A cyclic campaign is simulated showing the satisfactory performance of the DB-MVC control configuration.
Figure 5 BER performance of the PEG-ACE dB code and the code designed by Ma and Kwak [6] after 10 decoder iterations.
We can note that estimating the source spectrum improves considerably the localization performance (of about dB in the example).
The average PSNR performance of the proposed method is 2.31 dB, 5.15 dB, and 5.15 dB higher than that of BTC, DPCM-BTC, and a recently developed BTC scheme using error diffusion and bilateral filtering, respectively.
For an SNR of 0 dB, the best performance of the proposed method was 2.976 dB (β = 5), which is 0.338 dB less than that of audio-input NMF.
For example, in traditional single-node systems, the performance of Berkeley DB is high, but its shortcoming of not supporting parallel writing across multiple nodes is weakening its availability and scalability in the distributed environment.
We used the following definition of error for evaluating the performance of the proposed method, textrm{error (dB)}=10log_{10}left int_{0}^{infty}left|,f_{alpha,gamma}(r -f_{alpha,gamma}^{^{text{app}}}(r -f_{alpha,gamma}ght)^{frac{1}{2}}. (32) Fig. 3 Mixture approximation error.
For few pilot symbols, a loss in performance of 1-3 dB is observed, while if the number of pilot symbols is increased to S p = 10, the loss is small.
Obviously, query and report generation performance depends on the size of the DB, the schema structure, and the organization/distribution of data within and across the tables.
Furthermore, a specific MA performance of 12243 dB cm2 g−1 is realized, which is one of the best results among various MAMs.
