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Record W2162056584 · doi:10.1109/map.2013.6529319

An Engineered Conductor for Gain and Efficiency Improvement of Miniaturized Microstrip Antennas

2013· article· en· W2162056584 on OpenAlexafffund
Saeed I. Latif, L. Shafai, Cyrus Shafai

Bibliographic record

VenueIEEE Antennas and Propagation Magazine · 2013
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsConductorMicrostrip antennaMicrostripMaterials sciencePatch antennaElectrical conductorAntenna (radio)Directional antennaOptoelectronicsMiniaturizationElectronic engineeringElectrical engineeringEngineeringComposite materialNanotechnology

Abstract

fetched live from OpenAlex

This paper reviews the concept of an engineered conductor, introduced by the authors in order to reduce ohmic losses of miniaturized microstrip antennas [1-4]. Because of the miniaturization, the ohmic losses of microstrip antennas increase, which essentially significantly reduces their gain and efficiency. By the use of the engineered conductor concept, these two important parameters of such antennas can be improved without altering the antenna's geometry. The concept is based on using multiple laminated thin conductors, rather than one thick conducting layer, to form the microstrip antenna. The technique is applied to several miniaturized microstrip antennas in order to reduce their ohmic losses. The concept is explained using a microstrip line to demonstrate that the conductor loss can be reduced by increasing the number of layers in the lamination, while keeping the total thickness constant. Using conventional metallized substrates, the lamination reduces the thickness of each conductor layer to approximately or less than the skin depth in that conductor. Studies of two miniaturized antennas - namely, the square-ring antenna and the modified open-ring antenna - have respectively provided about 4.6 dB and 1.5 dB improvements in the gain, and from 30% to 40.7% improvement in the efficiency. Experimental investigations are also presented that confirmed the simulated results.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.786
Threshold uncertainty score0.703

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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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 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

Citations22
Published2013
Admission routes2
Has abstractyes

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