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This paper studies the reconstruction method of the end-to-end network traffic based on compressing sensing.
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To address this problem, recent approaches based on compressed sensing seek to reconstruct the signal from incomplete information.
In this work, we performed a feasibility study for image reconstruction in a circular digital tomosynthesis (CDTS) from limited-scan angle data based on compressed-sensing (CS) theory.
A new method based on compressed sensing is proposed.
In this paper, we provide a new approach for motif mining based on compressing the searching space.
Balances measure mass (in kg), while scales are based on compressing or expanding springs to measure your weight (in kgf).
We also calculate the Tspe with the compressed UWB echo data based on the compressed sensing theory.
Based on the compressed sensing theory [6, 7], an M×P (M≪N≤P) measurement matrix Φ can be employed to compress the EMV (widetilde {boldsymbol {Psi }}) due to its sparsity.
The target of low-dose medical CT image blind restoration reconstruction based on Bayesian compressed sensing is that the values of the sparse coefficient and the degradation matrix (H) are estimated based on the noisy projection (P) and parameters' prior distributions.
In this paper, the JSM-2 model was introduced for data compressed collection for sensor network based on distribution compressive sensing theory.
Spectrum sensing: For the channel assignment, CMNs estimate PN workload based on spectrum sensing.
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