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Record W1983639020 · doi:10.1049/iet-map.2010.0571

Frequency adjustable microstrip annular ring patch antenna with multi-band characteristics

2011· article· en· W1983639020 on OpenAlexaff
Kashish Jhamb, Lei Li, Karumudi Rambabu

Bibliographic record

VenueIET Microwaves Antennas & Propagation · 2011
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHFSSReturn lossPatch antennaMicrostrip antennaMulti-band deviceMicrostripMicrowaveAntenna (radio)Materials scienceCoaxial antennaAcousticsElectrical engineeringOptoelectronicsEngineeringElectronic engineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

This study introduces a frequency-tunable dual-band microstrip annular ring patch antenna design for wireless local area network (2.45 GHz) and Worldwide Interoperability for Microwave Access (3.5 GHz) applications. The proposed antenna produces realisable gain higher than 5 dBi and return loss better than 10 dB for the stated applications. The antenna design consists of an annular ring patch loaded with a slot or gap. This loaded ring excites higher-order modes around the dominant mode (TM11) of the annular ring thus making this design capable of dual-band operation. This design is realised on a 1.57 mm thick polytetrafluoroethylene substrate using a coaxial probe feed. In this study, a novel technique for independent frequency tuning has also been introduced by cutting grooves on the periphery of the ring at the desired locations. Furthermore, the design capability of multi-band operation (2.45 GHz/3.5 GHz/5.5 GHz) has also been explained in the later part of the study. Antenna operation has been verified with the help of two commercially available EM solvers (CST Microwave Studio and Ansoft HFSS Designer) and measurements. A close agreement has been found between the simulated and the measured results.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
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 communication0.0000.000
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 head0.019
GPT teacher head0.195
Teacher spread0.176 · 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 designSimulation or modeling
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

Citations35
Published2011
Admission routes1
Has abstractyes

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