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—The selection of the minimum number of sensors within a network to satisfy a certain estimation performance metric is an interesting problem with a plethora of applications. We explore the sparsity embedded within the problem and propose a relaxed sparsity-aware sensor selection approach which is equivalent to the unrelaxed problem under certain(More)
A total of 1528 pre-school children (mean age 4 years and 9 months), being identified as speech or language delayed, were evaluated with respect to micro-otoscopy, nose and throat pathology, hearing function, and speech-language abilities. Subjects were classified into groups of (I) constant normal hearing, (II) fluctuating conductive hearing loss and (III)(More)
Two novel cooperative localization algorithms for mobile wireless networks are proposed. To continuously localize the mobile network , given the pairwise distance measurements between different wireless sensor nodes, we propose to use subspace tracking to track the variations in signal eigenvectors and corresponding eigenvalues of the double-centered(More)
4485 The results clearly show that the sparse LMS outperforms the standard LMS, which gives a good estimate for the actual spectrum. Consequently , the periodic property of the source signal can be revealed based on the estimated spectral peaks. The results also imply that the proposed sparse algorithms are workable for a practical AR process as commented(More)
We consider the problem of cooperative localization in mobile wireless sensor networks (WSNs). To be able to continuously localize the mobile network, we propose to exploit the knowledge of the location of the anchor nodes to linearize the nonlinear distance measurements with respect to the location of the unknown nodes. Based on this linearized measurement(More)
—We study the problem of optimizing the symbol error probability (SEP) performance of cluster-based cooperative wireless sensor networks (WSNs). It is shown in the literature that an efficient relay selection protocol based on simple geographical information of the nodes to execute the cooperative diversity transmission, can significantly improve the SEP(More)
We tackle the problem of localizing multiple sources in multipath environments using received signal strength (RSS) measurements. The existing sparsity-aware fingerprinting approaches only use the RSS measurements (autocorrelations) at different access points (APs) separately and ignore the potential information present in the cross-correlations of the(More)
We extend one of our recently proposed anchorless mobile network localization algorithms (called PEST) to operate in a partially connected network. To this aim, we propose a geometric missing link reconstruction algorithm for noisy scenarios and repeat the proposed algorithm in a local-to-global fashion to reconstruct a complete distance matrix. This(More)