A Millimeter Wave Dual-Lens Antenna for IoT-Based Smart Parking Radar System
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".