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Fundamentals of linear algebra for signal processing

Record Type:	Electronic resources : Monograph/item
Title/Author:	Fundamentals of linear algebra for signal processingby James Reilly.
Author:	Reilly, James.
Published:	Cham :Springer Nature Switzerland :2025.
Description:	xvii, 321 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Signal processingMathematics.
Online resource:	https://doi.org/10.1007/978-3-031-68915-4
ISBN:	9783031689154$q(electronic bk.)

Fundamentals of linear algebra for signal processing
Reilly, James.

Fundamentals of linear algebra for signal processing[electronic resource] /by James Reilly. - Cham :Springer Nature Switzerland :2025. - xvii, 321 p. :ill. (some col.), digital ;24 cm.

Chapter 1. Fundamental Concepts -- Chapter 2. Eigenvalues, Eigenvectors and Correlation -- Chapter 3. The Singular Value Decomposition(SVD) -- Chapter 4. The Quadratic Form -- Chapter 5. Gaussian Elimination and Associated Numerical Issues -- Chapter 6. The QR Decomposition -- Chapter 7. Linear Least Squares Estimation -- Chapter 8. The Rank Deficient Least Squares Problem -- Chapter 9. Regularization -- Chapter 10. Toeplitz Systems.

Signal processing is ubiquitous in many fields of science and engineering. This textbook is tailored specifically for graduate students and presents linear algebra, which is requisite knowledge in these fields, in a form explicitly targeted to signal processing and related disciplines. Written by an experienced author with over 35 years of expertise in signal processing research and teaching, this book provides the necessary foundation in a focused and accessible manner, offering a practical approach to linear algebra without sacrificing rigor. Emphasis is placed on a deeper conceptualization of material specific to signal processing so students may more readily adapt this knowledge to actual problems in the field. Since other emerging areas, such as machine learning, are closely related to signal processing, the book also provides the necessary background in this discipline. The book includes many examples and problems relevant to signal processing, offering explanations and insights that are difficult to find elsewhere. Fundamentals of Linear Algebra for Signal Processing will allow students to master the essential knowledge of linear algebra for signal processing. It is also an essential guide for researchers and practitioners in biomedical, electrical, chemical engineering, and related disciplines. All necessary algebraic concepts commonly used in the signal processing context are covered; A rich set of carefully-constructed signal processing examples is provided throughout the text; Designed as a primary text in linear algebra for engineering and science graduate students.

ISBN: 9783031689154$q(electronic bk.)

Standard No.: 10.1007/978-3-031-68915-4doiSubjects--Topical Terms:

183873
Signal processing
--Mathematics.

LC Class. No.: TK5102.9

Dewey Class. No.: 621.38220151

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505 0 $a Chapter 1. Fundamental Concepts -- Chapter 2. Eigenvalues, Eigenvectors and Correlation -- Chapter 3. The Singular Value Decomposition(SVD) -- Chapter 4. The Quadratic Form -- Chapter 5. Gaussian Elimination and Associated Numerical Issues -- Chapter 6. The QR Decomposition -- Chapter 7. Linear Least Squares Estimation -- Chapter 8. The Rank Deficient Least Squares Problem -- Chapter 9. Regularization -- Chapter 10. Toeplitz Systems.
520 $a Signal processing is ubiquitous in many fields of science and engineering. This textbook is tailored specifically for graduate students and presents linear algebra, which is requisite knowledge in these fields, in a form explicitly targeted to signal processing and related disciplines. Written by an experienced author with over 35 years of expertise in signal processing research and teaching, this book provides the necessary foundation in a focused and accessible manner, offering a practical approach to linear algebra without sacrificing rigor. Emphasis is placed on a deeper conceptualization of material specific to signal processing so students may more readily adapt this knowledge to actual problems in the field. Since other emerging areas, such as machine learning, are closely related to signal processing, the book also provides the necessary background in this discipline. The book includes many examples and problems relevant to signal processing, offering explanations and insights that are difficult to find elsewhere. Fundamentals of Linear Algebra for Signal Processing will allow students to master the essential knowledge of linear algebra for signal processing. It is also an essential guide for researchers and practitioners in biomedical, electrical, chemical engineering, and related disciplines. All necessary algebraic concepts commonly used in the signal processing context are covered; A rich set of carefully-constructed signal processing examples is provided throughout the text; Designed as a primary text in linear algebra for engineering and science graduate students.
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