Although largely different concepts, echo state networks and compressed sensing models both rely on collections of random weights; as the reservoir dynamics for echo state networks, and the sensing coefficients in compressed sensing. Several methods for generating the random matrices and metrics to indicate desirable performance are well-studied in compressed sensing, but less so for echo state...

Introduction: Compressed sensing is a technique that allows accelerating data acquisition in the presence of sparse or compressible signals ([5], [6], [7]). Especially, in magnetic resonance imaging, where measurements may be time consuming, compressed sensing might give a chance to reduce the scan time. However, up to now there are no studies that examine basic imaging parameters like image no...

To solve the low efficiency of traditional synthetic aperture sonar (SAS) imaging problem, a sonar imaging system combining compressed sensing (CS) and template compressed sensing (TCS) was proposed. The system used sonar network to image scenes. With 10% amount of traditional SAS data, CS and TCS algorithms could recover the image exploiting the structured sparsity of the interested scene with...

Two datasets, one simplistic that assumes direct observation of paths and the other based on observations derived from compressed sensing and an assumed OFDM communications underpinning, simulate underwater acoustic channels. The Cardinalized Probability Hypothesis Density filter and the Multiple Hypothesis Tracker are applied to these wireless channels. The performances of the two trackers are...

Three-dimensional dynamic MRI (3D-DMRI) is a promising method to analyze respiratory mechanics. However, current 3D DMRI implementations o er limited temporal, spatial resolution and volume coverage. In this work we demonstrate the feasibility of three compressed sensing reconstruction methods along with view-sharing method with clinical evaluation on 8 healthy subjects by expert radiologists. ...

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The theory of compressive sensing (CS) has opened up new opportunities in the field of optical imaging. However, its implementation in this field is often not straight-forward. We list the implementation challenges that might arise in compressive imaging and present some solutions to overcome them.

This paper first present a new general completely perturbed compressed sensing (CS) model y=(A+E)(x+u)+e,called noise folding based on general completely perturbed CS system, where y ∈ Rm, u ∈ Rm, u 6= 0, e ∈ Rm, A ∈ Rm×n, m ≪ n, E ∈ Rm×n with incorporating general nonzero perturbation E to sensing matrix A and noise u into signal x simultaneously based on the standard CS model y=Ax+e. Our cons...