A Millimeter Wave Dual-Lens Antenna for IoT-Based Smart Parking Radar System
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it