Jonatan Lerga

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A method for components instantaneous frequency (IF) estimation of multicomponent signals in low signal-to-noise ratio (SNR) is proposed. The method combines a new proposed modification of a blind source separation (BSS) algorithm for components separation, with the improved adaptive IF estimation procedure based on the modified sliding pairwise(More)
In this letter, we have proposed a signal denoising method based on a modification of the intersection of confidence intervals (ICI) rule. The ICI rule is complemented by the relative intersection of confidence intervals length which is used as an additional criterion for adaptive filter support selection. It is shown that the proposed method outperforms(More)
A method for the instantaneous frequency (IF) estimation of a monocomponent nonlinear frequency modulated (FM) signal based on the pseudo Wigner-Ville distribution (PWVD) with an adaptive window width is presented. In order to improve the IF estimation accuracy, the original sliding pair-wise intersection of confidence intervals (SPICI) rule has been(More)
Stochastic electroencephalogram (EEG) signals are known to be nonstationary and often multicomponential. Detecting and extracting their components may help clinicians to localize brain neurological dysfunctionalities for patients with motor control disorders due to the fact that movement-related cortical activities are reflected in spectral EEG changes. A(More)
This study proposes an adaptive method for components instantaneous frequency (IF) estimation of noisy non-stationary multicomponent signals, combined with the components time-support estimation method based on the shorttime Rényi entropy (STRE). Components localisation and separation are done using a double-direction component tracking and extraction(More)
The performance of the recently proposed modification of the sliding pair-wise intersection of confidence intervals (SPICI) method for the instantaneous frequency (IF) estimation, based on the pseudo Wigner-Ville distribution (PWVD) with an adaptive window width, is analyzed on a noisy monocomponent nonlinear frequency modulated (FM) signal. The modified(More)
In this paper we have proposed a novel method for image denoising using local polynomial approximation (LPA) combined with the relative intersection of confidence intervals (RICI) rule. The algorithm performs separable column-wise and row-wise image denoising (i.e., independently by rows and by columns), combining the obtained results into the final image(More)
OBJECTIVES Inflammation is an underlying mechanism behind fibrotic processes and differentiation of cells into myofibroblasts. Presented study therefore provides new data on activation of autoimmune and inflammatory immune response genes that accompany activation of p38 and cell differentiation in primary cells derived from Dupuytren's disease (DD)(More)
This paper proposes an improved adaptive algorithm for components localization and extraction from a noisy multicompo-nent signal time-frequency distribution (TFD). The algorithm, based on the intersection of confidence intervals (ICI) rule, does not require any a priori knowledge of signal components and their mixture. Its efficiency is significantly(More)
In this paper we have analyzed the performance of the recently introduced LPA-RICI signal denoising method. The RICI method is an extension of the classical denoising method based on the intersection of confidence intervals (ICI) rule, whereby an additional criterion, the ratio of intersection of confidence intervals, has been included. The performance of(More)