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Record W2336256148 · doi:10.2528/pierc15071504

DIELECTRIC RESONATOR ANTENNA ARRAYS FOR MICROWAVE ENERGY HARVESTING AND FAR-FIELD WIRELESS POWER TRANSFER

2015· article· en· W2336256148 on OpenAlexafffund
Ahmed Z. Ashoor, Omar M. Ramahi

Bibliographic record

VenueProgress In Electromagnetics Research C · 2015
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Waterloo
FundersCMC Microsystems
KeywordsWireless power transferRectennaDielectric resonator antennaMicrowaveAntenna (radio)Maximum power transfer theoremNear and far fieldEnergy harvestingDielectricEnergy transferResonatorDielectric resonatorElectrical engineeringMaterials scienceWirelessPower (physics)AcousticsOptoelectronicsTelecommunicationsEngineeringPhysicsEngineering physicsOptics

Abstract

fetched live from OpenAlex

This paper presents dielectric resonator antennas (DRAs) for efficient energy harvesting or wireless power transfer in the microwaves regime. A single DRA and 1 × 3 array were used to build foundation profiles for DRAs as energy absorbers. The proposed structures were designed and fabricated to resonate around 5.5 GHz. The study examined different factors that affect the absorbed power efficiency. The size of ground plane and coupling between dielectric resonator (DR) elements in an array were studied, highlighting their effects on the overall efficiency of the antenna structure for different incident polarizations. A 5 × 5 array was built based on the studied factors and tested numerically and experimentally. Measurements showed that energy absorption efficiency as high as 67% can be achieved using an array of DR antennas.

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 score0.004

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.033
GPT teacher head0.292
Teacher spread0.259 · 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

Citations18
Published2015
Admission routes2
Has abstractyes

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Same venueProgress In Electromagnetics Research CSame topicEnergy Harvesting in Wireless NetworksFrench-language works237,207