Digital Signal Processing Fundamentals

Front Cover
CRC Press, 19. dets 2017 - 904 pages

Now 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.


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Signals and Systems
Ordinary Linear Differential and Difference Equations
Finite Wordlength Effects
Signal Representation and Quantization
AnalogtoDigital Conversion Architectures
Quantization of Discrete Time Signals
Fast Algorithms and Structures
Transform Domain Adaptive Filtering
Adaptive IIR Filters
Adaptive Filters for Blind Equalization
Inverse Problems and Signal Reconstruction
Inverse Problems Statistical Mechanics and Simulated
Signal Recovery from Partial Information
Algorithms for Computed Tomography
Robust Speech Processing as an Inverse Problem

Digital Filtering
PARTV Statistical Signal Processing
Overview of Statistical Signal Processing
Signal Detection and Classification
Spectrum Estimation and Modeling
From Gauss to Wiener
Validation Testing and Noise Modeling
Adaptive Filtering
Convergence Issues in the LMS Adaptive Filter
Robustness Issues in Adaptive Filtering
Recursive LeastSquares Adaptive Filters
Inverse Problems in Array Processing
Channel Equalization as a Regularized Inverse Problem
Inverse Problems in Microphone Arrays
Synthetic Aperture Radar Algorithms
Iterative Image Restoration Algorithms
TimeFrequency and Multirate Signal
Lapped Transforms
Cyclostationary Signal Analysis

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About the author (2017)

Vijay K. Madisetti is a professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology in Atlanta. He teaches graduate and undergraduate courses in digital signal processing and computer engineering, and leads a strong research program in digital signal processing, telecommunications, and computer engineering. Dr. Madisetti received his BTech (Hons) in electronics and electrical communications engineering in 1984 from the Indian Institute of Technology, Kharagpur, India, and his PhD in electrical engineering and computer sciences in 1989 from the University of California at Berkeley. He has authored or edited several books in the areas of digital signal processing, computer engineering, and software systems, and has served extensively as a consultant to industry and the government. He is a fellow of the IEEE and received the 2006 Frederick Emmons Terman Medal from the American Society of Engineering Education for his contributions to electrical engineering.

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