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DIGITAL SIGNAL PROCESSING USING MATLAB® : A PROBLEM SOLVING COMPANION, 4TH EDITION
Publisher:
CENGAGE INDIA
| Author:
VINAY K. INGLE | JOHN G. PROAKIS
| Language:
English
| Format:
Paperback
Publisher:
CENGAGE INDIA
Author:
VINAY K. INGLE | JOHN G. PROAKIS
Language:
English
Format:
Paperback
₹653
Save: 10%
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Ships within:
1-4 Days
In stock
| Book Type |
|---|
ISBN:
Page Extent:
672
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.
Description
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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