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Record W2126335272 · doi:10.1109/acssc.1992.269195

Asynchronous timing recovery in DSP based PSK modems

2003· article· en· W2126335272 on OpenAlexaff
B. Koblents, P.J. McLane

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicBlind Source Separation Techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsIntersymbol interferenceAsynchronous communicationNoise (video)Computer scienceDetectorSymbol rateFilter (signal processing)Pulse shapingInterference (communication)Polyphase systemZero crossingElectronic engineeringChannel (broadcasting)Speech recognitionBit error rateTelecommunicationsElectrical engineeringEngineeringPhysicsArtificial intelligencePower (physics)

Abstract

fetched live from OpenAlex

Fully digital asynchronous timing recovery has been demonstrated to give excellent results in laboratory experiments on speech rate (2400 and 4800 b/s) modems with both rectangular and RC pulse shaping. F.M. Gardner's (1986) zero crossing tracker timing error detector has been applied to rectangular and RC-pulse-shaped speech-rate modems with good results on the additive noise channel. The nondata-aided (NDA) form of the zero crossing tracker has been found to be immune to pattern noise for any data phase transition, making it ideal for timing error detection in M-PSK and M-DPSK systems with M>4. In RC-pulse-shaped systems with alpha down to 60%, intersymbol interference (ISI) induced self noise is sufficiently controlled by loop filtering. For systems with a limited number of samples per symbol, polyphase filter interpolation can be used to improve performance.>

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.024
GPT teacher head0.261
Teacher spread0.237 · 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

Citations3
Published2003
Admission routes1
Has abstractyes

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