In traditional framework of Compressive Sensing (CS), only sparse prior on the property of signals in time or frequency domain is adopted to guarantee the exact inverse recovery. ...
This paper presents a method for detection of sinusoidal signals corrupted by an additive noise in the short-time Fourier domain. The proposed method is based on probabilistic mod...
Two-dimensional (2D) optimal filter for highly nonstationary 2D signal estimation is developed. It is based on the real-time results of space/spatial-frequency (S/SF) analysis, on...
Veselin N. Ivanovic, Nevena Radovic, Srdjan Jovano...
We present a method for removing environmental noise from physiological recordings such as Magnetoencephalography (MEG) for which noise-sensitive reference channels are available....
In this paper, we develop algorithms for robust linear regression by leveraging the connection between the problems of robust regression and sparse signal recovery. We explicitly ...