We propose a new method for reconstruction of sparse signals with and without noisy perturbations, termed the subspace pursuit algorithm. The algorithm has two important characteristics: low computational complexity, comparable to that of orthogonal matching pursuit techniques, and reconstruction accuracy of the same order as that of LP optimization methods. The presented analysis shows that in...

This paper introduces a novel framework for compressive sensing of biomedical ultrasonic signals based on modelling data with stable distributions. We propose an approach to `p norm minimisation that employs the iteratively reweighted least squares (IRLS) algorithm but in which the parameter p is judiciously chosen by relating it to the characteristic exponent of the underlying alpha-stable dis...

template matching is a widely used technique in many of image processing and machine vision applications. in this paper we propose a new as well as a fast and reliable template matching algorithm which is invariant to rotation, scale, translation and brightness (rstb) changes. for this purpose, we adopt the idea of ring projection transform (rpt) of image. in the proposed algorithm, two novel s...

Fault diagnosis of induction motor anomalies is vital for achieving industry safety. This paper proposes a new hybrid Machine Learning methodology induction-motor fault detection. Some the parameters such as stator currents and vibration signals provide great deal information about motor’s conditions. Therefore, these were selected to test proposed model. The was assessed in laboratory under he...

Sparse signal expansions represent or approximate a signal using a small number of elements from a large collection of elementary waveforms. Finding the optimum sparse expansion is known to be NP hard in general and non-optimal strategies such as Matching Pursuit, Orthogonal Matching Pursuit, Basis Pursuit and Basis Pursuit De-noising are often called upon. These methods show good performance i...