View in EDS

Application of a Simple, Spiking, Locally Competitive Algorithm to Radionuclide Identification.

Saved in:
Bibliographic Details
Title: Application of a Simple, Spiking, Locally Competitive Algorithm to Radionuclide Identification.
Authors: Carson, Merlin1 mpc6@pdx.edu, Woods, Walt1 wwoods@pdx.edu, Reynolds, Sebastian2 sbtn.rey@gmail.com, Wetzel, Mark3 mwetzel@unm.edu, Morton, Adam J.3 ajmorton@unm.edu, Hecht, Adam A.3 hecht@unm.edu, Osinski, Marek4 osinski@unm.edu, Teuscher, Christof1 teuscher@pdx.edu
Source: IEEE Transactions on Nuclear Science. Mar2021, Vol. 68 Issue 3, p292-304. 13p.
Subjects: Radioisotopes, Compton scattering, Radioactive substances, Data dictionaries, Algorithms, Scintillators
Abstract: Many radionuclide identification algorithms use statistical inference to collect a variety of features from gamma-ray spectra to deduce the presence of particular radionuclides. More modern algorithms require large amounts of data to learn and use latent features from spectra for classification. Both approaches are computationally expensive, which is reflected in their power consumption, and require large amounts of user intervention to prepare. In this article, we introduce a low-power, neuromorphic algorithm for the real-time identification of radionuclides which simultaneously considers the entire shape of a gamma-ray spectrum. Utilizing the output of a traditional gamma-ray detector, our spiking, locally competitive algorithm uses sparse coding optimization to compare global patterns in a gamma-ray spectrum with a dictionary of radionuclide templates. This approach allows us to model informative global features resulting from both photoelectric absorption and Compton scattering. For the purpose of radiation threat reduction, the dictionary consists of data from the Nuclear Wallet Cards, a list of radionuclides and their properties compiled by the National Nuclear Data Center. To test our algorithm, we use a variety of gamma-ray spectra created using radionuclides measured under laboratory conditions with varying durations, distances, activity levels, and backgrounds, resulting in a wide range of signal-to-noise ratios. We have created test sets for three different gamma-ray detector types, with 57Co, 137Cs, 152Eu, 60Co, 239Pu, and 235U sources, to quantify the effect of resolution, efficiency, and background on the accuracy of the algorithm. We demonstrate a true positive accuracy of 91% with a high-resolution detector and 89% with a low-resolution detector on the corresponding test sets. Experimenting with the same radionuclides included in the test sets in a variety of special nuclear material (SNM) masking configurations, we show that our algorithm is capable of correctly identifying both SNM and mask even when the activity level of the mask is several times higher than that of the SNM. We also determine that our algorithm achieves over a 99% reduction in power consumption over other radionuclide identification software applications, which is critical for long-term, independent monitoring and is the goal of this research. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Nuclear Science is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 149418022
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Application of a Simple, Spiking, Locally Competitive Algorithm to Radionuclide Identification.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Carson%2C+Merlin%22">Carson, Merlin</searchLink><relatesTo>1</relatesTo><i> mpc6@pdx.edu</i><br /><searchLink fieldCode="AR" term="%22Woods%2C+Walt%22">Woods, Walt</searchLink><relatesTo>1</relatesTo><i> wwoods@pdx.edu</i><br /><searchLink fieldCode="AR" term="%22Reynolds%2C+Sebastian%22">Reynolds, Sebastian</searchLink><relatesTo>2</relatesTo><i> sbtn.rey@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Wetzel%2C+Mark%22">Wetzel, Mark</searchLink><relatesTo>3</relatesTo><i> mwetzel@unm.edu</i><br /><searchLink fieldCode="AR" term="%22Morton%2C+Adam+J%2E%22">Morton, Adam J.</searchLink><relatesTo>3</relatesTo><i> ajmorton@unm.edu</i><br /><searchLink fieldCode="AR" term="%22Hecht%2C+Adam+A%2E%22">Hecht, Adam A.</searchLink><relatesTo>3</relatesTo><i> hecht@unm.edu</i><br /><searchLink fieldCode="AR" term="%22Osinski%2C+Marek%22">Osinski, Marek</searchLink><relatesTo>4</relatesTo><i> osinski@unm.edu</i><br /><searchLink fieldCode="AR" term="%22Teuscher%2C+Christof%22">Teuscher, Christof</searchLink><relatesTo>1</relatesTo><i> teuscher@pdx.edu</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Nuclear+Science%22">IEEE Transactions on Nuclear Science</searchLink>. Mar2021, Vol. 68 Issue 3, p292-304. 13p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Radioisotopes%22">Radioisotopes</searchLink><br /><searchLink fieldCode="DE" term="%22Compton+scattering%22">Compton scattering</searchLink><br /><searchLink fieldCode="DE" term="%22Radioactive+substances%22">Radioactive substances</searchLink><br /><searchLink fieldCode="DE" term="%22Data+dictionaries%22">Data dictionaries</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Scintillators%22">Scintillators</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Many radionuclide identification algorithms use statistical inference to collect a variety of features from gamma-ray spectra to deduce the presence of particular radionuclides. More modern algorithms require large amounts of data to learn and use latent features from spectra for classification. Both approaches are computationally expensive, which is reflected in their power consumption, and require large amounts of user intervention to prepare. In this article, we introduce a low-power, neuromorphic algorithm for the real-time identification of radionuclides which simultaneously considers the entire shape of a gamma-ray spectrum. Utilizing the output of a traditional gamma-ray detector, our spiking, locally competitive algorithm uses sparse coding optimization to compare global patterns in a gamma-ray spectrum with a dictionary of radionuclide templates. This approach allows us to model informative global features resulting from both photoelectric absorption and Compton scattering. For the purpose of radiation threat reduction, the dictionary consists of data from the Nuclear Wallet Cards, a list of radionuclides and their properties compiled by the National Nuclear Data Center. To test our algorithm, we use a variety of gamma-ray spectra created using radionuclides measured under laboratory conditions with varying durations, distances, activity levels, and backgrounds, resulting in a wide range of signal-to-noise ratios. We have created test sets for three different gamma-ray detector types, with 57Co, 137Cs, 152Eu, 60Co, 239Pu, and 235U sources, to quantify the effect of resolution, efficiency, and background on the accuracy of the algorithm. We demonstrate a true positive accuracy of 91% with a high-resolution detector and 89% with a low-resolution detector on the corresponding test sets. Experimenting with the same radionuclides included in the test sets in a variety of special nuclear material (SNM) masking configurations, we show that our algorithm is capable of correctly identifying both SNM and mask even when the activity level of the mask is several times higher than that of the SNM. We also determine that our algorithm achieves over a 99% reduction in power consumption over other radionuclide identification software applications, which is critical for long-term, independent monitoring and is the goal of this research. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IEEE Transactions on Nuclear Science is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=149418022
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1109/TNS.2021.3054608
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 292
    Subjects:
      – SubjectFull: Radioisotopes
        Type: general
      – SubjectFull: Compton scattering
        Type: general
      – SubjectFull: Radioactive substances
        Type: general
      – SubjectFull: Data dictionaries
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Scintillators
        Type: general
    Titles:
      – TitleFull: Application of a Simple, Spiking, Locally Competitive Algorithm to Radionuclide Identification.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Carson, Merlin
      – PersonEntity:
          Name:
            NameFull: Woods, Walt
      – PersonEntity:
          Name:
            NameFull: Reynolds, Sebastian
      – PersonEntity:
          Name:
            NameFull: Wetzel, Mark
      – PersonEntity:
          Name:
            NameFull: Morton, Adam J.
      – PersonEntity:
          Name:
            NameFull: Hecht, Adam A.
      – PersonEntity:
          Name:
            NameFull: Osinski, Marek
      – PersonEntity:
          Name:
            NameFull: Teuscher, Christof
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 03
              Text: Mar2021
              Type: published
              Y: 2021
          Identifiers:
            – Type: issn-print
              Value: 00189499
          Numbering:
            – Type: volume
              Value: 68
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: IEEE Transactions on Nuclear Science
              Type: main
ResultId 1