Distributed compressed sensing is the extension of compressed sampling (CS) to sensor networks. The idea is to design a CS joint decoding scheme at the base station which exploits the inter-sensor correlations, in order to recover the whole observations from very few number of random measurements per node. Here, the questions are about modeling the correlations, design of the joint recovery alg...

Detection, Synchronization, Channel Estimation and Capacity in UWB Sensor Networks using Compressed Sensing by Shao-Yuan Chen Chair: Wayne E. Stark Conventional receivers in ultrawideband (UWB) communication system usually require high sampling rate and thus consume much power. With compressed sensing (CS), the sampling rate can potentially be reduced. In this thesis, the performance of CS used...

Introduction: Several methods are currently used to resolve multiple fibers within a voxel including Q-space imaging such as DSI and QBI. These approaches are generally limited by the burden of dense sampling in Q-space to avoid aliasing, which makes their application to clinical studies difficult. However, compressed sensing (CS) can attain almost perfect reconstruction from incomplete random ...

This paper compares and discusses four techniques for model order reduction based on compressed sensing (CS), less relevant basis removal (LRBR), principal component analysis (PCA) and partial least squares (PLS). CS and PCA have already been used for reducing the order of power amplifier (PA) behavioral models for digital predistortion (DPD) purposes. While PLS, despite being popular in some s...

A trend in compressed sensing (CS) is to exploit structure for improved reconstruction performance. In the basic CS model (i.e. the single measurement vector model), exploiting the clustering structure among nonzero elements in the solution vector has drawn much attention, and many algorithms have been proposed such as group Lasso (Yuan & Lin, 2006). However, few algorithms explicitly consider ...

In this work, we present a direction-of-arrival (DOA) estimation method for narrowband sources impinging from the far-field on a uniform linear array (ULA) of sensors, based on the multichannel compressed sensing (CS) framework. We discretize the angular space uniformly into a grid of possible locations, which is much larger than the number of sensors, and assume that only a few of them will co...

Mathematical approaches refer to make quantitative descriptions, deductions and calculations through the use of mathematics concepts, approaches and techniques, then draw some new conclusions and foresee by mathematical analysis and judgement. In recent years, Compressed Sensing theory (CS) provides solutions in alleviating the huge amount of information demand in the pressure of signal samplin...

Compressed sensing (CS) is an important recent advance that shows how to reconstruct sparse high dimensional signals from surprisingly small numbers of random measurements. The nonlinear nature of the reconstruction process poses a challenge to understanding the performance of CS. We employ techniques from the statistical physics of disordered systems to compute the typical behavior of CS as a ...

When the number of projections does not satisfy the Shannon/Nyquist sampling requirement, streaking artifacts are inevitable in x-ray computed tomography (CT) images reconstructed using filtered backprojection algorithms. In this letter, the spatial-temporal correlations in dynamic CT imaging have been exploited to sparsify dynamic CT image sequences and the newly proposed compressed sensing (C...

Wireless telemonitoring of physiological signals is an important topic in eHealth. In order to reduce on-chip energy consumption and extend sensor life, recorded signals are usually compressed before transmission. In this paper, we adopt compressed sensing (CS) as a low-power compression framework, and propose a fast block sparse Bayesian learning (BSBL) algorithm to reconstruct original signal...