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We consider the distributed detection problem of a temporally correlated random radio source signal using a wireless sensor network capable of measuring the energy of the received signals. It is well-known that optimal tests in the Neyman-Pearson setting are based on likelihood ratio tests (LRT), which, in this set-up, evaluate the quotient between the probability density functions (PDF) of the measurements when the source signal is present and absent. When the source is present, the computation of the joint PDF of the energy measurements at the nodes is a challenging problem. This is due to the statistical dependence introduced to the received signals by the propagation through fading channels of the radio signal emitted by the source. We deal with this problem using the characteristic function of the (intractable) joint PDF, and proposing an approximation to it. We derive bounds for the approximation error in two wireless propagation scenarios, slow and fast fading, and show that the proposed approximation is exponentially tight with the number of nodes when the time-bandwidth product is sufficiently high. The approximation is used as a substitute of the exact joint PDF for building an approximate LRT, which performs better than other well-known detectors, as verified by Monte Carlo simulations.

The goal of this work is to study a preprocessing method for the data measured by a two-dimensional optoacoustic tomograph in order to reduce or eliminate artifacts introduced by the low number of detectors in the experimental setup and their limited bandwidth. A generative adversarial deep neural network was used to accomplish this task and its performance was compared with a reference U-Net neural network. In most of the test cases carried out, a slight improvement was found by applying the proposed network when measuring the Pearson correlation and the peak signal noise ratio between the reconstructed image product of the data processed by the model and the high-resolution reference image.

We first present a detailed review of two popular classes of model based synchrophasor estimators. They are the discrete Fourier transform (DFT) and the Taylor-Fourier transform (TFT) class estimators, which arise from constant or polynomial phasor models, respectively. Limitations of these techniques, arising from the models themselves, are exposed. These limitations make the original DFT and TFT approaches unfitted for situations where the phasor can not be properly described in such simple terms. This issue can be addressed with the recently introduced convex semi-infinite programming (CSIP) based estimators, which are also reviewed. In particular, we present the constraints associated with the tests which are most critical for the model based methods, showing how to precisely control the estimators performance in those cases. Finally, we show that the DFT and TFT estimators are particular instances of the CSIP estimator, so that there exists a connection between these two apparently different approaches. This opens the door for future analyses and developments.