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Record W2152405972 · doi:10.1109/iscas.2011.5938173

A novel finite-wordlength particle swarm optimization technique for FRM IIR digital filters

2011· article· en· W2152405972 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
FieldComputer Science
TopicDigital Filter Design and Implementation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInfinite impulse responseParticle swarm optimization2D FiltersDigital filterFinite impulse responseComputer scienceMathematicsInterpolation (computer graphics)AlgorithmFilter (signal processing)Artificial intelligenceComputer vision

Abstract

fetched live from OpenAlex

A novel technique is presented for finite-wordlength (FW) particle swarm optimization (PSO) of BIBO stable FRM digital filters incorporating bilinear-LDI IIR interpolation subfilters. A novel LUT scheme is developed to ensure that the FWPSO automatically searches over permissible FW multiplier coefficient values only in the course of optimization. The salient feature of the proposed LUT scheme is that unlike the conventional PSO, there is no need to limit the search space in the course of optimization to prevent going over the boundaries of the search space. This is achieved by introducing barren layers in the LUTs. The usefulness of the proposed FWPSO is illustrated through its application to the design and simultaneous magnitude and group-delay optimization of a lowpass IIR-based FRM digital filter.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.716
Threshold uncertainty score0.443

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 communication0.0000.002
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 head0.074
GPT teacher head0.262
Teacher spread0.188 · 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

Citations6
Published2011
Admission routes1
Has abstractyes

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