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Low-Cost 3D printed Dielectric Hyperbolic Lens Antenna for Beam Focusing and Steering of a 79GHz MIMO Radar

2020· article· en· W3130724669 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsChipsetBeamwidthAntenna (radio)Computer scienceLens (geology)RadarAnechoic chamberElectronic engineeringAntenna gainEngineeringOpticsDipole antennaTelecommunicationsAntenna aperturePhysics

Abstract

fetched live from OpenAlex

Extensive research has been conducted on millimeter wave (mm-wave) chipset solutions led to reduction in size and cost while adding sensitivity and accuracy. Recent 79GHz chipset solutions using antenna-in package approach were developed for various applications with a wide beamwidth and low gain. However, for some applications such as multiple people vital signs detection and corridor gait monitoring, there is still a need to achieve higher gain with thinner beamwidth, to increase the signal-to-noise ratio (SNR) and the transmit/receive range of the system, mitigate the reflection from surrounding objects as well as reduce multi-path effects. The use of a lens is an appealing solution since it could improve the system performances while using existing chipset solution. Using low cost and rapid manufacturing 3D printing technology, we designed and fabricated a dielectric lens antenna for a 79GHz MIMO radar. Compared with the system without lens, the full-wave simulation demonstrated a 14dB improvement in gain which is in good agreement with measurement results.

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 score0.665
Threshold uncertainty score0.552

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 head0.029
GPT teacher head0.217
Teacher spread0.188 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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