Imperial College London
Portrait Picture

ProfessorDaniloMandic

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Machine Intelligence
 
 
 
//

Contact

 

+44 (0)20 7594 6271d.mandic Website

 
 
//

Assistant

 

Miss Vanessa Rodriguez-Gonzalez +44 (0)20 7594 6267

 
//

Location

 

813Electrical EngineeringSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@inproceedings{Brajović:2018:10.1109/MECO.2018.8406053,
author = {Brajovi, M and Stankovi, L and Dakovi, M and Mandi, D},
doi = {10.1109/MECO.2018.8406053},
pages = {1--4},
title = {Additive noise influence on the bivariate two-component signal decomposition},
url = {http://dx.doi.org/10.1109/MECO.2018.8406053},
year = {2018}
}
Download

RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Decomposition of multicomponent signals overlapping in the time-frequency domain is a challenging research topic. To solve this problem, many approaches have been proposed so far, but only to be efficient for some particular signal classes. Recently, we have proposed a decomposition approach for multivariate multicomponent signals, based on the time-frequency signal analysis and concentration measures. The proposed solution is efficient for multivariate signals partially overlapped in the time-frequency plane regardless of the non-stationarity type of particular signal components. This decomposition approach is shown to be also efficient in noisy environments. In this paper, we investigate the limits of the decomposition efficiency subject to the signal-to-noise ratio and initial phase differences between the signals from different channels. The paper is focused on the decomposition of bivariate two-component signals.
AU  - Brajovi,M
AU  - Stankovi,L
AU  - Dakovi,M
AU  - Mandi,D
DO  - 10.1109/MECO.2018.8406053
EP  - 4
PY  - 2018///
SP  - 1
TI  - Additive noise influence on the bivariate two-component signal decomposition
UR  - http://dx.doi.org/10.1109/MECO.2018.8406053
ER  -
Download