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Wireless Power Transfer using Triangular Patch Microstrip at 2.4GHz

2021· article· en· W3187571731 on OpenAlexaff
Sahereh Sahandabadi, Esam Abdel‐Raheem, Shahpour Alirezaee

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPatch antennaReflection coefficientMicrostrip antennaTransmitterTransmission coefficientMicrostripAcousticsAntenna (radio)Wireless power transferTransmission (telecommunications)Electronic engineeringElectrical engineeringComputer scienceTelecommunicationsWirelessOpticsPhysicsEngineering

Abstract

fetched live from OpenAlex

In this paper, a triangular microstrip patch antenna is used to transfer wireless power at 2.4 GHz. First, the simple patch antenna is studied and its performance is investigated. Then, the new design is introduced to boost the efficiency and transmission coefficient of the simple patch antenna. Since the simple patch aimed to operate at 2.4GHz, its dimension is optimized to be 56mm with a reflection coefficient of -28.2 dB at the resonance frequency. An identical patch acting as a receiver is then placed in a 50mm distance to the main patch acting as a transmitter to enable us to measure the power transferred to the receiver antenna. The transmission coefficient between the two simple patches is -3.5 dB at the resonance frequency of 2.4 GHz. Furthermore, the proposed array is designed and simulated yielding a reflection coefficient of -28 dB at 2.4 GHz. The same procedure for the simple patch is followed to obtain the transmission coefficient of -0.55 dB at 2.4 GHz. The simulation results show 89% efficiency for the proposed structure compared to the 44% efficiency of the single patch.

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 score0.006

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)0.0020.001

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 head0.200
Teacher spread0.188 · 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".

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Citations1
Published2021
Admission routes1
Has abstractyes

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