Digital Signal Processing FundamentalsNow available in a three-volume set, this updated and expanded edition of the bestselling The Digital Signal Processing Handbook continues to provide the engineering community with authoritative coverage of the fundamental and specialized aspects of information-bearing signals in digital form. Encompassing essential background material, technical details, standards, and software, the second edition reflects cutting-edge information on signal processing algorithms and protocols related to speech, audio, multimedia, and video processing technology associated with standards ranging from WiMax to MP3 audio, low-power/high-performance DSPs, color image processing, and chips on video. Drawing on the experience of leading engineers, researchers, and scholars, the three-volume set contains 29 new chapters that address multimedia and Internet technologies, tomography, radar systems, architecture, standards, and future applications in speech, acoustics, video, radar, and telecommunications. Emphasizing theoretical concepts, Digital Signal Processing Fundamentals provides comprehensive coverage of the basic foundations of DSP and includes the following parts: Signals and Systems; Signal Representation and Quantization; Fourier Transforms; Digital Filtering; Statistical Signal Processing; Adaptive Filtering; Inverse Problems and Signal Reconstruction; and Time–Frequency and Multirate Signal Processing. |
Contents
| 2-1 | |
Chapter 3 Finite Wordlength Effects | 3-1 |
Signal Representation and Quantization | 3-21 |
Chapter 4 On Multidimensional Sampling | 4-1 |
Chapter 5 AnalogtoDIgital Conversion Architectures | 5-1 |
Chapter 6 Quantization of Discrete Time Signals | 6-1 |
Fast Algorithms and Structures | 6-15 |
A Tutorial Review and State of the Art | 7-1 |
Chapter 21 Recrusive LeastSquares Adaptive Filters | 21-1 |
Chapter 22 Transform Domain Adaptive Filtering | 22-1 |
Chapter 23 Adaptive IIR Filters | 23-1 |
Chapter 24 Adaptive Filters for Blind Equalization | 24-1 |
Part VII Inverse Problems and Signal Reconstruction | 24-21 |
Chapter 25 Signal Recovery from Partial Information | 25-1 |
Chapter 26 Algorithms for Computed Tomography | 26-1 |
Chapter 27 Robust Speech Processing as an Inverse Problem | 27-1 |
Chapter 8 Fast Convolution and Filtering | 8-1 |
Chapter 9 Complexity Theory of Transforms in Signal Processing | 9-1 |
Chapter 10 Fast Matrix Computations | 10-1 |
Digital Filtering | 10-11 |
Chapter 11 Digital Filtering | 11-1 |
Statistical Signal Processing | 11-91 |
Chapter 12 Overview of Statistical Signal Processing | 12-1 |
Chapter 13 Signal Detection and Classification | 13-1 |
Chapter 14 Spectrum Estimation and Modeling | 14-1 |
From Gauss to Wiener to Kalman | 15-1 |
Chapter 16 Validation Testing and Noise Modeling | 16-1 |
Chapter 17 Cyclostationary Signal Analysis | 17-1 |
Adaptive Filtering | 17-33 |
Chapter 18 Introduction to Adaptive Filters | 18-1 |
Chapter 19 Convergence Issues in the LMS Adaptive Filter | 19-1 |
Chapter 20 Robustness Issues in Adaptive Filtering | 20-1 |
Chapter 28 Inverse Problems Statistical Mechanics and Simulated Annealing | 28-1 |
Chapter 29 Image Recovery Using the EM Algorithm | 29-1 |
Chapter 30 Inverse Problems in Array Processing | 30-1 |
Chapter 31 Channel Equalization as a Regularized Inverse Problem | 31-1 |
Chapter 32 Inverse Problems in Microphone Arrays | 32-1 |
Chapter 33 Synthetic Aperture Radar Algorithms | 33-1 |
Chapter 34 Iterative Image Restoration Algorithms | 34-1 |
TimeFrequency and Multirate Signal Processing | 34-21 |
Chapter 35 Wavelets and Filter Banks | 35-1 |
Chapter 36 Filter Bank Design | 36-1 |
Chapter 37 TimeVarying AnalysisSynthesis Filter Banks | 37-1 |
Chapter 38 Lapped Transforms | 38-1 |
| I-1 | |
Back cover | I-21 |