S
Srdjan Stankovic
Researcher at University of Montenegro
Publications - 219
Citations - 4431
Srdjan Stankovic is an academic researcher from University of Montenegro. The author has contributed to research in topics: Compressed sensing & Time–frequency analysis. The author has an hindex of 35, co-authored 210 publications receiving 4075 citations. Previous affiliations of Srdjan Stankovic include Villanova University & Technische Universität Darmstadt.
Papers
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Decentralized Parameter Estimation by Consensus Based Stochastic Approximation
TL;DR: An algorithm for decentralized multi-agent estimation of parameters in linear discrete-time regression models is proposed in the form of a combination of local stochastic approximation algorithms and a global consensus strategy, and an analysis of the asymptotic properties of the proposed algorithm is presented.
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Watermarking in the space/spatial-frequency domain using two-dimensional Radon-Wigner distribution
TL;DR: The watermark robustness with respect to some very important image processing attacks, such as the translation, rotation, cropping, JPEG compression, and filtering, is demonstrated and tested by using Stirmark 3.1.
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Digital watermarking in the fractional Fourier transformation domain
TL;DR: An application of the fractional Fourier transform for the multimedia copyright protection is proposed and the watermark robustness as well as statistical performance are considered.
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Fast communication: Missing samples analysis in signals for applications to L-estimation and compressive sensing
TL;DR: This paper provides statistical analysis for efficient detection of signal components when missing data samples are present and the determination of the sufficient number of observation and the minimum number of missing samples which still allow proper signal detection.
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Compressive Sensing Based Separation of Nonstationary and Stationary Signals Overlapping in Time-Frequency
TL;DR: This work focuses on sinusoidal desired signals with sparse frequency-domain representation but shows that the analysis can be straightforwardly generalized to nonsinusoidal signals with known structures.