Application of the angle measure technique as image texture analysis method for the identification of uranium ore concentrate samples: New perspective in nuclear forensics-Reference-Cited by-同舟云学术

Application of the angle measure technique as image texture analysis method for the identification of uranium ore concentrate samples: New perspective in nuclear forensics

Published:2016-05 Issue: Volume:152 Page:463-474
ISSN:0039-9140
Container-title:Talanta
language:en
Short-container-title:Talanta

Author:

Fongaro Lorenzo,Lin Ho Doris Mer,Kvaal Knut,Mayer Klaus,Rondinella Vincenzo V.

Funder

Australian Safeguards and Non-proliferation Office (ASNO)

Publisher

Elsevier BV

Subject

Analytical Chemistry

Reference59 articles.

1. Nuclear forensics-a methodology providing clues on the origin of illicitly trafficked nuclear materials;Mayer;Analyst,2005

2. Nuclear forensic investigations: two case studies;Wallenius;Forensic Sci. Int.,2006

3. Nuclear forensic science-from cradle to maturity;Mayer;J. Alloy. Compd.,2007

4. Nuclear forensic investigations with a focus on plutonium;Wallenius;J. Alloy. Compd.,2007

5. Analysis of uranium ore concentrates for origin assessment;Varga;Proc. Radiochim. Acta,2011

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