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Record W1957468967 · doi:10.1109/iscas.1999.777791

Jitter model of direct digital synthesis clock generators

2003· article· en· W1957468967 on OpenAlexaff
Dorin Calbaza, Yvon Savaria

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDigital Filter Design and Implementation
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsJitterComputer scienceNTSCDigital audioDigital televisionDigital clock managerFrequency dividerClock domain crossingDigital clockDirect digital synthesizerClock skewClock synchronizationElectronic engineeringClock signalHigh-definition televisionComputer hardwareAudio signalSynchronization (alternating current)Digital signal processingSynchronous circuitFrequency synthesizerTelecommunicationsPhase-locked loopEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

Direct Digital Synthesis (DDS) has become a popular technique for synthesis of very accurate clocks. For example, in Digital Television (DTV) an audio data stream must be inserted into a video data stream, which implies that we must synchronize the audio clock with the video clock. According to the digital audio standard, the audio clock frequency is 5,6448 MHz and with PAL digital television standard, the video clock frequency is 35.46895 MHz. In this case, the division ratio is 112896/709379. Other division ratios are required with other DTV standards such as NTSC, SECAM or HDTV, and with other digital audio standard frequencies. This paper outlines a jitter model applicable to direct digital synthesis (DDS) clock generators. It is based on a Fourier analysis of the output signal when the DDS is corrupted by the most common error sources. This model is applicable to the design of DDS subject to stringent jitter constraints.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.032
GPT teacher head0.231
Teacher spread0.199 · 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

Citations6
Published2003
Admission routes1
Has abstractyes

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