MétaCan
Menu
Back to cohort
Record W4313118191 · doi:10.1109/tap.2022.3231014

Analysis of Facet-Loaded Rectangular DR-Rectenna Designs for Multisource RF Energy-Harvesting Applications

2022· article· en· W4313118191 on OpenAlexaff
Daasari Surender, Md. Ahsan Halimi, Taimoor Khan, Fazal Ahmed Talukdar, Karumudi Rambabu, Ahmed A. Kishk

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2022
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsConcordia UniversityUniversity of Alberta
FundersScience and Engineering Research Board
KeywordsRectennaMaterials scienceEnergy harvestingOptoelectronicsWidebandImpedance matchingAntenna (radio)Energy conversion efficiencyElectrical engineeringResonatorRectifier (neural networks)Radio frequencyVoltageReflector (photography)OpticsPower (physics)Electrical impedancePhysicsComputer scienceRectificationEngineering

Abstract

fetched live from OpenAlex

In this article, the characteristics of four spiral-facet structures are studied for RF energy-harvesting (RFEH) applications for increasing the harvesting power from the surrounding atmosphere. A rectangular dielectric resonance antenna (RDRA) is selected and placed above an FR4-epoxy substrate. Metallic rectangular spirals are then placed on the dielectric resonator (DR) surface to create resonances with insensitive polarization characteristics. The proposed spirals help provide wideband/multiband characteristics at 4.85, 5.0, 5.5, 5.8, and 6.25 GHz that covers 5G 4.9, WLAN 5.0, WLAN 5.5, Wi-Fi 5.8, and Wi-Fi 6E bands, respectively. The minimum gain achieved is 5 dBi in all possible configurations. A broadband rectifier circuit (4.67–7.0 GHz) with a staircase multistage transmission line matching network (MN) covering all resonant frequencies in various facet-loaded antenna configurations is proposed for RF-to-dc conversion purposes. The rectifier’s maximum power conversion efficiency (PCE) is achieved as 77.3% at a 13.5 dBm input power level, and the corresponding output voltage is 4.92 V.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.964
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.229
Teacher spread0.208 · 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 designSimulation or modeling
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Antennas and PropagationSame topicEnergy Harvesting in Wireless NetworksFrench-language works237,207