PET-SWNT nanocomposites through ultrasound assisted dissolution-evaporation

Poly(ethylene terephthalate) (PET) based nanocomposites have been prepared with single walled carbon nanotubes (SWNTs) through an ultrasound assisted dissolution-evaporation method. Differential scanning calorimetry studies showed that SWNTs nucleate crystallization in PET at weight fractions as low as 0.3%, as the nanocomposite melt crystallized during cooling at temperature 24 °C higher than neat PET of identical molecular weight. Isothermal crystallization studies also revealed that SWNTs significantly accelerate the crystallization process. Mechanical properties of the PETSWNT nanocomposites improved as compared to neat PET indicating the effective reinforcement provided by nanotubes in the polymer matrix. Electrical conductivity measurements on the nanocomposite films showed that SWNTs at concentrations exceeding 1 wt% in the PET matrix result in electrical percolation. Comparison of crystallization, conductivity and transmission electron microscopy studies revealed that ultrasound assisted dissolution-evaporation method enables more effective dispersion of SWNTs in the PET matrix as compared to the melt compounding method

Ultrasound Image Despeckling using Local Binary Pattern Weighted Linear Filtering

Speckle noise formed as a result of the coherent nature of ultrasound imaging affects the lesion detectability. We have proposed a new weighted linear filtering approach using Local Binary Patterns (LBP) for reducing the speckle noise in ultrasound images. The new filter achieves good results in reducing the noise without affecting the image content. The performance of the proposed filter has been compared with some of the commonly used denoising filters. The proposed filter outperforms the existing filters in terms of quantitative analysis and in edge preservation. The experimental analysis is done using various ultrasound images

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

Analysis of Stochastic Volatility Sequences Generated by Product Autoregressive Models

The classical methods of analysing time series by Box-Jenkins approach assume that the observed series uctuates around changing levels with constant variance. That is, the time series is assumed to be of homoscedastic nature. However, the nancial time series exhibits the presence of heteroscedasticity in the sense that, it possesses non-constant conditional variance given the past observations. So, the analysis of nancial time series, requires the modelling of such variances, which may depend on some time dependent factors or its own past values. This lead to introduction of several classes of models to study the behaviour of nancial time series. See Taylor (1986), Tsay (2005), Rachev et al. (2007). The class of models, used to describe the evolution of conditional variances is referred to as stochastic volatility modelsThe stochastic models available to analyse the conditional variances, are based on either normal or log-normal distributions. One of the objectives of the present study is to explore the possibility of employing some non-Gaussian distributions to model the volatility sequences and then study the behaviour of the resulting return series. This lead us to work on the related problem of statistical inference, which is the main contribution of the thesis

Sono, Photo and Sonophoto Catalytic Removal of Chemical and Bacterial Pollutants from Wastewater

The term ‘water pollution’ broadly refers to the contamination of water and water bodies (e.g. lakes, rivers, oceans, groundwater etc). Water pollution occurs when pollutants are discharged directly or indirectly into water bodies without adequate treatment to remove the harmful contaminants. This affects not only the plants and organisms living in these bodies of water but also the entire natural biological communities and the biodiversity.Advanced Oxidation Processes (AOPs) have been tested as environment-friendly techniques for the treatment of contaminated water, in view of their ability to convert pollutants into harmless end products. These techniques refer to a set of treatment procedures designed to remove organic or inorganic contaminants in wastewater by oxidation. The contaminants are oxidized by different reagents such as air, oxygen, ozone, and hydrogen peroxide which are introduced in precise, preprogrammed dosages, sequences and combinations under appropriate conditions. The procedure when combined with light in presence of catalyst is known as photocatalysis. When ultrasound (US) is used as the energy source, the process is referred as sonication. Sonication in presence of catalyst is referred as sonocatalysis. Of late, combination of light and sound as energy sources has been tested for the decontamination of wastewater in the presence of suitable catalyst. In this case, the process is referred as sonophotocatalysis. These AOPs are specially advantageous in pollution control and waste water treatment because unlike many other technologies, they do not just transfer the pollutant from one phase to another but completely degrade them into innocuous substances such as CO2 and H2O.