Efficient Compressive Sampling of Spatially Sparse Fields in Wireless Sensor Networks

Wireless sensor networks (WSN), i.e. networks of autonomous, wireless sensing nodes spatially deployed over a geographical area, are often faced with acquisition of spatially sparse fields. In this paper, we present a novel bandwidth/energy efficient CS scheme for acquisition of spatially sparse fields in a WSN. The paper contribution is twofold. Firstly, we introduce a sparse, structured CS matrix and we analytically show that it allows accurate reconstruction of bidimensional spatially sparse signals, such as those occurring in several surveillance application. Secondly, we analytically evaluate the energy and bandwidth consumption of our CS scheme when it is applied to data acquisition in a WSN. Numerical results demonstrate that our CS scheme achieves significant energy and bandwidth savings wrt state-of-the-art approaches when employed for sensing a spatially sparse field by means of a WSN.Comment: Submitted to EURASIP Journal on Advances in Signal Processin

Optimal SP frame selection and bit budget allocation for mobile H.264 video streaming

n/

Hierarchical CoSaMP for compressively sampled sparse signals with nested structure

n/

RECONSTRUCTION OF COMPRESSIVELY SAMPLED TEXTURE IMAGES IN THE GRAPH-BASED TRANSFORM DOMAIN

ABSTRACT This paper addresses the problem of texture images recovery from compressively sampled measurements. Texture images hardly present a sparse, or even compressible, representation in transformed domains (e.g. wavelet) and are therefore difficult to deal with in the Compressive Sampling (CS) framework. Herein, we resort to the recently defined Graph-based transform (GBT), formerly introduced for depth map coding, as a sparsifying transform for classes of textures sharing the similar spatial patterns. Since GBT proves to be a good candidate for compact representation of some classes of texture, we leverage it for CS texture recovery. To this aim, we resort to a modified version of a state-of-the-art recovery algorithm to reconstruct the texture representation in the GBT domain. Numerical simulation results show that this approach outperforms state-of-the-art CS recovery algorithms on texture images

UWA interference analysis for cognitive access

Cognitive access is growing in importance in radio frequency wireless applications for occupying spectrum holes leaved free by licensed and unlicensed primary services. Recently the cognitive paradigm involved underwater communications aspects since this propagation scenario is, generally, rich of acoustic sources. This justifies the curiosity of the scientific community to investigate the possibility of using some of the concept borrowed by the cognitive paradigm. Specifically, we consider an underwater active sensor network where each cognitive node applies a Wigner-Ville image processing based pattern analysis procedure not only for evaluating the presence of interference but also its nature. In fact, if the source of interference is a network node, coexistence issues should be taken into account, while, when the interference source is external (mammals, bubbles or ship engines), in principle the transmission is possible in the interfered bands, provided the presence of the interference does not affect the overall transmission quality. © 2013 IEEE

Maximum likelihood scale parameter estimation: An application to gain estimation for QAM constellations

In this paper we address the problem of scale parameter estimation, introducing a reduced complexity Maximum Likelihood (ML) estimation procedure. The estimator stems from the observation that, when the estimandum acts as a shift parameter on a multinomially distributed statistic, direct maximization of the likelihood function can be conducted by an efficient DFT based procedure. A suitable exponential warping of the observation's domain is known to transform a scale parameter problem into a shift estimation problem, thus allowing the afore mentioned reduced complexity ML estimation for shift parameter to be applied also in scale parameter estimation problems. As a case study, we analyze a gain estimator for general QAM constellations. Simulation results and theoretical performance analysis show that the herein presented estimator outperforms selected state of the art high order moments estimator, approaching the Craḿer- Rao Lower Bound (CRLB) for a wide range of SNR. © EURASIP, 2010

Fast maximum likelihood scale parameter estimation from histogram measurements

Abstract-In this letter, we address the problem of estimating a parameter acting as a scale factor on the observations probability density function (pdf), i.e. a scale parameter.Histogram basedMaximum Likelihood (ML) estimation of a scale parameter requires the evaluation of a discrete scale correlation.We show howML estimation can be implemented by means of a computationally efficient Discrete Fourier Transform based procedure, when geometric histogram sampling is adopted. As a case study, we analyze a gain estimator for generalQAMconstellations. Simulation results and theoretical performance analysis show that the presentedMLestimator outperforms selected state of the art estimators, approaching the Cramér Rao Lower Bound (CRLB) for a wide range of SNR. © 2011 IEEE