Array pattern nulling by element position perturbations using a genetic algorithm

A genetic algorithm has been used for null steering in phased and adaptive arrays . It has been shown that it is possible to steer the array null s precisely to the required interference directions and to achieve any prescribed null depths . A comparison with the results obtained from the analytic solution shows the advantages of using the genetic algorithm for null steering in linear array patterns

Spectral and Structural Studies of Transition Metal Complexes of Thiosemicarbazones Containing Ring Incorporated at N(4)-position

The thesis is an introduction to evaluate the coordination behaviour of a few compounds of our interest. The crucial aim of these investigations was to synthesize and characterize some transition metal complexes using the ligands benzaldehyde, 2-hydroxybenzaldehyde and 4-methoxybenzaldehyde N(4)-ring incorporated thiosemicarbazones.The study involves a brief foreword of the metal complexes of thiosemicarbazones including their bonding, stereochemistry and biological activities.The different analytical and spectroscopic techniques used for the analysis of the ligands and their complexes are discussed.It also deals with the synthesis and spectral characterization of the thiosemicarbazones and single crystal X-ray diffraction study of one of them.Chapter 3 describes the synthesis, spectral characterization, single crystal X-ray diffraction studies of copper(ll) complexes with ONS/NS donor thiosemicarbazones. Chapter 4 deals with the synthesis, spectral characterization and single crystal X-ray diffraction studies of nickel(II) complexes. Chapter 5 contains the synthesis, structural and spectral characterization of the cobalt(III) complexes. Chapters 6 and 7 include the synthesis, structural and spectral characterization of zinc(II) and cadmium(ll) complexes with ONS/NS donor thiosemicarbazones.

Development of Hierarchical Clustering Techniques for Gridded Data from Mixed Data Sequences

Knowledge discovery in databases is the non-trivial process of identifying valid, novel potentially useful and ultimately understandable patterns from data. The term Data mining refers to the process which does the exploratory analysis on the data and builds some model on the data. To infer patterns from data, data mining involves different approaches like association rule mining, classification techniques or clustering techniques. Among the many data mining techniques, clustering plays a major role, since it helps to group the related data for assessing properties and drawing conclusions. Most of the clustering algorithms act on a dataset with uniform format, since the similarity or dissimilarity between the data points is a significant factor in finding out the clusters. If a dataset consists of mixed attributes, i.e. a combination of numerical and categorical variables, a preferred approach is to convert different formats into a uniform format. The research study explores the various techniques to convert the mixed data sets to a numerical equivalent, so as to make it equipped for applying the statistical and similar algorithms. The results of clustering mixed category data after conversion to numeric data type have been demonstrated using a crime data set. The thesis also proposes an extension to the well known algorithm for handling mixed data types, to deal with data sets having only categorical data. The proposed conversion has been validated on a data set corresponding to breast cancer. Moreover, another issue with the clustering process is the visualization of output. Different geometric techniques like scatter plot, or projection plots are available, but none of the techniques display the result projecting the whole database but rather demonstrate attribute-pair wise analysis