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Three of the presented FIs as examples are chosen in order to verify the developed method, perform their simulations and show their performance.
With respect to the recovery error shown in Fig. 6 a, the Bregman iterative algorithm [15] demonstrates the best performance while GPSR [31], SPGL1-Lasso [32], l1-ls [35], and the proposed method perform similarly and can be classified as the second performance tier.
An AUC score of less than 0.5 means that a method perform worse than random.
These results confirmed that our reconstruction method perform well in low scatter and attenuation conditions.
The positioning algorithms that exploit the soft information obtained by the linearization and the likelihood method perform equally well.
However, method-1 and proposed method perform correctly as observed from the dynamic response shown in Fig. 9.
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The backward elimination method performed better.
This method performs non-equilibrium molecular dynamics.
The method performs segmentation accuracy over 90%.
The CLS method performed poorly (RPET=38%).
This combined method performs energy absorption effectively.
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