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.
- Serves as an ideal MATLAB® supplement for any DSP course, supporting a wide range of core texts.
- Introduces key DSP topics early, such as quantization and error characterization, for foundational understanding.
- Simplifies complex concepts like the Parks-McClellan algorithm for easier student comprehension.
- Offers clear coverage of random variables and processes, suitable for both undergraduate and graduate levels.
- Adds graduate-level content on linear prediction, adaptive filters, and communication system applications.
- Emphasizes MATLAB® mastery to enable exploration of advanced DSP problems.
- Highlights MATLAB® functions and scripts with modifiable examples for deeper learning.
- Covers both basic and advanced DSP topics, making it suitable for all levels of learners.
- Provides detailed filter design and analysis, including spectrum analyzers.
- Fully integrates the latest MATLAB® version, including support for large data and new hardware.
- Moves lattice/ladder filter discussion to a more logical placement in the online chapter.
- Adds online chapters on random processes, linear prediction, and adaptive filters with MATLAB® examples.
- Includes LMS and RLS algorithms with practical applications like noise cancellation and system identification.
1. Introduction.
2. Discrete-time signals and systems.
3. The discrete-time fourier analysis.
4. The z-transform.
5. The discrete fourier transform.
6. Implementation of discrete-time filters.
7. FIR filter design.
8. IIR filter design.
9. Sampling rate conversion.
10. Round-off effects in digital filters.
11. Applications in adaptive filtering.
12. Applications in communications
13. Random processes
14. Linear prediction and optimum linear filters
15. Adaptive filters
Vinay K. Ingle, Northeastern University
Dr. Vinay K. Ingle is an Associate Professor of Electrical and Computer Engineering at Northeastern University.
John G. Proakis, Northeastern University
Affiliation: University of California, San Diego and Northeastern University Bio: Dr. John Proakis is an Adjunct Professor at the University of California at San Diego and a Professor Emeritus at Northeastern University.
