Z-scan and Degenerate Four Wave Mixing Studies in Certain Photonic Materials

International School of Photonics, Cochin University of Science & Technology

Investigations of Nonlinear Optical Properties of certain organic photonic materials using Z-Scan and DFWM techniques

Nonlinear optical processes in organic compounds have attracted considerable interest in the field of science and technology because of their compelling technological promises in fields of optical communication,computing,switching and signal processing.As a result of the synthesis of novel organic compounds with varying degree of nonlinear optical strength, many practical devices based on these are getting realised giving new theoretical insights into the nonolinear optical behaviour of materials.Organic compounds like phthalocyanines and porphyrins have evoked great deal of interest in the field of photonic technology.The present thesis describes the results obtained from the investigations carried out on the nonlinear optical properties of certain organo-metallic compounds using Z-Scan and DFWM techniques.

Third order nonlinear optical studies in europium naphthalocyanine using degenerate four wave mixing and Z-scan

Third order nonlinear susceptibility χ(3) and second hyperpolarizability (γ) of a bis-naphthalocyanine viz. europium naphthalocyanines, Eu(Nc)2, were measured in dimethyl formamide solution using degenerate four wave mixing at 532 nm under nanosecond pulse excitation. Effective nonlinear absorption coefficient, βeff and imaginary part of nonlinear susceptibility, Im(χ(3)) were obtained using open aperture /Z-scan technique at the same wavelength. Optical limiting property of the sample was also investigated. The role of excited state absorption in deciding the nonlinear properties of this material is discussed.

A Novel Decision Tree Algorithm for Numeric Datasets - C 4.5*Stat

Decision trees are very powerful tools for classification in data mining tasks that involves different types of attributes. When coming to handling numeric data sets, usually they are converted first to categorical types and then classified using information gain concepts. Information gain is a very popular and useful concept which tells you, whether any benefit occurs after splitting with a given attribute as far as information content is concerned. But this process is computationally intensive for large data sets. Also popular decision tree algorithms like ID3 cannot handle numeric data sets. This paper proposes statistical variance as an alternative to information gain as well as statistical mean to split attributes in completely numerical data sets. The new algorithm has been proved to be competent with respect to its information gain counterpart C4.5 and competent with many existing decision tree algorithms against the standard UCI benchmarking datasets using the ANOVA test in statistics. The specific advantages of this proposed new algorithm are that it avoids the computational overhead of information gain computation for large data sets with many attributes, as well as it avoids the conversion to categorical data from huge numeric data sets which also is a time consuming task. So as a summary, huge numeric datasets can be directly submitted to this algorithm without any attribute mappings or information gain computations. It also blends the two closely related fields statistics and data mining