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Earlier, we mentioned the methods for blind evaluation of noise variance which are accurate enough.
The authors of [9, 10] develop methods for blind recovery of convolutional encoder in turbo code configuration.
There is an essential interest in design and testing the methods for blind estimation of mixed noise parameters [9, 12, 15, 16, 21, 22].
Normalized mean square error (NMSE) versus the SNR for SIMO system with two sensors and T=1024: performance comparison between CCA based methods for blind channel identification.
This motivates a design of more accurate methods for blind estimation including analysis of dependences between the estimates of components of the mixed noise.
Normalized mean square error (NMSE) versus the SNR for SIMO system with two sensors and T=256: performance comparison between CCA based methods for blind channel identification Fig. 7 NMSE versus SNR for T=1024.
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Risk of performance and detection bias was judged by evaluating the methods for blinding of participants, personnel, and observers, as described in the studies.
However, there is as yet no efficient method for blind BWE of audio signals.
In this paper, we propose an adaptive method for blind separation of convolutively mixed sources.
In [4], we developed a method for blind recovery of a rate k/n convolutional encoder in turbocode configuration.
In [6, 11], a dual code method for blind identification of k/n-rate convolutional codes is proposed for cognitive radio receivers.
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