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Record W2163641896 · doi:10.1109/tap.2010.2055805

Analysis and Design of a Differentially-Fed Frequency Agile Microstrip Patch Antenna

2010· article· en· W2163641896 on OpenAlexaff
Sean V. Hum, Hui Xiong

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2010
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVaricapMicrostrip antennaPatch antennaEquivalent circuitAntenna (radio)Computer scienceMicrostripElectronic engineeringElectrical engineeringPhysicsTelecommunicationsEngineeringCapacitanceVoltage

Abstract

fetched live from OpenAlex

The design of a frequency agile microstrip patch antenna is described that is readily interfaced with differential RF transceivers. By integrating three pairs of varactor diodes with the patch antenna, and tuning them in unison, frequency tuning ratios approaching 2 are possible with the design. This is made possible by the intrinsically broadband nature of the differential feeding scheme used. An intuitive equivalent circuit for predicting the port characteristics of the antenna is presented. Moreover, the circuit is shown to be highly accurate in predicting losses produced by the varactor diodes and the consequent radiation efficiency of the antenna. To the best of the authors' knowledge, this is the first time a detailed analysis of the effect of varactor diode losses has been undertaken in frequency agile antennas using an equivalent circuit. The equivalent circuit model is subsequently validated using full-wave simulations and experimental measurements of an antenna operating in the 2-4 GHz range.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.208
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 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

Citations83
Published2010
Admission routes1
Has abstractyes

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