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Then according to the size of the encrypted biometric signals, the top QSWTs were selected for both host video objects to embed the signals.
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That can be done using a technology called spread spectrum, which is now common in mobile telephones.The trick is to embed the signal in a dense "pseudorandom" sequence (it is pseudorandom because it looks random but is actually generated by a computer program).
We must embed the signal into background noise somehow.
To avoid the influence of the external circuit on data transmission, the system directly embedded the signal conversion circuit on the central control board.
End For . End For . Afterwards, summation of the coefficients of QSWT for to is calculated, and if the encrypted biometric signal is of size (in case of 2-D signals), then the top QSWTs (based on the summation results) are selected for embedding the signal.
Let (boldsymbol {x} in mathbb {R}^{L}) denote a host signal vector in which we wish to embed the watermark signal.
We stress again that (i) the number of image pixels used to embed the watermark signal samples, and (ii) their locations in the original image can be chosen arbitrarily.
There are two common approaches to this, the first being to simulate the noise simultaneously with the disease model, and the second being to embed the simulated signal into separately generated noise.
As customary in data-hiding applications, if the vector is the host signal, after the watermark embedding the watermarked signal is denoted by and the watermark signal is by definition.
To fill the gaps after the QRST intervals are removed, SSA embeds the original signal V1 in a subspace of higher-dimension M. The M-lag covariance matrix is computed as usual.
By embedding the desired signal in the clutter-plus-noise cross-correlation matrix, (mathbf {G}_{T}in {mathbb {C}^{N_{r}times N_{r}}}) in the transform domain yields.
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