Compressed sensing is a processing method that significantly reduces the number of measurements needed to accurately resolve signals in many fields of science and engineering. We develop a two-dimensional variant of compressed sensing for multidimensional spectroscopy and apply it to experimental data. For the model system of atomic rubidium vapor, we find that compressed sensing provides an or...

We proposed to apply compressed sensing to realize information sharing of link quality for wireless mesh networks (WMNs) with grid topology. In this paper, we extend the link quality sharing method to be applied for WMNs with arbitrary topology. For arbitrary topology WMNs, we introduce a link quality matrix and a matrix formula for compressed sensing. By employing a diffusion wavelets basis, t...

speckle is a granular disturbance in coherent images such as synthetic aperture radar (sar) images, modeled as a multiplicative noise. this noise degrades the sar image and complicates the image exploitation using automated image analysis techniques. several approaches have been developed to reduce the effect of speckle noise. recently, the application of compressed sensing (cs) is explored in ...

The potential of large tactile arrays to improve robot perception for safe operation in human-dominated environments and of high-resolution tactile arrays to enable humanlevel dexterous manipulation is well accepted. However, the increase in the number of tactile sensing elements introduces challenges including wiring complexity, data acquisition, and data processing. To help address these chal...

As an emerging approach of signal processing, not only has compressed sensing (CS) successfully compressed and sampled signals with few measurements, but also has owned the capabilities of ensuring the exact recovery of signals. However, the above-mentioned properties are based on the (compressed) sensing matrices. Hence the construction of sensing matrices is the key problem. Compared with the...

The restricted isometry property (RIP) has become well-known in the compressed sensing community. Recently, a weaken version of RIP was proposed for exact sparse recovery under weak moment assumptions. In this note, we prove that the weaken RIP is also sufficient for stable and robust sparse recovery by linking it with a recently introduced robust width property in compressed sensing. Moreover,...

Compressed sensing and magnetic resonance imaging are hot topics in the field of signal processing. In this study we introduced in Lustig’s variable density sampling method, integrated it to compressed sensing, and applied it to brain MRI acquisition. The realistic experiment shows the variable density sampling recovery better than traditional random sampling method on a 256x256 brain magnetic ...

We consider compressed sampling over finite fields and investigate the number of compressed measurements needed for successful L0 recovery. Our results are obtained while the sparseness of the sensing matrices as well as the size of the finite fields are varied. One of interesting conclusions includes that unless the signal is “ultra” sparse, the sensing matrices do not have to be dense. Keywor...

This survey provides a brief introduction to compressed sensing as well as several major algorithms to solve it and its various applications to communications systems. We firstly review linear simultaneous equations as ill-posed inverse problems, since the idea of compressed sensing could be best understood in the context of the linear equations. Then, we consider the problem of compressed sens...

The paper presents a study of Compressed Sensing application in a passive radar, where the range resolution is limited by the bandwidth of signal used. The application of Compressed Sensing allows to obtain superresolution in a presence of a point target, which is useful e.g. when exploiting multipath information for estimating the target elevation. However, in such setup, Compressed Sensing al...