Jonatan Lerga

Nonlinear IF Estimation Based on the Pseudo WVD Adapted Using the Improved Sliding Pairwise ICI Rule

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 modified. An additional criterion for a proper window width selection is introduced, which takes into account the amount of overlap between the current and the previous confidence interval relative to the current interval length. The presented results show that the proposed method outperforms the original SPICI-based method by up to 42% in terms of the mean absolute error and up to 73% in terms of the mean squared error. It is also less sensitive to the window widths set selection than the original method.

An Efficient Algorithm for Instantaneous Frequency Estimation of Nonstationary Multicomponent Signals in Low SNR

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 intersection of confidence intervals (ICI) rule. The obtained results are compared to the multicomponent signal ICI-based IF estimation method for various window types and SNRs, showing the estimation accuracy improvement in terms of the mean squared error (MSE) by up to 23%. Furthermore, the highest improvement is achieved for low SNRs values, when many of the existing methods fail.

A Signal Denoising Method Based on the Improved ICI Rule

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 the original ICI method equipped with the local polynomial approximation (LPA), as well as various conventional wavelet shrinkage methods.

Multicomponent noisy signal adaptive instantaneous frequency estimation using components time support information

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 Renyi entropy (STRE). Components localisation and separation are done using a double-direction component tracking and extraction method presented here, while the IF estimation is done using the adaptive algorithms based on the intersection of confidence intervals (ICI) rule and the relative intersection of confidence intervals (RICI) rule. The results obtained using the ICI and RICI rules are compared for various window types, signal-to-noise ratios and time-frequency distributions, both with and without using the information on components time support. Most of the errors in IF estimation using the ICI and RICI-based methods are caused by imprecise components time-support estimation. The proposed methods combined with the STRE have achieved a significant accuracy improvement in terms of the mean absolute error and the mean squared error, reducing them by up to 73 and 93%, respectively. The method has been applied to real-life signals and proven to be an efficient tool for IF estimation of noisy non-stationary multicomponent signals.

Algorithm Based on the Short-Term Rényi Entropy And IF Estimation for Noisy EEG Signals Analysis

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 new algorithm for EEG signal components detection from its time-frequency distribution (TFD) has been proposed in this paper. The algorithm utilizes the modification of the Rényi entropy-based technique for number of components estimation, called short-term Rényi entropy (STRE), and upgraded by an iterative algorithm which was shown to enhance existing approaches. Combined with instantaneous frequency (IF) estimation, the proposed method was applied to EEG signal analysis both in noise-free and noisy environments for limb movements EEG signals, and was shown to be an efficient technique providing spectral description of brain activities at each electrode location up to moderate additive noise levels. Furthermore, the obtained information concerning the number of EEG signal components and their IFs show potentials to enhance diagnostics and treatment of neurological disorders for patients with motor control illnesses.

An ICI Based Algorithm for Fast Denoising of Video Signals

In this paper, we have proposed a fast method for video denoising using the modified intersection of confidence intervals (ICI) rule, called fast ICI (FICI) method. The goal of the new FICI based video denoising method is to maintain an acceptable quality level of the denoised video estimate, and at the same time to significantly reduce denoising execution time when compared to the original ICI based method. The methods are tested on real-life video signals and their performances are analyzed and compared. It is shown that the FICI method outperforms the ICI method in terms of the execution time reduction by up to 96% (or up to 25 times). However, practical application demands dictate the choice of the video denoising method. If one wants fast denoising method with decent denoising results, the FICI based video denoising method is a better choice. The original ICI method, however, should be used in applications where significant noise suppression is an imperative regardless the computational complexity.

Denoising of X-ray Images Using the Adaptive Algorithm Based on the LPA-RICI Algorithm

Diagnostics and treatments of numerous diseases are highly dependent on the quality of captured medical images. However, noise (during both acquisition and transmission) is one of the main factors that reduce their quality. This paper proposes an adaptive image denoising algorithm applied to enhance X-ray images. The algorithm is based on the modification of the intersection of confidence intervals (ICI) rule, called relative intersection of confidence intervals (RICI) rule. For each image pixel apart, a 2D mask of adaptive size and shape is calculated and used in designing the 2D local polynomial approximation (LPA) filters for noise removal. One of the advantages of the proposed method is the fact that the estimation of the noise free pixel is performed independently for each image pixel and thus, the method is applicable for easy parallelization in order to improve its computational efficiency. The proposed method was compared to the Gaussian smoothing filters, total variation denoising and fixed size median filtering and was shown to outperform them both visually and in terms of the peak signal-to-noise ratio (PSNR) by up to 7.99 dB.

An improved method for nonstationary signals components extraction based on the ICI rule

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 enhanced by using high resolution and reduced cross-terms TFDs. The obtained results are compared for different signal-to-noise ratios (SNRs) and various time and lag window types used in the modified B-distribution (MBD) calculation, proving the method to be a valuable tool in noisy multicomponent signals components extraction in the time-frequency (TF) domain.

Performance analysis of the LPA-RICI denoising method

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 the RICI method is studied on a several test signals for an extensive range of noise distributions and SNR values. It is shown that for all types of noise and all considered SNR values, the RICI method when compared to the ICI method significantly reduces the estimation mean squared error.

A Fast Signal Denoising Algorithm Based on the LPA-ICI Method for Real-Time Applications

We propose a new algorithm for denoising of additive white Gaussian noise-corrupted signals, based on the intersection of confidence intervals (ICI) algorithm, called the fast intersection of confidence intervals (FICI) algorithm. The proposed approach combines the FICI algorithm, used for the adaptive filter support size selection, with the local polynomial approximation (LPA) method, used as a filter design tool. The LPA-FICI method, when compared to the existing ICI-based denoising method, reduces the computational complexity by up to N times, where N is the number of signal samples, resulting in significantly faster algorithm execution time, while maintaining the estimation accuracy close to the one achieved using the original ICI-based method. Furthermore, the proposed modifications allow the use of the LPA-FICI method in real-time signal processing. In conducted simulations, we have confirmed advantages of the proposed method on two commonly used benchmark signals corrupted with various noise strengths.