Randomly distributed optical fibers in translucent mortar for privacy-preserving light transmission and digital image reconstruction
Bibliographic record
Abstract
Light-transmitting building materials often compromise visual privacy due to coherent light transmission. This study presents a novel composite utilizing randomly distributed optical fibers coupled with a computational image reconstruction system. A limestone-calcined clay cement (LC³) inspired matrix was designed for sustainability and material performance. An algorithmic approach assigned a random traceable fiber distribution via a bijective input-output mapping. The random fiber configuration achieves effective light diffusion, preserving physical privacy. However, using digital imaging and homography-based calibration, the network was computationally reconstructed to reverse the diffusion, recovering hidden visual information accurately. This demonstrates a dual functionality: architectural privacy combined with selective digital transparency. Geometric robustness tests confirmed a stable operational envelope (estimated error of 2.9%) across viewing distances of 30–110 cm and camera rotation angles up to ± 35º (pitch and yaw), establishing these fiber-instrumented cementitious composites as hybrid physical-digital materials for smart infrastructure applications.
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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".