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Record W2597320579 · doi:10.1109/tmtt.2017.2673821

Electromagnetic Energy Harvesting Using Full-Wave Rectification

2017· article· en· W2597320579 on OpenAlexaff
Faruk Erkmen, Thamer S. Almoneef, Omar M. Ramahi

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2017
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRectennaRectifier (neural networks)Dipole antennaAntenna (radio)RectificationEnergy harvestingElectrical engineeringElectronic engineeringHorn antennaPower (physics)PhysicsEngineeringSlot antennaComputer scienceVoltage

Abstract

fetched live from OpenAlex

This paper presents a new approach to realizing full-wave rectification for electromagnetic energy harvesting. Instead of using one antenna to feed one rectifier circuit, we propose utilizing two antennas connected to one rectifier to form a full-wave rectenna. This balanced configuration allows the received power to be rectified and transferred to a dc load between two antennas, making it very easy to channel the harvested power in rectenna arrays. The proposed concept is demonstrated in the microwave regime using an array of full-wave rectennas optimized for 2.45 GHz. The full-wave rectenna array is compared with an array of half-wave rectennas that occupy the same footprint and are optimized to maximize power absorption at the same frequency. Measurements showed that the proposed full-wave rectifier performed better than the half-wave rectifier and achieved 74% radiation-to-dc conversion efficiency. Here, without loss of generality, the proposed concept is demonstrated using T-matched dipole antennas at a specific frequency, but the concept can be applied to other antenna types and other frequencies.

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 categoriesMeta-epidemiology (narrow)
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 score0.753
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.019
GPT teacher head0.232
Teacher spread0.213 · 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.

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

Citations69
Published2017
Admission routes1
Has abstractyes

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