Nonlinear Digital Filtering With Python
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Nonlinear Digital Filtering With Python
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Author : Ronald K. Pearson
language : en
Publisher: CRC Press
Release Date : 2018-09-03
Nonlinear Digital Filtering With Python written by Ronald K. Pearson and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-03 with Medical categories.
Nonlinear Digital Filtering with Python: An Introduction discusses important structural filter classes including the median filter and a number of its extensions (e.g., weighted and recursive median filters), and Volterra filters based on polynomial nonlinearities. Adopting both structural and behavioral approaches in characterizing and designing nonlinear digital filters, this book: Begins with an expedient introduction to programming in the free, open-source computing environment of Python Uses results from algebra and the theory of functional equations to construct and characterize behaviorally defined nonlinear filter classes Analyzes the impact of a range of useful interconnection strategies on filter behavior, providing Python implementations of the presented filters and interconnection strategies Proposes practical, bottom-up strategies for designing more complex and capable filters from simpler components in a way that preserves the key properties of these components Illustrates the behavioral consequences of allowing recursive (i.e., feedback) interconnections in nonlinear digital filters while highlighting a challenging but promising research frontier Nonlinear Digital Filtering with Python: An Introduction supplies essential knowledge useful for developing and implementing data cleaning filters for dynamic data analysis and time-series modeling.
Nonlinear Digital Filtering With Python
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Author : Ronald Pearson
language : en
Publisher:
Release Date : 2018
Nonlinear Digital Filtering With Python written by Ronald Pearson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Digital filters (Mathematics) categories.
Nonlinear Digital Filtering with Python: An Introduction discusses important structural filter classes including the median filter and a number of its extensions (e.g., weighted and recursive median filters), and Volterra filters based on polynomial nonlinearities. Adopting both structural and behavioral approaches in characterizing and designing nonlinear digital filters, this book: Begins with an expedient introduction to programming in the free, open-source computing environment of Python Uses results from algebra and the theory of functional equations to construct and characterize behaviorally defined nonlinear filter classes Analyzes the impact of a range of useful interconnection strategies on filter behavior, providing Python implementations of the presented filters and interconnection strategies Proposes practical, bottom-up strategies for designing more complex and capable filters from simpler components in a way that preserves the key properties of these components Illustrates the behavioral consequences of allowing recursive (i.e., feedback) interconnections in nonlinear digital filters while highlighting a challenging but promising research frontier Nonlinear Digital Filtering with Python: An Introduction supplies essential knowledge useful for developing and implementing data cleaning filters for dynamic data analysis and time-series modeling.
Fundamentals Of Nonlinear Digital Filtering
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Author : Jaakko Astola
language : en
Publisher: CRC Press
Release Date : 2020-09-10
Fundamentals Of Nonlinear Digital Filtering written by Jaakko Astola and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-10 with Technology & Engineering categories.
Fundamentals of Nonlinear Digital Filtering is the first book of its kind, presenting and evaluating current methods and applications in nonlinear digital filtering. Written for professors, researchers, and application engineers, as well as for serious students of signal processing, this is the only book available that functions as both a reference handbook and a textbook. Solid introductory material, balanced coverage of theoretical and practical aspects, and dozens of examples provide you with a self-contained, comprehensive information source on nonlinear filtering and its applications.
Nonlinear Digital Filters
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Author : Ioannis Pitas
language : en
Publisher: Springer Science & Business Media
Release Date : 1990-01-31
Nonlinear Digital Filters written by Ioannis Pitas and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990-01-31 with Technology & Engineering categories.
To date, no unifying theory has been developed which encompasses nonlinear filters in the same theoretical framework. This book takes strides in that direction by organizing nonlinear filter classes in a concise way and stretching their common properties. Annotation copyrighted by Book News, Inc., Portland, OR
Nonlinear Filters
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Author : Peyman Setoodeh
language : en
Publisher: John Wiley & Sons
Release Date : 2022-03-04
Nonlinear Filters written by Peyman Setoodeh and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-04 with Technology & Engineering categories.
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 allows the book to act as a stand-alone, self-contained reference A thorough exploration of the notion of observability, nonlinear observers, and the theory of optimal nonlinear filtering that bridges the gap between different science and engineering disciplines A profound account of Bayesian filters including Kalman filter and its variants as well as particle filter A rigorous derivation of the smooth variable structure filter as a predictor-corrector estimator formulated based on a stability theorem, used to confine the estimated states within a neighborhood of their true values A concise tutorial on deep learning and reinforcement learning A detailed presentation of the expectation maximization algorithm and its machine learning-based variants, used for joint state and parameter estimation Guidelines for constructing nonparametric Bayesian models from parametric ones Perfect for researchers, professors, and graduate students in engineering, computer science, applied mathematics, and artificial intelligence, Nonlinear Filters: Theory and Applications will also earn a place in the libraries of those studying or practicing in fields involving pandemic diseases, cybersecurity, information fusion, augmented reality, autonomous driving, urban traffic network, navigation and tracking, robotics, power systems, hybrid technologies, and finance.
Digital Filter Design Using Python For Power Engineering Applications
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Author : Shivkumar Venkatraman Iyer
language : en
Publisher: Springer Nature
Release Date : 2020-11-30
Digital Filter Design Using Python For Power Engineering Applications written by Shivkumar Venkatraman Iyer and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-30 with Technology & Engineering categories.
This book is an in-depth description on how to design digital filters. The presentation is geared for practicing engineers, using open source computational tools, while incorporating fundamental signal processing theory. The author includes theory as-needed, with an emphasis on translating to practical application. The book describes tools in detail that can be used for filter design, along with the steps needed to automate the entire process. Breaks down signal processing theory into simple, understandable language for practicing engineers; Provides readers with a highly-practical introduction to digital filter design; Uses open source computational tools, while incorporating fundamental signal processing theory; Describes examples of digital systems in engineering and a description of how they are implemented in practice; Includes case studies where filter design is described in depth from inception to final implementation.
Nonlinear Digital Filters
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Author : Wing-Kuen Ling
language : en
Publisher: Academic Press
Release Date : 2007
Nonlinear Digital Filters written by Wing-Kuen Ling and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Technology & Engineering categories.
This book provides an easy to understand overview of nonlinear behavior in digital filters, showing how it can be utilized or avoided when operating nonlinear digital filters. It gives techniques for analyzing discrete-time systems with discontinuous linearity, enabling the analysis of other nonlinear discrete-time systems, such as sigma delta modulators, digital phase lock loops and turbo coders. Features: • Uses new methods based on symbolic dynamics, enabling the engineer more easily to operate reliable nonlinear digital filters • Gives practical, 'real-world' applications of nonlinear digital filter • Includes many examples, together with Matlab source code available on an accompanying website Nonlinear Digital Filters: Analysis and Applications is ideal for professional engineers working with signal processing applications, as well as advanced undergraduates and graduates conducting a nonlinear filter analysis project. Companion website at http://books.elsevier.com/9780123725363 · Uses new methods based on symbolic dynamics, enabling the engineer more easily to operate reliable nonlinear digital filters · Gives practical, 'real-world' applications of nonlinear digital filter · Includes many examples, together with Matlab source code available on an accompanying website
Linear And Nonlinear Filtering For Scientists And Engineers
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Author : Nasir Uddin Ahmed
language : en
Publisher: World Scientific
Release Date : 1999-01-22
Linear And Nonlinear Filtering For Scientists And Engineers written by Nasir Uddin Ahmed and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-01-22 with Mathematics categories.
The book combines both rigor and intuition to derive most of the classical results of linear and nonlinear filtering and beyond. Many fundamental results recently discovered by the author are included. Furthermore, many results that have appeared in recent years in the literature are also presented. The most interesting feature of the book is that all the derivations of the linear filter equations given in Chapters 3-11, beginning from the classical Kalman filter presented in Chapters 3 and 5, are based on one basic principle which is fully rigorous but also very intuitive and easily understandable. The second most interesting feature is that the book provides a rigorous theoretical basis for the numerical solution of nonlinear filter equations illustrated by multidimensional examples. The book also provides a strong foundation for theoretical understanding of the subject based on the theory of stochastic differential equations.
Exploratory Data Analysis Using R
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Author : Ronald K. Pearson
language : en
Publisher: CRC Press
Release Date : 2018-05-04
Exploratory Data Analysis Using R written by Ronald K. Pearson and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-04 with Business & Economics categories.
Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" – good, bad, and ugly – features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to explore and explain data. The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. The book ends with a chapter on "keeping it all together" that includes managing the R installation, managing files, documenting, and an introduction to reproducible computing. The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no prior exposure to data analysis, modeling, statistics, or programming. it keeps the treatment relatively non-mathematical, even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters, and an instructor's solution manual is available. About the Author: Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera, a property insurance company in Fairfield, California, and he has previously held similar positions in a variety of application areas, including software development, drug safety data analysis, and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineering, the Sciences, and Medicine (Oxford University Press, 2011) and Nonlinear Digital Filtering with Python. He is also the developer of the DataCamp course on base R graphics and is an author of the datarobot and GoodmanKruskal R packages available from CRAN (the Comprehensive R Archive Network).
Mining Imperfect Data
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Author : Ronald K. Pearson
language : en
Publisher: SIAM
Release Date : 2020-09-10
Mining Imperfect Data written by Ronald K. Pearson and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-10 with Computers categories.
It has been estimated that as much as 80% of the total effort in a typical data analysis project is taken up with data preparation, including reconciling and merging data from different sources, identifying and interpreting various data anomalies, and selecting and implementing appropriate treatment strategies for the anomalies that are found. This book focuses on the identification and treatment of data anomalies, including examples that highlight different types of anomalies, their potential consequences if left undetected and untreated, and options for dealing with them. As both data sources and free, open-source data analysis software environments proliferate, more people and organizations are motivated to extract useful insights and information from data of many different kinds (e.g., numerical, categorical, and text). The book emphasizes the range of open-source tools available for identifying and treating data anomalies, mostly in R but also with several examples in Python. Mining Imperfect Data: With Examples in R and Python, Second Edition presents a unified coverage of 10 different types of data anomalies (outliers, missing data, inliers, metadata errors, misalignment errors, thin levels in categorical variables, noninformative variables, duplicated records, coarsening of numerical data, and target leakage). It includes an in-depth treatment of time-series outliers and simple nonlinear digital filtering strategies for dealing with them, and it provides a detailed introduction to several useful mathematical characteristics of important data characterizations that do not appear to be widely known among practitioners, such as functional equations and key inequalities. While this book is primarily for data scientists, researchers in a variety of fields—namely statistics, machine learning, physics, engineering, medicine, social sciences, economics, and business—will also find it useful.