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Record W2914178620 · doi:10.1109/tmtt.2018.2885754

Polarization-Independent Cross-Dipole Energy Harvesting Surface

2019· article· en· W2914178620 on OpenAlexaff
Ahmed Z. Ashoor, Omar M. Ramahi

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2019
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRectennaEnergy harvestingDipoleDipole antennaPolarization (electrochemistry)DiodeRadiationOpticsPhysicsWireless power transferOptoelectronicsElectrical engineeringMaterials sciencePower (physics)Antenna (radio)EngineeringChemistry

Abstract

fetched live from OpenAlex

We present an energy harvesting surface (EHS) designed for electromagnetic energy harvesting and wireless power transfer consisting of an array of cross-dipole metallic elements. The EHS unit cell of the proposed harvester is based on two cross-dipoles that enable capturing the energy from various angles of illuminations at an operating frequency of 3 GHz. The simulation results yielded a radiation to ac efficiency of 94% at multiple angles of polarization. As a proof of concept, a finite array of 7 × 7 unit cells was fabricated and tested experimentally. The experimental results of the EHS energy harvesting array show an overall radiation to dc harvesting efficiency of 74% at various polarization angles. A critical design feature of the proposed cross-dipole EHS array is that it allows direct matching to a rectifying circuitry at the dipoles plane. The design also enhances the available power per diode, thus, maximizing the rectifying diodes' turn-on time.

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 score0.001
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 science0.0000.000
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 head0.005
GPT teacher head0.211
Teacher spread0.206 · 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
Published2019
Admission routes1
Has abstractyes

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Same venueIEEE Transactions on Microwave Theory and TechniquesSame topicEnergy Harvesting in Wireless NetworksFrench-language works237,207