Seeded Localized Averaging

Different radioactive materials and their associated decay chains produce different radiation energy spectra.  Naturally, this has lead to the study of identifying a radioactive material based on its energy spectrum, called radiation spectroscopy.  One of the central problems in radiation spectroscopy is the resolution of the radiation detector.  As radiation hits the detector, the noise from the detection equipment and the random process of radiation smears out the radiation spectrum of the radioactive material.

Seeded Localized Averaging (SLA) is a real-time spectrum aquisition method that allows us to sharpen the peaks in aquired energy spectra.  SLA was patented by my co-author, Valentin Jordanov, in 2007.  In this talk, for the first time, we prove some basic theoretical properties of the SLA transformation.  We also describe the transformation in detail, and show some practical examples of how the transformation behaves.


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