Nonlinear Digital Filters

Nonlinear Digital Filters 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. It uses new methods based on symbolic dynamics, enabling the engineer to easily operate reliable nonlinear digital filters. It gives practical, 'real-world' applications of nonlinear digital filters and contains many examples. The book is ideal for professional engineers working with signal processing applications, as well as advanced undergraduates and graduates conducting a nonlinear filter analysis project. 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.

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  • Author : W. K. Ling
  • Publisher : Academic Press
  • Pages : 216 pages
  • ISBN : 9780080550015
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKNonlinear Digital Filters

Nonlinear Digital Filters

Nonlinear Digital Filters
  • Author : W. K. Ling
  • Publisher : Academic Press
  • Release : 27 July 2010
GET THIS BOOKNonlinear Digital Filters

Nonlinear Digital Filters 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. It uses new methods based on symbolic dynamics, enabling the engineer to easily operate reliable nonlinear digital filters. It gives practical, 'real-world'

Nonlinear Digital Filters

Nonlinear Digital Filters
  • Author : Ioannis Pitas,Anastasios N. Venetsanopoulos
  • Publisher : Springer Science & Business Media
  • Release : 14 March 2013
GET THIS BOOKNonlinear Digital Filters

The function of a filter is to transform a signal into another one more suit able for a given purpose. As such, filters find applications in telecommunica tions, radar, sonar, remote sensing, geophysical signal processing, image pro cessing, and computer vision. Numerous authors have considered deterministic and statistical approaches for the study of passive, active, digital, multidimen sional, and adaptive filters. Most of the filters considered were linear although the theory of nonlinear filters is developing rapidly, as it is

Nonlinear Digital Filtering with Python

Nonlinear Digital Filtering with Python
  • Author : Ronald K. Pearson,Moncef Gabbouj
  • Publisher : CRC Press
  • Release : 03 September 2018
GET THIS BOOKNonlinear Digital Filtering with Python

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

Fundamentals of Nonlinear Digital Filtering

Fundamentals of Nonlinear Digital Filtering
  • Author : Jaakko Astola,Pauli Kuosmanen
  • Publisher : CRC Press
  • Release : 10 September 2020
GET THIS BOOKFundamentals of Nonlinear Digital Filtering

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

Introduction to Digital Filters

Introduction to Digital Filters
  • Author : Julius O. Smith
  • Publisher : Julius Smith
  • Release : 17 June 2021
GET THIS BOOKIntroduction 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,

Nonlinear Image Processing

Nonlinear Image Processing
  • Author : Giovanni L. Sicuranza
  • Publisher : Academic Press
  • Release : 17 June 2021
GET THIS BOOKNonlinear Image Processing

This state-of-the-art book deals with the most important aspects of non-linear imaging challenges. The need for engineering and mathematical methods is essential for defining non-linear effects involved in such areas as computer vision, optical imaging, computer pattern recognition, and industrial automation challenges. * Presents the latest developments in a variety of filter design techniques and algorithms * Contains essential information for development of Human Vision Systems (HVS) * Provides foundations for digital imaging and image capture technology

Nonlinear Filters for Image Processing

Nonlinear Filters for Image Processing
  • Author : Edward R. Dougherty,Jaakko Astola
  • Publisher : SPIE Press
  • Release : 25 June 1999
GET THIS BOOKNonlinear Filters for Image Processing

"This text covers key mathematical principles and algorithms for nonlinear filters used in image processing. Readers will gain an in-depth understanding of the underlying mathematical and filter design methodologies needed to construct and use nonlinear filters in a variety of applications. The 11 chapters explore topics of contemporary interest as well as fundamentals drawn from nonlinear filtering's historical roots in mathematical morphology and digital signal processing. This book examines various filter options and the types of applications for which they are

Nonlinear Filtering

Nonlinear Filtering
  • Author : Jitendra R. Raol,Girija Gopalratnam,Bhekisipho Twala
  • Publisher : CRC Press
  • Release : 12 July 2017
GET THIS BOOKNonlinear Filtering

Nonlinear Filtering covers linear and nonlinear filtering in a comprehensive manner, with appropriate theoretic and practical development. Aspects of modeling, estimation, recursive filtering, linear filtering, and nonlinear filtering are presented with appropriate and sufficient mathematics. A modeling-control-system approach is used when applicable, and detailed practical applications are presented to elucidate the analysis and filtering concepts. MATLAB routines are included, and examples from a wide range of engineering applications - including aerospace, automated manufacturing, robotics, and advanced control systems - are

Nonlinear Filters

Nonlinear Filters
  • Author : Hisashi Tanizaki
  • Publisher : Springer Science & Business Media
  • Release : 09 March 2013
GET THIS BOOKNonlinear Filters

Nonlinear and nonnormal filters are introduced and developed. Traditional nonlinear filters such as the extended Kalman filter and the Gaussian sum filter give biased filtering estimates, and therefore several nonlinear and nonnormal filters have been derived from the underlying probability density functions. The density-based nonlinear filters introduced in this book utilize numerical integration, Monte-Carlo integration with importance sampling or rejection sampling and the obtained filtering estimates are asymptotically unbiased and efficient. By Monte-Carlo simulation studies, all the nonlinear filters are

Stochastic Processes and Filtering Theory

Stochastic Processes and Filtering Theory
  • Author : Andrew H. Jazwinski
  • Publisher : Courier Corporation
  • Release : 15 April 2013
GET THIS BOOKStochastic Processes and Filtering Theory

This unified treatment of linear and nonlinear filtering theory presents material previously available only in journals, and in terms accessible to engineering students. Its sole prerequisites are advanced calculus, the theory of ordinary differential equations, and matrix analysis. Although theory is emphasized, the text discusses numerous practical applications as well. Taking the state-space approach to filtering, this text models dynamical systems by finite-dimensional Markov processes, outputs of stochastic difference, and differential equations. Starting with background material on probability theory and

Complex Valued Nonlinear Adaptive Filters

Complex Valued Nonlinear Adaptive Filters
  • Author : Danilo P. Mandic,Vanessa Su Lee Goh
  • Publisher : John Wiley & Sons
  • Release : 20 April 2009
GET THIS BOOKComplex Valued Nonlinear Adaptive Filters

This book was written in response to the growing demand for a text that provides a unified treatment of linear and nonlinear complex valued adaptive filters, and methods for the processing of general complex signals (circular and noncircular). It brings together adaptive filtering algorithms for feedforward (transversal) and feedback architectures and the recent developments in the statistics of complex variable, under the powerful frameworks of CR (Wirtinger) calculus and augmented complex statistics. This offers a number of theoretical performance gains,

Nonlinear Signal Processing

Nonlinear Signal Processing
  • Author : Gonzalo R. Arce
  • Publisher : John Wiley & Sons
  • Release : 03 January 2005
GET THIS BOOKNonlinear Signal Processing

Nonlinear Signal Processing: A Statistical Approach focuses onunifying the study of a broad and important class of nonlinearsignal processing algorithms which emerge from statisticalestimation principles, and where the underlying signals arenon-Gaussian, rather than Gaussian, processes. Notably, byconcentrating on just two non-Gaussian models, a large set of toolsis developed that encompass a large portion of the nonlinear signalprocessing tools proposed in the literature over the past severaldecades. Key features include: * Numerous problems at the end of each chapter to aid developmentand

Adaptive Nonlinear System Identification

Adaptive Nonlinear System Identification
  • Author : Tokunbo Ogunfunmi
  • Publisher : Springer Science & Business Media
  • Release : 05 September 2007
GET THIS BOOKAdaptive Nonlinear System Identification

Focuses on System Identification applications of the adaptive methods presented. but which can also be applied to other applications of adaptive nonlinear processes. Covers recent research results in the area of adaptive nonlinear system identification from the authors and other researchers in the field.

Nonlinear System Identification

Nonlinear System Identification
  • Author : Stephen A. Billings
  • Publisher : John Wiley & Sons
  • Release : 29 July 2013
GET THIS BOOKNonlinear System Identification

Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains describes a comprehensive framework for the identification and analysis of nonlinear dynamic systems in the time, frequency, and spatio-temporal domains. This book is written with an emphasis on making the algorithms accessible so that they can be applied and used in practice. Includes coverage of: The NARMAX (nonlinear autoregressive moving average with exogenous inputs) model The orthogonal least squares algorithm that allows models to be built term by