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Digital signal processing (DSP) is used in a wide range of applications including voice processing, image processing, digital communications, the transfer of data over the internet, and image and data compression. Engineers who develop DSP applications today, and in the future, need to understand the fundamental theories and mathematical algorithms, and will need to address implementation issues like mapping algorithms to hardware, computational efficiency, and the effects of finite precision arithmetic. Alexander covers all these topics at a level appropriate for senior undergraduates or first year graduate students, making this text the ideal bridge between the theory and analytical procedures that form the basis for modern DSP, and pratical implementation. 1. Covers fundamental theories and analytical procedures that form the basis of modern DSP 2. Shows practical implementation of DSP in software and hardware 3. Includes Matlab for design and implementation of signal processing algorithms and related discrete time systems 4. Bridges the gap between reference texts and the knowledge needed to implement DSP applications in software or hardware
Table of Contents
Signals and Signal Processing
Discrete Time Signals and Systems
The Z-Transform and its Applications
Frequency Analysis of Signals
Frequency Domain Analysis of Discrete Time Systems
Sampling and Reconstruction of Signals
The Discrete Fourier Transform
The Fast Fourier Transform
Implementation of Discrete Time Systems
Design of Digital Filters
Finite Word Length Effects
Computational Structures for Hardware/Software Implementation
Design of Digital Signal Processing Systems
Multirate Digital Signal Processing
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