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Record W2150494964 · doi:10.1109/jstqe.2004.841467

A direct temporal domain approach for pulse-repetition rate multiplication with arbitrary envelope shaping

2005· article· en· W2150494964 on OpenAlexaff
Bing Xia, Lawrence R. Chen

Bibliographic record

VenueIEEE Journal of Selected Topics in Quantum Electronics · 2005
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsOpticsAmplitudePulse shapingPhysicsFilter (signal processing)Envelope (radar)Pulse-amplitude modulationPulse waveInterferometryImpulse responseBandwidth-limited pulseAmplitude modulationPulse (music)Optical filterComputer scienceLaserMathematicsFrequency modulationTelecommunicationsBandwidth (computing)Mathematical analysisUltrashort pulseDetector

Abstract

fetched live from OpenAlex

We present a direct temporal domain approach for pulse-repetition rate multiplication (PRRM) with envelope shaping using spectrally periodic optical filters. We show that the repetition rate of an input pulse train can be multiplied by a factor N using an optical filter with a free spectral range that does not need to constrained to an integer multiple of N. Individual output pulses in the newly generated pulse train have exactly the same intensity shape as those at the input. Furthermore, the amplitude of each individual output pulse can be manipulated separately to form an arbitrary envelope (profile) by optimizing N discrete values of the optical filter impulse response h(t). We demonstrate the direct temporal domain approach by designing a combined amplitude-and-phase filter based on a lattice-form Mach-Zehnder interferometer and simulation results show that PRRM with uniform and arbitrary profiles can be performed using these type of filters.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.228
Teacher spread0.214 · 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 designBench or experimental
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

Citations33
Published2005
Admission routes1
Has abstractyes

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