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...

We introduce a new combinatorial structure: superselectors. We show that superselectors subsume several important combinatorial structures used in the past few years to solve problems in group testing, compressed sensing, multi-channel conflict resolution and data security. We prove close upper and lower bounds on the size of superselectors and we provide efficient algorithms for their construc...

In this paper we introduce a new image representation for texture classification. Our work is motivated by recent developments in the field of local patch based features, compressive sensing and descriptor encoding methods. Novel features called Compressed Random Pixel Difference (CRPD) are proposed. These features are low in dimensionality, highly discriminative, and easy to compute. Combined ...