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Record W4386320551 · doi:10.1109/trs.2023.3310860

The Detection of Concealed Explosives Using the MiDSIX System

2023· article· en· W4386320551 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Radar Systems · 2023
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsCentre For Cold Ocean Resources Engineering
FundersPublic Works and Government Services Canada
KeywordsExplosive materialExplosive detectionSIGNAL (programming language)Computer scienceAcousticsDoppler effectRadarLoudspeakerComputer securityReal-time computingRemote sensingTelecommunicationsGeologyPhysicsGeography

Abstract

fetched live from OpenAlex

The detection of concealed explosives is an active area of concern for defence and security forces. Radar technology, with the ability to penetrate barriers and detect the motion of vibrating objects, provides an attractive method for detecting concealed threats and, thereby, protecting assets and lives. A system has been developed to detect concealed threats by inducing acoustically driven vibrations using a loudspeaker and measuring the resulting micro-Doppler signal. The prototype, known as the Micro-Doppler Signatures Indicating eXplosives (MiDSIX) system, is evaluated against replica improvised explosive devices (IEDs) concealed behind a variety of barriers that are constructed from common materials. The characterization of the induced micro-Doppler signal and the detection of concealed IEDs explosives are demonstrated in multiple settings. The MiDSIX system offers a new method for detecting concealed explosives that can be easily deployed with a flexible operation.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.857
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.030
GPT teacher head0.257
Teacher spread0.227 · 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