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Record W4213290269 · doi:10.1109/lmwc.2022.3146883

Compact and Efficient Broadband Rectifier Using T-type Matching Network

2022· article· en· W4213290269 on OpenAlexfundno aff
Sha Yu, Fei Cheng, Chao Gu, Ce Wang, Kama Huang

Bibliographic record

VenueIEEE Microwave and Wireless Components Letters · 2022
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesState Key Laboratory of Millimeter WavesQueen's UniversityNational Natural Science Foundation of ChinaQueen's University Belfast
KeywordsRectifier (neural networks)Voltage doublerImpedance matchingBroadbandElectronic engineeringElectrical impedanceElectrical engineeringTopology (electrical circuits)Input impedanceVoltageMatching (statistics)Power (physics)Computer scienceEngineeringPhysicsTelecommunicationsMathematicsVoltage source

Abstract

fetched live from OpenAlex

In this letter, a multioctave voltage doubler rectifier using a T-type matching network is demonstrated. The voltage doubler topology is used to flatten the load impedance, which makes it easier to achieve broadband impedance matching. The T-type matching network is used for the broadband input impedance matching. The design procedure of the proposed broadband rectifier is provided and discussed. For validation, a rectifier prototype is fabricated, and the performance is evaluated. The measured results exhibit power conversion efficiency (PCE) of more than 50% over a frequency band of 0.2–3.2 GHz at 15 dBm input power and 70% from 1 to 2.7 GHz. Moreover, the peak PCE is 78.2% at 1.9 GHz when the input power is 18 dBm.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.000
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 head0.018
GPT teacher head0.208
Teacher spread0.191 · 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

Citations62
Published2022
Admission routes1
Has abstractyes

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Same venueIEEE Microwave and Wireless Components LettersSame topicEnergy Harvesting in Wireless NetworksFrench-language works237,207