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Record W4292566563 · doi:10.1080/03772063.2022.2112986

Design of a Frequency Selectable Rectifier Using Tuned Matching Circuit for RFEH Applications

2022· article· en· W4292566563 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIETE Journal of Research · 2022
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsConcordia University
FundersScience and Engineering Research Board
KeywordsRectifier (neural networks)Precision rectifierInductorElectronic engineeringElectrical engineeringAntenna (radio)Power (physics)Computer scienceEngineeringVoltagePower factorPhysics

Abstract

fetched live from OpenAlex

RF energy harvesting (RFEH) is an emerging technique in the field of wireless technology. The key components of this system are receiving antenna, matching network, and rectifier circuit. A rectifier circuit based on a tuned matching circuit is demonstrated in this paper for RFEH applications. The topology of this rectifier circuit is suitable for selecting a wide range of operating frequency bands, realized by changing the value of the inductor in the matching network. For validation purpose, a rectifier has been designed, developed, and tested at 2.45 GHz. The rectifier achieved peak power conversion efficiency (PCE) of 64.5% at 0 dBm. The PCE is higher than 50% for input power in the range of −8.5–2 dBm. The proposed rectifier has a compact size of 20 × 15 × 1.524 mm3. By changing the value of the inductor in the matching network this rectifier can be redesigned for any other operating frequency in the range of 0.6–2.6 GHz.

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.

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)0.003
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 score0.843
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
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 head0.125
GPT teacher head0.348
Teacher spread0.223 · 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