### 2010

Vinuelas-Peris, Pablo; Artés-Rodríguez, Antonio

Bayesian Joint Recovery of Correlated Signals in Distributed Compressed Sensing Artículo en actas

En: 2010 2nd International Workshop on Cognitive Information Processing, pp. 382–387, IEEE, Elba, 2010, ISBN: 978-1-4244-6459-3.

Resumen | Enlaces | BibTeX | Etiquetas: Bayes methods, Bayesian joint recovery, Bayesian methods, correlated signal, Correlation, correlation methods, Covariance matrix, Dictionaries, distributed compressed sensing, matrix decomposition, Noise measurement, sensors, sparse component correlation coefficient

@inproceedings{Vinuelas-Peris2010,

title = {Bayesian Joint Recovery of Correlated Signals in Distributed Compressed Sensing},

author = {Pablo Vinuelas-Peris and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5604103},

isbn = {978-1-4244-6459-3},

year = {2010},

date = {2010-01-01},

booktitle = {2010 2nd International Workshop on Cognitive Information Processing},

pages = {382--387},

publisher = {IEEE},

address = {Elba},

abstract = {In this paper we address the problem of Distributed Compressed Sensing (DCS) of correlated signals. We model the correlation using the sparse components correlation coefficient of signals, a general and simple measure. We develop an sparse Bayesian learning method for this setting, that can be applied to both random and optimized projection matrices. As a result, we obtain a reduction of the number of measurements needed for a given recovery error that is dependent on the correlation coefficient, as shown by computer simulations in different scenarios.},

keywords = {Bayes methods, Bayesian joint recovery, Bayesian methods, correlated signal, Correlation, correlation methods, Covariance matrix, Dictionaries, distributed compressed sensing, matrix decomposition, Noise measurement, sensors, sparse component correlation coefficient},

pubstate = {published},

tppubtype = {inproceedings}

}

### 2009

Vinuelas-Peris, Pablo; Artés-Rodríguez, Antonio

Sensing Matrix Optimization in Distributed Compressed Sensing Artículo en actas

En: 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, pp. 638–641, IEEE, Cardiff, 2009, ISBN: 978-1-4244-2709-3.

Resumen | Enlaces | BibTeX | Etiquetas: Compressed sensing, Computer Simulation, computer simulations, correlated signal, Correlated signals, correlation theory, Dictionaries, distributed coding strategy, distributed compressed sensing, Distributed control, efficient projection method, Encoding, joint recovery method, Matching pursuit algorithms, Optimization methods, orthogonal matching pursuit, Projection Matrix Optimization, sensing matrix optimization, Sensor Network, Sensor phenomena and characterization, Sensor systems, Signal processing, Sparse matrices, Technological innovation

@inproceedings{Vinuelas-Peris2009,

title = {Sensing Matrix Optimization in Distributed Compressed Sensing},

author = {Pablo Vinuelas-Peris and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5278496},

isbn = {978-1-4244-2709-3},

year = {2009},

date = {2009-01-01},

booktitle = {2009 IEEE/SP 15th Workshop on Statistical Signal Processing},

pages = {638--641},

publisher = {IEEE},

address = {Cardiff},

abstract = {Distributed compressed sensing (DCS) seeks to simultaneously measure signals that are each individually sparse in some domain(s) and also mutually correlated. In this paper we consider the scenario in which the (overcomplete) bases for common component and innovations are different. We propose and analyze a distributed coding strategy for the common component, and also the use of efficient projection (EP) method for optimizing the sensing matrices in this setting. We show the effectiveness of our approach by computer simulations using the orthogonal matching pursuit (OMP) as joint recovery method, and we discuss the configuration of the distribution strategy.},

keywords = {Compressed sensing, Computer Simulation, computer simulations, correlated signal, Correlated signals, correlation theory, Dictionaries, distributed coding strategy, distributed compressed sensing, Distributed control, efficient projection method, Encoding, joint recovery method, Matching pursuit algorithms, Optimization methods, orthogonal matching pursuit, Projection Matrix Optimization, sensing matrix optimization, Sensor Network, Sensor phenomena and characterization, Sensor systems, Signal processing, Sparse matrices, Technological innovation},

pubstate = {published},

tppubtype = {inproceedings}

}