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Record W2042933400 · doi:10.1109/tap.2013.2267718

Compact Multi-Band PIFAs on a Semi-Populated Mobile Handset With Tunable Isolation

2013· article· en· W2042933400 on OpenAlexaff
Kasra Payandehjoo, Ramesh Abhari

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2013
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsHandsetVaricapMiniaturizationComputer scienceBandwidth (computing)Electronic engineeringMIMOElectrical engineeringCapacitancePhysicsTelecommunicationsEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

In this communication, miniaturized tunable two-antenna systems composed of printed inverted-F antennas (PIFAs) are developed for a semi-populated mobile phone handset. The PIFAs are loaded with a series combination of an inductor and a varactor to simultaneously achieve miniaturization and tunability. The compact 32 mm-long PIFAs demonstrate tuning range of more than 240 MHz covering personal telecommunication bands from LTE-band13 to GSM900 MHz. As well a miniaturized tunable parasitic element is integrated in the handset to efficiently suppress coupling between the PIFAs to below$-$28 dB across the entire operational bandwidth of the antennas. Simulation and measurement results demonstrate the successful implementation of a tunable MIMO system with passive adjustable coupling reduction mechanism for mobile handsets and achievement of a channel capacity profile close to that of an un-correlated system.

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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

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 integrity0.0000.000
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 head0.012
GPT teacher head0.209
Teacher spread0.197 · 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

Citations29
Published2013
Admission routes1
Has abstractyes

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