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Record W2129633054 · doi:10.1109/tmtt.2005.852764

Analysis and design of bridged NRD-guide coupler for millimeter-wave applications

2005· article· en· W2129633054 on OpenAlexaff
Duochuan Li, Y. Cassivi, Ping Yang, Ke Wu

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2005
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMillimeterPower dividers and directional couplersHybrid couplerExtremely high frequencyWaveguideCoupling coefficient of resonatorsCoupling (piping)Bandwidth (computing)Materials scienceOpticsRat-race couplerPhysicsElectrical engineeringOptoelectronicsEngineeringTelecommunications

Abstract

fetched live from OpenAlex

A new nonradiative-dielectric (NRD)-guide directional coupler using two NRD waveguides interconnected with a bridge is proposed and demonstrated. Propagation constants of the bridged NRD-guide couplers are investigated with an electric-field integral-equation method, and modeling results show that there is a maximum of coupling coefficient when the thickness of the bridge is made around 0.68 of the plate separation. In this case, the coupling length is reduced approximately 60% and the bandwidth under the tolerance limits of /spl plusmn/0.5 dB of deviation for 3-dB coupling is nearly doubled with reference to its conventional counterparts. The use of a bridge not only improves the mechanical stability, but also makes the coupler performance reproducible. Experimental prototypes are fabricated with two 90/spl deg/ elbow bends that are used for the arms and the bridge-connected coupler section is terminated at both ends by tapered half-circle sections. A calibration procedure is used to remove mismatch effects between the NRD-guide and the rectangular waveguide in the measured results. Advantages of the new coupler as a passive component are shown for millimeter-wave integrated circuits.

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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.885
Threshold uncertainty score0.842

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 head0.013
GPT teacher head0.233
Teacher spread0.219 · 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
GenreMethods

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

Citations18
Published2005
Admission routes1
Has abstractyes

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