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Record W2152447094 · doi:10.1109/icccn.1995.540182

Dual-hop LANs using station wavelength routing

2002· article· en· W2152447094 on OpenAlexaff
Nima Ahmadvand, T.D. Todd

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsComputer scienceNetwork packetComputer networkWavelength-division multiplexingPacket switchingMetropolitan area networkReuseRouting (electronic design automation)WavelengthDistributed computingLocal area networkEngineering

Abstract

fetched live from OpenAlex

In future WDM local area networks, the number of available wavelengths may initially be fairly modest. As a result, spatial reuse is required in order to obtain designs which will support a reasonable number of stations. A dual-hop architecture is considered. The network is partitioned into two stages. In the first, the wavelength agility of the user stations is used to route packets from a given local optical network (LON) to a destination LON. When packets arrive at their destination LON, they are buffered and transmitted onto the required wavelength. There are a number of significant advantages to this arrangement. In addition, the proposed design takes advantage of increasingly available commercial ATM buffer/switch components. Several hybrid electro-optic designs are discussed. We propose electronic implementations for the buffering stage and also consider the "almost" all-optical approaches first introduced by Haas (1993). The performance of the proposed systems is considered using various traffic models. Simplified control strategies are also proposed and multiple wavelength sharing is used to decrease the number of required buffers.

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 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 score0.360
Threshold uncertainty score0.332

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)0.0000.000

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 head0.025
GPT teacher head0.231
Teacher spread0.206 · 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

Citations1
Published2002
Admission routes1
Has abstractyes

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