Fluorescence characterization and gain studies on a dye-doped graded index polymer optical-fiber preform

Preparation of an appropriate optical-fiber preform is vital for the fabrication of graded-index polymer optical fibers (GIPOF), which are considered to be a good choice for providing inexpensive high bandwidth data links, for local area networks and telecommunication applications. Recent development of the interfacial gel polymerization technique has caused a dramatic reduction in the total attenuation in GIPOF, and this is one of the potential methods to prepare fiber preforms for the fabrication of dye-doped polymer-fiber amplifiers. In this paper, the preparation of a dye-doped graded-index poly(methyl methacrylate) (PMMA) rod by the interfacial gel polymerization method using a PMMA tube is reported. An organic compound of high-refractive index, viz., diphenyl phthalate (DPP), was used to obtain a graded-index distribution, and Rhodamine B (Rh B), was used to dope the PMMA rod. The refractive index profile of the rod was measured using an interferometric technique and the index exponent was estimated. The single pass gain of the rod was measured at a pump wavelength of 532 nm. The extent of doping of the Rh B in the preform was studied by axially exciting a thin slice of the rod with white light and measuring the spatial variation of the fluorescence intensity across the sample.

Unconstrained Handwritten Malayalam Character Recognition using Wavelet Transform and Support vector Machine Classifier

This paper presents the application of wavelet processing in the domain of handwritten character recognition. To attain high recognition rate, robust feature extractors and powerful classifiers that are invariant to degree of variability of human writing are needed. The proposed scheme consists of two stages: a feature extraction stage, which is based on Haar wavelet transform and a classification stage that uses support vector machine classifier. Experimental results show that the proposed method is effective

Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576