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Record W2337440476 · doi:10.1109/tap.2016.2554199

A Novel 2-D Multibeam Antenna Without Beamforming Network

2016· article· en· W2337440476 on OpenAlexaff
Dongfang Guan, Yingsong Zhang, Zuping Qian, Yujian Li, Muftah Asaadi, Can Ding

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2016
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsConcordia University
FundersState Key Laboratory of Millimeter WavesSoutheast UniversityNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsBeamformingAntenna (radio)Smart antennaComputer scienceDirectional antennaReconfigurable antennaAntenna arrayOmnidirectional antennaAcousticsTelecommunicationsPhysicsCoaxial antenna

Abstract

fetched live from OpenAlex

A novel design of multibeam array antenna without feeding network is presented in this communication. This array antenna consists of 3×3 microstrip patches as radiators. In this design, a feeding network is avoided where each patch is fed by a probe. Furthermore, whatever patch is excited, the input power can be coupled to all patches through four microstrip lines located between the radiating elements. In addition, nine radiation beams can be implemented depending on different field distributions that are generated by exciting each patch individually. The proposed antenna has a simple single-layered structure and does not suffer from a complex feeding network compared with traditional multibeam antennas. The experimental results demonstrate that the scanning ranges of the nine beams are ±24° and ±45° in the vertical and horizontal directions, respectively. Moreover, measured gain for the nine beams of the implemented antenna varies from 9.06 to 10.45 dBi.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.212
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations27
Published2016
Admission routes1
Has abstractyes

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