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Record W2756348097 · doi:10.1109/lmwc.2017.2746685

A Novel Tri-Band Wilkinson Power Divider for Multiband Wireless Applications

2017· article· en· W2756348097 on OpenAlexaff
Basem M. Abdelrahman, Hesham N. Ahmed, A. I. Nashed

Bibliographic record

VenueIEEE Microwave and Wireless Components Letters · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsWilkinson power dividerPower dividers and directional couplersCurrent dividerFrequency dividerElectrical engineeringBandwidth (computing)Electrical impedanceTransmission lineElectric power transmissionInsertion lossRadio spectrumElectronic engineeringFrequency bandEngineeringComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

This letter presents a novel and simple technique for the design of an arbitrary tri-band Wilkinson power divider. The proposed technique is based on the use of quarter-wave open stubs (QWOS) at the desired operation frequencies, placed at appropriate positions along the divider's 50-Ω output transmission lines, to create the targeted tri-band response. These stubs present a high impedance at the frequencies of interest, while introducing transmission zeroes between the operation bands. For a compact design, slow-wave structure has been adopted to miniaturize the conventional divider's arms. To validate the above concept, a power divider-intended to operate in the 1.5-, 1.9-, and 2.35-GHz frequency bands-is designed. The results of the fabricated circuit present low insertion loss of less than 0.45 dB while offering an isolation better than 15 dB in the three bands of operation. Compared to recently published results, the proposed divider exhibits the largest fractional bandwidth at each operation frequency. Furthermore, it can be easily extended to more bands by adding QWOS depending on the required number of bands.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.230
Teacher spread0.210 · 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

Citations26
Published2017
Admission routes1
Has abstractyes

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