Digital Signal Processing with Examples in MATLAB® (Electrical Engineering & Applied Signal Processing Series)
Based on fundamental principles from mathematics, linear systems, and signal analysis, digital signal processing (DSP) algorithms are useful for extracting information from signals collected all around us. Combined with today’s powerful computing capabilities, they can be used in a wide range of application areas, including engineering, communications, geophysics, computer science, information technology, medicine, and biometrics.
Updated and expanded, Digital Signal Processing with Examples in MATLAB®, Second Edition introduces the basic aspects of signal processing and presents the fundamentals of DSP. It also relates DSP to continuous signal processing, rather than treating it as an isolated operation.
New to the Second Edition
- Discussion of current DSP applications
- New chapters on analog systems models and pattern recognition using support vector machines
- New sections on the chirp z-transform, resampling, waveform reconstruction, discrete sine transform, and logarithmic and nonuniform sampling
- A more comprehensive table of transforms
Developing the fundamentals of DSP from the ground up, this bestselling text continues to provide readers with a solid foundation for further work in most areas of signal processing. For novices, the authors review the basic mathematics required to understand DSP systems and offer a brief introduction to MATLAB. They also include end-of-chapter exercises that not only provide examples of the topics discussed, but also introduce topics and applications not covered in the chapters.
Why Read This Book
You should read this book if you want a practical, mathematically grounded introduction to digital signal processing with extensive MATLAB examples so you can quickly prototype and visualize DSP algorithms. It balances theory and application, helping you move from continuous-time concepts to discrete implementations and real numerical experiments.
Who Will Benefit
Upper-level undergraduates, graduate students, and practicing engineers who need a hands-on DSP reference for algorithm development and filter design using MATLAB.
Level: Intermediate — Prerequisites: Single-variable calculus, basic linear algebra, and introductory signals-and-systems concepts; familiarity with MATLAB or Octave is strongly recommended.
Key Takeaways
- Understand the relationship between continuous-time signals and their discrete-time counterparts and the implications of sampling and aliasing.
- Compute and interpret the DTFT, DFT, and use FFT algorithms for efficient spectral analysis.
- Design and analyze FIR and IIR digital filters, including window methods and common IIR design techniques.
- Implement DSP algorithms and experiments in MATLAB, using scripts to visualize frequency responses, filtering, and spectra.
- Apply basic spectral estimation methods and understand resolution, leakage, and trade-offs in practical analysis.
- Evaluate numerical issues in DSP implementations, including quantization effects and stability considerations.
Topics Covered
- 1. Review of Mathematical Tools for DSP (complex numbers, transforms, linear algebra)
- 2. Discrete-Time Signals and Systems
- 3. Sampling Theory and Connections to Continuous-Time Processing
- 4. The Discrete-Time Fourier Transform (DTFT) and Frequency Response
- 5. The Discrete Fourier Transform (DFT) and FFT Algorithms
- 6. FIR Filter Design and Windowing Methods
- 7. IIR Filter Design and Realization Structures
- 8. Filter Implementation Issues and Quantization Effects
- 9. Spectral Analysis and Estimation Techniques
- 10. Multirate Concepts and Decimation/Interpolation (overview)
- 11. Practical MATLAB Examples and Case Studies
- Appendix: MATLAB Tips and Signal Processing Toolbox Usage
Languages, Platforms & Tools
How It Compares
Less comprehensive and mathematically deep than Oppenheim & Schafer or Proakis & Manolakis, but far more hands-on and MATLAB-focused — a good practical complement to those classics.
