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The Hilbert-Huang Transform in Engineering

โœ Scribed by Norden E. Huang, Nii O. Attoh-Okine


Publisher
Taylor & Francis
Year
2005
Tongue
English
Leaves
306
Category
Library

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๐Ÿ“œ SIMILAR VOLUMES


The Hilbert-Huang Transform in Engineeri
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Data used to develop and confirm models suffer from several shortcomings: the total data is too limited, the data are non-stationary, and the data represent nonlinear processes. The Hilbert-Huang transform (HHT) is a relatively new method that has grown into a robust tool for data analysis and is re

The Hilbert-Huang transform and its appl
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The Hilbertโ€“Huang Transform (HHT) represents a desperate attempt to break the suffocating hold on the field of data analysis by the twin assumptions of linearity and stationarity. Unlike spectrograms, wavelet analysis, or the Wignerโ€“Ville Distribution, HHT is truly a time-frequency analysis, but it

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โœ Norden E. Huang, Samuel S. Shen ๐Ÿ“‚ Library ๐Ÿ“… 2005 ๐Ÿ› World Scientific Publishing Company ๐ŸŒ English

The Hilbertโ€“Huang Transform (HHT) represents a desperate attempt to break the suffocating hold on the field of data analysis by the twin assumptions of linearity and stationarity. Unlike spectrograms, wavelet analysis, or the Wignerโ€“Ville Distribution, HHT is truly a time-frequency analysis, but it

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This is a high quality introduction to what is known as the Hilbert-Huang transform. The chapters are well-prepared and nicely ordered. The examples are valuable for those who might consider using this new technique. The relevant software is not provided.

Hilbert Huang Transform and Its Applicat
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This book is written for scientists and engineers who use HHT (Hilbert Huang Transform) to analyze data from nonlinear and non-stationary processes. It can be treated as a HHT user manual and a source of reference for HHT applications. The book contains the basic principle and method of HHT and vari

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<p><P>The Hilbert-Huang Transform ((HHT) is a recently developed technique which is used to analyze nonstationary data. Hydrologic and environmental series are, in the main, analyzed by using techniques which were developed for stationary data. This has led to problems of interpretation of the resul