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Record W2066404290 · doi:10.1109/isplc.2012.6201294

On the design of impedance matching circuits for vehicular power line communication systems

2012· article· en· W2066404290 on OpenAlexaff
Nima TaheriNejad, Roberto Rosales, Shahriar Mirabbasi, Lutz Lampe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInductorInductanceImpedance matchingElectronic engineeringElectrical impedanceOverhead (engineering)Electronic circuitComputer scienceElectrical engineeringPower-line communicationEngineeringPower (physics)VoltagePhysics

Abstract

fetched live from OpenAlex

The design of power line communication (PLC) systems for vehicles, i.e., vehicular power line communication (VPLC), is a challenging task as propagation conditions are harsh and devices need to be low cost and highly integrated (to have minimal overhead on vehicle's cost and weight). One particular challenge, which is common to many PLC application scenarios, is the temporal and spatial variation of the input impedance. In this paper, we investigate on this issue and, based on previous studies and measurements on access impedances for a PLC network in a car, we discuss the design of adaptive impedance matching circuits for VPLC. This includes a study on frequency range of operation, suggestions for impedance matching circuits, and proposing a circuit structure. In particular, since inductors are an integral part of matching circuitry and given that over the typical frequency range of operation for VPLC their integration is challenging, if not impractical, we advocate the use of active inductors in matching circuits. As compared to passive inductors, they occupy a smaller on-chip size and their inductance is adjustable. We also propose an active inductor structure that provides a wide range of inductance values which are suitable for VPLC applications.

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)0.001
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 score0.821
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.040
GPT teacher head0.257
Teacher spread0.217 · 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

Citations15
Published2012
Admission routes1
Has abstractyes

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