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Record W2950995151 · doi:10.1109/tmtt.2019.2918519

Miniaturized Folded Ridged Half-Mode and Quarter-Mode Substrate Integrated Waveguides for Filter Design

2019· article· en· W2950995151 on OpenAlexafffund
Thomas R. Jones, Mojgan Daneshmand

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology FuturesCMC Microsystems
KeywordsMiniaturizationBand-pass filterMaterials scienceSubstrate (aquarium)OpticsBandwidth (computing)WaveguideOptoelectronicsCapacitive sensingElectrical engineeringEngineeringPhysicsTelecommunications

Abstract

fetched live from OpenAlex

This paper characterizes the performance of a miniaturized folded ridged half-mode substrate integrated waveguide (SIW) and explores its use in quarter-mode bandpass filter (BPF) design. By folding the fundamental mode of a ridged half-mode SIW (RHMSIW) around the capacitive ridge structure, an additional 50% of miniaturization can be achieved over RHMSIW and up to 87.5% compared with the standard SIW. The monomode bandwidths of both folded ridged half-mode and ridged half-mode waveguide technologies are studied, yielding 1:4.28 or greater fundamental to the second-order mode ratios for all cases and approximately twice the monomode bandwidth compared with half-mode technology. Furthermore, the study of folded ridged half-mode and quarter-mode substrate integrated cavities is presented, yielding miniaturizations compared with standard substrate integrated cavities with respect to free-space lambda equal to 96.6% and 98.0%, respectively. The design of BPFs using folded ridged quarter-mode cavity is demonstrated, with two different prototypes measured to study the choice of substrate thickness with respect to filter performance. By increasing the substrate thickness while maintaining similar cavity miniaturization, an improvement of 26.1% in unloaded quality factor between the filters is realized experimentally.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.677
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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

Citations48
Published2019
Admission routes2
Has abstractyes

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