MétaCan
Menu
Back to cohort
Record W4206262259 · doi:10.18280/i2m.200605

Bandwidth Enhancement of Circular Ring Patch by Loading Single Split Complementary Split Ring Resonator

2021· article· en· W4206262259 on OpenAlexvenueno aff
Louis Magthelin Therase, Jayanthy Thangappan

Bibliographic record

VenueInstrumentation Mesure Métrologie · 2021
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHFSSGround planeSplit-ring resonatorMetamaterialBandwidth (computing)ResonatorPatch antennaMetamaterial antennaMicrostrip antennaMicrostripElectrical impedanceAcousticsWidebandOpticsAntenna (radio)PhysicsElectronic engineeringMaterials scienceComputer scienceEngineeringElectrical engineeringTelecommunicationsAntenna factor

Abstract

fetched live from OpenAlex

Metamaterials have gained a lot of interest in modern wireless devices due to their electromagnetic characteristics and it is employed to improve antenna parameters such as bandwidth, gain and efficiency. The study presents a novel strategy of single split non-uniform width Complementary Split Ring Resonator (CSRR) equipped Circular ring Microstrip Antenna (CRMA). The radiating element dimensions are seen to be 20 mm x 20 mm at the initial working frequency wherein the CSRR is incorporated on the ground plane of CRMA. The single split non-uniform width metamaterial (CSRR) structure displays -10 dB impedance bandwidth at 11.1 and 13.56 GHz which is beneficial for applications pertaining to X and Ku band. The total bandwidth of the said metamaterial antenna is 4 GHz (10.73-14.75). Optimization is undertaken with the help of commercially assessable simulation software tool Ansys HFSS 2019 version and practically measured using network analyzer. It is quite obvious from the experimentation that the results calculated get along well with the assumed outcomes.

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: Empirical · Consensus signal: Empirical
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.252
Teacher spread0.228 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInstrumentation Mesure MétrologieSame topicAntenna Design and AnalysisFrench-language works237,207