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However, estimating RTFs usually requires estimation of the source activity or a double-talk detector, as noise-only frames or time segments where both the transfer functions (TFs) and the noise signals are assumed to be stationary need to be available for RTF estimation [25 27].
In Section 3, a maximum likelihood estimation of the source location and attenuation parameters is proposed.
In Section 5.3 we described a simple method of estimation of the source spectrum.
We can obtain an ML estimation of the source location using any suitable nonlinear optimization technique.
These codes are able to calculate a conservative estimation of the source term, i.e. quantity and duration of radionuclide release.
From (4), we see that is actually the minimum-mean-squared-error (MMSE) estimation of the source signal.
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In doing so, the estimation of the source-relay channel is greatly simplified, and the computational complexity at the destination is reduced.
Furthermore, this makes the estimation of the estimation of the source-relay channel transition probabilities independent of the channel model and quantization method, making the results applicable to a large variety of systems.
The LMMSE disintegrated estimation from [5] involves the estimation of the source-relay channel at the relay (which significantly increases relay complexity) and the relay-destination channel at the destination, whereas [7] considers maximum-likelihood (ML) estimation of both these channels at the destination.
The SC decoder outputs bit by bit estimation of the sources.
In summary, the API measures the quality of the estimation of the mixing matrix, while the SNR measures the quality of the estimation of the sources themselves.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com