706 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of AntwerpBE540 papers
- A. Alikhanyan National LaboratoryAM538 papers
- Charles UniversityCZ537 papers
- Deutsches Elektronen-Synchrotron DESYDE530 papers
- RWTH Aachen UniversityDE527 papers
- Rutherford Appleton LaboratoryGB526 papers
- Universität HamburgDE524 papers
- University of ZurichCH523 papers
- ETH ZurichCH519 papers
- European Organization for Nuclear ResearchCH492 papers
- University of BelgradeRS490 papers
- Centre National de la Recherche ScientifiqueFR475 papers
8 papers · 1 filter
Improved Coherence Index-Based Bound in Compressive Sensing
Ljubisa Stankovic, Milos Brajovic, Danilo Mandic +2
Within the Compressive Sensing (CS) paradigm, sparse signals can be reconstructed based on a reduced set of measurements. Reliability of the solution is determined by the uniquenes…
The Support Uncertainty Principle and the Graph Rihaczek Distribution: Revisited and Improved
Ljubisa Stankovic
The classical support uncertainty principle states that the signal and its discrete Fourier transform (DFT) cannot be localized simultaneously in an arbitrary small area in the tim…
Analysis of Gradient based Algorithm for Signal reconstruction in the Presence of Noise
Slavoljub Jokić, Ljindita Niković, Jelena Kadović
Common problem in signal processing is reconstruction of the missing signal samples. Missing samples can occur by intentionally omitting signal coefficients to reduce memory requir…
Separation of sinusoidal and chirp components using Compressive sensing approach
Zoja Vulaj, Faris Kardovic
In this paper we deal with the linear frequency modulated signals and radar signals that are affected by disturbance which is the inevitable phenomenon in everyday communications.…
An Approach to 2D Signals Recovering in Compressive Sensing Context
Srdjan Stankovic, Irena Orovic
In this paper we study the compressive sensing effects on 2D signals exhibiting sparsity in 2D DFT domain. A simple algorithm for reconstruction of randomly under-sampled data is p…
Polynomial Fourier Domain as a Domain of Signal Sparsity
Srdjan Stankovic, Irena Orovic, Ljubisa Stankovic
A compressive sensing (CS) reconstruction method for polynomial phase signals is proposed in this paper. It relies on the Polynomial Fourier transform, which is used to establish a…
On the ISAR Image Analysis and Recovery with Unavailable or Heavily Corrupted Data
Ljubisa Stankovic
Common ISAR radar images and signals can be reconstructed from much fewer samples than the sampling theorem requires since they are usually sparse. Unavailable randomly positioned…
Adaptive Variable Step Algorithm for Missing Samples Recovery in Sparse Signals
Ljubisa Stankovic, Milos Dakovic, Stefan Vujovic
Recovery of arbitrarily positioned samples that are missing in sparse signals recently attracted significant research interest. Sparse signals with heavily corrupted arbitrary posi…