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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 OpenAlex
Dongfang Guan, Yingsong Zhang, Zuping Qian, Yujian Li, Muftah Asaadi, Can Ding

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 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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.553

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 head0.014
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