A robust chaos radar for collision detection and vehicular ranging in intelligent transportation systems
Why this work is in the frame
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Bibliographic record
Abstract
This work presents a robust chaos radar system for collision detection and vehicular ranging in intelligent transportation systems (ITS). The robustness of the scheme lies in its multipath mitigation characteristics. By exploiting the spread spectrum (SS) nature of chaos, a high resolution radar system is designed. A cost effective receiver architecture for multipath mitigation in vehicular channel is proposed here. The receiver adaptively equalizes the vehicular multi-path channel minimizing a non-linear prediction error (MNPE) criteria. The MNPE receiver performance is derived to analyze its multi-path mitigation performance. Numerical simulations are performed to validate the theoretical results. The performance of the proposed radar is compared with the conventional direct sequence spread spectrum (DS-SS) ranging scheme. It is shown that the proposed chaos radar outperforms the conventional DS-SS ranging scheme in the vehicular multi-path environment.
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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