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The sparsity rate was set to 0.1.
The sparsity rate was fixed at 0.1.
The sparsity rate and the measurement rate were set to 0.15 and 0.45, respectively.
The average sparsity rate in each test image was set to 0.1.
SABMP algorithm, like other Bayesian algorithms, utilizes statistics of noise and sparsity rate.
SABMP algorithm assumes prior Gaussian statistics of the additive noise and the sparsity rate.
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The sparsity rates (k/N) of blocks in an image were intentionally varied to demonstrate that one set of thresholds was applicable for various sparsity rates.
Fig. 2 Impact of sparsity order, compression rate, and SNR. a The probability of false alarm versus δ, when γ k = 9 dB.
In what follows, we thus investigate the impact of sparsity order, compression rate, and SNR on the sensing performance of the proposed scheme.
b The probability of false alarm for different pairs of (ρ, γ k ) as δ = 0.5 Fig. 3 Impact of sparsity order, compression rate, and SNR. a The probability of detection versus δ, when γ k = 9 dB.
A numerical example illustrates that sparsity reduces bit-rates, thereby making our proposal suited to control over unreliable and bit-rate limited networks.
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