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Record W1997808414 · doi:10.1109/aps.2013.6711763

Squint-free beamforming in series-fed antenna arrays using synthesized non-foster elements

2013· article· en· W1997808414 on OpenAlexaff
Hassan Mirzaei, George V. Eleftheriades

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBeamformingAntenna (radio)InductanceTransmission lineSeries (stratigraphy)Computer scienceBroadbandCapacitanceElectronic engineeringTransmission (telecommunications)Electric power transmissionAcousticsTelecommunicationsEngineeringPhysicsElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

A method for squint-free arbitrary-angle broadband beamforming in series-fed antenna arrays is introduced. This method originates from the idea of loading a feedline with non-Foster elements. This type of loading reduces the per unit length inductance and capacitance of the transmission line and a fast-wave non-dispersive propagation, required for squint-free beamforming in series-fed antenna arrays, is obtained. Importantly, the challenges associated with traditional implementations of non-Foster reactive elements (e.g. stability) are circumvented by introducing a new methodology using negative-group-delay (NGD) networks. This method is established by showing that non-Foster reactive elements and NGD networks influence propagating waves in a similar manner. Subsequently, a series feeding network for linear antenna arrays is designed by loading a host transmission line with loss-compensated NGD networks. In summary, this paper introduces a new method for synthesizing stable non-Foster reactances, using NGD networks, which is utilized to present a solution to the beam squinting problem in series-fed antenna arrays.

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score1.000

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 head0.011
GPT teacher head0.201
Teacher spread0.190 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations12
Published2013
Admission routes1
Has abstractyes

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