Advanced signal processing: a concise guide

By:

Najmi, Amir-Homayoon

Contributor(s):

Moon, Todd [co-author]

Publication details: New York: McGraw Hill, 2020Description: xiii; 328p.: hbk: 24cmISBN:

9781260458930

Subject(s):

DDC classification:

621.3822 NAJ

Summary: This text is an expanded version of a graduate course on advanced signal processing at the Johns Hopkins University Whiting School program for professionals, with students from electrical engineering, physics, computer and data science, and mathematics backgrounds. It covers the theory underlying applications in statistical signal processing, including spectral estimation, linear prediction, adaptive filters, and optimal processing of uniform spatial arrays. Unique among books on the subject, it also includes a comprehensive introduction to modern neural networks with examples in time series forecasting and image classification. Coverage includes: -Mathematical structures of signal spaces and matrix factorizations -Linear time-invariant systems and transforms -Least squares filters -Random variables, estimation theory, and random processes -Spectral estimation and autoregressive signal models -Linear prediction and adaptive filters -Optimal processing of linear arrays -Neural networks https://www.accessengineeringlibrary.com/content/book/9781260458930?implicit-login=true

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Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Books	IIT Gandhinagar	General	621.3822 NAJ (Browse shelf(Opens below))	1	Available		033070

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Includes bibliographical references and index.

This text is an expanded version of a graduate course on advanced signal processing at the Johns Hopkins University Whiting School program for professionals, with students from electrical engineering, physics, computer and data science, and mathematics backgrounds. It covers the theory underlying applications in statistical signal processing, including spectral estimation, linear prediction, adaptive filters, and optimal processing of uniform spatial arrays. Unique among books on the subject, it also includes a comprehensive introduction to modern neural networks with examples in time series forecasting and image classification.
Coverage includes:
-Mathematical structures of signal spaces and matrix factorizations
-Linear time-invariant systems and transforms
-Least squares filters
-Random variables, estimation theory, and random processes
-Spectral estimation and autoregressive signal models
-Linear prediction and adaptive filters
-Optimal processing of linear arrays
-Neural networks

https://www.accessengineeringlibrary.com/content/book/9781260458930?implicit-login=true

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