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A simpler, although less efficient, reconstruction solution also much used in literature, defines as the reconstructed value the side-information value, if this side information value in within the decoded bin; if not, the reconstructed value assumes the lowest intensity value or the highest intensity value within the decoded quantized bin, following a saturation approach.
Another important issue for efficient reconstruction is the data access pattern, which affects cache utilization.
Reconstruction of full fMRI data from this less or undersampled data requires efficient reconstruction algorithm.
Trace enables efficient reconstruction of large-scale datasets on multicore clusters where adequate memory is available.
Researchers have proposed various methods for efficient reconstruction from undersampled k-space measurements [11, 12, 13, 14, 15, 16, 17].
An efficient reconstruction of the Delaunay triangulation at each step makes the method accurate for long times.
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Our work extends the functionality of the existing ASTRA Tomography toolbox by allowing efficient reconstructions of volumes that do not fit in the memory of a single GPU, on either a single node or using multiple nodes of a GPU cluster.
To address this issue, Fazel et al. proposed a random access compressed sensing (RACS) scheme in [13, 14] for energy-efficient reconstruction of sensing fields.
Since in this work one aims at the energy-efficient reconstruction of a monitored process by a WSN, it is important to evaluate how γ affects the energy consumption of sensor nodes.
The ASTRA toolbox provides highly efficient tomographic reconstruction methods implemented for graphic processing units (GPUs).
Recent results in compressive sensing (CS) can provide an efficient signal reconstruction method for WSNs.
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