8 results on '"Lerga, Jonatan"'
Search Results
2. Block-Adaptive Rényi Entropy-Based Denoising for Non-Stationary Signals.
- Author
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Saulig, Nicoletta, Lerga, Jonatan, Miličić, Siniša, and Tomasović, Željka
- Subjects
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SIGNAL denoising , *RENYI'S entropy , *HILBERT-Huang transform , *ENTROPY , *SPECTROGRAMS - Abstract
This paper approaches the problem of signal denoising in time-variable noise conditions. Non-stationary noise results in variable degradation of the signal's useful information content over time. In order to maximize the correct recovery of the useful part of the signal, this paper proposes a denoising method that uses a criterion based on amplitude segmentation and local Rényi entropy estimation which are limited over short time blocks of the signal spectrogram. Local estimation of the signal features reduces the denoising problem to the stationary noise case. Results, presented for synthetic and real data, show consistently better performance gained by the proposed adaptive method compared to denoising driven by global criteria. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
3. RANSAC-Based Signal Denoising Using Compressive Sensing.
- Author
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Stanković, Ljubiša, Brajović, Miloš, Stanković, Isidora, Lerga, Jonatan, and Daković, Miloš
- Subjects
SIGNAL denoising ,SIGNAL reconstruction ,DISCRETE Fourier transforms ,SIGNAL theory ,SIGNAL sampling ,FOURIER transforms - Abstract
In this paper, we present an approach to the reconstruction of signals exhibiting sparsity in a transformation domain, having some heavily disturbed samples. This sparsity-driven signal recovery exploits a carefully suited random sampling consensus (RANSAC) methodology for the selection of a subset of inlier samples. To this aim, two fundamental properties are used: A signal sample represents a linear combination of the sparse coefficients, whereas the disturbance degrades the original signal sparsity. The properly selected samples are further used as measurements in the sparse signal reconstruction, performed using algorithms from the compressive sensing framework. Besides the fact that the disturbance degrades signal sparsity in the transformation domain, no other disturbance-related assumptions are made—there are no special requirements regarding its statistical behavior or the range of its values. As a case study, the discrete Fourier transform is considered as a domain of signal sparsity, owing to its significance in signal processing theory and applications. Numerical results strongly support the presented theory. In addition, the exact relation for the signal-to-noise ratio of the reconstructed signal is also presented. This simple result, which conveniently characterizes the RANSAC-based reconstruction performance, is numerically confirmed by a set of statistical examples. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
4. Adaptive state estimator with intersection of confidence intervals based preprocessing.
- Author
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Lerga, Jonatan, Kirinčić, Vedran, Franković, Dubravko, and Štajduhar, Ivan
- Subjects
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STATE estimation in electric power systems , *CONFIDENCE intervals , *SIGNAL processing , *SIGNAL denoising , *LEAST squares - Abstract
The paper presents an effective solution for improving power system state estimation performance by applying the intersection of confidence intervals (ICI) algorithm, a state-of-the-art adaptive signal processing technique used in signal denoising. Since many power utilities worldwide still run state estimators based on the weighed least squares (WLS) algorithm using supervisory control and data acquisition (SCADA) measurements, the ICI algorithm is added to pre-process SCADA measurements without changing the structure of the WLS algorithm. Due to its adaptive window size and high sensitivity to noise in the input measurement series, the proposed ICI-based solution results in an enhancement of the state estimator output and overall performance when compared to the original algorithm. As test beds, the IEEE systems with 30 and 118 buses were used, while as an example of the real power system, the complete mathematical model of the Croatian transmission power system was simulated. Several case studies indicate that the ICI-based state estimator reduces the input measurements mean squared error by up to 30.8%, the mean absolute error by up to 20.8%, and the maximum estimation error by up to 22.6%. Furthermore, this also led to an enhanced final output of the state estimator for all tested systems in view of the state estimation accuracy and convergence. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
5. An adaptive method based on the improved LPA-ICI algorithm for MRI enhancement.
- Author
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Lerga, Jonatan, Mandić, Ivica, Peić, Hajdi, and Brščić, Dražen
- Subjects
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DIAGNOSTIC imaging , *MAGNETIC resonance imaging , *SIGNAL denoising , *IMAGE analysis , *PIXELS , *CONFIDENCE intervals - Abstract
Various diseases are diagnosed using medical imaging used for analysing internal anatomical structures. However, medical images are susceptible to noise introduced in both acquisition and transmission processes. We propose an adaptive data-driven image denoising algorithm based on an improvement of the intersection of confidence intervals (ICI), called relative ICI (RICI) algorithm. The 2D mask of the adaptive size and shape is calculated for each image pixel independently, and utilized in the design of the 2D local polynomial approximation (LPA) filters. Denoising performances, in terms of the PSNR, are compared to the original ICI-based method, as well as to the fixed sized filtering. The proposed adaptive RICI-based denoising outperformed the original ICI-based method by up to 1.32 dB, and the fixed size filtering by up to 6.48 dB. Furthermore, since the denoising of each image pixel is done locally and independently, the method is easy to parallelize. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
6. An ICI Based Algorithm for Fast Denoising of Video Signals.
- Author
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Lerga, Jonatan, Grbac, Edi, and Sucic, Victor
- Subjects
CONFIDENCE intervals ,VIDEO compression ,COMPUTATIONAL complexity ,SIGNAL denoising ,ELECTRONIC noise - Abstract
Copyright of Automatika: Journal for Control, Measurement, Electronics, Computing & Communications is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2014
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7. Adaptive filter support selection for signal denoising based on the improved ICI rule
- Author
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Sucic, Victor, Lerga, Jonatan, and Vrankic, Miroslav
- Subjects
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ADAPTIVE filters , *SIGNAL processing , *CONFIDENCE intervals , *PERFORMANCE evaluation , *SIMULATION methods & models , *POLYNOMIAL approximation , *SAMPLING errors , *STANDARD deviations - Abstract
Abstract: Performance and simulation-based optimization of the improved intersection of confidence intervals (ICI) rule for adaptive filter support selection are presented. The improved ICI rule (refereed to as the relative intersection of confidence intervals (RICI) rule) is combined with the local polynomial approximation (LPA) method and applied to signal denoising, with the aim to enhance the signal estimation accuracy and reduce the estimation error energy. The results achieved using the RICI rule are compared to those obtained using the classical ICI rule, showing the reduction of the root mean-square error (RMSE) of up to 10 times for various classes of analyzed signals. The proposed procedure for the selection of the RICI parameters Γ and , for which the RMSE is minimum, has been shown to significantly improve the quality of denoised signals. [Copyright &y& Elsevier]
- Published
- 2013
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8. Gravitational-Wave Burst Signals Denoising Based on the Adaptive Modification of the Intersection of Confidence Intervals Rule.
- Author
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Lopac, Nikola, Lerga, Jonatan, and Cuoco, Elena
- Subjects
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SIGNAL denoising , *GRAVITATIONAL waves , *CONFIDENCE intervals , *STANDARD deviations , *POLYNOMIAL approximation , *ACQUISITION of data , *MEAN field theory - Abstract
Gravitational-wave data (discovered first in 2015 by the Advanced LIGO interferometers and awarded by the Nobel Prize in 2017) are characterized by non-Gaussian and non-stationary noise. The ever-increasing amount of acquired data requires the development of efficient denoising algorithms that will enable the detection of gravitational-wave events embedded in low signal-to-noise-ratio (SNR) environments. In this paper, an algorithm based on the local polynomial approximation (LPA) combined with the relative intersection of confidence intervals (RICI) rule for the filter support selection is proposed to denoise the gravitational-wave burst signals from core collapse supernovae. The LPA-RICI denoising method's performance is tested on three different burst signals, numerically generated and injected into the real-life noise data collected by the Advanced LIGO detector. The analysis of the experimental results obtained by several case studies (conducted at different signal source distances corresponding to the different SNR values) indicates that the LPA-RICI method efficiently removes the noise and simultaneously preserves the morphology of the gravitational-wave burst signals. The technique offers reliable denoising performance even at the very low SNR values. Moreover, the analysis shows that the LPA-RICI method outperforms the approach combining LPA and the original intersection of confidence intervals (ICI) rule, total-variation (TV) based method, the method based on the neighboring thresholding in the short-time Fourier transform (STFT) domain, and three wavelet-based denoising techniques by increasing the improvement in the SNR by up to 118.94% and the peak SNR by up to 138.52%, as well as by reducing the root mean squared error by up to 64.59%, the mean absolute error by up to 55.60%, and the maximum absolute error by up to 84.79%. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
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