Artificial Compound Eye for Clear Vision in Harsh Environment
Bibliographic record
Abstract
Abstract Artificial compound eyes (CEs) exhibit unique optical properties, including wide‐angle vision, high light sensitivity, and enhanced motion detection, making them ideal for applications in drones, robots, and cameras. An easily fabricated, transparent, and self‐cleaning superhydrophobic compound eyes are designed to achieve clear vision in harsh environments. The CEs demonstrated exceptional motion tracking and imaging capabilities. A crawling spider and a swinging object are captured by the CE with a wide field of view. A clear vision is demonstrated by imaging 3D‐printed alphabetic letters through the CE in rainy and foggy environments. The superhydrophobic CE demonstrated fog resistance 6 times higher than a hydrophilic CE, and 14 times higher than a simple eye, and the projected image remains visible 3 times longer in heavy fog. A machine learning model is trained using 300 000 CE‐produced images of five vowels in various fog densities, showing CE‐produced images can better obtain information in a blurred situation. The superhydrophobic CEs are highly promising for applications in outdoor visualization, motion detection, and signal identification in adverse weather conditions.
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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 | 0.000 |
| 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) | 0.001 | 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".