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Hence, using the proposed ΣCI method, the signal can be segmented in three states in a deterministic manner.
Unlike the conventional method, the signal subspace approach does not require many microphones to work reasonably well.
In the proposed method, the signal subspace and the beamforming weights are updated in each snapshot to handle the dynamic cases.
In this pixel-based method, the signal at the pixel under test is compared with an adaptive threshold, generated from a sliding window of reference pixels from the background.
In this method, the signal s ∈ R n is assumed to be an instance of a vector random variable s = ( s 1, …, s n ) t, where t denotes transpose operator, and the ith row of Φ is constructed using the value of the previous sample yi−1.
For the first normalization method, the signal intensities of each spot of the experiment as well as of each spot of the control were totalised.
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We proposed that the signals under experiment has been added with muscle noise and after applying different FIR method, the signals according to signal noise ratio (SNR) and MSE mean square error) are evaluated.
In the proposed multi-channel method, the signals captured at different microphones are linearly combined to increase SNR.
We can see that the PDR-based method outperformed both the Fixed method and the signal strength method.
In the conventional coding methods, the signal is segmented into short time segments and transformed into frequency domain coefficients.
Results: Using three different computational methods, the signal of tRNA decoding in codon usage bias is identified.
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