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The following detection problems are studied in this article, to assess the capability of learning machines trained to minimize the Cross-Entropy error to implement good approximations of the NP optimum detector: Case study 1: Detection of Gaussian fluctuating targets in presence of Additive White Gaussian Noise AWGNN).
A handful of disgruntled shareholders late last year filed a civil suit against Cubic, alleging fraudulent activity by Cubic executives and officers in connection with the defense procurement investigation and the mine detector case.
Reconstructed background and inclusion hemoglobin levels were much lower when the detector case was in place.
Resin and agar phantom experiments using the detector case provided useful data.
In seeking the optimal detector case material, several options were studied further.
The average total hemoglobin recovery for the background region was 70% of the expected value without the detector case, but only 54% with it in place – a 23% decrease due to the detector case.
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There was no overlap in subjects between the training and validation sets for either the confocal or split detector cases.
We trained separate networks for the confocal and split detector cases, which we label the confocal CNN (C-CNN) and split detector CNN (SD-CNN).
This very substantial radiation hardness should also favor the survival of the detector in case of accidental beam losses.
On the other hand, light from the Raman excitation laser which would be detected by the DHM detector in case of imperfections in the dichroic mirror would not interfere with the reference arm light, and would be filtered out during the Fourier reconstruction process.
The separation gets better compared to the single detector imitating case for SACF from two detectors using independent filter generation.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com