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Record W2020444353 · doi:10.1109/mwscas.2010.5548768

Particle swarm optimization of FRM FIR digital filters over the CSD multiplier coefficient space

2010· article· en· W2020444353 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
KeywordsParticle swarm optimizationFinite impulse responseMultiplier (economics)Digital filterLookup tableMathematicsComputer scienceFilter (signal processing)Algorithm

Abstract

fetched live from OpenAlex

This paper presents a novel technique for particle swarm optimization (PSO) of FRM FIR digital filters over the CSD multiplier coefficient space. In this technique, a FRM FIR digital filter is represented as a point in a multidimensional CSD multiplier coefficient space. In order to limit the search space, a CSD LUT is generated to include promising points in the multidimensional multiplier coefficient space. Candidate CSD FRM FIR digital filters generated in the course of particle swarm optimization are guaranteed to remain automatically within the CSD LUT boundaries during the constituent PSO move operation without any recourse to backtracking. This is achieved by augmenting the LUT with barren regions. An example is given to illustrate the application of the proposed PSO to the design of a lowpass FRM FIR 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.814
Threshold uncertainty score0.352

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 communication0.0000.001
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 head0.014
GPT teacher head0.245
Teacher spread0.232 · 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

Citations4
Published2010
Admission routes1
Has abstractyes

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