Continuous Functional Calculus for Quaternionic Bounded Normal Operators

In this article we give an approach to define continuous functional calculus for bounded quaternionic normal operators defined on a right quaternionic Hilbert space.Comment: Submitted to a journal. There was a gap in the previous version. We have corrected it and stated all the results for bounded cas

The role of taxes in capital structure: evidence from taxed and non-taxed Arab economies

The Arab economies present a unique opportunity to test the tax model of capital structure. These economies may be dichotomized into taxable and non-taxable states. The results support a number of implications of the tax-based theories of capital structure. We document relatively higher leverage in economies that impose a corporate income tax. We also document that leverage is significantly positive in the proxy for corporate marginal tax rate. In addition, we find that non-debt tax shield is a positive and significant determinant of capital structure in non-taxed economies, but is insignificant in taxed economies. Additionally, we find that leverage is systematically related to size, collateral, and profitability. The overall results are suggestive of the portability of capital structure theory(ies) across diverse economies.Capital Struacutre; Arab Economies; Taxes; Debt Tax Shiled

On the polar decomposition of right linear operators in quaternionic Hilbert spaces

In this article we prove the existence of the polar decomposition for densely defined closed right linear operators in quaternionic Hilbert spaces: If $T$ is a densely defined closed right linear operator in a quaternionic Hilbert space $H$, then there exists a partial isometry $U_{0}$ such that $T = U_{0}|T|$. In fact $U_{0}$ is unique if $N(U_{0}) = N(T)$. In particular, if $H$ is separable and $U$ is a partial isometry with $T = U|T|$, then we prove that $U = U_{0}$ if and only if either $N(T) = \{0\}$ or $R(T)^{\bot} = \{0\}$.Comment: 17 page

Spoken Word Recognition Using Hidden Markov Model

The main aim of this project is to develop isolated spoken word recognition system using Hidden Markov Model (HMM) with a good accuracy at all the possible frequency range of human voice. Here ten different words are recorded by different speakers including male and female and results are compared with different feature extraction methods. Earlier work includes recognition of seven small utterances using HMM with the use only one feature extraction method. This spoken word recognition system mainly divided into two major blocks. First includes recording data base and feature extraction of recorded signals. Here we use Mel frequency cepstral coefficients, linear cepstral coefficients and fundamental frequency as feature extraction methods. To obtain Mel frequency cepstral coefficients signal should go through the following: pre emphasis, framing, applying window function, Fast Fourier transform, filter bank and then discrete cosine transform, where as a linear frequency cepstral coefficients does not use Mel frequency. Second part describes HMM used for modeling and recognizing the spoken words. All the raining samples are clustered using K-means algorithm. Gaussian mixture containing mean, variance and weight are modeling parameters. Here Baum Welch algorithm is used for training the samples and re-estimate the parameters. Finally Viterbi algorithm recognizes best sequence that exactly matches for given sequence there is given spoken utterance to be recognized. Here all the simulations are done by the MATLAB tool and Microsoft window 7 operating system