Sentence examples for square reconstruction from inspiring English sources

S. K. Narang in [9] proposed an iterative least square reconstruction (ILSR) algorithm for reconstructing bandlimited graph signal from partially observed samples.

In [16], a simple least square reconstruction technique was used to recover the samples.

The correlation between the original and the reconsturcted F0 signal was calculated with root mean square reconstruction error.

PCA is the optimal linear transformation with respect to minimizing the mean square reconstruction error but it only considers second-order statistics.

According to the principle of Papoulis-Gerchberg Algorithm, an iterative least square reconstruction (ILSR) algorithm is proposed in [9, 17] for the signal processing on graphs.

These scaling studies show that for problems with reasonable load balance, our new algorithms for both spline interpolation and moving least square reconstruction demonstrate both strong and weak scalability using more than 100,000 MPI processes with billions of degrees of freedom in the data transfer operation.

Simulation results reveal high performance in terms of symbol error rate (SER) and mean-square reconstruction error (MSE).

The second term in Eq. (4) is the residual of the reconstruction, based on the mean-square reconstruction error estimate in the same way as in the PCA method.

The convergence of the curvature calculation is ensured by using a least squares reconstruction.

The new scheme, called DD-L2-ENO, uses a data-dependent weighted least-squares reconstruction with a fixed stencil.

Encouraging preliminary two-dimensional flow solutions obtained using DD-L2-ENO reconstruction are also shown and compared with solutions using limited least-squares reconstruction.