Digital Signal Processing Using MATLAB: A Problem Solving Companion (Activate Learning with these NEW titles from Engine
Help your student learn to maximize MATLAB as a computing tool to explore traditional Digital Signal Processing (DSP) topics, solve problems and gain insights. An extremely valuable supplementary text, DIGITAL SIGNAL PROCESSING USING MATLAB: A PROBLEM SOLVING COMPANION, 4E greatly expands the range and complexity of problems that students can effectively study in your course. Since DSP applications are primarily algorithms implemented on a DSP processor or software, they require a significant amount of programming. Using interactive software, such as MATLAB, makes it possible to place more emphasis on learning new and difficult concepts than on programming algorithms. This engaging supplemental text introduces interesting practical examples and shows students how to explore useful problems. New, optional online chapters introduce advanced topics, such as optimal filters, linear prediction, and adaptive filters, to further prepare your students for graduate-level success.
Why Read This Book
You will learn to use MATLAB as a powerful laboratory for exploring and solving a wide range of DSP problems, so you can focus on concepts instead of low-level programming. The book emphasizes hands-on problem solving and practical examples—letting you prototype filters, run spectral analyses, and experiment with audio, speech, radar, and communications algorithms quickly.
Who Will Benefit
Undergraduate or graduate engineering students and practicing engineers who already know basic signals & systems and want a MATLAB-centered, problem-driven companion to deepen their practical DSP skills.
Level: Intermediate — Prerequisites: Introductory signals & systems (continuous and discrete-time), basic calculus and linear algebra, and elementary programming experience (familiarity with MATLAB is helpful but not strictly required).
Key Takeaways
- Implement and interpret FFT-based spectral analyses and use MATLAB to visualize frequency-domain behavior
- Design, analyze, and implement FIR and IIR digital filters using practical MATLAB routines and filter structures
- Apply adaptive filtering (LMS, RLS) and evaluate performance for noise cancellation and system identification
- Use wavelet transforms and multiresolution analysis in MATLAB for time–frequency signal processing tasks
- Perform statistical signal processing tasks such as power spectral density estimation, parameter estimation, and detection
- Prototype DSP algorithms for audio/speech, radar, and communications scenarios and evaluate them with realistic test problems
Topics Covered
- Introduction and MATLAB Fundamentals for DSP
- Review of Continuous and Discrete-Time Signals and Systems
- Sampling, Quantization, and Time-Domain Signal Operations
- The Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT)
- Windowing, Spectral Leakage, and Practical Spectral Analysis
- FIR Filter Design and Implementation
- IIR Filter Design and Realization Structures
- Linear Phase, Filter Trade-offs, and Filter Optimization
- Adaptive Filtering: LMS, NLMS, and RLS Algorithms
- Wavelets and Multiresolution Signal Analysis
- Statistical Signal Processing: Estimation and Detection
- Communications Signal Processing: Modulation, Matched Filtering, Channel Effects
- Audio and Speech Processing Applications
- Radar Signal Processing and Target Detection Examples
- MATLAB Projects, Problem Sets, and Appendix: MATLAB/Toolbox Reference
Languages, Platforms & Tools
How It Compares
Similar in hands-on spirit to Richard Lyons' Understanding Digital Signal Processing but focused on MATLAB problem solving; compared with Oppenheim & Schafer, this title is more application- and MATLAB-oriented and less theory-focused.
