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Record W2103272361 · doi:10.1109/iscas.2007.378848

Peak-Contrained WLS Strategy for FIR Digital Filter Design

2007· article· en· W2103272361 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Adaptive Filtering Techniques
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPassbandFilter (signal processing)Filter designFinite impulse responseDigital filterSet (abstract data type)MathematicsControl theory (sociology)Computer scienceAlgorithmMathematical optimizationBand-pass filterElectronic engineeringEngineeringControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

In this paper, a peak-constrained weighted least-squares (PCWLS) method is presented for designing a general FIR digital filter with constant group delay passband, using the projected least-squares (PLS) algorithm. In the design of a general FIR filter, peak error constraints are nonlinear functions of filter coefficients and can not be accepted by the PLS algorithm. In the proposed method, peak error constraints are formulated, through some approximation, as a set of linear inequalities to be incorporated into the design problem. Design examples show the effectiveness of the peak error control of the proposed method.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.824
Threshold uncertainty score0.627

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 head0.046
GPT teacher head0.271
Teacher spread0.225 · 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

Quick stats

Citations3
Published2007
Admission routes1
Has abstractyes

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