Modeling and Analysis of Some Heavy Tailed Time Series

The thesis has covered various aspects of modeling and analysis of finite mean time series with symmetric stable distributed innovations. Time series analysis based on Box and Jenkins methods are the most popular approaches where the models are linear and errors are Gaussian. We highlighted the limitations of classical time series analysis tools and explored some generalized tools and organized the approach parallel to the classical set up. In the present thesis we mainly studied the estimation and prediction of signal plus noise model. Here we assumed the signal and noise follow some models with symmetric stable innovations.We start the thesis with some motivating examples and application areas of alpha stable time series models. Classical time series analysis and corresponding theories based on finite variance models are extensively discussed in second chapter. We also surveyed the existing theories and methods correspond to infinite variance models in the same chapter. We present a linear filtering method for computing the filter weights assigned to the observation for estimating unobserved signal under general noisy environment in third chapter. Here we consider both the signal and the noise as stationary processes with infinite variance innovations. We derived semi infinite, double infinite and asymmetric signal extraction filters based on minimum dispersion criteria. Finite length filters based on Kalman-Levy filters are developed and identified the pattern of the filter weights. Simulation studies show that the proposed methods are competent enough in signal extraction for processes with infinite variance.Parameter estimation of autoregressive signals observed in a symmetric stable noise environment is discussed in fourth chapter. Here we used higher order Yule-Walker type estimation using auto-covariation function and exemplify the methods by simulation and application to Sea surface temperature data. We increased the number of Yule-Walker equations and proposed a ordinary least square estimate to the autoregressive parameters. Singularity problem of the auto-covariation matrix is addressed and derived a modified version of the Generalized Yule-Walker method using singular value decomposition.In fifth chapter of the thesis we introduced partial covariation function as a tool for stable time series analysis where covariance or partial covariance is ill defined. Asymptotic results of the partial auto-covariation is studied and its application in model identification of stable auto-regressive models are discussed. We generalize the Durbin-Levinson algorithm to include infinite variance models in terms of partial auto-covariation function and introduce a new information criteria for consistent order estimation of stable autoregressive model.In chapter six we explore the application of the techniques discussed in the previous chapter in signal processing. Frequency estimation of sinusoidal signal observed in symmetric stable noisy environment is discussed in this context. Here we introduced a parametric spectrum analysis and frequency estimate using power transfer function. Estimate of the power transfer function is obtained using the modified generalized Yule-Walker approach. Another important problem in statistical signal processing is to identify the number of sinusoidal components in an observed signal. We used a modified version of the proposed information criteria for this purpose.

Artificial neural network modeling for surface roughness prediction in cylindrical grinding of Al‐SiCp metal matrix composites and ANOVA analysis

Metal matrix composites (MMC) having aluminium (Al) in the matrix phase and silicon carbide particles (SiCp) in reinforcement phase, ie Al‐SiCp type MMC, have gained popularity in the re‐cent past. In this competitive age, manufacturing industries strive to produce superior quality products at reasonable price. This is possible by achieving higher productivity while performing machining at optimum combinations of process variables. The low weight and high strength MMC are found suitable for variety of components

Photosensitivity of Laser Dye Mixtures in Polymer Matrix – A Photoacoustic Study

Laser induced photoacoustic (PA) technique is used in the study of photostability of polymethyl methacrylate (PMMA) films doped with Rhodamine 6G -Rhodamine B dye system. Energy transfer from a donor molecule to an acceptor molecule in a dye mixture affects the output of the dye system. Details of investigations on the role of laser power, modulation frequency and the irradiation wavelength on the photosensitivity of the dye mixture doped PMMA films are presented.

Complex methylene-blue-sensitized polyvinyl chloride: a polymer matrix for hologram recording

A new polymer matrix sensitized with methylene blue for use as an optical recording material is described here. The characterization is done to determine the optimal recording conditions. These films need no chemical development and are found to be stable for several months. The matrix has excellent shelf life and needs an exposure only as short as 20 s. Direct imaging was done on this material.

Preparation and Characterisation of Spinel Ferrites - Their Incorporation in Rubber Matrix and Evaluation of Properties

The objectives of the proposed work are preparation of ceramic nickel zinc ferrite belonging to the series Ni1-XZnXFe2O4 with x varying from 0 to 1in steps of 0.2, structrural, magnetic and electrical characterization of Ni1-XZnXFe2O4, preparation and evaluation of Cure characteristics of Rubber Ferrite Composites (RFCs), magnetic characterization of RFCs using vibrating sample magnetometer (VSM), electrical characterization of RFCs and estimation of magnetostriction constant form HL parameters. The study deals with the structural and magnetic properties of ceramic fillers, variation of coercivity with composition and the variation of magnetization for different filler loadings are compared and correlated. The dielectric properties of ceramic Ni1-XZnXFe2O4 and rubber ferrite composites containing Ni1-XZnXFe2O4 were evaluated and the ac electrical conductivity (ac) of ceramic as well as composite samples can be calculated by using a simple relationship of the form ac = 2f tan 0r, with the data available from dielectric measurements. The results suggest that the ac electrical conductivity is directly proportional to the frequency

Laser-induced thermal characterization of nano Ag metal dispersed ceramic alumina matrix

In this paper, we report the measurements of thermal diffusivity of nano Ag metal dispersed ceramic alumina matrix sintered at different temperatures using laser induced non-destructive photoacoustic technique. Measurements of thermal diffusivity also have been carried out on specimens with various concentration of nano metal. Analysis of the data is done on the basis of one-dimensional model of Rosencwaig and Gersho. The present measurements on the thermal diffusivity of nano metal dispersed ceramic alumina shows that porosity has a great influence on the heat transport and the thermal diffusivity value. The present analysis also shows that the inclusion of nano metal into ceramic matrix increases its interconnectivity and hence the thermal diffusivity value. The present study on the samples sintered at different temperature shows that the porosity of the ceramics varies considerably with the change in sintering temperature. The results are interpreted in terms of phonon assisted heat transfer mechanism and the exclusion of pores with the increase in sintering temperature.

Laser-induced thermal characterization of nano Ag metal dispersed ceramic alumina matrix

In this paper, we report the measurements of thermal diffusivity of nano Ag metal dispersed ceramic alumina matrix sintered at different temperatures using laser induced non-destructive photoacoustic technique. Measurements of thermal diffusivity also have been carried out on specimens with various concentration of nano metal. Analysis of the data is done on the basis of one-dimensional model of Rosencwaig and Gersho. The present measurements on the thermal diffusivity of nano metal dispersed ceramic alumina shows that porosity has a great influence on the heat transport and the thermal diffusivity value. The present analysis also shows that the inclusion of nano metal into ceramic matrix increases its interconnectivity and hence the thermal diffusivity value. The present study on the samples sintered at different temperature shows that the porosity of the ceramics varies considerably with the change in sintering temperature. The results are interpreted in terms of phonon assisted heat transfer mechanism and the exclusion of pores with the increase in sintering temperature

Laser-induced thermal characterization of nano Ag metal dispersed ceramic alumina matrix

In this paper, we report the measurements of thermal diffusivity of nano Ag metal dispersed ceramic alumina matrix sintered at different temperatures using laser induced non-destructive photoacoustic technique. Measurements of thermal diffusivity also have been carried out on specimens with various concentration of nano metal. Analysis of the data is done on the basis of one-dimensional model of Rosencwaig and Gersho. The present measurements on the thermal diffusivity of nano metal dispersed ceramic alumina shows that porosity has a great influence on the heat transport and the thermal diffusivity value. The present analysis also shows that the inclusion of nano metal into ceramic matrix increases its interconnectivity and hence the thermal diffusivity value. The present study on the samples sintered at different temperature shows that the porosity of the ceramics varies considerably with the change in sintering temperature. The results are interpreted in terms of phonon assisted heat transfer mechanism and the exclusion of pores with the increase in sintering temperature

Emission spectral studies of phthalocyanines in borate glass matrix

Cochin University of Science & Technology

Spectral studies of naphthalocyanine (Nc) and rare earth phthalocyanine (RePc) molecules in an inorganic glassy borate matrix

Optical absorption and emission spectral studies of free and metal naphthalocyanine doped borate glass matrix are reported for the first time. Absorption spectra recorded in the UV- VIS-NIR region show the characteristic absorption bands, namely, the B-band and Q-band of the naphthalocyanine (Nc) molecule. Some of the important spectral parameters, namely, the optical absorption coefficient (α), molar extinction coefficient (ε) and absorption cross section (σa) of the principal absorption transitions are determined. Optical band gap (Eg) of the materials evaluated from the functional dependence of absorption coefficient on photon energy lies in the range 1.6 eV≤Eg≤2.1 eV. All fluorescence spectra except that of EuNc consist of an intense band in the 765 nm region corresponding to the excitation of Q-band. In EuNc the maximum fluorescence intensity band is observed at 824 nm. The intensity of the principal fluorescence band is maximum in ZnNc, whereas it is minimum in H2Nc. Radiative parameters of the principal fluorescence transitions corresponding to the Q-band excitation are also reported for the naphthalocyanine and phthalocyanine based matrices.

Optical properties of phthalocyanine molecules in cyano acrylate polymer matrix

Optical absorption and emission spectral studies of various phthalocyanine molecules, viz., LaPc, NdPc, SmPc, EuPc, CuPc and ZnPc in a polymer matrix of cyano acrylate are reported for the first time. All the absorption spectra show an intense B band (Soret) in the UV region followed by a weaker Q band in the visible region. The positions of the Q and B bands are found to have dependence on the metallic substitution. Values of the important spectral parameters, viz., molar extinction coefficient (ϵ), oscillator strength (f), radiative transition rate and decay time of the excited singlet state are also presented and compared with other solid matrices. The recorded fluorescence spectrum shows two broad emission bands in the case of NdPc, whereas for ZnPc only a very weak band is observed. The absence of emission bands for the other metallated phthalocyanines is attributed to increased spin orbit interaction and intersystem crossing.

Optical properties of porphyrins in borate glassy matrix

Optical properties of free and substituted porphyrins (PP) doped borate glass matrix are reported for the first time. Absorption spectral measurements of H2TPP, CdTPP, MgTPP and ZnTPP doped borate glass matrix have been made in the 200–1100 nm region and the spectra obtained are analyzed to obtain the optical bandgap (Eg) and other important spectral parameters viz. oscillator strength (f), molar extinction coefficient (ε), electric dipole strength (q2), absorption cross-section (σa) and molecular concentration (N). Intense fluorescence was observed in the region 668–685 nm for CdTPP, ZnTPP and MgTPP doped matrices, whereas no such fluorescence was observed in H2TPP doped matrix. Fluorescence intensity was observed to be almost similar in all the metallated porphyrine matrices. Fluorescence bandwidth (Δλ), decay time (τ), stimulated emission cross-section (σ) and optical gain (G) of the principal fluorescence transitions corresponding to the Q-band excitation were also evaluated and discussed.

Optical-limiting response of rare-earth metallo-phthalocyanine-doped copolymer matrix

The nanosecond optical-limiting characteristics (at 532 nm) of some rare-earth metallo-phthalocyanines (Sm(Pc)2, Eu(Pc)2, and LaPc) doped in a copolymer matrix of poly(methyl methacrylate) and methyl-2-cyanoacrylate have been studied for the first time to our knowledge. The optical-limiting response is attributed to reverse saturable absorption due to excited-state absorption. The performance of LaPc in a copolymer host is studied at different linear transmissions. The laser damage thresholds of all the samples are also reported.

Characterization of Probability Distributions by Variance Bounds and its Applications

Department of Statistics, Cochin University of Science and Technology

Mean Squared Residue Based Biclustering Algorithms for the Analysis of Gene Expression Data

Computational Biology is the research are that contributes to the analysis of biological data through the development of algorithms which will address significant research problems.The data from molecular biology includes DNA,RNA ,Protein and Gene expression data.Gene Expression Data provides the expression level of genes under different conditions.Gene expression is the process of transcribing the DNA sequence of a gene into mRNA sequences which in turn are later translated into proteins.The number of copies of mRNA produced is called the expression level of a gene.Gene expression data is organized in the form of a matrix. Rows in the matrix represent genes and columns in the matrix represent experimental conditions.Experimental conditions can be different tissue types or time points.Entries in the gene expression matrix are real values.Through the analysis of gene expression data it is possible to determine the behavioral patterns of genes such as similarity of their behavior,nature of their interaction,their respective contribution to the same pathways and so on. Similar expression patterns are exhibited by the genes participating in the same biological process.These patterns have immense relevance and application in bioinformatics and clinical research.Theses patterns are used in the medical domain for aid in more accurate diagnosis,prognosis,treatment planning.drug discovery and protein network analysis.To identify various patterns from gene expression data,data mining techniques are essential.Clustering is an important data mining technique for the analysis of gene expression data.To overcome the problems associated with clustering,biclustering is introduced.Biclustering refers to simultaneous clustering of both rows and columns of a data matrix. Clustering is a global whereas biclustering is a local model.Discovering local expression patterns is essential for identfying many genetic pathways that are not apparent otherwise.It is therefore necessary to move beyond the clustering paradigm towards developing approaches which are capable of discovering local patterns in gene expression data.A biclusters is a submatrix of the gene expression data matrix.The rows and columns in the submatrix need not be contiguous as in the gene expression data matrix.Biclusters are not disjoint.Computation of biclusters is costly because one will have to consider all the combinations of columans and rows in order to find out all the biclusters.The search space for the biclustering problem is 2 m+n where m and n are the number of genes and conditions respectively.Usually m+n is more than 3000.The biclustering problem is NP-hard.Biclustering is a powerful analytical tool for the biologist.The research reported in this thesis addresses the problem of biclustering.Ten algorithms are developed for the identification of coherent biclusters from gene expression data.All these algorithms are making use of a measure called mean squared residue to search for biclusters.The objective here is to identify the biclusters of maximum size with the mean squared residue lower than a given threshold. All these algorithms begin the search from tightly coregulated submatrices called the seeds.These seeds are generated by K-Means clustering algorithm.The algorithms developed can be classified as constraint based,greedy and metaheuristic.Constarint based algorithms uses one or more of the various constaints namely the MSR threshold and the MSR difference threshold.The greedy approach makes a locally optimal choice at each stage with the objective of finding the global optimum.In metaheuristic approaches particle Swarm Optimization(PSO) and variants of Greedy Randomized Adaptive Search Procedure(GRASP) are used for the identification of biclusters.These algorithms are implemented on the Yeast and Lymphoma datasets.Biologically relevant and statistically significant biclusters are identified by all these algorithms which are validated by Gene Ontology database.All these algorithms are compared with some other biclustering algorithms.Algorithms developed in this work overcome some of the problems associated with the already existing algorithms.With the help of some of the algorithms which are developed in this work biclusters with very high row variance,which is higher than the row variance of any other algorithm using mean squared residue, are identified from both Yeast and Lymphoma data sets.Such biclusters which make significant change in the expression level are highly relevant biologically.