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- W2085651305 abstract "This paper deals with the problem of discriminating samples that contain only noise from samples that contain a signal embedded in noise. The focus is on the case when the variance of the noise is unknown. We derive the optimal soft decision detector using a Bayesian approach. The complexity of this optimal detector grows exponentially with the number of observations and as a remedy, we propose a number of approximations to it. The problem under study is a fundamental one and it has applications in signal denoising, anomaly detection, and spectrum sensing for cognitive radio. We illustrate the results in the context of the latter." @default.
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- W2085651305 date "2009-04-01" @default.
- W2085651305 modified "2023-09-26" @default.
- W2085651305 title "A Bayesian approach to spectrum sensing, denoising and anomaly detection" @default.
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- W2085651305 doi "https://doi.org/10.1109/icassp.2009.4960088" @default.
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