Matching Pursuit decomposes a signal into a linear expansion of functions selected from a redundant dictionary, isolating the signal structures that are coherent with respect to a given dictionary. In this paper we focus on the Matching Pursuit representation of the displaced frame difference (dfd). In particular, we introduce a new dictionary for Matching Pursuit that efficiently exploits the ...

Abstract As an effective spectrum utilization technology, non-contiguous orthogonal frequency division multiplexing (NC-OFDM) can be used in the environment of discrete spectrum. Compared with the traditional algorithm, channel estimation algorithm based on compressive sensing can get better performance with fewer pilots and improve the effectiveness of the system spectrum. In NC-OFDM systems, ...

In our research group, we also focus on methods for automatic detection of event-related potentials in the EEG signal. We published the algorithm for event-related potential detection based on the matching pursuit algorithm in one of our previous papers. As usual, this method does not work well under special circumstances which can occur (it is a situation when the waveform of event-related pot...

We consider the problem of recovering a structured sparse representation of a signal in an overcomplete time-frequency dictionary with a particular structure. For infinite dictionaries that are the union of a nice wavelet basis and a Wilson basis, sufficient conditions are given for the Basis Pursuit and (Orthogonal) Matching Pursuit algorithms to recover a structured representation of an admis...

This article proposes a stochastic version of the matching pursuit algorithm for Bayesian variable selection in linear regression. In the Bayesian formulation, the prior distribution of each regression coefficient is assumed to be a mixture of a point mass at 0 and a normal distribution with zero mean and a large variance. The proposed stochastic matching pursuit algorithm is designed for sampl...

This thesis describes a new adaptive approximation technique called high resolution pursuit (HRP), and demonstrates how HRP can be used to extract features that are suitable for object recognition. Recently, adaptive approximation techniques have become popular for obtaining representations of large classes of signals. These techniques include method of frames, matching pursuit, and basis pursu...

Representing signals with sparse vectors has a wide range of applications that from image and video coding to shape representation health monitoring. In many real-time requirements, or deal high-dimensional signals, the computational complexity encoder finds plays an important role. Quantum computing recently shown promising speed-ups in learning tasks. this work, we propose quantum version wel...