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Record W2324724876 · doi:10.1109/tap.2014.2336655

MMW Sensor for Hidden Targets Detection and Warning Based on Reflection/Scattering Approach

2014· article· en· W2324724876 on OpenAlexaff
Ayman Elboushi, A. Sebak

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2014
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsAntenna (radio)Computer scienceReflectometryExtremely high frequencyRadarObject detectionRadiation patternAcousticsTime domainOpticsRemote sensingArtificial intelligenceTelecommunicationsPhysicsComputer visionPattern recognition (psychology)Geology

Abstract

fetched live from OpenAlex

An antenna sensor for millimeter wave (MMW) hidden target detection and warning applications is introduced. The sensor consists of three adjacent high gain microstrip/horn hybrid antenna elements. The central antenna acts as bi-static radar, while the two side antennas are used to receive the scattered back signals from a hidden object. The proposed antenna sensor has been employed in a detection/imaging system prototype based on time domain reflectometry (TDR). A very short pulse generated by a vector network analyzer is used to illuminate a three layers body model made from cotton, natural leather and reinforced papers. The model construction is chosen to emulate the presence of human body. Experimental scanning of a hidden metallic target has been conducted for three different orientations of the target. Compared to a single antenna sensor, the triple sensor shows a great enhancement in the detection ability and the constructed image resolution of a hidden 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.217
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), 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

Citations29
Published2014
Admission routes1
Has abstractyes

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