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Record W2016658097 · doi:10.1109/iscas.2012.6272083

Broadband beamfoming using Nested Planar Arrays and 3D FIR frustum filters

2012· article· en· W2016658097 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
FieldEngineering
TopicAntenna Design and Optimization
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsFrustumPlanarBroadbandAperture (computer memory)Planar arrayPassbandComputer scienceBeamformingNarrowbandFilter (signal processing)Cube (algebra)Electronic engineeringAcousticsPhysicsBand-pass filterTelecommunicationsEngineeringMathematicsGeometryComputer graphics (images)

Abstract

fetched live from OpenAlex

A topology of planar array called Nested Planar Arrays (NPAs) is used for broadband beamforming. The NPAs consist of several Uniform Planar Arrays (UPAs), each one with the double element distance of the previous array. The signals from these arrays are fed into different subbands which process different octaves of temporal frequency bands. The combination of NPAs and multirate techniques leads to the same 3D frustum filter frequency specifications for all subbands. The passband of these 3D frustum filters does not include the low temporal frequencies where it is difficult to achieve high selectivity. Simulation results indicate that with the same number of sensors, NPA can achieve longer aperture size compared to a UPA and thus higher selectivity particularly for lower temporal frequencies. For the same aperture size, NPA can be implemented with much less sensors and much less computations than a UPA with small deterioration in the performance.

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 score0.686
Threshold uncertainty score0.361

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 head0.018
GPT teacher head0.206
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

Citations5
Published2012
Admission routes1
Has abstractyes

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