Fundamentals Of Linear Algebra For Signal Processing
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Fundamentals of Linear Algebra for Signal Processing
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.
Linear Algebra for Signal Processing
Author: Adam Bojanczyk
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Signal processing applications have burgeoned in the past decade. During the same time, signal processing techniques have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This trend will continue as many new signal processing applications are opening up in consumer products and communications systems. In particular, signal processing has been making increasingly sophisticated use of linear algebra on both theoretical and algorithmic fronts. This volume gives particular emphasis to exposing broader contexts of the signal processing problems so that the impact of algorithms and hardware can be better understood; it brings together the writings of signal processing engineers, computer engineers, and applied linear algebraists in an exchange of problems, theories, and techniques. This volume will be of interest to both applied mathematicians and engineers.
Linear Algebra, Signal Processing, and Wavelets - A Unified Approach
This book offers a user friendly, hands-on, and systematic introduction to applied and computational harmonic analysis: to Fourier analysis, signal processing and wavelets; and to their interplay and applications. The approach is novel, and the book can be used in undergraduate courses, for example, following a first course in linear algebra, but is also suitable for use in graduate level courses. The book will benefit anyone with a basic background in linear algebra. It defines fundamental concepts in signal processing and wavelet theory, assuming only a familiarity with elementary linear algebra. No background in signal processing is needed. Additionally, the book demonstrates in detail why linear algebra is often the best way to go. Those with only a signal processing background are also introduced to the world of linear algebra, although a full course is recommended. The book comes in two versions: one based on MATLAB, and one on Python, demonstrating the feasibility and applications of both approaches. Most of the MATLAB code is available interactively. The applications mainly involve sound and images. The book also includes a rich set of exercises, many of which are of a computational nature.