This paper proposes a method to ascertain the stability of two dimensional linear time invariant discrete system within the shifted unit circle which is represented by the form of characteristic equation. Further an e...This paper proposes a method to ascertain the stability of two dimensional linear time invariant discrete system within the shifted unit circle which is represented by the form of characteristic equation. Further an equivalent single dimensional characteristic equation is formed from the two dimensional characteristic equation then the stability formulation in the left half of Z-plane, where the roots of characteristic equation f(Z) = 0 should lie within the shifted unit circle. The coefficient of the unit shifted characteristic equation is suitably arranged in the form of matrix and the inner determinants are evaluated using proposed Jury’s concept. The proposed stability technique is simple and direct. It reduces the computational cost. An illustrative example shows the applicability of the proposed scheme.展开更多
A new simple and efficient dual tree analytic wavelet transform based on Discrete Cosine Harmonic Wavelet Transform DCHWT (ADCHWT) has been proposed and is applied for signal and image denoising. The analytic DCHWT ha...A new simple and efficient dual tree analytic wavelet transform based on Discrete Cosine Harmonic Wavelet Transform DCHWT (ADCHWT) has been proposed and is applied for signal and image denoising. The analytic DCHWT has been realized by applying DCHWT to the original signal and its Hilbert transform. The shift invariance and the envelope extraction properties of the ADCHWT have been found to be very effective in denoising speech and image signals, compared to that of DCHWT.展开更多
In recent years,the development in the field of computer-aided diagnosis(CAD)has increased rapidly.Many traditional machine learning algorithms have been proposed for identifying the pathological brain using magnetic ...In recent years,the development in the field of computer-aided diagnosis(CAD)has increased rapidly.Many traditional machine learning algorithms have been proposed for identifying the pathological brain using magnetic resonance images.The existing algorithms have drawbacks with respect to their accuracy,efficiency,and limited learning processes.To address these issues,we propose a pathological brain tumour detection method that utilizes the Weiner filter to improve the image contrast,2D-discrete wavelet transformation(2D-DWT)to extract the features,probabilistic principal component analysis(PPCA)and linear discriminant analysis(LDA)to normalize and reduce the features,and a feed-forward neural network(FNN)and modified particle swarm optimization(MPSO)with ant colony optimization(ACO)to improve the accuracy,stability,and overcome fitting issues in the classification of brain magnetic resonance images.The proposed method achieves better results than other existing algorithms.展开更多
文摘This paper proposes a method to ascertain the stability of two dimensional linear time invariant discrete system within the shifted unit circle which is represented by the form of characteristic equation. Further an equivalent single dimensional characteristic equation is formed from the two dimensional characteristic equation then the stability formulation in the left half of Z-plane, where the roots of characteristic equation f(Z) = 0 should lie within the shifted unit circle. The coefficient of the unit shifted characteristic equation is suitably arranged in the form of matrix and the inner determinants are evaluated using proposed Jury’s concept. The proposed stability technique is simple and direct. It reduces the computational cost. An illustrative example shows the applicability of the proposed scheme.
文摘A new simple and efficient dual tree analytic wavelet transform based on Discrete Cosine Harmonic Wavelet Transform DCHWT (ADCHWT) has been proposed and is applied for signal and image denoising. The analytic DCHWT has been realized by applying DCHWT to the original signal and its Hilbert transform. The shift invariance and the envelope extraction properties of the ADCHWT have been found to be very effective in denoising speech and image signals, compared to that of DCHWT.
文摘In recent years,the development in the field of computer-aided diagnosis(CAD)has increased rapidly.Many traditional machine learning algorithms have been proposed for identifying the pathological brain using magnetic resonance images.The existing algorithms have drawbacks with respect to their accuracy,efficiency,and limited learning processes.To address these issues,we propose a pathological brain tumour detection method that utilizes the Weiner filter to improve the image contrast,2D-discrete wavelet transformation(2D-DWT)to extract the features,probabilistic principal component analysis(PPCA)and linear discriminant analysis(LDA)to normalize and reduce the features,and a feed-forward neural network(FNN)and modified particle swarm optimization(MPSO)with ant colony optimization(ACO)to improve the accuracy,stability,and overcome fitting issues in the classification of brain magnetic resonance images.The proposed method achieves better results than other existing algorithms.
