Analog-to-digital conversion comprises of two fundamental discretization steps: sampling and quantization. Recent results in compressive sensing (CS) have overhauled the conventional wisdom related to the sampling step, by demonstrating that sparse or compressible signals can be sampled at rates much closer to their sparsity rate, rather than their bandwidth. This work further overhauls the con...

Compressed sensing (CS), a reconstruction method for undersampled MR data, was recently introduced [1]. Since only undersampled data are acquired, CS allows a significant reduction in the time needed for MR experiments. The basic requirement for CS, however, is sparsity in the data. The lack of F background signal in living tissue leads to an intrinsically sparse signal distribution in the F im...

This paper addresses the experimental study of the wide band signal estimation and reconstruction using the established compressive sampling (CS) methods. For this purpose, a hardware test bed was setup inter-connecting a wide band SDR based hand held military radio (SWAVE HH or HH), vector signal generator, bi-directional coupler, attenuators, PC and other auxiliaries. Real-world communication...

In compressive sensing (CS) theory, as the number of samples is decreased below a minimum threshold, the average error of the recovery increases. Sufficient sampling is either required for quality reconstruction or the error is resignedly accepted. However, most CS work has not taken advantage of the inherent structure in a variety of signals relevant to engineering applications. Hence, this pa...

Recently, deep learning-based compressed sensing (CS) has achieved great success in reducing the sampling and computational cost of systems improving reconstruction quality. These approaches, however, largely overlook issue cost; they rely on complex structures task-specific operator designs, resulting extensive storage high energy consumption CS imaging systems. In this article, we propose a l...

INTRODUCTION: Parallel MRI has been used widely to accelerate image acquisition [1,2]. Recently, application of compressed sensing (CS) to MRI has been proposed and showed great promise [3]. It is reported that combining parallel MRI and CS can enable further acceleration in MRI acquisition [4,5]. SparseSENSE directly applies CS to SENSE by using a random under-sampling pattern and a regulariza...

Abstract. Let φ(θ) ∼ ∑∞ −∞ ake ikθ (where ak is the k-th Fourier coefficient of φ) be a bounded measurable function on the unit circle T. Consider the operator Sφ(m,n) on L2(T) whose matrix with respect to the standard basis { eikθ : k ∈ Z } is given by (ami−nj )i,j∈Z. In this paper, we give upper and lower bound estimation for r(Sφ(m,n)), the spectral radius of Sφ(m,n). Furthermore, we will sh...