Using optical code-division multiple-access techniques in Michelson interferometer vibration sensor networks
Bibliographic record
Abstract
This study proposes a spectral-amplitude-coding optical code-division multiple-access (SAC-OCDMA) framework for accessing the vibration frequency of a test object by using a Michelson interferometer vibration sensor (MIVS). Each sensor node possesses an individual signature codeword, and liquid crystal spatial light modulator (LC-SLM) encoders/decoders (codecs) are adopted to provide excellent orthogonal properties in the frequency domain. The proposed LC-SLM-based OCDMA system mitigates multiple access interference among all sensor nodes. When optical beams strike and are reflected by the object, the sensing interferometer becomes sensitive to external physical parameters such as temperature, strain, and vibration. The MIVS includes a Michelson interferometer placed at a specific distance from the test object on a designed vibration platform. A balanced photodetector (BPD) was used to convert the light output of the LC-SLM decoders into electrical signals, and a digitizing oscilloscope was used to Fourier transform the BPD electrical signal output, thereby yielding the vibration frequency of the test object. The results showed that the proposed sensor network with an interferometer can be used as a distributed highly sensitive sensor to obtain mechanical values. This study provides a new optical sensor network for current vibration frequency measurements.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".