Bibliographic record
Abstract
This work proposes a numerical technique for the analysis of Doppler radar systems, which are used in many applications, including but not limited to aircraft detection, vital signs monitoring, and hand gesture control. The proposed approach consists of using the finite-difference time-domain (FDTD) method with the implementation of moving objects, where the order of magnitude of the speed of light is considered for the numerical movements. This ensures that nonprohibitive computational time is required. The dynamic interactions between electromagnetic waves and moving targets are precisely captured. Medically accurate videos are used for heartbeat and respiration detections. Postprocessing is applied to obtain realistic radar responses, enabling the simulation results to closely mimic those measured by Doppler radars. Several problems are investigated and the numerical results are compared with experimental data reported in the literature. Additionally, an experimental setup is introduced for the analysis of the proposed numerical method, by using a Doppler radar and an object in motion that is video-recorded. The video is then inserted in the FDTD code to compare the simulated and experimental results. Two scenarios are studied: an oscillating metronome and hand gestures. The obtained results further validate the proposed method.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".