Cyclostationarity: Theory and Methods - II : Contributions to the 7th Workshop on Cyclostationary Systems And Their Applications, Grodek, Poland, 2014

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  • 電子書籍

Cyclostationarity: Theory and Methods - II : Contributions to the 7th Workshop on Cyclostationary Systems And Their Applications, Grodek, Poland, 2014

  • 言語:ENG
  • ISBN:9783319163291
  • eISBN:9783319163307

ファイル: /

Description

This book reports on the latest advances in the analysis of non-stationary signals, with special emphasis on cyclostationary systems. It includes cutting-edge contributions presented at the 7th Workshop on “Cyclostationary Systems and Their Applications,” which was held in Gródek nad Dunajcem, Poland, in February 2014. The book covers both the theoretical properties of cyclostationary models and processes, including estimation problems for systems exhibiting cyclostationary properties, and several applications of cyclostationary systems, including case studies on gears and bearings, and methods for implementing cyclostationary processes for damage assessment in condition-based maintenance operations. It addresses the needs of students, researchers and professionals in the broad fields of engineering, mathematics and physics, with a special focus on those studying or working with nonstationary and/or cyclostationary processes.

 

Table of Contents

Simulation Study of Performance of MBBin Overall Mean Estimation Problemfor APC Time Series.- Parametric Estimation Problem for a Time‐Periodic Signal in a Periodic Noise.- Damage Assessment of Rolling Element Bearing Using Cyclostationary Processing of AE Signals with Electromagnetic Interference.- The Stochastic Recurrence Structure of Geophysical Phenomena.- Influence of Different Signal Characteristics to PAR Model Stability.- Limiting Distributions For Explosive PAR(1) Timeseries with Strongly Mixing Innovation.- PARMA Models with Applications in R.- Multidimensional Analysis of New Zealand Electricity Prices.- Imputation of Missing Observations for Heavy Tailed Cyclostationary Time Series.- The Dependence Structure for Symmetricα‐Stable CARMA(p,q) Processes.

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