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Record W2898801294 · doi:10.1002/mmce.21501

Miniaturized planar filters exhibiting flexible placement of three transmission zeros suitable for duplexer design

2018· article· en· W2898801294 on OpenAlexaff
Ahmed Elzayat, Ammar B. Kouki

Bibliographic record

VenueInternational Journal of RF and Microwave Computer-Aided Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsPassbandStub (electronics)DuplexerMicrostripPlanarStriplineElectronic engineeringTopology (electrical circuits)Band-pass filterInsertion lossResonatorMaterials sciencePole–zero plotBandwidth (computing)AcousticsOptoelectronicsComputer scienceEngineeringPhysicsElectrical engineeringTelecommunicationsTransfer function

Abstract

fetched live from OpenAlex

In this work, we show that a stub-loaded open-loop double resonator filter can produce three transmission zeros at finite frequencies close to the passband when designed using our proposed asymmetric feed point topology. We demonstrate that by selecting the length of the stub, the placement of double transmission zeros on either the high side of the passband or the low side of the passband can be selected. This selection can be achieved by using a single design parameter: the stub length. The flexibility in the double zero placement makes these filters particularly advantageous for duplexer designs. Two duplexers are fabricated using the proposed filter topology in a microstrip on a duroid substrate and substrate-embedded stripline via Low-Temperature Cofired Ceramic technology (LTCC). The measurements show steep attenuation of approximately 30 dB, close to the filters' passband as well as a compact size down to 0.075λ × 0.17λ.

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: Simulation or modeling · Consensus signal: none
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)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.018
GPT teacher head0.226
Teacher spread0.208 · 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 designSimulation or modeling
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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