kth.sePublications
System disruptions
We are currently experiencing disruptions on the search portals due to high traffic. We are working to resolve the issue, you may temporarily encounter an error message.
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Analysis of the state of the art on non-intrusive object-screening techniques
Turiba Univ, Graudu St 68, LV-1058 Riga, Latvia.;Kremenchuk Mykhailo Ostrohradskyi Natl Univ, Pershotravneva 20, UA-39600 Kremenchuk, Ukraine..
Riga Tech Univ, Inst Informat Technol, Kalku St 1, LV-1658 Riga, Latvia..
Univ Politecn Madrid, Sch Aerosp Engn, Madrid 28040, Spain..
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.ORCID iD: 0000-0001-6570-5499
2022 (English)In: Przeglad Elektrotechniczny, ISSN 0033-2097, E-ISSN 2449-9544, Vol. 98, no 2, p. 168-173Article in journal (Refereed) Published
Abstract [en]

The paper is devoted to an analysis of the modern methods and techniques used for non-intrusive object screening. First, currently used technology and the principle of equipment operation are described. Next, the ways for improving the reliability and efficiency of the screening process and ways for its automation are indicated. Finally, a schematic of an automated screening system that uses additional sensors and implements AI-based analysis for automatic detection and distinguishing between legal, illegal and illicit items inside the object under inspection is proposed.

Place, publisher, year, edition, pages
Wydawnictwo SIGMA-NOT, sp. z.o.o. , 2022. Vol. 98, no 2, p. 168-173
Keywords [en]
X-ray machine, odour analysis, spectroscopy, artificial intelligence, deep learning, image recognition
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:kth:diva-308666DOI: 10.15199/48.2022.02.39ISI: 000748729800026Scopus ID: 2-s2.0-85124513794OAI: oai:DiVA.org:kth-308666DiVA, id: diva2:1637910
Note

QC 20220215

Available from: 2022-02-15 Created: 2022-02-15 Last updated: 2022-06-25Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Vinuesa, Ricardo

Search in DiVA

By author/editor
Vinuesa, Ricardo
By organisation
Fluid Mechanics and Engineering AcousticsLinné Flow Center, FLOW
In the same journal
Przeglad Elektrotechniczny
Software Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 117 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf