@inproceedings{Plata-Chaves2011bb,

title = {Optimal Neyman-Pearson Fusion in Two-Dimensional Densor Networks with Serial Architecture and Dependent Observations},

author = {Jorge Plata-Chaves and Marcelino Lazaro and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5977545\&searchWithin%3Dartes+rodriguez%26sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A5977431%29},

isbn = {978-1-4577-0267-9},

year  = {2011},

date = {2011-01-01},

booktitle = {Information Fusion (FUSION), 2011 Proceedings of the 14th International Conference on},

pages = {1--6},

address = {Chicago},

abstract = {In this correspondence, we consider a sensor network with serial architecture. When solving a binary distributed detection problem where the sensor observations are dependent under each one of the two possible hypothesis, each fusion stage of the network applies a local decision rule. We assume that, based on the information available at each fusion stage, the decision rules provide a binary message regarding the presence or absence of an event of interest. Under this scenario and under a Neyman-Pearson formulation, we derive the optimal decision rules associated with each fusion stage. As it happens when the sensor observations are independent, we are able to show that, under the Neyman-Pearson criterion, the optimal fusion rules of a serial configuration with dependent observations also match optimal Neyman-Pearson tests.},

keywords = {Bayesian methods, binary distributed detection problem, decision theory, dependent observations, Joints, local decision rule, Measurement uncertainty, Network topology, Neyman-Pearson criterion, optimal Neyman-Pearson fusion, optimum distributed detection, Parallel architectures, Performance evaluation, Probability density function, sensor dependent observations, sensor fusion, serial architecture, serial network topology, two-dimensional sensor networks, Wireless Sensor Networks},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}