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Record W2771811450 · doi:10.1109/tcsi.2017.2769685

Harvesting Energy From Aviation Data Lines: Implementation and Experimental Results

2017· article· en· W2771811450 on OpenAlexafffund
Maryam Mohajertehrani, Yvon Savaria, Mohamad Sawan

Bibliographic record

VenueIEEE Transactions on Circuits and Systems I Regular Papers · 2017
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaThales GroupCMC Microsystems
KeywordsAvionicsPower (physics)IdleEthernetEfficient energy useEmbedded systemAutomotive engineeringEnergy harvestingScheme (mathematics)EngineeringComputer sciencePower managementElectrical engineeringComputer hardwareAerospace engineeringOperating system

Abstract

fetched live from OpenAlex

Wiring is one of the main challenges in aircrafts. The avionics industry is exploring new schemes to minimize the number of power and data cables to build lighter, more reliable and fuel-efficient aircrafts. In this paper, we describe a novel integrated power harvesting interface to procure power required for avionic sensors. The implemented power harvesting approach is based on a modified power over Ethernet scheme in which the power in the ARINC 825 field (data) bus during its idle times serves as the source for the power conversion chain. A transistor-level design is carried out in CMOSP 0.35 μm (AMS) 3.3 V/5 V technology and the system performance is investigated under various conditions to improve its efficiency. From the experimental tests, an overall efficiency of 60 % was achieved and the harvesting device provided an output power of 10.08 mW for feeding sensors. Reported experimental results proved that the proposed power recovery scheme could serve as a power recovery unit to supply embedded sensors.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score0.603

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 head0.050
GPT teacher head0.286
Teacher spread0.236 · 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 designOther design
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

Citations4
Published2017
Admission routes2
Has abstractyes

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