Luminescence studies of strontium sulphide based phosphors for display applications

This thesis has focused on the synthesis and analysis of some important phosphors (nano, bulk and thin film) for display applications. ACTFEL device with SrS:Cu as active layer was also fabricated.Three bulk phosphors: SrS:Cu,CI; SrS:Dy,Cl; and SrS:Dy,Cu,Cl were synthesized and their structural, optical and electrical properties were investigated. Special emphasis was given to, the analysis of the role of defects and charge compensating centers, on the structural changes of the host and hence the luminance. A new model describing the sensitizing behaviour of Cu in SrS:Dy,Cu,Cl two component phosphor was introduced. It was also found that addition of NH4CI as flux in SrS:Cu caused tremendous improvement in the structural and luminescence properties.A novel technique for ACTFEL phosphor deposition at low temperature was introduced. Polycrystalline films of SrS:Cu,F were synthesized at low temperature by concomitant evaporation of host and dopant by electron beam evaporation and thermal evaporatin methods.Copper doped strontium sulphide nanophosphor was synthesized for the first time. Improvement in the luminescence properties was observed in the nanophosphor with respect to it' s bulk counterpart.

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