A wavelength detection method for a wavelength division multiplexing (WDM) FBG sensor network is proposed based on least squares support vector regression (LS-SVR). As a kind of promising machine learning technique, LS-SVR is employed to approximate the inverse function of the reflection spectrum. The LS-SVR detection model is established from the training samples, and then the Bragg wavelength of each FBG can be directly identified by inputting the measured spectrum into the well-trained model. We also discuss the impact of the sample size and the pre-process of the input spectrum on the performance of the training effectiveness. The results demonstrate that our approach is effective in improving the accuracy for sensor networks with a large number of FBGs.
Wavelength-division demultiplexing by superimposed gratings (supergrating) in a planar waveguide is modeled, accounting for intergrating coupling, and a theoretical decoupling criterion that serves as a guideline for practical design is presented. Superimposed gratings were formed by direct photoinscription in a photosensitive silica planar waveguide, which is a promising method for practical optical circuit applications. A 1x8 channel wavelength-division multiplexer is demonstrated experimentally as a proof-of-concept study. The effects on experimental performance of wavelength-selectivity broadening and finite photosensitive index-modulation depth are discussed.
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