This work presents a low-power radiation detection and radioisotope identification System
on Chip (SoC) featuring mixed-signal sensory electronics and on-chip neural network (NN) …

This article explores the role of digital technologies (DTs) in enhancing the safety, efficiency,
and accuracy of radioactive waste management (RadWM). With the increasing generation of …

Low-pressure ultrafiltration membranes (LPM) are a promising technology for the
decentralized water supply. However, currently available ultrafiltration membranes cannot …

The adoption of machine learning approaches for gamma-ray spectroscopy has received
considerable attention in the literature. Many studies have investigated the deployment of …

As a rapid and automatic method, multiple radionuclide identification using deep learning
has drawn wide interest from researchers in the field of nuclear safety and nuclear security …

In current neural network algorithms for nuclide identification in high-background, poor-
resolution detectors, traditional network paradigms including back-propagation networks …

Determining the position of interaction is of great interest for gamma-ray imaging in various
nuclear applications. Among all gamma-ray detectors, scintillation detectors are commonly …

This dissertation presents the design of a novel radiation detection and identification system
that can operate continuously over a period of 8 days while detecting at 30,000 counts per …

With increasing popularity of machine learning methods used in gamma ray spectroscopy
for radioisotope identification, quality and scope of the data, to train and evaluate models …