The design and packaging of simple, small, and low cost sensor heads, used for continuous liquid level measurement using uniformly thinned (etched) optical fiber Bragg grating (FBG) are proposed. The sensor system consists of only an FBG and a simple detection system. The sensitivity of sensor is found to be 23 pm∕cm of water column pressure. A linear optical fiber edge filter is designed and developed for the conversion of Bragg wavelength shift to its equivalent intensity. The result shows that relative power measured by a photo detector is linearly proportional to the liquid level. The obtained sensitivity of the sensor is nearly −15 mV∕cm. Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 05/16/2015 Terms of Use: http://spiedl.org/terms
Crystal-field parameters appropriate for S4 symmetry were fit to existing data on the I49/2 and I411/2 sublevel splittings of LiYF4:Nd3+. Intermediate coupling wavefunctions obtained previously from fits to the CaWO4:Nd3+ spectrum were used. Although ground-state EPR data were not available in this system, the symmetries of the levels were uniquely assignable from group-theoretical considerations and the available polarization data. The parameter values found are (in cm−1): B02 = 482.7, B04 = − 1452.5, B06 = − 122.1, B44 = − 1222.0, B46 = − 981.9, B4′4 = − 0.3, and B4′6 = − 0.2. The rms deviation of this fit was 10. We calculate ground-state g∥ and g⊥ values of 0.483 and 2.571, respectively. The crystal-field parameters found here are compared with those of CaWO4:Nd3+ and those of the “mirror” ion Er3+ in LiYF4:Er3+. We conclude that simple geometrical considerations as to the algebraic signs of the crystal-field components in these systems may be misleading.
Semi-supervised classifiers combine labeled and unlabeled data during the learning phase in order to increase classifier's generalization capability. However, most successful semi-supervised classifiers involve complex ensemble structures and iterative algorithms which make it difficult to explain the outcome, thus behaving like black boxes. Furthermore, during an iterative self-labeling process, mistakes can be propagated if no amending procedure is used. In this paper, we build upon an interpretable self-labeling grey-box classifier that uses a black box to estimate the missing class labels and a white box to make the final predictions. We propose a Rough Set based approach for amending the self-labeling process. We compare its performance to the vanilla version of our self-labeling grey-box and the use of a confidence-based amending. In addition, we introduce some measures to quantify the interpretability of our model. The experimental results suggest that the proposed amending improves accuracy and interpretability of the self-labeling greybox, thus leading to superior results when compared to state-ofthe-art semi-supervised classifiers.
In this work, we demonstrate the measurement of the Brillouin gain spectra of vector modes in a few-mode fiber for the first time using a simple heterodyne detection technique. A tunable long period fiber grating is used to selectively excite the vector modes supported by the few-mode fiber. Further, we demonstrate the non-destructive measurement of the absolute effective refractive indices (n
eff) of vector modes with ~10−4 accuracy based on the acquired Brillouin frequency shifts of the modes. The proposed technique represents a new tool for probing and controlling vector modes as well as modes carrying orbital angular momentum in optical fibers with potential applications in advanced optical communications and multi-parameter fiber-optic sensing.
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