Introduction to Radioisotope Identification

The paper also considers solving real optimization problem in the field of nuclear engineering, radioisotope identification.

PURPOSE This is a computational study to develop a rugged self-powered Radioisotope Identification Device (RIID).

We report the implementation of a deep convolutional neural network to train a high-resolution room-temperature CdZnTeSe based gamma ray spectrometer for accurate and precise determination of gamma ray energies for radioisotope identification.

This paper details FPGA implementation methodology for Convolutional Spiking Neural Networks (CSNN) and applies this methodology to low-power radioisotope identification using high resolution data.

76% at 662 keV, demonstrating good energy performance of the detector design, which could benefit radioisotope identification capability of the gamma imager.

Radioisotope identification of an energy spectrum obtained from a radiation portal monitor (RPM) is a challenging task due to its insufficient data acquisition time.

Considering the application to a handheld radioisotope identification device, different hand motions are investigated and compared, where waving apparently has the best angular detection rates.

Significant complexities in the emission spectra made automated identification of radioisotopes inaccurate; complexities were resolved using a manual radioisotope identification procedure.

Radioisotope identification using a plastic scintillation detector has been a challenging issue because of the poor spectral resolution and low cross-sections of these types of detectors when used for photoelectric absorption.

A representative sample of three archetypes (body-worn, human-carried, and other/large-detection-volume equipment) encompassed six categories: personal radiation detector, extended-range personal radiation detector, personal emergency radiation detector, radioisotope identification device, human-portable detector/backpack, and vehicle-mounted large-detection-volume detector.