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Given that there are L different pM-dimension signals in the ensemble of y. p is the ratio of the sampled signal's size to the original size.
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Selective control depends on the statistics of signaling in the ensemble of networks, and we analyze the effects of superposition, non-linearity and feedback on these statistics.
First, we investigated for the size of the ensemble (L) and the size of the signal in the ensemble for the optimum performance of OMP-PKS+RS and DCS-SOMP.
We hypothesize that all randomly sub-sampled y are corrupted with the noise of the same mean and variance; therefore, we can remove the effect of Gaussian noise by averaging the reconstruction results of the signals in the ensemble.
The size of the signal in the ensemble was investigated in term of the ratio to the size of y (p). Figure 6 shows the PSNR of the reconstruction images at different L and p. The measurement rate (M/N) was set to 0.4.
DCS uses the concept of joint sparsity, which is the sparsity of every signal in the ensemble.
The Gaussian denoising was performed by averaging the image reconstruction of every signal in the ensemble.
The second requirement is necessary because the reconstruction must be performed L times (L is the number of the signal in the ensemble).
The i th signal in the ensemble, y i.
Output: The i th signal in the ensemble, y i.
the reconstruction of the signal at low measurement rate (M/N), fast reconstruction, independent reconstruction result for each signal in the ensemble.
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