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Compressed sensing is a signal processing method that acquires data directly in a compressed form. This allows one to make less measurements than what was considered necessary to record a signal, enabling faster or more precise measurement protocols in a wide range of applications. Using an interdisciplinary approach, we have recently proposed in [1] a(More)
Compressed sensing is triggering a major evolution in signal acquisition. It consists in sampling a sparse signal at low rate and later using computational power for its exact reconstruction, so that only the necessary information is measured. Currently used reconstruction techniques are, however, limited to acquisition rates larger than the true density of(More)
In this paper we propose a novel Bayesian method to improve the robustness of cooperative spectrum sensing against misbehaving secondary users, which may send wrong sensing reports in order to artificially increase or reduce the throughput of a cognitive network. We adopt a statistical attack model in which every malicious node is characterized by a certain(More)
—Unexpected disasters, both naturally occurring and those caused through human actions, result in severe damage to communication infrastructure. Additionally, such events are accompanied by sharp spikes in the usage of commercially licensed spectrum, when affected victims of the tragedy attempt to transmit information about themselves and capture high(More)
Recent underwater sensor network research has focused on developing physical, medium access control, and network layer protocols to enable high data rate, energy-efficient and reliable acoustic communications. However, it is now essential to design and standardize architectures that will enhance the usability and interoperability of underwater networks. (More)