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Record W3019648268 · doi:10.1109/tmtt.2020.2984740

An Efficient Technique for Tuning and Design of Filters and Diplexers

2020· article· en· W3019648268 on OpenAlexaff
Huayong Jia, Raafat R. Mansour

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDiplexerElectronic engineeringNetwork synthesis filtersComputer scienceBand-pass filterPrototype filterTopology (electrical circuits)EngineeringElectrical engineeringLow-pass filterTelecommunicationsBandwidth (computing)

Abstract

fetched live from OpenAlex

This article introduces a new technique for filter design and tuning using the phase of the input impedance (PII) as the design parameter. This novel method is applicable to both narrow and wideband filters. Compared with conventional methods, this approach requires less computation time and provides a clear step-by-step procedure for identifying the proper interresonator coupling and the resonant frequencies of the resonators. In practice, the physical realization of the filter always has a nonideal I/O port, which can introduce an unexpected unknown transmission line between the physical reference plane and the port of the corresponding inverter in the circuit model. In this article, the PII response is used to determine the equivalent electrical length of this unknown transmission line. The technique is demonstrated through the design of a wideband planar filter with a fractional bandwidth of 72%, and the tuning of a 4-2 combline filter with two transmission zeros. Finally, a wideband diplexer is developed using the proposed technique.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.880
Threshold uncertainty score0.735

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 head0.014
GPT teacher head0.226
Teacher spread0.211 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations15
Published2020
Admission routes1
Has abstractyes

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