TY - GEN
T1 - A Fast and Noise Rejecting Kolmogorov-Smirnvo Sorting Algorithm in X-ray Diamond sorting
AU - Modise, Ernest Gomolemo
AU - Zungeru, Adamu Murtala
AU - Mtengi, Bokani
AU - Ude, Albert
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - X-ray fluorescence and transmission have found common use in large-scale diamond sorting. These methods have progressed independently with each providing a solution for a specific range of particle size. Fluorescence is applicable for diamond sizes between 1.25 - 32 mm and is subject to self-absorption, while transmission has been used on diamond fractions between 10 mm - 100 mm. Transmission sorting suffers poor contrast at particle sizes below 10 mm. Parametric x-ray absorption fine structure models of fluorescence, with five describing features, and a transmission one with five parameters, are developed for both phenomena and simulated against literature for correctness. Furthermore, we define a new x-ray signature parameter by combining high-density ensemble data from both modes for a calibrated sample which we use as a benchmark in our sorting criteria. Finally, we construct random theoretical pure compounds and implore a fast noise rejecting Kolmogorov-Smirnov sorting algorithm to test random samples against the calibrated sample.
AB - X-ray fluorescence and transmission have found common use in large-scale diamond sorting. These methods have progressed independently with each providing a solution for a specific range of particle size. Fluorescence is applicable for diamond sizes between 1.25 - 32 mm and is subject to self-absorption, while transmission has been used on diamond fractions between 10 mm - 100 mm. Transmission sorting suffers poor contrast at particle sizes below 10 mm. Parametric x-ray absorption fine structure models of fluorescence, with five describing features, and a transmission one with five parameters, are developed for both phenomena and simulated against literature for correctness. Furthermore, we define a new x-ray signature parameter by combining high-density ensemble data from both modes for a calibrated sample which we use as a benchmark in our sorting criteria. Finally, we construct random theoretical pure compounds and implore a fast noise rejecting Kolmogorov-Smirnov sorting algorithm to test random samples against the calibrated sample.
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U2 - 10.1109/SmartNets55823.2022.9994005
DO - 10.1109/SmartNets55823.2022.9994005
M3 - Conference contribution
AN - SCOPUS:85146622905
T3 - 2022 International Conference on Smart Applications, Communications and Networking, SmartNets 2022
BT - 2022 International Conference on Smart Applications, Communications and Networking, SmartNets 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Smart Applications, Communications and Networking, SmartNets 2022
Y2 - 29 November 2022 through 1 December 2022
ER -