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Record W2611479053

Frequency-scanned waveguide-fed slot array for millimetre-wave radar applications

2016· dissertation· en· W2611479053 on OpenAlexfundno aff
Michael Royle

Bibliographic record

VenueTSpace (University of Toronto) · 2016
Typedissertation
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsMillimetre waveRadarBandwidth (computing)Extremely high frequencyMillimeterMicrowaveAntenna (radio)OpticsRadar imagingAntenna arrayAcousticsElectronic engineeringEngineeringPhysicsElectrical engineeringTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

Millimetre-wave designs have become increasingly common in radar applications due to advantages when compared with traditional microwave designs. The increased operating frequencies offer greater bandwidths and allow for reduced antenna sizes. In radar systems, large bandwidths are used to achieve high range resolutions. At millimetre-wave frequencies, there is sufficient bandwidth for additional uses, most notably, beam steering using a frequency-scanning antenna. Due to their simplicity, these antennas are particularly attractive for commercial radar applications. In this work, a millimetre-wave frequency-scanned slot array was designed based on a serpentine waveguide for a 1.8 GHz bandwidth centred at 34.3 GHz. The antenna was fabricated, and measurements indicated a range resolution better than 1.25 metres over an angular scanning range of 20 degrees. The antenna was incorporated in a simplified radar system, which successfully demonstrated the ability to detect and locate multiple targets simultaneously.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.213
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 designSimulation or modeling
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

Citations3
Published2016
Admission routes1
Has abstractyes

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