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Record W1999704582 · doi:10.1109/isplc.2014.6812364

Adaptive impedance matching for Vehicular Power Line Communication systems

2014· article· en· W1999704582 on OpenAlexaff
Nima TaheriNejad, Lutz Lampe, Shahriar Mirabbasi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsImpedance matchingElectrical impedanceComputer scienceMatching (statistics)Electronic engineeringOutput impedanceSIGNAL (programming language)Power (physics)Impedance bridgingStanding wave ratioCommunications systemCharacteristic impedanceElectrical engineeringDamping factorAntenna (radio)EngineeringTelecommunicationsMathematicsPhysics

Abstract

fetched live from OpenAlex

The growing number of electronic devices inside vehicles has motivated research and development activities in Vehicular Power Line Communication (VPLC) systems. Advantages of the VPLC approach include reduced complexity and cost of the wiring harness. Among the design challenges of VPLC systems is the problem of impedance matching. The access impedance at the modem port is a time varying quantity which also depends on the location of the VPLC modem. Impedance mismatch degrades the signal-to-noise ratio (SNR) and thus the signal integrity. Given the variable nature of the access impedance, a fixed matching circuit will be inefficient. A potential solution to cope with the access impedance variability is an adaptive impedance matching system which is the subject of this work. Here we have designed an adaptive impedance matching system. The system is simulated and its performance is evaluated under extreme changes in access impedance.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.014
GPT teacher head0.236
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2014
Admission routes1
Has abstractyes

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