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Mathematics for Circuits and Filters
Every engineering professional needs a practical, convenient mathematics resource, without extensive theory and proofs. Mathematics for Circuits and Filters stresses the fundamental theory behind professional applications, making an excellent, flexible resource that enables easy access to the information needed to deal with circuits and filters. The sections feature frequent examples and illustrations, reinforcing the basic theory. The examples also demonstrate applications of the concepts. References at the end of each section are drawn from not only traditional sources, but from relevant, nontraditional ones as well, including software, databases, standards, seminars, and conferences. This leads advanced researchers quickly to the data they may need for more specialized problems. An international panel of experts developed the chapters for practicing engineers, concentrating on the problems that they encounter the most and have the most difficulty with. Mathematics for Circuits and Filters aids in the engineer's understanding and recall of vital mathematical concepts and acts as the engineer's primary resource when looking for solutions to a wide range of problems.
Fundamentals of Stochastic Filtering
This book provides a rigorous mathematical treatment of the non-linear stochastic filtering problem using modern methods. Particular emphasis is placed on the theoretical analysis of numerical methods for the solution of the filtering problem via particle methods. The book should provide sufficient background to enable study of the recent literature. While no prior knowledge of stochastic filtering is required, readers are assumed to be familiar with measure theory, probability theory and the basics of stochastic processes. Most of the technical results that are required are stated and proved in the appendices. Exercises and solutions are included.
Filtering Theory
Authors are experts in the field and have published books as well as articles in first-rate journals Comprehensive resource that contains many MATLAB-based examples
Bayesian Filtering and Smoothing
A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.
Mathematics of Kalman-Bucy Filtering
The second edition has not deviated significantly from the first. The printing of this edition, however, has allowed us to make a number of corrections which escaped our scrutiny at the time of the first printing, and to generally improve and tighten our presentation of the material. Many of these changes were suggested to us by colleagues and readers and their kindness in doing so is greatly appreciated. Delft, The Netherlands and P. A. Ruymgaart Buffalo, New York, December, 1987 T. T. Soong Preface to the First Edition Since their introduction in the mid 1950s, the filtering techniques developed by Kalman, and by Kalman and Bucy have been widely known and widely used in all areas of applie...
Introduction to Digital Filters
A digital filter can be pictured as a "black box" that accepts a sequence of numbers and emits a new sequence of numbers. In digital audio signal processing applications, such number sequences usually represent sounds. For example, digital filters are used to implement graphic equalizers and other digital audio effects. This book is a gentle introduction to digital filters, including mathematical theory, illustrative examples, some audio applications, and useful software starting points. The theory treatment begins at the high-school level, and covers fundamental concepts in linear systems theory and digital filter analysis. Various "small" digital filters are analyzed as examples, particularly those commonly used in audio applications. Matlab programming examples are emphasized for illustrating the use and development of digital filters in practice.
Nonlinear Filters
NONLINEAR FILTERS Discover the utility of using deep learning and (deep) reinforcement learning in deriving filtering algorithms with this insightful and powerful new resource Nonlinear Filters: Theory and Applications delivers an insightful view on state and parameter estimation by merging ideas from control theory, statistical signal processing, and machine learning. Taking an algorithmic approach, the book covers both classic and machine learning-based filtering algorithms. Readers of Nonlinear Filters will greatly benefit from the wide spectrum of presented topics including stability, robustness, computability, and algorithmic sufficiency. Readers will also enjoy: Organization that allow...
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