Design and Implementation of a Plastic Optical Fiber-Based Displacement Sensor System
Bibliographic record
Abstract
This paper presents the design, development, and implementation of a displacement sensor system using Plastic Optical Fiber (POF) technology. The system utilizes a laser diode, four optical receivers, a mirror, and a servo motor, all of which are controlled through an ATmega328P microcontroller. Displacement is detected by analyzing the angular reflection of a laser beam on a mirror, with light directed into POF-guided sensors. Two computational methods are proposed for fiber alignment based on mirror tilt angle and sensor-mirror geometry, enabling precise displacement measurements. A functional prototype was fabricated using custom PCBs and 3Dprinted enclosures, and its performance was validated through experiments that correlated mirror tilt angles (5° to 20°) with sensor activation. A dedicated software interface was developed for real-time monitoring and control. The system achieved accurate displacement detection with well-defined angle-sensor mappings. This low-cost, compact, and efficient design demonstrates high potential for applications in industrial monitoring, robotics, and healthcare, where precision and electromagnetic immunity are essential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".