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Record W1595498052 · doi:10.1109/iscas.2003.1205116

Reducing the computational complexity of narrowband 2D fan filters using shaped 2D window functions

2003· article· en· W1595498052 on OpenAlexaff
L. Khademi, L.T. Bruton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Adaptive Filtering Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsWindow functionComputational complexity theoryImpulse responseFinite impulse responseNarrowbandMathematicsAlgorithmWedge (geometry)Impulse (physics)Function (biology)Reduction (mathematics)Filter (signal processing)Window (computing)Computer scienceMathematical analysisPhysicsGeometryOptics

Abstract

fetched live from OpenAlex

Two-dimensional (2D) discrete-domain FIR narrow fan filters may be used for the selective filtering of sampled broadband 2D plane waves on the basis of their directions of arrival (DOA). We show that the 2D region of support (ROS) of the unit impulse response h(n/sub 1/,n/sub 2/) of such filters may be reduced in size by using a shaped 2D window function, where the shape is determined by the angle and angular width of the fan. Relative to the widely used square-shaped window function, significant reductions in computational complexity are demonstrated using various shaped window functions. An approximately parabolically-bounded (PB) shaped 2D window function is shown to be especially effective. The method may be extended to 3D cone filters, for which the reduction in computational complexity is expected to be larger than for the 2D case.

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)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.064
GPT teacher head0.273
Teacher spread0.208 · 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
GenreEmpirical

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

Citations21
Published2003
Admission routes1
Has abstractyes

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