Periodicities in Linear-Phase FIR Responses
Note 35. Designing FIR Filters: Basic Window Method
Note 36. Designing FIR Filters: Kaiser Window Method
Note 37. Designing FIR Filters: Parks-McClellan Algorithm
Part V. Analog Prototype Filters
Note 38. Laplace Transform
Note 39. Characterizing Analog Filters
Note 40. Butterworth Filters
Note 41. Chebyshev Filters
Note 42. Elliptic Filters
Note 43. Bessel Filters
Part VI. z-Transform Analysis
Note 44. The z Transform
Note 45. Computing the Inverse z Transform Using the Partial Fraction Expansion
Note 46. Inverse z Transform via Partial Fraction Expansion: Case 1: All Poles Distinct with M < N in System Function
Note 47. Inverse z Transform via Partial Fraction Expansion: Case 2: All Poles Distinct with M ≥ N in System Function (Explicit Approach)
Note 48. Inverse z Transform via Partial Fraction Expansion: Case 3: All Poles Distinct with M ≥ N in System Function (Implicit Approach)
Part VII. IIR Filter Design
Note 49. Designing IIR Filters: Background and Options
Note 50. Designing IIR Filters: Impulse Invariance Method
Note 51. Designing IIR Filters: Bilinear Transformation
Part VIII. Multirate Signal Processing
Note 52. Decimation: The Fundamentals
Note 53. Multistage Decimators
Note 54. Polyphase Decimators
Note 55. Interpolation Fundamentals
Note 56. Multistage Interpolation
Note 57. Polyphase Interpolators
Part IX. Bandpass and Quadrature Techniques
Note 58. Sampling Bandpass Signals
Note 59. Bandpass Sampling: Wedge Diagrams
Note 60. Complex and Analytic Signals
Note 61. Generating Analytic Signals with FIR Hilbert Transformers
Note 62. Generating Analytic Signals with Frequency-Shifted FIR Lowpass Filters
Note 63. IIR Phase-Splitting Networks for Generating Analytic Signals
Note 64. Generating Analytic Signals with Complex Equiripple FIR Filters
Note 65. Generating I and Q Channels Digitally: Rader’s Approach
Note 66. Generating I and Q Channels Digitally: Generalization of Rader’s Approach
Part X. Statistical Signal Processing
Note 67. Parametric Modeling of Discrete-Time Signals
Note 68. Autoregressive Signal Models
Note 69. Fitting AR Models to Stochastic Signals: The Yule-Walker Method
Note 70. Fitting All-Pole Models to Deterministic Signals: Autocorrelation Method
Note 71. Fitting All-Pole Models to Deterministic Signals: Covariance Method
Note 72. Autoregressive Processes and Linear Prediction Analysis
Note 73. Estimating Coefficients for Autoregressive Models: Burg Algorithm
Index
Footnotes
Chapter 14
Chapter 16
Chapter 23
Chapter 60
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Note 57. Polyphase Interpolators
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Note 58. Sampling Bandpass Signals
Part IX. Bandpass and Quadrature Techniques
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