Performance evaluation of a few- and multimode fiber optic perimeter sensor with selective mode excitation
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it