Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Nasser Mourad"'
Autor:
Nasser Mourad
Publikováno v:
IET Signal Processing, Vol 15, Iss 5, Pp 323-336 (2021)
Abstract Penalized least squares (PLS) is a popular data smoothing technique. However, existing PLS smoothing algorithms behave as low‐pass filters (LPF), and, hence, they may introduce distortions to bandpass signals. These algorithms also have di
Externí odkaz:
https://doaj.org/article/9362cb416266499dbe109de8ffcf516e
Autor:
Nasser Mourad
Publikováno v:
Digital Signal Processing. 137:104024
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::be2a3982668a029c923d53ffdc7b175f
https://doi.org/10.1016/j.dsp.2023.104024
https://doi.org/10.1016/j.dsp.2023.104024
Autor:
Nasser Mourad
Publikováno v:
IET Signal Processing. 13:596-605
A new algorithm for denoising electrocardiogram (ECG) signals contaminated by additive white Gaussian noise is proposed in this study. In the proposed algorithm, a clean ECG signal is modelled as a combination of a smooth signal representing the P-wa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c36fd785719240c5c2f60f455643d865
https://doi.org/10.1049/iet-spr.2018.5458
https://doi.org/10.1049/iet-spr.2018.5458
Autor:
Nasser Mourad
Publikováno v:
Biomedical Signal Processing and Control. 50:62-71
A new algorithm for denoising ECG signals contaminated by additive white Gaussian noise is proposed here. In the proposed algorithm, a clean ECG signal is modeled as a combination of two morphologically different signals. The first signal is a spike-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d8f3e60d7b3d89ae3a8bd897563e05b
https://doi.org/10.1016/j.bspc.2019.01.018
https://doi.org/10.1016/j.bspc.2019.01.018
Autor:
Nasser Mourad
Publikováno v:
IET Signal Processing. 12:549-557
A new algorithm for correcting short-duration artefacts in single-channel EEG recording is proposed here. A short-duration artefact has the characteristics of a group-sparse (GS) signal, i.e. a sparse signal in which its non-zero entries tend to conc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7293d2c2c2612ee0f5bc65cc2fbb94d7
https://doi.org/10.1049/iet-spr.2017.0344
https://doi.org/10.1049/iet-spr.2017.0344
Publikováno v:
Signal Processing. 131:120-133
In this paper we propose the Majorization-Minimization Blind Spare Source Separation (MM-BSSS) algorithm for solving the blind source separation (BSS) problem when the source signals are known a priori to be sparse, or can be sparsely represented in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f14d9c5460a673230333bd70d9ebd15c
https://doi.org/10.1016/j.sigpro.2016.08.015
https://doi.org/10.1016/j.sigpro.2016.08.015
Autor:
Nasser Mourad
Publikováno v:
Biomedical Signal Processing and Control. 59:101935
A new algorithm for denoising ECG data contaminated by wideband noise signals (e.g., muscle artifacts and instrumentation noise) is proposed in this paper. In contrast to the wideband noise, a clean ECG signal s ( t ) has the property that its p th o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ed2522247b99da69d46f9104cbdf2471
https://doi.org/10.1016/j.bspc.2020.101935
https://doi.org/10.1016/j.bspc.2020.101935
Publikováno v:
Arabian Journal of Geosciences. 8:3565-3585
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::62047acdabec07db5044097e5bdfbc8c
https://doi.org/10.1007/s12517-014-1466-z
https://doi.org/10.1007/s12517-014-1466-z
Publikováno v:
2016 IEEE 12th International Colloquium on Signal Processing & Its Applications (CSPA).
Orthogonal Matching Pursuit (OMP) is the most popular greedy algorithm that has been developed to find a sparse solution vector to an under-determined linear system of equations. OMP follows the projection procedure to identify the indices of the sup
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e1815cc89cb6373e0b33a5cdc51f03c2
https://doi.org/10.1109/cspa.2016.7515840
https://doi.org/10.1109/cspa.2016.7515840