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Record W1988509817 · doi:10.1109/nrsc.2012.6208514

B14. Active millimeter-wave imaging system for hidden weapons detection

2012· article· en· W1988509817 on OpenAlexaff
Ayman Elboushi, Abdel‐Razik Sebak

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Imaging and Scattering Analysis
Canadian institutionsConcordia University
FundersKing Abdulaziz City for Science and Technology
KeywordsExtremely high frequencyComputer scienceAntenna (radio)Synthetic aperture radarMicrowave imagingRadarFrench hornRadar imagingArtificial intelligenceRemote sensingComputer visionAcousticsTelecommunicationsPhysicsMicrowaveGeology

Abstract

fetched live from OpenAlex

In recent years, the need for developing an accurate human body imaging systems for concealed weapons becomes urgent due to the increase of the international terrorism. In this paper, an experimental system for hidden weapons detection is introduced. The system is based on the principles of Synthetic Aperture Radar (SAR) and monostatic radar. The proposed system transmits a very short pulse generated by a vector network analyzer (VNA) to illuminate a three layers target made from jeans, natural leather and reinforced papers. The target construction is chosen to emulate the presence of the human body. The imaging process is carried out by interpolating successive time domain measurements of the probe reflection coefficient S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sub> at different points. Two types of scanning probes are used the first one is a standard millimetre-wave (MMW) horn antenna while the other one is a hybrid microstrip/horn antenna. The system shows a great ability for imaging and detecting a hidden metallic targets under the jeans layer of the target.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.605
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.209
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2012
Admission routes1
Has abstractyes

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