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Record W4377832630 · doi:10.18280/ts.400231

A Kagome Crest Fractal Optimized Quad-Band Antenna for Wireless Applications

2023· article· en· W4377832630 on OpenAlexvenueno aff
Paresh Chandra Sau, Purnima K. Sharma, Tammireddy J V S Rao, Emandi Kusuma Kumari, T. V. N. L. Aswini, Shilpa Jindal, Dinesh Sharma

Bibliographic record

VenueTraitement du signal · 2023
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsFractal antennaFractalCrestWirelessGeologyAntenna (radio)Computer scienceRemote sensingEnvironmental scienceTelecommunicationsRadiation patternPhysicsMathematicsOpticsAntenna efficiency

Abstract

fetched live from OpenAlex

The design of multiband antenna plays a vital role and enhances the functionality of mobile equipment for various wireless services over the last decade.This paper presents a sophisticated quad-band fractal microstrip patch circular monopole antenna with a star shaped slot.The proposed structure is iterated two times to have a Kagome crest fractal antenna.In the paper, optimization of quad band antenna using the Quasi newton Optimizer (QNO) is also presented.Optimizing various design parameters for the proposed antenna in HFSS is simulated.The antenna dimensions are 100mmx100mmx1.6mmoccupying an area of 100cm 2 .Model simulation and experimental validation is done for three frequency ranges, 2. resonating at 3.1GHz, 5.2GHz, 6.8GHz and 8.7GHz respectively.The proposed antenna operates for several wireless applications including RF Energy harvesting, dedicated short range communication, Satellite communication, wireless power transfer to a micro aerial vehicle, etc. can be covered with these band of frequencies.VSWR, return loss, radiation pattern, gain is some of the parameters considered to prove the performance of the fabricated design 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.

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 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 score0.869
Threshold uncertainty score0.611

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)0.0000.000

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 head0.027
GPT teacher head0.247
Teacher spread0.220 · 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.

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

Citations2
Published2023
Admission routes1
Has abstractyes

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