An Improved Color Video Super-Resolution Using Kernel Regression and Fuzzy Enhancement

An improved color video super-resolution technique using kernel regression and fuzzy enhancement is presented in this paper. A high resolution frame is computed from a set of low resolution video frames by kernel regression using an adaptive Gaussian kernel. A fuzzy smoothing filter is proposed to enhance the regression output. The proposed technique is a low cost software solution to resolution enhancement of color video in multimedia applications. The performance of the proposed technique is evaluated using several color videos and it is found to be better than other techniques in producing high quality high resolution color videos

Studies on Some Aspects of the Physics of the Early Universe Using Gravitationally Coupled Scalar Field –

This thesis deals with some aspects of the Physics of the early universe, like phase transitions, bubble nucleations and premodial density perturbations which lead to the formation structures in the universe. Quantum aspects of the gravitational interaction play an essential role in retical high-energy physics. The questions of the quantum gravity are naturally connected with early universe and Grand Unification Theories. In spite of numerous efforts, the various problems of quantum gravity remain still unsolved. In this condition, the consideration of different quantum gravity models is an inevitable stage to study the quantum aspects of gravitational interaction. The important role of gravitationally coupled scalar field in the physics of the early universe is discussed in this thesis. The study shows that the scalar-gravitational coupling and the scalar curvature did play a crucial role in determining the nature of phase transitions that took place in the early universe. The key idea in studying the formation structure in the universe is that of gravitational instability.

Changes in Protein, Carbohydrate and lipid content of cashew kernels during storage under two humidity conditions

The changes occuring to cashew kernels during storage at two humidity levels - 80% to 20% with respect to organoleptic characteristics, protein content, carbohydrate content, oil content, iodine and peroxide values were studied. From the present study it is concluded that organoleptic characteristics of cashew kernels deteriorates with increase in humidity. Decrease in protein and carbohydrate content of stored cashew kernel is dependent on humidity. Humidity increased oxidative rancidification.

A Study on the Water Retention Characteristics of Soils and their Improvements

Soil moisture plays a cardinal role in sustaining eclological balance and agricultural development – virtually the very existence of life on earth. Because of the growing shortage of water resources, we have to use the available water most efficiently by proper management. Better utilization of rainfall or irrigation management depends largely on the water retention characteristics of the soil.Soil water retention is essential to life and it provides an ongoing supply of water to plants between periods of irrigation so as to allow their continued growth and survival.It is essential to maintain readily available water in the soil if crops are to sustain satisfactory growth. The plant growth may be retarded if the soil moisture is either deficient or excessive. The optimum moisture content is that moisture which leads to optimum growth of plant. When watering is done, the amount of water supplied should be such that the water content is equal to the field capacity that is the water remained in the saturated soil after gravitational drainage. Water will gradually be utilized consumptively by plants after the water application, and the soil moisture will start falling. When the water content in the soil reaches the value known as permanent wilting point (when the plant starts wilting) fresh dose of irrigation may be done so that water content is again raised to the field capacity of soil.Soil differ themselves in some or all the properties depending on the difference in the geotechnical and environmental factors. Soils serve as a reservoir of the nutrients and water required for crops.Study of soil and its water holding capacity is essential for the efficient utilization of irrigation water. Hence the identification of the geotechnical parameters which influence the water retention capacity, chemical properties which influence the nutrients and the method to improve these properties have vital importance in irrigation / agricultural engineering. An attempt in this direction has been made in this study by conducting the required tests on different types of soil samples collected from various locations in Trivandrum district Kerala, with and without admixtures like coir pith, coir pith compost and vermi compost. Evaluation of the results are presented and a design procedure has been proposed for a better irrigation scheduling and management.

Development of a New Transform:MRT

Fourier transform methods are employed heavily in digital signal processing. Discrete Fourier Transform (DFT) is among the most commonly used digital signal transforms. The exponential kernel of the DFT has the properties of symmetry and periodicity. Fast Fourier Transform (FFT) methods for fast DFT computation exploit these kernel properties in different ways. In this thesis, an approach of grouping data on the basis of the corresponding phase of the exponential kernel of the DFT is exploited to introduce a new digital signal transform, named the M-dimensional Real Transform (MRT), for l-D and 2-D signals. The new transform is developed using number theoretic principles as regards its specific features. A few properties of the transform are explored, and an inverse transform presented. A fundamental assumption is that the size of the input signal be even. The transform computation involves only real additions. The MRT is an integer-to-integer transform. There are two kinds of redundancy, complete redundancy & derived redundancy, in MRT. Redundancy is analyzed and removed to arrive at a more compact version called the Unique MRT (UMRT). l-D UMRT is a non-expansive transform for all signal sizes, while the 2-D UMRT is non-expansive for signal sizes that are powers of 2. The 2-D UMRT is applied in image processing applications like image compression and orientation analysis. The MRT & UMRT, being general transforms, will find potential applications in various fields of signal and image processing.

Studies on Quasinormal Modes and Late-time Tails in Black Hole Spacetimes

This thesis Entitled Studies on Quasinormal modes and Late-time tails black hole spacetimes. In this thesis, the signature of these new theories are probed on the evolution of field perturbations on the black hole spacetimes in the theory. Chapter 1 gives a general introduction to black holes and its perturbation formalism. Various concepts in the area covered by the thesis are also elucidated in this chapter. Chapter 2 describes the evolution of massive, charged scalar field perturbations around a Reissner-Nordstrom black hole surrounded by a static and spherically symmetric quintessence. Chapter 3 comprises the evolution of massless scalar, electromagnetic and gravitational fields around spherically symmetric black hole whose asymptotes are defined by the quintessence, with special interest on the late-time behavior. Chapter 4 examines the evolution of Dirac field around a Schwarzschild black hole surrounded by quintessence. Detailed numerical simulations are done to analyze the nature of field on different surfaces of constant radius . Chapter 5is dedicated to the study of the evolution of massless fields around the black hole geometry in the HL gravity.

Geometric Algebra and Einstein’s Electron

This thesis entitled Geometric algebra and einsteins electron: Deterministic field theories .The work in this thesis clarifies an important part of Koga’s theory.Koga also developed a theory of the electron incorporating its gravitational field, using his substitutes for Einstein’s equation.The third chapter deals with the application of geometric algebra to Koga’s approach of the Dirac equation. In chapter 4 we study some aspects of the work of mendel sachs (35,36,37,).Sachs stated aim is to show how quantum mechanics is a limiting case of a general relativistic unified field theory.Chapter 5 contains a critical study and comparison of the work of Koga and Sachs. In particular, we conclude that the incorporation of Mach’s principle is not necessary in Sachs’s treatment of the Dirac equation.

Squeezed Coherent State Representation of Scalar Field and Particle Production in the Early Universe

The present work is an attempt to explain particle production in the early univese. We argue that nonzero values of the stress-energy tensor evaluated in squeezed vacuum state can be due to particle production and this supports the concept of particle production from zero-point quantum fluctuations. In the present calculation we use the squeezed coherent state introduced by Fan and Xiao [7]. The vacuum expectation values of stressenergy tensor defined prior to any dynamics in the background gravitational field give all information about particle production. Squeezing of the vacuum is achieved by means of the background gravitational field, which plays the role of a parametric amplifier [8]. The present calculation shows that the vacuum expectation value of the energy density and pressure contain terms in addition to the classical zero-point energy terms. The calculation of the particle production probability shows that the probability increases as the squeezing parameter increases, reaches a maximum value, and then decreases.

Squeezed Coherent State Representation of Scalar Field and Particle Production in the Early Universe

The present work is an attempt to explain particle production in the early univese. We argue that nonzero values of the stress-energy tensor evaluated in squeezed vacuum state can be due to particle production and this supports the concept of particle production from zero-point quantum fluctuations. In the present calculation we use the squeezed coherent state introduced by Fan and Xiao [7]. The vacuum expectation values of stressenergy tensor defined prior to any dynamics in the background gravitational field give all information about particle production. Squeezing of the vacuum is achieved by means of the background gravitational field, which plays the role of a parametric amplifier [8]. The present calculation shows that the vacuum expectation value of the energy density and pressure contain terms in addition to the classical zero-point energy terms. The calculation of the particle production probability shows that the probability increases as the squeezing parameter increases, reaches a maximum value, and then decreases.

Unconstrained Handwritten Malayalam Character Recognition using Wavelet Transform and Support vector Machine Classifier

This paper presents the application of wavelet processing in the domain of handwritten character recognition. To attain high recognition rate, robust feature extractors and powerful classifiers that are invariant to degree of variability of human writing are needed. The proposed scheme consists of two stages: a feature extraction stage, which is based on Haar wavelet transform and a classification stage that uses support vector machine classifier. Experimental results show that the proposed method is effective

A System for Offline Recognition of Handwritten Characters in Malayalam Script

In this paper, we propose a handwritten character recognition system for Malayalam language. The feature extraction phase consists of gradient and curvature calculation and dimensionality reduction using Principal Component Analysis. Directional information from the arc tangent of gradient is used as gradient feature. Strength of gradient in curvature direction is used as the curvature feature. The proposed system uses a combination of gradient and curvature feature in reduced dimension as the feature vector. For classification, discriminative power of Support Vector Machine (SVM) is evaluated. The results reveal that SVM with Radial Basis Function (RBF) kernel yield the best performance with 96.28% and 97.96% of accuracy in two different datasets. This is the highest accuracy ever reported on these datasets

Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576