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Record W2167311437 · doi:10.1109/ssp.2005.1628610

A Partially Adaptive Beamforming with Slepian-Based Quiescent Response

2005· article· en· W2167311437 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/SP 13th Workshop on Statistical Signal Processing, 2005 · 2005
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsQueen's University
Fundersnot available
KeywordsAdaptive beamformerRobustness (evolution)Computer scienceAlgorithmControl theory (sociology)BeamformingSIGNAL (programming language)Interference (communication)Chebyshev filterMathematicsArtificial intelligenceTelecommunications

Abstract

fetched live from OpenAlex

We propose and investigate a partially adaptive beamformer that employs Slepian sequences in both the quiescent weight vector and the signal blocking matrix. This technique maintains robustness in the sense that it preserves low sidelobe levels under conditions of low training data support, signal steering vector mismatch, and moving interference. The adaptive degrees of freedom are chosen based on the properties of Slepian sequences, in order to mitigate the effects of signal self-nulling. Numerical comparisons with adaptive beamformers with Slepian-based and Chebyshev-based quiescent responses support the efficacy of this 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.948
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.276
Teacher spread0.255 · 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