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We can conclude from this experiment that using TV3D norm in our model can guarantee a high object detection performance at low sampling rate.
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Regarding the performance on the SensorScope data, one can observe that in this case RegEM achieves a significantly better performance compared to the other methods, followed by MC at low sampling rates and SS-MC at large ones.
At low sampling rates, nonlinear simulations show better performance using the sampled-data H∞-controller than those of other controllers.
In this special case, the poor video reconstruction performance has become the bottleneck that precludes good video background and foreground reconstruction at low sampling rate.
However, this approach has ghosting artifacts at low sampling rates.
Aimed at improving the performance under low sampling rates, a bandwidth criterion for intrinsic mode function (IMF) is proposed.
The size of each window is chosen to be 64 samples, which gives a good performance at low signal to noise ratio (SNR).
The autocorrelation among the received samples will result in more noise terms yielding poor performance at low SNR.
The SMA shows better performance at low noise level.
It has poorer performance at low normalized Doppler frequencies.
ML decoding exhibits a very low complexity, but it presents a poor performance at low SNRs.
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CEO of Professional Science Editing for Scientists @ prosciediting.com