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Record W2041832977 · doi:10.1109/mms.2011.6068565

Early demonstration of a passive millimeter-wave imaging system using substrate integrated waveguide technology

2011· preprint· en· W2041832977 on OpenAlexaff
Ali Doghri, Anthony Ghiotto, Tarek Djerafi, Ke Wu

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsExtremely high frequencyOperabilityPrinted circuit boardWaveguideBandwidth (computing)Computer scienceIntegrated circuitSoftwareElectronic engineeringEngineeringElectrical engineeringTelecommunicationsOptoelectronicsMaterials science

Abstract

fetched live from OpenAlex

Majority of the reported millimeter-wave (MMW) imaging systems are constructed on the basis of conventional waveguide technology as this technology is known to provide the best performance over higher frequency ranges. This paper proposes and presents an alternative platform of technology based on the substrate integrated waveguide (SIW) technology. Early results in the development of an SIW passive millimeter-wave imaging system are reported and discussed in this work. This technology presents many advantages over the conventional waveguide technology such as lower cost, smaller size, integrability with printed circuit board (PCB) technologies, compactness, as well as low interference susceptibility. An SIW passive imaging system operating at 35 GHz intended for concealed weapon detection is demonstrated at its early stage. It consists of a mechanically scanning reflector antenna, an SIW direct receiver and a computer running data acquisition software. The direct receiver presents a 1.5 GHz bandwidth centered at 35 GHz, 2.7 dB noise figure, and 48 dB gain. It was built upon interconnecting SIW sub-circuits in a LEGO manner which is appropriate for system prototyping. Resulting images for different scenarios are presented and the system operability is demonstrated.

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 categoriesMeta-epidemiology (narrow)
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 score0.431
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.021
GPT teacher head0.208
Teacher spread0.187 · 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.

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

Citations13
Published2011
Admission routes1
Has abstractyes

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