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Record W2128248218 · doi:10.1109/wescan.1993.270554

ATM framing acquisition

2002· article· en· W2128248218 on OpenAlexaff
D.E. Dodds, Longqing Du

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCellular Automata and Applications
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsFraming (construction)Computer scienceByteHeaderTheoretical computer scienceComputer hardwareComputer networkEngineering

Abstract

fetched live from OpenAlex

There are two kinds of framing strategies in asynchronous transfer mode (ATM) networks: special word framing (SWF) and cell delineation framing (CDF). A simple design for the realization of cell delineation framing is proposed. In contrast to SWF which may include many cells to a frame, the CDF technique has only one cell per frame. Both framing methods detect the frame boundary using correlation. The SWF method directly compares the incoming data with a known special word. In contrast, the proposed CDF technique uses a sliding window cycle redundancy check (CRC) for the previous 40 b to produce an 8-b syndrome byte. This syndrome byte will be zero in all bit positions when a 5-byte cell header is positional within the 40-b window. The authors describe the concept and framing acquisitions model for CDF, and give a practical framing receiver design. The mathematical analysis and the synchronization performance are considered.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.946
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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)0.0000.001

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 head0.016
GPT teacher head0.214
Teacher spread0.198 · 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.

Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2002
Admission routes1
Has abstractyes

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