MétaCan
Menu
Back to cohort
Record W2911942459 · doi:10.1109/mwscas.2018.8623832

Efficient Dual-band Ultra-Low-Power RF Energy Harvesting Front-End for Wearable Devices

2018· article· en· W2911942459 on OpenAlexafffund
Seyed Mohammad Noghabaei, Rafael Luciano Radin, Mohamad Sawan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsMulti-band deviceElectrical engineeringEnergy harvestingRadio frequencyAntenna (radio)CapacitorCMOSCapacitive sensingSensitivity (control systems)VoltageComputer sciencedBmISM bandPower (physics)Wearable computerElectronic engineeringEngineeringPhysicsAmplifierEmbedded system

Abstract

fetched live from OpenAlex

This paper presents a novel dual-band ultra-low power RF energy harvesting front-end for wearable devices, wireless sensor networks, and the Internet of things (IoT) designed in standard 130 nm CMOS technology. The proposed dual-band RF energy harvester operates at ISM bands of 915 MHz and 1.85 GHz and uses an efficient summation network to combine power from different frequency bands. A dual-band antenna receives signals from two different bands which are boosted by two different matching networks. Then, two self-compensated cross-coupled rectifiers convert the RF signals provided by the two different bands into DC output voltages. At the output of the two rectifiers, a summation network using switches and a control circuit combines power from different bands, charging the output capacitor. The post-layout simulation results demonstrate a sensitivity of -33 dBm for 1 V output at a capacitive load and the peak end-to-end efficiency is 43.2% at -18 dBm when two frequency bands are available. When only 915 MHz or 1.85 GHz is available, the results demonstrate a high peak efficiency of 42.3% and 44% at -16 dBm and -17 dBm, respectively..

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
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 studies0.0000.000
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 head0.008
GPT teacher head0.208
Teacher spread0.199 · 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 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

Citations23
Published2018
Admission routes2
Has abstractyes

Explore more

Same topicEnergy Harvesting in Wireless NetworksFrench-language works237,207