MétaCan
Menu
Back to cohort
Record W1973104408 · doi:10.1109/ccece.2013.6567766

Performance of unique word timing offset estimator

2013· article· en· W1973104408 on OpenAlexaff
Suresh Kalle, Mushtajizur Rahman Choudhury, Francis M. Bui, D.E. Dodds

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDigital Filter Design and Implementation
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsComputer scienceOffset (computer science)EstimatorWord (group theory)Speech recognitionNatural language processingStatisticsMathematicsProgramming language

Abstract

fetched live from OpenAlex

A quadrature amplitude modulation receiver must include a timing recovery circuit to identify the correct points in time to sample the output. Timing recovery is handled differently in synchronous systems than it is in burst mode systems. In synchronous systems the convergence (or dwell) time can be quite long without penalty. In burst mode systems timing must be recovered rapidly and is therefore aided by placing a unique word at the beginning of the preamble. The output of the matched filter is correlated with the unique word to find the timing offset required to align the time origin. This timing offset is used as the “delay” input to the fractional delay filter that synchronizes the signal in the receiver to the timebase [1][3][5]. This paper discusses the performance of the timing recovery circuit using a specific unique word: length-11 Barker sequence preceded by a cyclic prefix.

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)0.005
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.260
Teacher spread0.227 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

Explore more

Same topicDigital Filter Design and ImplementationFrench-language works237,207