The Hessian matrix is reconstructed by linear or quadratic fitting.
The interference covariance matrix is reconstructed based on the Capon spectrum in (11), generally, not an optimal method [27].
Once the elastic energy in the shear band is dissipated its internal structure begins to recover, with the solid-like matrix being reconstructed, resulting in shear band arrest.
Expanding X E, the data matrix is reconstructed and denoted as Y ∈ ¡ 8 M × L Y = Re X E Im X E (8).
Images with a slice thickness of 0.6 mm, an increment of 0.6 mm, 150 mm FOV and 512 × 512 image matrix were reconstructed using the B30f kernel.
And the projected matrix is reconstructed based on weighted subspace basis: R i, p = f ̂ i, l × W l, p + 1 MN ∑ i = 1 MN f i, p (12).
The convergence of (12) can be recast as (tilde {sigma }_{i}^{2} = max left { { hat P {theta _{i1}}), mbox ldots, ldots, hat P {theta _{iL}})} right }), which means that the interference covariance matrix is reconstructed to create null notches towards the sampled points in angular sectors of the nonstationary interference, and thus, the wide null notches can be formed.
Therefore, the eigenvalues of G ROH are checked, negative eigenvalues are set to 0, and the matrix is reconstructed using only the positive eigenvalues.
In the proposed algorithm, two equivalent covariance matrices are reconstructed to achieve the decorrelation of the coherent signals and the estimated angle parameters are pair-matched automatically.
This advantage is suitable in non-linear study because the problem is solved iteratively and the non-linear stiffness matrices are reconstructed throughout each iteration.
