Glitches are transients of environmental or instrumental origin appearing in the data channel of existing large baseline interferometer based gravitational wave detectors. Their presence limits severely the performance of gravitational wave detection/estimation algorithms based on the customary Gaussian noise assumption, and requires adopting ad-hoc data laundering techniques. Modeling glitches...

Matching pursuit (MP) is an adaptive signal decomposition technique and can be applied to process Lamb waves, such as denoising, wave parameter estimation, and feature extraction, for health monitoring applications. This paper explored matching pursuit decomposition using Gaussian and chirplet dictionaries to decompose/approximate Lamb waves and extract wave parameters. While Gaussian dictionar...

Analysis of nonstationary signals, such as music signals, is a challenging task. The purpose of this study is to explore an efficient and powerful technique to analyze and classify music signals in higher frequency range (44.1 kHz). The pursuit methods are good tools for this purpose, but they aimed at representing the signals rather than classifying them as in Y. Paragakin et al., 2009. Among ...

Polytope Faces Pursuit (PFP) is a greedy algorithm that approximates the sparse solutions recovered by 1 regularised least-squares (Lasso) [4,10] in a similar vein to (Orthogonal) Matching Pursuit (OMP) [16]. The algorithm is based on the geometry of the polar polytope where at each step a basis function is chosen by finding the maximal vertex using a path-following method. The algorithmic comp...

In this paper, the application of the emerging compressed sensing (CS) theory and the geometric characteristics of the targets in radar images are investigated. Currently, the signal detection algorithms based on the CS theory require knowing the prior knowledge of the sparsity of target signals. However, in practice, it is often impossible to know the sparsity in advance. To solve this problem...

We introduce a new greedy algorithm to find approximate sparse representations s of x = As by finding the Basis Pursuit (BP) solution of the linear program min{‖s‖ 1 | x = As}. The proposed algorithm is based on the geometry of the polar polytope P ∗ = {c | à c ≤ 1} where à = [A,−A] and searches for the vertex c ∈ P ∗ which maximizes x c using a path following method. The resulting algorithm is...