Digital Signal Processing using MATLAB (Activate Learning with these NEW titles from Engineering!)
Focus on the development, implementation, and application of modern DSP techniques with DIGITAL SIGNAL PROCESSING USING MATLAB, 3E. Written in an engaging, informal style, this edition immediately captures your attention and encourages you to explore each critical topic. Every chapter starts with a motivational section that highlights practical examples and challenges that you can solve using techniques covered in the chapter. Each chapter concludes with a detailed case study example, a chapter summary with learning outcomes, and practical homework problems cross-referenced to specific chapter sections for your convenience. DSP Companion software accompanies each book to enable further investigation. The DSP Companion software operates with MATLAB and provides intriguing demonstrations as well as interactive explorations of analysis and design concepts.
Why Read This Book
You will gain a hands-on, application-driven grounding in modern DSP techniques and learn how to implement them quickly using MATLAB and the book’s DSP Companion demos. The text emphasizes practical examples, end-of-chapter case studies, and clear motivation so you can move from theory to working algorithms for audio, speech, radar, and communications problems.
Who Will Benefit
Upper-level undergraduate or graduate students in electrical engineering, and practicing engineers who need a practical, MATLAB-centered guide to DSP algorithms and real-world signal-processing applications.
Level: Intermediate — Prerequisites: Introductory calculus and linear algebra, basic signals & systems (discrete-time signals, sampling, and linear systems), and basic familiarity with MATLAB.
Key Takeaways
- Implement core DSP algorithms in MATLAB, including FFT-based spectral analysis and efficient DFT computation
- Design and evaluate digital filters (FIR and IIR) for audio, communications, and radar applications
- Apply time‑frequency methods such as wavelets and short-time Fourier transforms to analyze nonstationary signals
- Develop and test adaptive filtering routines (LMS, RLS) for noise cancellation and channel equalization
- Perform statistical signal-processing tasks including estimation, detection, and practical spectral estimation techniques
- Build end-to-end processing chains for audio/speech and radar/communications case studies using real-data examples
Topics Covered
- 1. Introduction to Digital Signal Processing and MATLAB
- 2. Discrete-Time Signals and Systems — Review and MATLAB Tools
- 3. The z-Transform and Discrete-Time Fourier Transform
- 4. The Discrete Fourier Transform (DFT) and FFT Algorithms
- 5. Digital Filter Design: FIR Techniques and Window Methods
- 6. Digital Filter Design: IIR Filters and Bilinear Transform
- 7. Spectral Analysis and Parametric Methods
- 8. Short-Time Analysis and Time-Frequency Representations
- 9. Wavelets and Multiresolution Signal Analysis
- 10. Adaptive Filtering: LMS, NLMS, and RLS Algorithms
- 11. Statistical Signal Processing: Estimation and Detection
- 12. Audio and Speech Processing Applications
- 13. Radar and Communications Signal Processing Applications
- 14. Case Studies, Projects, and Using the DSP Companion with MATLAB
Languages, Platforms & Tools
How It Compares
More application- and MATLAB-focused than Oppenheim & Willsky's Discrete-Time Signal Processing and less theory-heavy than Proakis & Manolakis, making it a pragmatic complement to those classics.
