A Fast and Noise Rejecting Kolmogorov-Smirnvo Sorting Algorithm in X-ray Diamond sorting

• Published in: 2022 International Conference on Smart Applications, Communications and Networking (SmartNets) pp. 1 - 5
• Main Authors: Modise, Ernest Gomolemo, Zungeru, Adamu Murtala, Mtengi, Bokani, Ude, Albert
• Format: Conference Proceeding
• Language: English
• Published: IEEE 29.11.2022
• Subjects: Absorption; Absorption coefficient; Algorithm; Benchmark testing; Calibrated sample; Calibration; Compounds; Contrast; Detection; Diamond sorting. Diamond; Diamonds; Edge jump; Fluorescence; Kolmogorov-Smirnov; Parametric; Self-absorption; Task analysis; X-ray absorption fine structure; X-ray fluorescence; X-transmission
• DOI: 10.1109/SmartNets55823.2022.9994005

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. For compounds that are closely related to the calibration sample, we obtained a percent similarity in the order of 98 %, while completely dissimilar theoretical compound similarity can be as low as 6 %