This text provides a broad introduction to the field of digital signal processing and contains sufficient material for a two-semester sequence in this multifaceted subject. It is also written with the practicing engineer or scientist in mind, having many observations and examples of practical significance drawn from the author's industrial experience. The first semester, at the junior, senior, or first-year graduate level, could cover chapters 2 through 7 with topics perhaps from chapters 8 and 9, depending upon the background of the students. The only requisite background is linear systems theory for continuous-time systems, including Fourier and Laplace trans- forms. Many students will also have had some previous exposure to discrete-time systems, in which case chapters 2 through 4 may serve to review and expand that preparation. Note, in particular, that knowledge of probability theory and random processes is not required until chapters 10 and 11, except for section 7. 6 on the periodogram. A second, advanced course could utilize material from chapters 8 through 13. A comprehensive one-semester course for suitably prepared graduate students might cover chapters 4 through 9 and additional topics from chapters 10 through 13. Sections marked with a dagger Ct) cover advanced or specialized topics and may be skipped without loss of continuity. Notable features of the book include the following: 1. Numerous useful filter examples early in the text in chapters 4 and 5. 2. State-space representation and structures in chapters 4 and 11.

Rese�a del editor

This text provides a broad introduction to the field of digital signal processing and contains sufficient material for a two-semester sequence in this multifaceted subject. It is also written with the practicing engineer or scientist in mind, having many observations and examples of practical significance drawn from the author's industrial experience. The first semester, at the junior, senior, or first-year graduate level, could cover chapters 2 through 7 with topics perhaps from chapters 8 and 9, depending upon the background of the students. The only requisite background is linear systems theory for continuous-time systems, including Fourier and Laplace trans� forms. Many students will also have had some previous exposure to discrete-time systems, in which case chapters 2 through 4 may serve to review and expand that preparation. Note, in particular, that knowledge of probability theory and random processes is not required until chapters 10 and 11, except for section 7. 6 on the periodogram. A second, advanced course could utilize material from chapters 8 through 13. A comprehensive one-semester course for suitably prepared graduate students might cover chapters 4 through 9 and additional topics from chapters 10 through 13. Sections marked with a dagger Ct) cover advanced or specialized topics and may be skipped without loss of continuity. Notable features of the book include the following: 1. Numerous useful filter examples early in the text in chapters 4 and 5. 2. State-space representation and structures in chapters 4 and 11.

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