Performance evaluation of a few- and multimode fiber optic perimeter sensor with selective mode excitation
Bibliographic record
Abstract
We present a study of the performance of a simple fiber-optic perimeter snsor based on selective mode excitation in few- and multimode optical fibers. The electronic unit identifies an intrusion on the basis of a pre-defined threshold and number of detected pulses per unit time. The sensor may be controlled using the keypad, a PC or a FSK modem. The statistical properties of the responses are analyzed using Weibull distributions. Full Text: PDF References: A. Yariv, On transmission and recovery of three-dimensional image information in optical waveguides, J. Opt. Soc. Amer., 66, 301(1976) [CrossRef] A. S. Wu, S. Yin and F.T.S. Yu, Sensing with fiber specklegrams, Appl. Opt. 30, 4468 (1991). [CrossRef] S. Yin, P. Purwosumarto and F.T.S.Yu, Application of fiber specklegram sensor to fine angular alignment, Opt. Commun. 170, 15 (1999) [CrossRef] A. F. T.S. Yu, K.Pan, C. Uang and P.B. Ruffin, specklegram sensing by means of an adaptive joint transform correlator , Opt. Eng. 32, 2884 (1993). [CrossRef] F. T.S. Yu, K.Pan, D. Zhao and P.B. Ruffin, Dynamic fiber specklegram sensing, Appl. Opt. 34, 622 (1995). [CrossRef] K.Pan, C.-M.Uang, F.Cheng and F.T.S. Yu, fiber sensing by using mean-absolute speckle-intensity variation, Appl. Opt. 33, 2095 (1994). [CrossRef] A. Malki, R. Gafsi, L. Michel, M. Labarr?re and P. Lecoy, Impact and vibration detection in composite materials by using intermodal interference in multimode optical fibers, Appl. Opt. 35, 5198 (1996). [CrossRef] J. Park, J. of the Korean Phys. Soc. 50, 529 (2007). [DirectLink] D. Anderson, Fiber SenSys White Paper R.Arnaudov, W. Bock, R.Miletiev, Y. Angelov and T. Eftimov, IMTC 2007, Warsaw, Poland, paper IM-7352(2007). T.Eftimov and T.Kortenski, Mode Pattern Rottation Effect in Spirals of Multimode Optical Fibres, Bulg. J. Phys. 14, 456(1987). [DirectLink]
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".