"I like the way the authors discuss the solutions to the problems. They provide enough steps in their solution to help the student keep up, but not so many steps that the student doesn't have to think...This book is very accessible compared to many texts. It is written clearly...It has excellent integration with MATLAB. I like the fact that the authors have no trouble introducing functions to complement the MATLAB functions."
"This book has developed a suite of MATLAB functions that can be used to determine standard digital filter structures including the direct form, cascade form, parallel form, and lattice form. The MATLAB scripts are provided with detailed explanations. Numerous examples along with block diagrams and discussions are included to show how these functions are used. Overall, I find the authors' treatment of the subject, including the MATLAB functions and examples, is among the best compared with other textbooks I know of...In terms of the treatment of using MATLAB for DSP teaching at the undergraduate level, counting the number and variety of MATLAB examples and problems, this is the most competitive book on the market in my opinion."
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.
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