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Record W2099484109 · doi:10.1109/ccece.2004.1349615

Implementing a digitally synthesized adaptive pre-emphasis algorithm for use in a high-speed backplane interconnection

2004· article· en· W2099484109 on OpenAlexaff
Lei Lin, P. Noel, T. Kwaśniewski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Adaptive Filtering Techniques
Canadian institutionsCarleton University
Fundersnot available
KeywordsApplication-specific integrated circuitComputer scienceVery-large-scale integrationEmphasis (telecommunications)InterconnectionBackplaneCMOSStandard cellElectronic engineeringDigital electronicsSimple (philosophy)FootprintModulation (music)Integrated circuit layoutElectronic circuitComputer architectureIntegrated circuitEmbedded systemComputer hardwareEngineeringElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

This paper presents a novel implementation technique using simple digital ASIC synthesis to generate a silicon layout of a multi-level PAM modulation circuit that incorporates a digitally adaptive pre-emphasis scheme. While computationally complex, the actual VLSI implementation is relatively simple, requires minimal power and generates a layout that minimizes the footprint. The results of the digital synthesis of several comparable adaptive circuits are detailed and compared. Several devices have been submitted for fabrication, via CMC, using the TSMC 0.18 /spl mu/m CMOS generic standard cell process.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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)0.0030.001

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 head0.021
GPT teacher head0.254
Teacher spread0.233 · 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

Citations3
Published2004
Admission routes1
Has abstractyes

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