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Record W1530813257 · doi:10.1109/array.2010.5613250

Dense transmit and receive phased arrays

2010· article· en· W1530813257 on OpenAlex
Chen‐Pang Yeang, Gregory W. Wornell, Lizhong Zheng, James D. Krieger

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 Toronto
FundersDefense Advanced Research Projects Agency
KeywordsBeamformingOversamplingComputer scienceElectronic engineeringQuantization (signal processing)Phased arrayAntenna arrayBandwidth (computing)Antenna (radio)TelecommunicationsAlgorithmEngineering

Abstract

fetched live from OpenAlex

A dense antenna array architecture is developed to ease the circuit requirements of the radio frequency (RF) front-end in beamforming applications. In the architecture, antennas are spaced more closely than required by the sampling principle to exploit the available degrees of freedom. This array structure is analogous to temporally oversampled data conversion systems, which have reduced quantizer resolution requirements. In, we have developed a spatial-domain version of ΔΣ quantization, and have shown that with binary quantization for the in-phase and quadrature components of antenna weights, modest amounts of oversampling can reproduce beamforming patterns of interest to practically useful levels of accuracy. In this paper, we incorporate mutual coupling between antennas and impedance matching in the over-sampling scheme and analyze their effects on the performance of this dense-array architecture. These effects do not change the validity of the dense array.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.872
Threshold uncertainty score0.274

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 head0.005
GPT teacher head0.183
Teacher spread0.178 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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