The operation of hyperspectral imaging systems in industrial environments can be a challenge. In the nuclear industry, partially transparent elements such as gloveboxes or panels are often used to cover samples for protection against the risk of contamination. In practical terms, this means that the hyperspectral sensors can only capture data through partially transparent media, which interferes the vision between sensor and sample. Representative examples of these media are Polymethyl Methacrylate (PMMA) or acrylic and Polycarbonate (PC). In this work, we evaluate the effect that the transparent media can have on the data when captured under these conditions, where transparent materials are placed between sensor and sample. Experiments include hyperspectral images of the same samples captured with and without panel obstruction for a direct comparison of spectral responses, suggesting potential artificial intelligence techniques and methods to identify these effects and mitigate them.
The stand-off, range-resolved detection of hydrogen production rates is a valuable mechanism for the long-term condition monitoring of packages containing intermediate-level nuclear materials. To exploit this effect we have developed a long-range optical sensor system which uses Raman detection of hydrogen. Our need for operation over extended ranges (up to 100m) results in very low Raman signals. We therefore use time-correlated (with respect to the outgoing excitation laser pulse) and spectrally-resolved single-photon detection to ascertain molecular species, position and concentration as revealed by photon energy, arrival time and number, respectively.
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