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Record W2171267979 · doi:10.1109/pacrim.1989.48395

Jitter analysis for DS-3 to SONET interface circuit with reduced complexity

2003· article· en· W2171267979 on OpenAlexaff
T. E. Moore, J.J. Brown, Witold A. Krzymień

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsAlberta Energy
FundersAdvanced Technology Research Council
KeywordsSynchronous optical networkingJitterAsynchronous communicationOptical mesh networkInterface (matter)Computer scienceMultiplexingElectronic engineeringEngineeringComputer networkTelecommunicationsOperating system

Abstract

fetched live from OpenAlex

The feasibility is discussed of designing a DS-3 to 28 VT 1.5 synchronous optical network (SONET) interface circuit without using intermediate DS-2 and DS-1 desynchronizer phase-lock loops (PLLs). Elimination of intermediate PLLs results in a significant reduction in the cost and complexity of SONET interface circuits for the existing asynchronous digital multiplex hierarchy. The primary concern of implementing such an interface is the effect on accumulated DS-1 waiting time jitter. In order to analyze jitter accumulation, two multiplex models are used. Both models consist of back-to-back M13 multiplexing followed by back-to-back DS-1 to VT 1.5 mapping. The first model includes intermediate DS-2 and DS-1 desynchronizer PLLs, while the second model does not. The jitter analysis and results for both models are given. It is estimated that elimination of these PLLs can reduce the circuit complexity by 14000 gates in a DS-3 to 28 VT1.5 interface design.>

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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.041
GPT teacher head0.248
Teacher spread0.208 · 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

Citations0
Published2003
Admission routes1
Has abstractyes

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