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Record W2907178208 · doi:10.1109/tmtt.2018.2886847

Compact Wideband Reflective/Absorptive Bandstop Filter With Multitransmission Zeros

2018· article· en· W2907178208 on OpenAlexaff
Mengdan Kong, Yongle Wu, Zheng Zhuang, Yuanan Liu, Ahmed A. Kishk

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia University
FundersState Key Laboratory of Information Photonics and Optical CommunicationsNational Natural Science Foundation of China
KeywordsStopbandPassbandWidebandBand-stop filterStub (electronics)Bandwidth (computing)Return lossPrototype filterTransition bandBand-pass filterElectronic engineeringPole–zero plotMaterials scienceTopology (electrical circuits)Low-pass filterComputer scienceElectrical engineeringEngineeringTelecommunicationsTransfer function

Abstract

fetched live from OpenAlex

Novel wideband reflective bandstop filters (RBSFs) and absorptive bandstop filters (ABSFs) with high stopband rejection (SR) and good frequency selectivity are proposed. Based on a coupled line (CL) with an open-circuited stub, a broad stopband response with three transmission zeros (TZs) is implemented in a compact circuit configuration. Two modified CL structures are loaded at the ports resulting in two additional TZs. In addition, a grounded resistor is introduced to absorb unwanted signals in the stopband. For demonstration, the five-zero RBSFs and three-zero ABSFs operating at 2 GHz are designed and built. The reflective filter shows a 35-dB SR with 79.5% relative bandwidth (RB). The absorptive filter with 69.5% RB of 24-dB SR achieves an all-passband 12.2-dB return loss. Good agreements between simulated and measured results are observed.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.231
Teacher spread0.222 · 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

Citations49
Published2018
Admission routes1
Has abstractyes

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