MétaCan
Menu
Back to cohort
Record W2803611450 · doi:10.1109/tcpmt.2018.2834730

Wideband and Ultrawideband Phase Shifter Designs Based on Low-Pass/Bandpass/High-Pass Networks

2018· article· en· W2803611450 on OpenAlexafffund
Mohammad Mahdi Honari, Rashid Mirzavand, Pedram Mousavi

Bibliographic record

VenueIEEE Transactions on Components Packaging and Manufacturing Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology Futures
KeywordsBand-pass filterPhase shift moduleWidebandMicrostripPassbandInductorCapacitorElectronic engineeringPhase (matter)Insertion lossElectrical engineeringEngineeringPhysicsComputer scienceMaterials scienceVoltage

Abstract

fetched live from OpenAlex

This paper presents the design of wideband and ultrawideband 90° phase shifters using coupled-line sections and low-pass/bandpass/high-pass networks with inductors and capacitors. By relaxing the tight-coupling requirement, the proposed phase shifters are realized with much easier fabrication process. It is demonstrated how the embedded low-pass/bandpass/high-pass networks make the phase shifters wideband. The fabricated phase shifters with low-pass, bandpass, and high-pass networks using discrete elements achieve the operating bandwidths of 44%, 83%, and 107%, respectively. Later, discrete elements are replaced by their equivalent short and open microstrip lines to make the system monolithic and more reliable, especially at higher frequencies. The measured results show approximately equal bandwidths of 40%, 87%, and 94% for the phase shifters with low-pass, bandpass, and high-pass networks, respectively.

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: Methods · Consensus signal: Methods
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 communication0.0000.001
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 head0.011
GPT teacher head0.217
Teacher spread0.206 · 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
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

Citations20
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Components Packaging and Manufacturing TechnologySame topicMicrowave Engineering and WaveguidesFrench-language works237,207