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Dual Tunable Multifunctional Reconfigurable Vivaldi Antenna for Cognitive/Multi-Standard Radio Applications

2019· article· en· W2982605837 on OpenAlexaff
Chinmoy Saha, Jawad Y. Siddiqui, Yahia M. M. Antar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsVivaldi antennaReconfigurable antennaVaricapAntenna (radio)NarrowbandCognitive radioOptoelectronicsOmnidirectional antennaElectrical engineeringComputer scienceElectronic engineeringAntenna measurementPhysicsAntenna efficiencyTelecommunicationsEngineeringWirelessCapacitance

Abstract

fetched live from OpenAlex

A novel tunable triple band-notch vivaldi antenna which can also be reconfigured as a tunable narrow band antenna is proposed in this paper. The triple band notch characteristic is obtained by placing three split ring resonators (SRR) on the slotline and tuning it using a varactor diode. A tuning range from 800 MHz-1.1 GHz is contributed by each of the three varactor loaded SRRs yielding an overall tuning range of 3.5 GHz. The gain of the standalone Vivaldi is around 6-7 dBi in the entire Ultra wide Band (UWB) spectrum. The same antenna is reconfigured into narrowband antenna by introducing switches on the slotline above the SRR. This reconfigurable antenna is suitable for both underlay and interweave spectrum access in Cognitive Radio.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.019
GPT teacher head0.237
Teacher spread0.218 · 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 teacher head, not a consensus.

Study designSimulation or modeling
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

Citations10
Published2019
Admission routes1
Has abstractyes

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