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Record W4408859820 · doi:10.1109/memc.2024.10942574

Design Challenges for High-Speed Digital Links for Automotive Applications

2024· article· en· W4408859820 on OpenAlexaff
José Enrique Hernández-Bonilla, Christian Klein, Golzar Alavi, Abdessamad El Ouardi, Torsten Reuschel, Christian Schuster

Bibliographic record

VenueIEEE Electromagnetic Compatibility Magazine · 2024
Typearticle
Languageen
FieldEngineering
Topic3D IC and TSV technologies
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsAutomotive industryComputer scienceComputer architectureEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

This paper outlines the state of the art and future directions for high-speed links in modern automotive applications. The technological constraints of signal integrity (SI) are discussed in the context of challenges of the mechanical and atmospheric environment. The requirements are illustrated by example of automotive Ethernet and its use to establish communication between electronic control units (ECUs). The overview covers design elements and parts inside the ECU enclosure as well as the link segment in between control units. Moreover, the compound system is subject to operational testing and verification of compliance with applicable standards.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.005

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 head0.025
GPT teacher head0.246
Teacher spread0.221 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2024
Admission routes1
Has abstractyes

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