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Record W2148810903 · doi:10.1109/tmtt.2009.2025463

Optimum Design of Wideband Compensated and Uncompensated Marchand Baluns With Step Transformers

2009· article· en· W2148810903 on OpenAlexaff
Zhan Xu, Leonard MacEachern

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2009
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsCarleton University
Fundersnot available
KeywordsBalunWidebandImpedance matchingElectronic engineeringTransformerPassbandChebyshev filterInsertion lossElectrical impedanceBandwidth (computing)Return lossElectrical engineeringBand-pass filterComputer scienceEngineeringTelecommunications

Abstract

fetched live from OpenAlex

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper presents a design approach for wideband compensated and uncompensated Marchand baluns with stepped-impedance transformers. In order to obtain an equal-ripple bandpass response, conventional Chebyshev polynomials are modified to compensate the effect of the transfer function's dc poles. Unlike the available microwave filter design approaches, which usually require redundant elements, this approach leads to an optimum design by using the minimum number of equal length transmission line elements. Based on this design approach, both compensated and uncompensated Marchand baluns are studied. It is found that increasing difficulty arises when implementing a large bandwidth balun using the widely adopted compensated balun structure. Hence, the uncompensated balun structure becomes a better choice. To validate the proposed design approach, an uncompensated balun is designed on a standard two-sided printed circuit board. The measured results indicate that a return loss greater than 20 dB can be observed from 1 to 7.5 GHz. The phase imbalance is less than 4° and the amplitude is less than 0.5 dB from dc to 7.2 GHz. </para>

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.855
Threshold uncertainty score1.000

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 integrity0.0000.000
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 head0.008
GPT teacher head0.205
Teacher spread0.197 · 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.

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

Citations25
Published2009
Admission routes1
Has abstractyes

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