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Record W2290270839 · doi:10.1109/tap.2015.2513099

Design of Compact F-Shaped Slot Triple-Band Antenna for WLAN/WiMAX Applications

2015· article· en· W2290270839 on OpenAlexaff
A. K. Gautam, Lalit Kumar, Binod Kumar Kanaujia, Karumudi Rambabu

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2015
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWiMAXGround planeAntenna (radio)Omnidirectional antennaMicrostrip antennaRadiator (engine cooling)MicrostripSlot antennaPhysicsPlanarElectrical engineeringComputer scienceTelecommunicationsOpticsEngineeringWireless

Abstract

fetched live from OpenAlex

This communication presents a small, low-profile planar triple-band microstrip antenna for WLAN/WiMAX applications. The goal of this communication is to combine WLAN and WiMAX communication standards simultaneously into a single device by designing a single antenna that can excite triple-band operation. The designed antenna has a compact size of $19 \times 25\;\text{mm}^{2}$ ($0.152 \lambda_{0}\;\times 0.2 \lambda_{0}$). The proposed antenna consists of F-shaped slot radiators and a defected ground plane. Since only two F-shaped slots are etched on either sides of the radiator for triple-band operation, the radiator is very compact in size and simple in structure. The antenna shows three distinct bands I from 2.0 to 2.76, II from 3.04 to 4.0, and III from 5.2 to 6.0 GHz, which covers entire WLAN (2.4/5.2/5.8 GHz) and WiMAX (2.5/3.5/5.5) bands. To validate the proposed design, an experimental prototype has been fabricated and tested. Thus, the simulation results along with the measurements show that the antenna can simultaneously operate over WLAN (2.4/5.2/5.8 GHz) and WiMAX (2.5/3.5/5.5 GHz) frequency bands.

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)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.043
GPT teacher head0.253
Teacher spread0.209 · 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

Citations151
Published2015
Admission routes1
Has abstractyes

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