MétaCan
Menu
Back to cohort
Record W3017195965 · doi:10.1109/tie.2020.2987278

Harmonic Characterizations of Loaded Resonators for Waveguide Filters

2020· article· en· W3017195965 on OpenAlexaff
King Yuk Chan, Rodica Ramer, Raafat R. Mansour, Roberto Sorrentino

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Waterloo
FundersAustralian Research Council
KeywordsBand-pass filterResonatorHarmonicsWaveguide filterHarmonicElectronic engineeringCoupling coefficient of resonatorsCapacitive sensingPrototype filterFilter (signal processing)Materials scienceAcousticsEngineeringFilter designPhysicsOptoelectronicsElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

A new method of controlling and predicting the harmonic responses, for spurious-free window improvement of millimeter-wave bandpass filters, with complete mathematical derivation, is presented in this article. This method relies on loading a transmission line resonator with inverters, permitting the increase/decrease of the harmonics-to-fundamental ratios, for predetermined inverter coupling values. It is a universal method based on circuit models and mathematical theories, independent of the fabrication implementation; only typical bandpass filter inverters are necessary. It does not affect or include the design of input/ output resonator ports, with materials and dimensions, introducing the least design complexity, cf. other earlier approaches. Resonators and bandpass filters using substrate integrated waveguide technology are designed, simulated, fabricated, measured and validated. The derived models, simulations and measured results are in good agreement. The control of the harmonic performance is demonstrated by comparing the measurements of two bandpass filters fabricated with-waveguides, in the millimeter-wave band 27.9 to 29.5 GHz. The filter, incorporating single capacitive inverter-loaded resonators, is analyzed versus a conventional iris filter operating with the TE10 mode. The comparisons of the simulations and measurements of the two filters demonstrate experimentally the possibility of the systematic control of the harmonics-to-fundamental ratios of the proposed theoretical analysis.

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: none
Teacher disagreement score0.948
Threshold uncertainty score0.942

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 integrity0.0000.001
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 head0.035
GPT teacher head0.222
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.

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

Citations9
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Industrial ElectronicsSame topicMicrowave Engineering and WaveguidesFrench-language works237,207