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According to the HVS characteristic analysis of each sub-block, we learn that the maximum embedding capacity of each sub-block is 5 bits, so we use the singular value of LL, HL, and LH to embed the watermark.
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Currently, it is recommended to use the singular values that corresponds to a given percentile, such as the 95th of the distribution of singular values derived from the random data.
Composition matrices for different hydrological conditions are analyzed by using the singular value decomposition technique.
The overdetermined system of linear equations is robustly solved using the singular value decomposition (SVD) method.
We then calculate the closest orthonormal basis to B using the singular value decomposition.
To estimate the parameters, and, we have used the singular value decomposition (SVD) approach.
Rank reduction is achieved by using the singular value decomposition (SVD).
The theoretical control properties of those schemes were obtained using the singular value decomposition technique in all frequency domain.
This method uses the singular value decomposition (SVD) as the primary analysis tool and has shown some promise.
Five independent members of the alignment tensor were determined by using the singular value decomposition (SVD) method.
Using the singular value decomposition (SVD), the system matrix Ψ ˜ ˜ can be decomposed as: Ψ ˜ ˜ = UΣ V H, (4).
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