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Digital Signal Processing Matlab by Giron Sierra Jose (77 results)
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paperback. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Digital Signal Processing with Matlab Examples, Volume 3: Model-Based Actions and Sparse Representation (Signals and Communication Technology)
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Digital Signal Processing with Matlab Examples, Volume 1: Signals and Data, Filtering, Non-stationary Signals, Modulation (Signals and Communication Technology)
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Digital Signal Processing with Matlab Examples, Volume 2: Decomposition, Recovery, Data-Based Actions (Signals and Communication Technology)
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Digital Signal Processing with Matlab Examples, Volume 3: Model-Based Actions and Sparse Representation (Signals and Communication Technology)
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Digital Signal Processing With Matlab Examples : Model-based Actions and Sparse Representation
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Digital Signal Processing with Matlab Examples, Volume 1: Signals and Data, Filtering, Non-stationary Signals, Modulation (Signals and Communication Technology)
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Add to basketHardcover. Condition: As New. 1. Auflage. Unread, like new. Immediately dispatched from Germany.
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Digital Signal Processing with Matlab Examples, Volume 3
Published by Springer Nature Singapore, Springer Nature Singapore Jul 2018, 2018
ISBN 10: 9811096449 ISBN 13: 9789811096440
Language: English
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Add to basketTaschenbuch. Condition: Neu. Neuware -This is the third volume in a trilogy on modern Signal Processing. The three books provide a concise exposition of signal processing topics, and a guide to support individual practical exploration based on MATLAB programs.This book includes MATLAB codes to illustrate each of the main steps of the theory, offering a self-contained guide suitable for independent study. The code is embedded in the text, helping readers to put into practice the ideas and methods discussed.The book primarily focuses on filter banks, wavelets, and images. While the Fourier transform is adequate for periodic signals, wavelets are more suitable for other cases, such as short-duration signals: bursts, spikes, tweets, lung sounds, etc. Both Fourier and wavelet transforms decompose signals into components. Further, both are also invertible, so the original signals can be recovered from their components. Compressedsensing has emerged as a promising idea. One of the intended applications is networked devices or sensors, which are now becoming a reality; accordingly, this topic is also addressed. A selection of experiments that demonstrate image denoising applications are also included. In the interest of reader-friendliness, the longer programs have been grouped in an appendix; further, a second appendix on optimization has been added to supplement the content of the last chapter.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 448 pp. Englisch.
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