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In both simulation and experimental studies, the proposed method delivered better deconvolution performance than the classical minimum entropy deconvolution method, especially in the case of low signal-to-noise ratio.
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Upon analyzing the extracted images, it is observed that the proposed method delivers better results compared to other two methods.
Upon analyzing the metrics, it is evident that the proposed method delivers better embedding and extraction for the images of different contrast and background.
Furthermore, we validate the proposed method with a numerical design example, which shows that the proposed method delivers better performance than traditional ones.
For these two attacks, DWT SVD and RDWT SVD methods are able to extract the WM with less imperceptibility but the proposed methods delivers better results.
Overall, the CCQ method delivered a better assessment of the accuracy of the extracted data as it was assessed solely in relation to the high-quality reference dataset and there was no bias towards large homogenous areas i.e. non-drain.
The multiframe method, however, delivers better edge preservation in any case.
Therefore, in this particular case the optical methods for digitizing the coin and calculating its volume delivered better results.
The results show that all three methods improved the manipulation performance with regard to the defined problem where Magnet and Dual-gaze delivered better performance than Scaling.
Our proposed two-level prediction method delivers significantly better accuracy in predicting the request rate of one month workload.
Compared with the fixed feedback rate scheme, the proposed method delivers a better performance without incurring additional feedback bandwidth.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com