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Record W3037331710 · doi:10.1109/jiot.2020.3004403

A Millimeter Wave Dual-Lens Antenna for IoT-Based Smart Parking Radar System

2020· article· en· W3037331710 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

VenueIEEE Internet of Things Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsUniversity of WaterlooWestern University
FundersNational Natural Science Foundation of China
KeywordsBeamwidthComputer scienceExtremely high frequencyAntenna (radio)OpticsAcousticsElectrical engineeringTelecommunicationsPhysicsEngineering

Abstract

fetched live from OpenAlex

With a rapid increase in the number of vehicles over recent years, urban parking systems have encountered more and more challenges. In this article, a dual-lens millimeter wave (MMW) radar antenna is designed for a smart parking system in the context of the Internet of Things (IoT). A flat dielectric punch lens is used to increase the gain of the transmitting antenna in order to compensate for the penetration loss in MMW. In addition, a dielectric rod lens is used to correct beam direction and maintain a wide beamwidth in order to overcome received energy loss due to scattering of the car chassis. The combined dual-lens antenna can improve the accuracy and stability of MMW radar operating at 24 GHz. The measured gain is 15.8 dBi for the transmitting antenna and 7.9 dBi for the receiving antenna, and the 3-dB beamwidth is approximately 65°. The system measurement results show that the proposed antenna has stable measurement effect and is suitable for the MMW radar smart parking system.

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 score0.799
Threshold uncertainty score0.861

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 head0.044
GPT teacher head0.223
Teacher spread0.179 · 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