It can be seen from Table 1 when the size of the pascal matrix is 2 or 4, the reconstruction property of the SKP measurement matrices is better than that of the random Gaussian measurement matrices and random Bernoulli measurement matrices from PSNR values.
In terms of alignment sensitivity, SSEARCH with sensitive matrices is better than BLAST-based methods, partly because the BLAST algorithm including CS-BLAST, and also SSEARCH with MIQS.SCOP40-v, tends to generate shorter alignment (Supplementary Fig. S4).
Furthermore, given the sufficient randomness of the Gaussian random measurement matrix elements, the reconstruction performance of the five reconstruction algorithms under the Gaussian random measurement matrix are better than those under the Bernoulli random measurement matrix.
As, the commuting matrix is better conditioned.
The accuracy of the core solution based on the matrix is better than a traditional collaborative filtering approach.
According to Figure 5, the symmetry along with range direction of the result based on our proposed matrix is better than those based on the previous matrix.
We demonstrate that, the IE system matrix is better conditioned than that of the finite-difference (FD) method, and discuss its relation to a specially preconditioned FD matrix.
Also, it is easy to find the symmetry of the range result after CS, and the final result by our matrix is better.
The fatigue life of UD composite with a brittle epoxide matrix is better than that with a ductile thermoplastic matrix by using the same fibre reinforcement.
According to Figure 1, using the different compression ratios, we can see that the PSNR of the reconstructed image of the Gaussian random measurement matrix is better than the other measurement matrix.
