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Record W2595006481 · doi:10.1049/iet-map.2016.0671

Dual‐frequency DRA‐based MIMO antenna system for wireless access points

2017· article· en· W2595006481 on OpenAlexaff
Mohammad S. Sharawi, Symon K. Podilchak, Muhammad U. Khan, Yahia M. M. Antar

Bibliographic record

VenueIET Microwaves Antennas & Propagation · 2017
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsRoyal Military College of Canada
FundersKing Fahd University of Petroleum and Minerals
KeywordsMIMODual (grammatical number)WirelessComputer science3G MIMOAntenna (radio)TelecommunicationsElectronic engineeringComputer networkEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

An eight‐element, dual‐frequency, multiple‐input–multiple‐output (MIMO) antenna system is proposed. Cylindrical dielectric resonator antennas (cDRAs) were used as the radiating elements in the MIMO antenna system. One group of four cDRAs covers the 2.45 GHz band, whereas another four cover the second band at 5.8 GHz. A reflector element was also proposed to tilt the radiated beam patterns and reduce field correlations for the MIMO antenna system. The high‐band antenna elements were rotated 45° with respect to their low‐band counterparts, on the antenna ground plan, for compactness and printed circuit board integration such that the complete antenna system occupies a volume of 160 mm×160 mm×14.8 mm. The measured bandwidths (BWs) were at least 90 and 200 MHz for the two bands of operation while the envelope correlation coefficient (ECC) was <0.17. A study of different metallic reflectors was also provided in terms of the impedance matching, isolation, BW, andECC. Specific applications for the proposed design include wireless local area networks and other fourth generation access points.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.022
GPT teacher head0.258
Teacher spread0.236 · 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

Citations93
Published2017
Admission routes1
Has abstractyes

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