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Editorial: Deep learning for high-dimensional sense, non-linear signal processing and intelligent diagnosis, vol II. [PDF]

Front Psychiatry
Ke H, Cai C, Yao L, Chen D.
europepmc   +1 more source

Modulation classification by Hilbert-Huang Transform [PDF]

2013 21st Signal Processing and Communications Applications Conference (SIU), 2013
The problem of identifying the modulation type of the signals at the receiver, is very helpful intermediate step for spectrum sensing which the cognitive radio systems are commonly used today. In this study, Hilbert-Huang Transform is proposed for identifying the modulation type of digitally modulated signals in the noisy environments.
Yesim Hekim Tanc, Aydin Akan
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Mode Decomposition and the Hilbert-Huang Transform [PDF]

2019 Russian Open Conference on Radio Wave Propagation (RWP), 2019
The paper presents a relatively new method for the analysis of nonstationary and nonlinear processes called Hilbert-Huang transform. The Hilbert-Huang transform consists of two stages: Empirical mode decomposition and Hilbert Spectral Analysis. The empirical mode decomposition is a signal analysis method that separates multi-component signals into ...
V. D. Ompokov, V. V. Boronoev
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A new envelope algorithm of Hilbert–Huang Transform

Mechanical Systems and Signal Processing, 2006
Abstract The algorithm to compute the envelope-line in Hilbert–Huang Transform (HHT) has major drawbacks. This paper first introduces the problem of an envelope-line algorithm in HHT, analyses the shortcomings of two classic algorithms, cubic spline interpolation algorithm and the Akima interpolation algorithm, and then proposes an important theory ...
S.R. Qin, Y.M. Zhong
exaly   +2 more sources

Hilbert-Huang Transform and the Application

2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS), 2020
The short-time fourier transform and wavelet transform are efficient non-stationary signal processing methods, but have their own limit. N.E. Huang put forward a new method, that is, to calculate the instantaneous frequency of the signal through empirical mode decomposition (EMD) of the signal and the Hilbert transform of intrinsic mode functions (IMF).
Yi Liu, Hao An, Shuangshuang Bian
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2D Hilbert-Huang Transform

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
This paper presents a 2D transposition of the Hilbert-Huang Transform (HHT), an empirical data analysis method designed for studying instantaneous amplitudes and phases of non-stationary data. The principle is to adaptively decompose an image into oscillating parts called Intrinsic Mode Functions (IMFs) using an Empirical Mode Decomposition method (EMD)
Jeremy Schmitt, Nelly Pustelnik, Pierre Borgnat, Patrick Flandrin   +3 more
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Parallelizing Hilbert-Huang Transform on a GPU

2010 First International Conference on Networking and Computing, 2010
In this paper, we show parallel implementation of Hilbert-Huang Transform on GPU. This implementation focused on the reducing the computation complexity from O(N) on a single CPU to O(N/P log (N)) on GPU, as well as the use of 'shared-global' switching method to increase performance.
Pulung Waskito, Shinobu Miwa, Yasue Mitsukura, Hironori Nakajo   +3 more
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The summary of Hilbert-Huang transform

SPIE Proceedings, 2013
The widely investigated signals are mainly nonstationary and nonlinear signals, thus it is difficult to get the precise information from the nonstationary and nonlinear signals. Here we introduce a new method to process the nonstationary and nonlinear signals. And this new algorithm makes a good performance on processing the nonstationary and nonlinear
Shi-De Song, Zhi-chao Yao, Xiao-Na Wang
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