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Record W2142708000 · doi:10.1109/cca.2005.1507243

A computationally efficient algorithm for order-reduction of IIR filters using control techniques

2005· article· en· W2142708000 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
FieldPhysics and Astronomy
TopicModel Reduction and Neural Networks
Canadian institutionsConcordia University
Fundersnot available
KeywordsInfinite impulse response2D FiltersFinite impulse responseRealization (probability)AlgorithmDigital filterControl theory (sociology)Computer scienceReduction (mathematics)LTI system theoryImpulse responseFilter (signal processing)MathematicsLinear systemControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

In this paper, an algorithm is presented for reducing the order of a single-input single-output (SISO) linear time-invariant (LTI) infinite impulse response (IIR) digital filter. The algorithm uses balanced realization method which is widely used in control systems, in order to find a reduced-order approximate model and an estimate of the error bound. The process consists of an HR-to-FIR conversion step which speeds up the algorithm and makes the subsequent calculations easier. This method can be applied to any high-order digital filter used in communication and digital signal processing

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: Methods · Consensus signal: none
Teacher disagreement score0.753
Threshold uncertainty score0.280

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 head0.016
GPT teacher head0.277
Teacher spread0.262 · 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

Citations5
Published2005
Admission routes1
Has abstractyes

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