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Victor Sucic
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2020 – today
- 2024
- [j19]Ivan Volaric
, Victor Sucic
A Fast Intersection of Confidence Intervals Method-Based Adaptive Thresholding for Sparse Image Reconstruction Using the Matrix Form of the Wavelet Transform. Inf. 15(2): 71 (2024) - 2021
- [j18]Vedran Jurdana
Sparse time-frequency distribution reconstruction based on the 2D Rényi entropy shrinkage algorithm. Digit. Signal Process. 118: 103225 (2021) - [c15]Vedran Jurdana
A Sparse TFD Reconstruction Approach Using the S-method and Local Entropies Information. ISPA 2021: 4-9
2010 – 2019
- 2019
- [j17]Ivan Volaric, Victor Sucic
Sparse time-frequency distributions based on the $$\ell _1$$ ℓ 1 -norm minimization with the fast intersection of confidence intervals rule. Signal Image Video Process. 13(3): 499-506 (2019) - [c14]Ivan Volaric
Sparse Time-Frequency Distribution Calculation with an Adaptive Thresholding Algorithm. ISPA 2019: 341-346 - 2017
- [j16]Ivan Volaric, Jonatan Lerga
A Fast Signal Denoising Algorithm Based on the LPA-ICI Method for Real-Time Applications. Circuits Syst. Signal Process. 36(11): 4653-4669 (2017) - [j15]Ivan Volaric, Victor Sucic
A data driven compressive sensing approach for time-frequency signal enhancement. Signal Process. 141: 229-239 (2017) - [j14]Gordan Segon, Jonatan Lerga
Improved LPA-ICI-based estimators embedded in a signal denoising virtual instrument. Signal Image Video Process. 11(2): 211-218 (2017) - 2016
- [j13]Nicoletta Saulig
Optimization of quadratic time-frequency distributions using the local Rényi entropy information. Signal Process. 129: 17-24 (2016) - 2015
- [c13]Ivan Volaric, Victor Sucic
A compressive sensing based method for cross-terms suppression in the time-frequency plane. BIBE 2015: 1-4 - 2014
- [j12]Victor Sucic
Analysis of local time-frequency entropy features for nonstationary signal components time supports detection. Digit. Signal Process. 34: 56-66 (2014) - [j11]Srdjan Stankovic, Thayananthan Thayaparan, Victor Sucic
Guest Editorial: Special Issue on Compressive Sensing and Robust Transforms. IET Signal Process. 8(3): 221-222 (2014) - [j10]Victor Sucic
Multicomponent noisy signal adaptive instantaneous frequency estimation using components time support information. IET Signal Process. 8(3): 277-284 (2014) - [c12]Irena Jurdana
Submarine optical networks: How to make them greener. ICTON 2014: 1-4 - 2013
- [j9]Victor Sucic
Adaptive filter support selection for signal denoising based on the improved ICI rule. Digit. Signal Process. 23(1): 65-74 (2013) - [c11]Nicoletta Saulig, Nelly Pustelnik, Pierre Borgnat, Patrick Flandrin, Victor Sucic:
Instantaneous counting of components in nonstationary signals. EUSIPCO 2013: 1-5 - [c10]Damir Malnar
On the reference signal parameters in the cross wigner-ville distribution based instantaneous frequency estimation method. ISPA 2013: 421-424 - 2012
- [j8]Srdjan Stankovic, Irena Orovic
Averaged multiple L-spectrogram for analysis of noisy nonstationary signals. Signal Process. 92(12): 3068-3074 (2012) - [c9]Damir Malnar
A cross-terms geometry based method for components instantaneous frequency estimation using the Cross Wigner-Ville distribution. ISSPA 2012: 1217-1222 - 2011
- [j7]Jonatan Lerga
An Efficient Algorithm for Instantaneous Frequency Estimation of Nonstationary Multicomponent Signals in Low SNR. EURASIP J. Adv. Signal Process. 2011 (2011) - [j6]Victor Sucic
Estimating the number of components of a multicomponent nonstationary signal using the short-term time-frequency Rényi entropy. EURASIP J. Adv. Signal Process. 2011: 125 (2011) - [j5]Jonatan Lerga, Victor Sucic, Miroslav Vrankic:
Separable Image Denoising Based on the Relative Intersection of Confidence Intervals Rule. Informatica 22(3): 383-394 (2011) - [c8]Paolo Zenzerovic, Victor Sucic:
Remote laboratory for microcontroller systems design. MIPRO 2011: 1685-1688 - 2010
- [j4]Miroslav Vrankic, Damir Sersic, Victor Sucic
Adaptive 2-D Wavelet Transform Based on the Lifting Scheme With Preserved Vanishing Moments. IEEE Trans. Image Process. 19(8): 1987-2004 (2010) - [c7]Jonatan Lerga
An instantaneous frequency estimation method based on the improved sliding pair-wise ICI rule. ISSPA 2010: 161-164
2000 – 2009
- 2009
- [j3]Jonatan Lerga
Nonlinear IF Estimation Based on the Pseudo WVD Adapted Using the Improved Sliding Pairwise ICI Rule. IEEE Signal Process. Lett. 16(11): 953-956 (2009) - 2008
- [j2]Jonatan Lerga
A Signal Denoising Method Based on the Improved ICI Rule. IEEE Signal Process. Lett. 15: 601-604 (2008) - 2007
- [c6]M. Balakrishnan Malarvili
Renyi entropy of quadratic time-frequency distributions: effects of signal's parameters. ISSPA 2007: 1-4 - 2003
- [j1]Boualem Boashash, Victor Sucic
Resolution measure criteria for the objective assessment of the performance of quadratic time-frequency distributions. IEEE Trans. Signal Process. 51(5): 1253-1263 (2003) - [c5]Victor Sucic
An approach for selecting a real-life signal best-performing time-frequency distribution. ISSPA (1) 2003: 125-128 - 2002
- [c4]Victor Sucic, Boualem Boashash:
Selecting the optimal time-frequency distribution for real-life multicomponent signals under given constraints. EUSIPCO 2002: 1-4 - 2001
- [c3]Victor Sucic, Boualem Boashash:
Parameter selection for optimising time-frequency distributions and measurements of time-frequency characteristics of non-stationary signals. ICASSP 2001: 3557-3560 - [c2]Victor Sucic
Optimisation algorithm for selecting quadratic time-frequency distributions: performance results and calibration. ISSPA 2001: 331-334
1990 – 1999
- 1999
- [c1]Victor Sucic
Performance evaluation of the B-distribution. ISSPA 1999: 267-270
Coauthor Index
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