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Record W2885068114 · doi:10.1109/mwsym.2018.8439556

Fully Reconfigurable Dual-Mode Bandpass Filter

2018· article· en· W2885068114 on OpenAlexaff
Wentao Lin, Tae‐Hak Lee, Ke Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsBand-pass filterVaricapMiniaturizationCenter frequencyInsertion lossBandwidth (computing)Materials sciencePhysicsTopology (electrical circuits)Electronic engineeringOptoelectronicsOpticsEngineeringElectrical engineeringTelecommunicationsCapacitance

Abstract

fetched live from OpenAlex

This paper presents a fully reconfigurable dual-mode bandpass filter based on substrate integrated circular cavity technique (SICC). Dual-modes are excited orthogonally for miniaturization, which can be mutually coupled by the use of a cavity perturbation technique. Varactor diodes are used to tune cavity resonant frequencies independently since the arranged tuning units are only set for affecting one of the related modes. Combining controlled external coupling, all parametric tunings are achieved such as center frequency, bandwidth, transmission zero and other reconfigurable filtering characteristics. On the basis of tunable resonant frequencies, quasi-elliptical, linear phase and intrinsic-switching capability are obtained. For an experimental validation of the proposed structure, a dual-mode bandpass filter was fabricated and measured. As one example of the quasi-elliptical response, a constant absolute bandwidth of 200 MHz in the tuning range 2.83-3.55 GHz with insertion loss of 1.76-5.08 dB is observed in our experiments.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.212
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 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

Citations1
Published2018
Admission routes1
Has abstractyes

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