The sensing characteristics of long period gratings in three different types of fibre

In this paper, sensing characteristics of long period gratings in three different types of single mode optical fibre: a step-index fibre, a progressive three layered fibre and a suppressed-cladding fibre were investigated. It was found that variation in the refractive index profile and material constants of the fibre can produce a dramatic change in the spectral sensitivity for a particular measurand for a given long period grating attenuation band. © 2003 IEEE.

Use of dual-grating sensors formed by different types of fiber Bragg gratings for simultaneous temperature and strain measurements

We report on a systematic investigation of the dependence of both temperature and strain sensitivities on the fiber Bragg grating type, including the well-known Type I, Type IIA, and a new type that we have designated Type IA, using both hydrogen-free and hydrogenated B/Ge codoped fibres. We have identified distinct sensitivity characteristics for each grating type, and we have used them to implement a novel dual-grating, dual-parameter sensor device. Three dual-grating sensing schemes with different combinations of grating type have been constructed and compared, and that of a Type IA-Type IIA combination exhibits the best performance, which is also superior to that of previously reported grating-based structures. The characteristics of the measurement errors in such dual-grating sensor systems is also presented in detail. © 2004 Optical Society of America.

The New Software Package for Dynamic Hierarchical Clustering for Circles Types of Shapes

In data mining, efforts have focused on finding methods for efficient and effective cluster analysis in large databases. Active themes of research focus on the scalability of clustering methods, the effectiveness of methods for clustering complex shapes and types of data, high-dimensional clustering techniques, and methods for clustering mixed numerical and categorical data in large databases. One of the most accuracy approach based on dynamic modeling of cluster similarity is called Chameleon. In this paper we present a modified hierarchical clustering algorithm that used the main idea of Chameleon and the effectiveness of suggested approach will be demonstrated by the experimental results.