Discrete Random Signal Processing and Filtering Primer with MATLAB (Electrical Engineering & Applied Signal Processing)

Computers & Technology

Poularikas, Alexander D.

Networking & Cloud Computing

CRC-Press

The book discusses receiving signals that most electrical engineers detect and study. The vast majority of signals could never be detected due to random additive signals, known as noise, that distorts them or completely overshadows them. Such examples include an audio signal of the pilot communicating with the ground over the engine noise or a bioengineer listening for a fetus’ heartbeat over the mother’s. The text presents the methods for extracting the desired signals from the noise. Each new development includes examples and exercises that use MATLAB to provide the answer in graphic forms for the reader's comprehension and understanding.

Understanding Digital Signal Processing with MATLAB® and Solutions

The second edition of this comprehensive text continues to explore basic concepts and applications of signal system analysis. It is assumed students have completed a course in circuits and have a proficiency in the use of differential equations. Modern applications are included on such topics as optical system processing and photonics. The text contains abundant examples, illustrations, and problems. Detailed appendixes cover complex variable theory and matrix theory. The second edition features a combined section on optical and acousto-optical principles and applications in signal processing studies. New discussions on the Hartley transform and the discrete Hartley transform have been added where appropriate, and new examples from a variety of disciplines are included. A solutions manual is available to qualified instructors.

Signals and systems

Engineering & Transportation

Engineering

PWS Engineering

This text provides an essential parallel treatment of continuous time and discrete time methods in signals and signal processing modelling and simulation, digital signal processing, filter theory and digital filters. Every topic is illustrated by applications and examples from both the students' background in electrical circuits, and from practical unrelated fields, such as: economics, physics, biomedical engineering, chemistry and heat transfer. The authors take into consideration the fact that students' previous mathematical studies may be too limited for many of the topics considered and, therefore, all essential mathematical details are included. Where a given development might involve extensive mathematical demands, this is indicated by use of a symbol at the section title. An appendix on matrix theory is included as support material to state equation development. This book should be of interest to degree and diploma students on courses in signals and systems; a knowledge of circuit theory and mathematics through differential equations is a prerequisite.

Elements of Signals and Systems

Seely, Samuel

Kent Pub. Co.

Signals and Systems (Pws-Kent Series in Electrical Engineering)

PWS Pub. Co.

Signals and Systems Primer with MATLAB® equally emphasizes the fundamentals of both analog and digital signals and systems. To ensure insight into the basic concepts and methods, the text presents a variety of examples that illustrate a wide range of applications, from microelectromechanical to worldwide communication systems. It also provides MATLAB functions and procedures for practice and verification of these concepts. Taking a pedagogical approach, the author builds a solid foundation in signal processing as well as analog and digital systems. The book first introduces orthogonal signals, linear and time-invariant continuous-time systems, discrete-type systems, periodic signals represented by Fourier series, Gibbs's phenomenon, and the sampling theorem. After chapters on various transforms, the book discusses analog filter design, both finite and infinite impulse response digital filters, and the fundamentals of random digital signal processing, including the nonparametric spectral estimation. The final chapter presents different types of filtering and their uses for random digital signal processing, specifically, the use of Wiener filtering and least mean squares filtering. Balancing the study of signals with system modeling and interactions, this text will help readers accurately develop mathematical representations of systems.

Signals and Systems Primer with MATLAB (Electrical Engineering & Applied Signal Processing Series)

Signals and Systems

Science & Math

Technology

Krieger Pub. Co

Understanding Digital Signal Processing with MATLAB® and Solutions (The Electrical Engineering and Applied Signal Processing)

Adaptive Filtering

Adaptive filters are used in many diverse applications, appearing in everything from military instruments to cellphones and home appliances. Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB covers the core concepts of this important field, focusing on a vital part of the statistical signal processing area�the least mean square (LMS) adaptive filter. This largely self-contained text: Discusses random variables, stochastic processes, vectors, matrices, determinants, discrete random signals, and probability distributions Explains how to find the eigenvalues and eigenvectors of a matrix and the properties of the error surfaces Explores the Wiener filter and its practical uses, details the steepest descent method, and develops the Newton�s algorithm Addresses the basics of the LMS adaptive filter algorithm, considers LMS adaptive filter variants, and provides numerous examples Delivers a concise introduction to MATLAB, supplying problems, computer experiments, and more than 110 functions and script files Featuring robust appendices complete with mathematical tables and formulas, Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB clearly describes the key principles of adaptive filtering and effectively demonstrates how to apply them to solve real-world problems.

Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB®

Because of the wide use of adaptive filtering in digital signal processing and, because most of the modern electronic devices include some type of an adaptive filter, a text that brings forth the fundamentals of this field was necessary. The material and the principles presented in this book are easily accessible to engineers, scientists, and students who would like to learn the fundamentals of this field and have a background at the bachelor level. Adaptive Filtering Primer with MATLAB clearly explains the fundamentals of adaptive filtering supported by numerous examples and computer simulations. The authors introduce discrete-time signal processing, random variables and stochastic processes, the Wiener filter, properties of the error surface, the steepest descent method, and the least mean square (LMS) algorithm. They also supply many MATLAB functions and m-files along with computer experiments to illustrate how to apply the concepts to real-world problems. The book includes problems along with hints, suggestions, and solutions for solving them. An appendix on matrix computations completes the self-contained coverage. With applications across a wide range of areas, including radar, communications, control, medical instrumentation, and seismology, Adaptive Filtering Primer with MATLAB is an ideal companion for quick reference and a perfect, concise introduction to the field.

Adaptive Filtering Primer with MATLAB (Electrical Engineering Primer Series)

Ramadan, Zayed M.

Physics

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