Empirical mode decomposition revisited by multicomponent non-smooth convex optimization-Reference-Cited by-同舟云学术

Empirical mode decomposition revisited by multicomponent non-smooth convex optimization

Published:2014-09 Issue: Volume:102 Page:313-331
ISSN:0165-1684
Container-title:Signal Processing
language:en
Short-container-title:Signal Processing

Author:

Pustelnik Nelly,Borgnat Pierre,Flandrin Patrick

Funder

Agence Nationale de la Recherche

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software,Control and Systems Engineering

Reference64 articles.

1. N. Pustelnik, P. Borgnat, P. Flandrin, A multicomponent proximal algorithm for Empirical Mode Decomposition, in: Proceedings of the European Signal Processing Conference, Bucharest, Romania, 2012, pp. 1880–1884.

2. Non-Linear and Non-Stationary Time Series Analysis;Priestley,1989

3. Théorie et applications de la notion de signal analytique;Ville;Câbles Transm.,1948

4. Wave modulation and breakdown;Meville;J. Fluid Mech.,1983

Cited by 40 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. IRCNN: A novel signal decomposition approach based on iterative residue convolutional neural network;Pattern Recognition;2024-11

2. Ordinal Pattern-Based Mode Decomposition: A New Approach to Time Series Analysis;2024

3. Novel optimization-based bidimensional empirical mode decomposition;Digital Signal Processing;2023-03

4. Data-driven nonstationary signal decomposition approaches: a comparative analysis;Scientific Reports;2023-01-31

5. Theoretical Framework for a Succinct Empirical Mode Decomposition;IEEE Signal Processing Letters;2023