Deepmarks: A secure fingerprinting framework for digital rights management of deep learning models
Deep Neural Networks (DNNs) are revolutionizing various critical fields by providing an
unprecedented leap in terms of accuracy and functionality. Due to the costly training …
unprecedented leap in terms of accuracy and functionality. Due to the costly training …
Detecting explosives by PGNAA using KNN Regressors and decision tree classifier: A proof of concept
Radiation based techniques such as PGNAA provided a good alternative to conventional
explosives detection methods due to the simplicity and efficiency of the quantitative isotopic …
explosives detection methods due to the simplicity and efficiency of the quantitative isotopic …
DeepAttest: An end-to-end attestation framework for deep neural networks
Emerging hardware architectures for Deep Neural Networks (DNNs) are being
commercialized and considered as the hardware-level Intellectual Property (IP) of the device …
commercialized and considered as the hardware-level Intellectual Property (IP) of the device …
Real-time classification of radiation pulses with piled-up recovery using an FPGA-based artificial neural network
Artificial neural networks (ANNs) have shown several benefits over the traditional
classification methods for radiation detector data, such as greater accuracy and the ability to …
classification methods for radiation detector data, such as greater accuracy and the ability to …
Pulse characteristics of CLYC and piled-up neutron–gamma discrimination using a convolutional neural network
J Han, J Zhu, Z Wang, G Qu, X Liu, W Lin, Z Xu… - Nuclear Instruments and …, 2022 - Elsevier
A method of describing the characteristics of Cs 2 LiYCl 6 scintillator pulses is proposed by
fitting all individual pulses with multiple exponential functions. Simulated pulses under very …
fitting all individual pulses with multiple exponential functions. Simulated pulses under very …
[HTML][HTML] Intelligent Radiation: A review of Machine learning applications in nuclear and radiological sciences
Modern advancements in computing power and the ability of machine learning (ML) to
model complex relationships between input and output have opened new prospects for data …
model complex relationships between input and output have opened new prospects for data …
K-nearest neighbors regression for the discrimination of gamma rays and neutrons in organic scintillators
M Durbin, MA Wonders, M Flaska… - Nuclear Instruments and …, 2021 - Elsevier
Certain organic scintillators, such as EJ-299 and stilbene, have the ability to distinguish
gamma rays and neutrons through the process of pulse shape discrimination (PSD). This …
gamma rays and neutrons through the process of pulse shape discrimination (PSD). This …
Pulse shape discrimination and exploration of scintillation signals using convolutional neural networks
J Griffiths, S Kleinegesse, D Saunders… - Machine Learning …, 2020 - iopscience.iop.org
We demonstrate the use of a convolutional neural network to perform neutron-gamma pulse
shape discrimination, where the only inputs to the network are the raw digitised silicon …
shape discrimination, where the only inputs to the network are the raw digitised silicon …
An artificial neural network system for photon-based active interrogation applications
Active interrogation (AI) is a promising technique to detect shielded special nuclear
materials (SNMs). At the University of Michigan, we are developing a photon-based AI …
materials (SNMs). At the University of Michigan, we are developing a photon-based AI …
[HTML][HTML] Labeling strategy to improve neutron/gamma discrimination with organic scintillator
Organic scintillators are widely used for neutron/gamma detection. Pulse shape
discrimination algorithms have been commonly used to discriminate the detected radiations …
discrimination algorithms have been commonly used to discriminate the detected radiations …