In Table 7, it can be seen that among all lossless methods, our scheme has the smallest shadow size.
Compared with traditional sensing methods, our scheme can effectively explore potential gain carried by PU's working states and hence significantly reinforce sensing performance.
Compared with central WENO methods, the spatial reconstruction used here is much more compact; and unlike the original HWENO methods, our proposed schemes require neither flux splitting nor the use of numerical fluxes.
Unlike standard finite element methods, our discretization scheme relies on macro- and microfinite elements.
In other words, if there is any change in the authentication and encryption methods, our pre-authentication scheme will still work correctly because it does not involve any modification and encryption methods.
In this experiment, we verified that "(1,7,4) scheme" is worth the steganography method, because our scheme shows reasonable embedding rate and quality as a data hiding scheme.
Compared with the state-of-the-art sparse library and the latest GPU SpMV method, our adaptive scheme improves the performance of sparse matrix multiplications by 2.1× for single-precision and 1.6× for double-precision formats, on average.
The comparison with the other SVD-related watermarking methods indicates that our scheme is comparable to, if not better than, the selected methods.
Assuming that the current node receives AP signal successfully, the local congestion processing method used in our scheme is shown in Algorithm 1, where R is the local node's data transmission rate.
With a weighted-averaging-based interpolation method, our proposed encoding scheme attains an improvement of about 0.5 1.5 dB in PSNR over other similar methods.
