This text provides a broad introduction to the field of digital signal processing and contains sufficient material for a two-semester sequence in this multifaceted subject. It is also written with the practicing engineer or scientist in mind, having many observations and examples of practical significance drawn from the author's industrial experience. The first semester, at the junior, senior, or first-year graduate level, could cover chapters 2 through 7 with topics perhaps from chapters 8 and 9, depending upon the background of the students. The only requisite background is linear systems theory for continuous-time systems, including Fourier and Laplace trans- forms. Many students will also have had some previous exposure to discrete-time systems, in which case chapters 2 through 4 may serve to review and expand that preparation. Note, in particular, that knowledge of probability theory and random processes is not required until chapters 10 and 11, except for section 7. 6 on the periodogram. A second, advanced course could utilize material from chapters 8 through 13. A comprehensive one-semester course for suitably prepared graduate students might cover chapters 4 through 9 and additional topics from chapters 10 through 13. Sections marked with a dagger Ct) cover advanced or specialized topics and may be skipped without loss of continuity. Notable features of the book include the following: 1. Numerous useful filter examples early in the text in chapters 4 and 5. 2. State-space representation and structures in chapters 4 and 11.
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1 Introduction: Terminology and Motivation.- 2 Discrete-Time Signals and Systems.- 2.0 Introduction.- 2.1 Discrete-Time Signals, or Sequences.- 2.2 Discrete-Time Systems and Filters.- 2.3 Stability and Causality.- Problems.- 3 The z Transform.- 3.0 Introduction.- 3.1 Definition of the z Transform.- 3.2 Inverse z Transform.- 3.3 Inverse z Transform for Causal Sequences.- 3.4 Properties of the z Transform.- Problems.- 4 Input/Output Relationships.- 4.0 Introduction.- 4.1 System Function and Frequency Response.- 4.2 Difference Equations.- 4.3 Geometric Evaluations of H(z) and H’(?).- 4.4 State Variables.- Problems.- 5 Discrete-Time Networks.- 5.0 Introduction.- 5.1 Flow Graph Properties.- 5.2 Network Structures.- 5.3 Properties of Network Coefficients.- 5.4 Special Discrete-Time Networks.- Problems.- 6 Sampling Continuous-Time Signals.- 6.0 Introduction.- 6.1 Fourier Transform Relationships.- 6.2 Discrete-Time Fourier Transform.- 6.3 Laplace Transform Relationships.- 6.4 Prefilters, Postfilters and D/A Converters.- Problems.- 7 Discrete Fourier Transform.- 7.0 Introduction.- 7.1 Derivation and Properties of the DFT.- 7.2 Zero Padding.- 7.3 Windows in Spectrum Analysis.- 7.4 FFT Algorithms.- 7.5 Prime-Factor FFT’s.- 7.6 Periodogram.- Problems.- 8 IIR Filter Design by Transformation.- 8.0 Introduction.- 8.1 Classical Filter Designs.- 8.2 Impulse-Invariant Transformation.- 8.3 Bilinear Transformation.- 8.4 Spectral Transformation.- Problems.- 9 FIR Filter Design Techniques.- 9.0 Introduction.- 9.1 Window-Function Technique.- 9.2 Frequency-Sampling Technique.- 9.3 Equiripple Designs.- Problems.- 10 Filter Design by Modeling.- 10.0 Introduction.- 10.1 Autoregressive (all-pole) Filters.- 10.2 Moving-Average (all-zero) Filters.- 10.3 ARMA (pole/zero) Filters.- 10.4 Lattice Structures.- 10.5 Spectrum Analysis by Modeling.- Problems.- 11 Quantization Effects.- 11.0 Introduction.- 11.1 Coefficient Quantization.- 11.2 Signal Quantization.- 11.3 Dynamic Range and Scaling.- 11.4 Parallel and Cascade Forms.- 11.5 Limit-Cycle Oscillations.- 11.6 State-Space Structures.- Problems.- 12 Digital Filter Implementation.- 12.0 Introduction.- 12.1 Bit-Serial Arithmetic and VLSI.- 12.2 Distributed Arithmetic.- 12.3 Block IIR Implementations.- Problems.- 13 Filter and Systems Examples.- 13.0 Introduction.- 13.1 Interpolation and Decimation.- 13.2 Hilbert Transformation.- 13.3 Digital Oscillators and Synthesizers.- 13.4 Speech Synthesis.- 13.5 Cepstrum.- Problems.- Answers to Selected Problems.- References.
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