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Record W2172574424 · doi:10.1109/pacrim.2015.7334879

Implementation and performance analysis of 3-D cone and frustum filters

2015· article· en· W2172574424 on OpenAlexaff
Hussam Shubayli, Chamira U. S. Edussooriya, Iman Moazzen, P. Agathoklis, L.T. Bruton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDigital Filter Design and Implementation
Canadian institutionsSimon Fraser UniversityUniversity of Victoria
Fundersnot available
KeywordsFrustumFinite impulse responseCone (formal languages)ImplementationFilter (signal processing)Computer sciencePassbandInfinite impulse responseAlgorithmPrototype filterDigital filterLow-pass filterMathematicsElectronic engineeringBand-pass filterEngineeringComputer visionGeometry

Abstract

fetched live from OpenAlex

Two novel computationally efficient implementations for 3-D FIR cone filters are proposed in this paper. In the proposed implementations, the well-known 1-D quadrature mirror cosine modulated and 1-D directly designed temporal filter banks are cascaded with 2-D FIR circularly symmetric lowpass spatial filters to approximate the cone-shaped passband. Furthermore, the 3-D FIR frustum filters are derived from the proposed 3-D FIR cone filter implementations. The proposed implementations provide additional 2 dB signal-to-interference-and-noise ratio improvement compared to previously reported 3-D FIR cone and frustum filter implementations with reduced or equivalent computational complexity.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.298
Teacher spread0.253 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2015
Admission routes1
Has abstractyes

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