MétaCan
Menu
Back to cohort
Record W2110478878 · doi:10.1109/jlt.2004.838848

A broad-band digital filtering approach for time-domain Simulation of pulse propagation in optical fiber

2005· article· en· W2110478878 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Lightwave Technology · 2005
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsFrequency domainBandwidth (computing)Time domainElectronic engineeringComputer scienceFilter (signal processing)Pulse shapingAlgorithmOpticsTelecommunicationsEngineeringPhysics

Abstract

fetched live from OpenAlex

A broad-band digital filtering approach for the simulation of pulse propagation in the optical fiber has been developed. Unlike the most popular frequency-domain split-step method, the pulse propagation is realized by letting the signal samples pass through a preextracted digital filter where the convolution is simply made by a series of operations that consist of shift and multiplication only. It also differs from the existing time-domain split-step method in a sense that the digital filter is extracted to match the frequency-domain fiber linear transfer function in the full bandwidth range rather than in a reduced portion. This approach is verified through comparisons made with the conventional frequency-domain split-step method and is applied to the simulation of multiple-channel narrow-pulse propagation over the long-haul fiber. The main advantage brought by this approach lies in that the simulator is fully realized in a "data-flow" fashion; that is, the signal (long sample stream) is treated sample by sample, rather than block (a collection of neighboring samples) by block. Matching the fiber frequency-domain response over the full bandwidth does not require any further reduction on the propagation step since the error can be controlled through the filter length. The authors' preliminary effort on the filter length reduction on a given error reveals that a savings on both memory and computation time is also achievable in comparison with the frequency-domain split-step method.

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.

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: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.008
GPT teacher head0.224
Teacher spread0.216 · 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