Bibliographic record
Abstract
Recently Vehicular Ad-hoc Networks (VANETs) have drawn the attention of academic and industry researchers due to their potential applications in enabling Intelligent Transportation System (ITS), including safe driving, entertainment, emergency response, and content sharing. Another potential application for VANET lies in vehicle tracking, where a tracking system is used to visually track a specific vehicle or to monitor a particular area. In this case, and in similar applications such as multimedia content sharing, a large volume of information is required to be transferred between vehicles, which can easily congest the wireless network in a VANET if not designed properly. The development of low-delay, low-overhead, and precise tracking system in VANET is a major challenge requiring novel techniques to guarantee performance and reduce network congestion. \nAmong the several proposed data dissemination and management methods implemented in VANETs, clustering has been used to reduce data propagation traffic and to facilitate network management. However, clustering for target tracking in VANETs is still a challenge. In this thesis, we propose two clustering algorithms for vehicle tracking in VANETs. These algorithms provide a reliable and stable platform for tracking specific vehicles based on their visual features under various conditions. These algorithms have also been tested and evaluated in the context of vehicular tracking under various scenarios. Performance evaluation results demonstrate that the proposed schemes provide a more stable clustering structure with reduced overhead.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".