Electrical & Computer Engineering, Department of
First Advisor
Sina Balkir
Second Advisor
Michael Hoffman
Date of this Version
Summer 7-26-2018
Citation
S. Murray, CMOS Radioactive Isotope Identification with Multichannel Analyzer and Embedded Neural Network. Master's thesis, Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, 2018.
Abstract
A radiation detection and identification system is designed and implemented to perform gamma ray spectroscopy on radioactive sources and identify which isotopes are present in the sources. A multichannel analyzer is implemented on an ASIC to process the signal produced from gamma rays detected by a scintillator and photomultiplier tube and to quantize the gamma ray energies to build a histogram. A fast, low memory embedded neural network is implemented on a microcontroller ASIC to identify the isotopes present in the gamma ray histogram produced by the multichannel analyzer in real time.
Advisors: Sina Balkir and Michael Hoffman
Included in
Computer Engineering Commons, Electrical and Electronics Commons, Other Electrical and Computer Engineering Commons, Systems and Communications Commons, VLSI and Circuits, Embedded and Hardware Systems Commons
Comments
A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science. Major: Electrical Engineering. Under the Supervision of Professors Sina Balkir and Michael Hoffman. Lincoln, Nebraska: August, 2018
Copyright (c) 2018 Samuel Murray