Optical fiber and genetically optimized computer-generated hologram force detection and classification

Quasi-monomode optical fiber sensors, used an input of systems for external force detection and classification, are widely described in references. Feature extractors in such systems are often based on computer generated holograms (CGH) and classifiers are usually built as artificial neural networks (ANN). The use of CGH instead of ring-wedge detector gives possibility of easy change of ring and wedge sizes. In this paper we present our method of CGH optimization. This method is based on evolutionary algorithms and elements algorithms and elements from rough set theory (RST). The results of classification of features obtained by applying optimized by our method CGH confirm that proposed approach can be successfully used for detection and classification of external force. All what is needed for this purpose is to pass coherent light through quasi-monomode optical fiber, and to place CGH in a focal plane of the lens. As CGH regions are the subject to be optimized to given application and therefore minimized in size, the resulting hybrid optic-digital system can be compact and relatively cheap. The experimental results for classification of generated by optimized CGH features confirmed the good overall quality of the proposed system.