Digital Signal Processing with Examples in MATLAB®, Second Edition (Electrical Engineering & Applied Signal Processing
In a field as rapidly expanding as digital signal processing, even the topics relevant to the basics change over time both in their nature and their relative importance. It is important, therefore, to have an up-to-date text that not only covers the fundamentals, but that also follows a logical development that leaves no gaps readers must somehow bridge by themselves.
Digital Signal Processing with Examples in MATLAB® is just such a text. The presentation does not focus on DSP in isolation, but relates it to continuous signal processing and treats digital signals as samples of physical phenomena. The author also takes care to introduce important topics not usually addressed in signal processing texts, including the discrete cosine and wavelet transforms, multirate signal processing, signal coding and compression, least squares systems design, and adaptive signal processing. He also uses the industry-standard software MATLAB to provide examples of signal processing, system design, spectral analysis, filtering, coding and compression, and exercise solutions. All of the examples and functions used in the text are available online at www.crcpress.com.
Designed for a one-semester upper-level course but also ideal for self-study and reference, Digital Signal Processing with Examples in MATLAB is complete, self-contained, and rigorous. For basic DSP, it is quite simply the only book you need.
Why Read This Book
You should read this book if you want a hands-on DSP text that ties theory to worked MATLAB examples so you can quickly move from equations to experiments. It emphasizes practical algorithms (filter design, FFT, DCT) and includes less-common but important topics such as multirate processing and wavelets.
Who Will Benefit
Engineers or grad students with some signals & systems background who want an applied DSP reference for algorithm development and MATLAB-based experimentation.
Level: Intermediate — Prerequisites: Basic signals and systems (continuous and discrete), undergraduate calculus and linear algebra, and basic familiarity with MATLAB.
Key Takeaways
- Explain the relationship between continuous and discrete signals and how sampling affects spectral content.
- Design and evaluate FIR and IIR digital filters using MATLAB visualization and tools.
- Compute and use the FFT for spectral analysis and efficient convolution in MATLAB.
- Apply the discrete cosine transform (DCT) and basic wavelet transforms to signal compression and analysis tasks.
- Design and analyze multirate systems (decimation, interpolation, polyphase structures) and filter banks.
- Simulate and prototype DSP algorithms in MATLAB to validate performance on practical signals.
Topics Covered
- 1. Introduction: Continuous vs. Discrete Signals and Systems
- 2. Discrete-Time Signals, Systems, and Linear Time-Invariant Analysis
- 3. Fourier Analysis for Discrete-Time Signals (DTFT, DFT, FFT)
- 4. Z-Transform and Stability; IIR Filter Concepts
- 5. FIR Filters: Design Methods and Windowing
- 6. Filter Implementation and Quantization Effects
- 7. Spectral Analysis and the Use of the FFT
- 8. The Discrete Cosine Transform and Applications
- 9. Multirate Signal Processing: Decimation, Interpolation, Polyphase
- 10. Wavelet Transforms and Multiresolution Analysis
- 11. Practical MATLAB Examples and Case Studies
Languages, Platforms & Tools
How It Compares
More hands-on and MATLAB-focused than Oppenheim & Schafer's Discrete-Time Signal Processing, and less mathematically rigorous than Proakis but more applied and example-driven.
