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Record W2801066721 · doi:10.1049/iet-map.2018.0012

Design of ultra‐compact UWB antenna with band‐notched characteristics for MIMO applications

2018· article· en· W2801066721 on OpenAlexaff
A. K. Gautam, Swati Varun Yadav, Karumudi Rambabu

Bibliographic record

VenueIET Microwaves Antennas & Propagation · 2018
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsElectronic engineeringMIMOAntenna (radio)Ultra-widebandComputer scienceAcousticsTelecommunicationsEngineeringPhysicsBeamforming

Abstract

fetched live from OpenAlex

This study describes the design of a very compact dual‐band‐notched ultrawide band (UWB) multiple‐input multiple‐output (MIMO) antenna. The main features of the proposed antenna are its compact dimensions and high isolation between the two antenna elements. The UWB performance of the antenna is attained by designing two identical triangular‐shaped patch elements, which are designed opposite to each other and connected to a tapered microstrip feedline. The ground plane of the proposed antenna consists of a funnel‐shaped patch connected with two triangles at the lower end. In addition, two J‐shaped slits are etched in the radiator to get the band rejection characteristics at wireless local area network band from 5.1 to 5.8 GHz and IEEE INSAT/Super‐Extended C‐band from 6.7 to 7.1 GHz. The designed antenna has the smallest dimension of . Results show that the MIMO antenna has a large impedance bandwidth of 31.9 GHz from 3.1 to 35 GHz. The diversity performance of the proposed antenna is also analysed using different parameters such as envelope correlation coefficient (ECC), diversity gain, total active reflection coefficient and so on. Due to the low mutual coupling of <−24 dB and ECC of <0.2 across the frequency band this antenna is a good candidate for portable UWB applications.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 integrity0.0010.000
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 head0.016
GPT teacher head0.227
Teacher spread0.212 · 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

Citations108
Published2018
Admission routes1
Has abstractyes

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