MétaCan
Menu
Back to cohort
Record W3028336855 · doi:10.1002/mmce.22290

Detection of metallic and<scp>nonmetallic</scp>concealed targets based on millimeter‐wave inverse scattering approach

2020· article· en· W3028336855 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.

Bibliographic record

VenueInternational Journal of RF and Microwave Computer-Aided Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsConcordia University
FundersKing Saud University
KeywordsExtremely high frequencyAntenna (radio)ScatteringMaterials scienceDielectricRadarAcousticsOpticsMillimeterFrequency bandOptoelectronicsComputer sciencePhysicsTelecommunications

Abstract

fetched live from OpenAlex

A millimeter-wave radar based on active invers scattering approach for two dimensional screening of metallic and nonmetallic concealed targets is presented. The perceived challenges of detecting a nonmetallic target exhibiting poor dynamic range for measurement systems are analyzed and discussed by comparing the performance of three different antenna sensors. A short time duration pulse with frequency sweep covering 27 to 33 GHz band is used to feed the antenna sensors. In our experimental test, we buried a concealed target consists of metallic or dielectric strips under a dielectric layer that simulates the human body model. Waveguide and printed antipodal Vivaldi antennas are considered to study the target detectability and the quality of the measured millimeter-wave images. The use of proposed AVA resulted in a better-quality image with lower noise effect for both metallic and nonmetallic cases.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score0.793

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 head0.012
GPT teacher head0.189
Teacher spread0.177 · 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