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Record W2159693415 · doi:10.1109/lsp.2003.817852

Adaptive beamforming with sidelobe control: a second-order cone programming approach

2003· article· en· W2159693415 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

VenueIEEE Signal Processing Letters · 2003
Typearticle
Languageen
FieldComputer Science
TopicDirection-of-Arrival Estimation Techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAdaptive beamformerBeamformingSecond-order cone programmingMinimum-variance unbiased estimatorControl theory (sociology)Cone (formal languages)Computer scienceMathematicsConvex optimizationMathematical optimizationRegular polygonAlgorithmControl (management)TelecommunicationsStatistics

Abstract

fetched live from OpenAlex

A new approach to adaptive beamforming with sidelobe control is developed. The proposed beamformer represents a modification of the popular minimum variance distortionless response (MVDR) beamformer. It minimizes the array output power while maintaining the distortionless response in the direction of the desired signal and a sidelobe level that is strictly guaranteed to be lower than some given (prescribed) threshold value. The resulting modified MVDR problem is shown to be convex, and its second-order cone (SOC) formulation is obtained that facilitates a computationally efficient way to implement our beamformer using the interior point method.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.715
Threshold uncertainty score0.898

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.228
Teacher spread0.213 · 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