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Record W4386996097 · doi:10.3390/electronics12194016

3D-Printed Dielectric Rods for Radar Range Enhancement

2023· article· en· W4386996097 on OpenAlexaff
Mohammad Omid Bagheri, Hajar Abedi, George Shaker

Bibliographic record

VenueElectronics · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRadarAntenna (radio)Materials scienceMicrostripOpticsContinuous-wave radarPrinted circuit boardMicrostrip antennaRadar imagingElectronic engineeringEngineeringAcousticsElectrical engineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

A design strategy to alter the radiation characteristics of modular radar printed circuit boards without the need for expensive retooling and remanufacturing is presented in this paper. To this end, a compact radar module including microstrip array antennas integrated with a dielectric rod lens is considered for a demonstration of an X-band radar antenna gain improvement leading to radar detection range enhancement. Using travelling wave theory, the proposed lens is designed to target the excitation of HE11 mode to achieve gain improvement without disturbing reflection coefficients. Using a low-cost rapid-manufacturing 3D-printing technology, two pairs of the 3D-printed dielectric rods integrated with a dielectric housing are designed and fabricated uniformly for a commercially available off-the-shelf radar module. The radar integration with the dielectric rod lens leads to a low-cost and easy-to-fabricate long-range radar system. Compared with the radar without the rods, the design system achieved a measured 6.6 dB gain improvement of the transmitter and receiver antennas which causes doubling the detection range for both elevation and azimuth directions at 10.525 GHz.

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 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 score0.518
Threshold uncertainty score0.584

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 head0.014
GPT teacher head0.251
Teacher spread0.237 · 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.

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

Citations3
Published2023
Admission routes1
Has abstractyes

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