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Record W2015829457

Analysis of varactor diode-tuned frequency agile antennas

2010· article· en· W2015829457 on OpenAlex
Sean V. Hum

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEuropean Conference on Antennas and Propagation · 2010
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVaricapAntenna (radio)Electronic engineeringReconfigurable antennaDiodeFrequency agilityEquivalent circuitComponent (thermodynamics)Computer scienceElectrical engineeringAntenna efficiencyRadiation patternEngineeringPhysicsCapacitanceTelecommunicationsVoltageRadar
DOInot available

Abstract

fetched live from OpenAlex

Applications requiring frequency agility can benefit from a class of antennas known as frequency agile antennas (FAAs). Such antennas are based on integrating tunable components and/or switches within the structure of the antenna, and show significant promise for use in multi-frequency systems. However, tuning components often have losses associated with them that can affect the performance of an FAA. In this paper, we present the design and analysis of a differentially-fed frequency-tunable patch antenna employing varactor diodes that achieves a 1.8–3.2 GHz frequency tuning range. An equivalent circuit model is developed which is shown to accurately predict both port and radiation characteristics of the antenna. In particular, the ability of the circuit to rapidly and accurately predict the radiation efficiency of FAAs enables particular insight into the effect of tuning component losses to be extracted from the model. Predictions are compared to both full-wave simulations and experimental measurements of an FAA prototype.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.970
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.223
Teacher spread0.204 · 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