PSWT Based Linear Predictive Coding and Development of a Parallel Multiple Subsequence Structure for DWT Computation

During 1990's the Wavelet Transform emerged as an important signal processing tool with potential applications in time-frequency analysis and non-stationary signal processing.Wavelets have gained popularity in broad range of disciplines like signal/image compression, medical diagnostics, boundary value problems, geophysical signal processing, statistical signal processing,pattern recognition,underwater acoustics etc.In 1993, G. Evangelista introduced the Pitch- synchronous Wavelet Transform, which is particularly suited for pseudo-periodic signal processing.The work presented in this thesis mainly concentrates on two interrelated topics in signal processing,viz. the Wavelet Transform based signal compression and the computation of Discrete Wavelet Transform. A new compression scheme is described in which the Pitch-Synchronous Wavelet Transform technique is combined with the popular linear Predictive Coding method for pseudo-periodic signal processing. Subsequently,A novel Parallel Multiple Subsequence structure is presented for the efficient computation of Wavelet Transform. Case studies also presented to highlight the potential applications.

Photoacoustic Thermal Characterization of Porous Rare-Earth Phosphate Ceramics

The laser induced non-destructive photoacoustic technique has been employed to measure the thermal diffusivity of lanthanum phosphate ceramics prepared by the sol–gel route. The thermal diffusivity value was evaluated by knowing the transition frequency between the thermally thin to thermally thick region from the log–log plot of photoacoustic amplitude versus chopping frequency. Analysis of the data was carried out on the basis of the one-dimensional model of Rosencwaig and Gersho. The present investigation reveals that the sintering temperature has great influence on the propagation of heat carriers and hence on the thermal diffusivity value. The results were interpreted in terms of variations in porosity with sintering temperature as well as with changes in grain size.

Age and growth of Gerres filamentosus (Cuvier, 1829) from Kodungallur, Azhikode Estuary, Kerala

The age and growth, length – weight relationship and relative condition factor of Gerres filamentosus (Cuvier, 1829) from Kodungallur, Azhikode Estuary were studied by examination of 396 specimens collected between May 2008 to October 2008. Here, length frequency method was used to study age and growth in fishes. L∞, K and t 0 obtained from seasonal and non - seasonal growth curves. Gerres filamentosus showed a low mortality rate (Z) 3.702 y-1. G. filamentosus has moderately low K value and long life span. The relation between the total length and weight of G. filamentosus was described as Log W = 1.321+2.5868 log L for males, Log W = 1.467 + 2.7227 log L for females and Log W = 1.481 + 2.7316 log L for sexes combined. The mean relative condition factor (Kn) values ranged from 0.9 to 1.14 for males, 0.89 to 1.11 for females and 0.73 to 1.08 for sexes combined. The length weight relationship and relative condition factor showed that the wellbeing of G. filamentosus were good. The morphometric measurements of various body parts were recorded. The morphometric measurements were found to be nonlinear and there is no significant difference observed between the two sexes.

DEVELOPMENT AND ANALYSIS OF DUAL FREQUENCY MICROSTRIP ANTENNAS

Department of Elecctronics, Cochin University of Science and Technology

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.