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Record W2128521582 · doi:10.1109/tcsii.2006.875371

An Efficient Implementation of the Delay Compensation for Sub-Band Filtered-x Least-Mean-Square Algorithm

2006· article· en· W2128521582 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

VenueIEEE Transactions on Circuits and Systems II Analog and Digital Signal Processing · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Adaptive Filtering Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsPath (computing)Compensation (psychology)AlgorithmStability (learning theory)Filter (signal processing)Group delay and phase delayMean squared errorMinimum mean square errorNoise (video)Computer scienceComputational complexity theorySquare (algebra)Control theory (sociology)MathematicsControl (management)StatisticsArtificial intelligence

Abstract

fetched live from OpenAlex

The sub-band filtered-x least-mean-square algorithm used in the active control of noise and vibration suffers from reduced stability bounds due to the error-path delay. The error-path delay results from both the secondary path and the analysis filter bank. To restore the stability bounds, a method has been suggested, whereby the delay is compensated. However, this results in an increased computational complexity. In this brief, we derive an efficient implementation of the error-path delay compensation that results in substantial computational saving.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.751
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

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 science0.0000.000
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 head0.015
GPT teacher head0.243
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