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Steven R. Long
Researcher at Wallops Flight Facility
Publications - 38
Citations - 25449
Steven R. Long is an academic researcher from Wallops Flight Facility. The author has contributed to research in topics: Wind wave & Hilbert–Huang transform. The author has an hindex of 22, co-authored 38 publications receiving 22109 citations. Previous affiliations of Steven R. Long include Goddard Space Flight Center.
Papers
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Journal ArticleDOI
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Norden E. Huang,Zheng Shen,Steven R. Long,Man-Li C. Wu,Hsing H. Shih,Quanan Zheng,Nai-Chyuan Yen,C. C. Tung,Henry H. Liu +8 more
TL;DR: In this paper, a new method for analysing nonlinear and nonstationary data has been developed, which is the key part of the method is the empirical mode decomposition method with which any complicated data set can be decoded.
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A new view of nonlinear water waves: the Hilbert spectrum
TL;DR: In this paper, Hilbert spectral analysis is proposed as an alternative to wavelet analysis, which provides not only a more precise definition of particular events in time-frequency space, but also more physically meaningful interpretations of the underlying dynamic processes.
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A confidence limit for the empirical mode decomposition and Hilbert spectral analysis
Norden E. Huang,Man-Li C. Wu,Steven R. Long,Samuel S. P. Shen,Wendong Qu,Per Gloersen,Kuang L. Fan +6 more
TL;DR: The confidence limit of the method here termed EMD/HSA (for empirical mode decomposition/Hilbert spectral analysis) is introduced by using various adjustable stopping criteria in the sifting processes of the EMD step to generate a sample set of intrinsic mode functions (IMFs) as mentioned in this paper.
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On the trend, detrending, and variability of nonlinear and nonstationary time series
TL;DR: A simple and logical definition of trend is given for any nonlinear and nonstationary time series as an intrinsically determined monotonic function within a certain temporal span, or a function in which there can be at most one extremum within that temporal span.
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On instantaneous frequency
TL;DR: This paper offers an overview of the difficulties involved in using AS, and two new methods to overcome the difficulties for computing IF, and finds that the NHT and direct quadrature gave the best overall performance.