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Record W4226397034 · doi:10.1080/00207217.2022.2062797

A triple band rectenna for RF energy harvesting in smart city applications

2022· article· en· W4226397034 on OpenAlexaff
Daasari Surender, Md. Ahsan Halimi, Taimoor Khan, Fazal Ahmed Talukdar, Yahia M. M. Antar

Bibliographic record

VenueInternational Journal of Electronics · 2022
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsRoyal Military College of CanadaQueen's University
FundersMinistry of Education, India
KeywordsRectennaRectifier (neural networks)Electrical engineeringEnergy harvestingAntenna (radio)Energy conversion efficiencyRadio frequencyVoltageDiodePower (physics)Frequency bandEngineeringElectronic engineeringPhysicsComputer scienceRectification

Abstract

fetched live from OpenAlex

Radio Frequency Energy Harvesting is found to be the best alternative to conventional batteries for providing endless power to the sensor networks connected to perform various applications in the smart city. The electromagnetic energy density in the ambient environment is very low and not uniform, which demands a high gain multi-band rectenna system operating at mostly available energy bands and a suitable rectifier circuit that offers large PCE performance. This paper introduces a triple-band monopole rectenna with enhanced performance for smart city applications. A defective ground structure (DGS) has been investigated for a significant increase in gain with an increasing number of resonant frequencies. The proposed antenna operates over a band of frequencies that lies within 1.25–3 GHz, which covers 1.8/2.1/2.45 GHz frequencies. The antenna offers the gain values of 4.16/6.54/10.2 dB at 1.8/2.1/2.45 GHz frequencies, respectively. A single diode series-connected rectifier has been opted to efficiently operate over a wide range of input power levels from −10 dBm to 5 dBm and offers a minimum conversion efficiency of 60% within the operating bands at 0 dBm input power. The rectenna system is experimentally measured and the measured output voltage at 2.45 GHz frequency is 1.123 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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.688
Threshold uncertainty score0.485

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 studies0.0000.000
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 head0.010
GPT teacher head0.233
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

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

Citations26
Published2022
Admission routes1
Has abstractyes

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