Novel applications of an affordable short-range digital Lidar
Bibliographic record
Abstract
The design and application results of an affordable short range (less than 100 m) digital LIDAR (LIght Detection And Ranging) system will be presented. This work was initiated because many short-range standoff detection applications would benefit from such a system. The lidar features a fiber-based component integrated in the optical module, which allows for hardware partial compensation of the backscattered signal losses observed at short distances due to a biaxial configuration of the lidar optics. This is an important advantage for particle density computations. The digitized backscattered laser signals are available for signal processing. A dedicated FPGA (Field Programmable Gate Array) allows for real-time averaging of the signal waveforms captured at the maximum 50-kHz pulse repetition frequency of the laser. Several application-specific tests have been performed. The first of these was real-time onboard monitoring of pesticide drift in agricultural spraying applications. The signal levels were sufficient for control of the spraying operations and prevention of pesticide drift into sensitive areas. The second was a dust monitoring application. The lidar was installed in a quarry and signals from dust clouds were recorded. Real-time monitoring capabilities were shown to be promising. Other applications involving detection of solid targets in degraded visibility conditions caused by fog or snowfalls were also tested and are discussed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".