The Experts below are selected from a list of 279 Experts worldwide ranked by ideXlab platform
M. H. Barati - One of the best experts on this subject based on the ideXlab platform.
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Probability Density of relativistic spinless particles
Physical Review A, 2018Co-Authors: M. J. Kazemi, H. Hashamipour, M. H. BaratiAbstract:In this paper, a new conserved current for Klein-Gordon equation is derived. It is shown, for $1+1$-dimensions, the first component of this current is non-negative and reduces to $|\phi|^2$ in non-relativistic limit. Therefore, it can be interpreted as the Probability Density of spinless particles. In addition, main issues pertaining to localization in relativistic quantum theory are discussed, with a demonstration on how this definition of Probability Density can overcome such obstacles. Our numerical study indicates that the Probability Density deviates significantly from $|\phi|^2$ only when the uncertainty in momentum is greater than $m_0c$.
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On The Probability Density of Spinless Particles
2018Co-Authors: M. J. Kazemi, H. Hashamipour, M. H. BaratiAbstract:In this paper, we shall derive a new conserved current for Klein-Gordon equation. The first component of this current is non-negative and reduces to $|\phi|^2$ in the non-relativistic limit. Therefore, it can be interpreted as a suitable Probability Density for spinless particles. In addition, this current is time-like and so prevents faster than light particle propagation. We will see the Probability Density has a considerable deviation from $|\phi|^2$ providing the uncertainty in momentum is much greater than $m_0c$.
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On The Probability Density of Relativistic Spinless Particles
arXiv: Quantum Physics, 2018Co-Authors: M. J. Kazemi, H. Hashamipour, M. H. BaratiAbstract:In this paper, we shall derive a new conserved current for Klein-Gordon equation. The first component of this current is non-negative and reduces to $|\phi|^2$ in the non-relativistic limit. Therefore, it can be interpreted as a suitable Probability Density for spinless particles. In addition, this current is time-like and so prevents faster than light particle propagation. We will see the Probability Density has a considerable deviation from $|\phi|^2$ providing the uncertainty in momentum is much greater than $m_0c$.
B. L. S. Prakasa Rao - One of the best experts on this subject based on the ideXlab platform.
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Another Esseen-type inequality for multivariate Probability Density functions
Statistics & Probability Letters, 2002Co-Authors: B. L. S. Prakasa RaoAbstract:An upper bound for the supremum of the absolute value of the difference of two multivariate Probability Density functions is obtained. The upper bound involves integrals of the absolute value of suitable transforms of the characteristic functions of the Probability Density functions. Results are similar to the work of Gamkrelidze (Theory Probab. Appl. 22 (1977) 877–880) on the Esseen's inequality for multidimensional distribution functions.
M. J. Kazemi - One of the best experts on this subject based on the ideXlab platform.
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Probability Density of relativistic spinless particles
Physical Review A, 2018Co-Authors: M. J. Kazemi, H. Hashamipour, M. H. BaratiAbstract:In this paper, a new conserved current for Klein-Gordon equation is derived. It is shown, for $1+1$-dimensions, the first component of this current is non-negative and reduces to $|\phi|^2$ in non-relativistic limit. Therefore, it can be interpreted as the Probability Density of spinless particles. In addition, main issues pertaining to localization in relativistic quantum theory are discussed, with a demonstration on how this definition of Probability Density can overcome such obstacles. Our numerical study indicates that the Probability Density deviates significantly from $|\phi|^2$ only when the uncertainty in momentum is greater than $m_0c$.
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On The Probability Density of Spinless Particles
2018Co-Authors: M. J. Kazemi, H. Hashamipour, M. H. BaratiAbstract:In this paper, we shall derive a new conserved current for Klein-Gordon equation. The first component of this current is non-negative and reduces to $|\phi|^2$ in the non-relativistic limit. Therefore, it can be interpreted as a suitable Probability Density for spinless particles. In addition, this current is time-like and so prevents faster than light particle propagation. We will see the Probability Density has a considerable deviation from $|\phi|^2$ providing the uncertainty in momentum is much greater than $m_0c$.
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On The Probability Density of Relativistic Spinless Particles
arXiv: Quantum Physics, 2018Co-Authors: M. J. Kazemi, H. Hashamipour, M. H. BaratiAbstract:In this paper, we shall derive a new conserved current for Klein-Gordon equation. The first component of this current is non-negative and reduces to $|\phi|^2$ in the non-relativistic limit. Therefore, it can be interpreted as a suitable Probability Density for spinless particles. In addition, this current is time-like and so prevents faster than light particle propagation. We will see the Probability Density has a considerable deviation from $|\phi|^2$ providing the uncertainty in momentum is much greater than $m_0c$.
Richard Barakat - One of the best experts on this subject based on the ideXlab platform.
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Probability Density of the radial gradient of aperture-averaged isotropic-speckle intensity
Journal of the Optical Society of America A, 1991Co-Authors: Richard BarakatAbstract:The Probability-Density function governing the radial gradient of the aperture-integrated isotropic-speckle intensity is obtained by inverting the Abel integral equation between the above Probability Density and the known Probability-Density function of the corresponding spatial derivative. The final result is that the sought-for Probability Density is expressible in terms of modified Bessel functions of the second kind.
Hong Wang - One of the best experts on this subject based on the ideXlab platform.
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Estimating unknown Probability Density functions for random parameters of stochastic ARMAX systems
IFAC Proceedings Volumes, 2003Co-Authors: Hong Wang, Yongji WangAbstract:Abstract Different from existing parameter estimation algorithms where the values of parameters are required to be estimated, this paper presents a new method to estimate the unknown Probability Density functions of random parameters for non-Gaussian dynamic stochastic systems. The System is represted by an ARMAX model, where the parameters and the system noise term are random processes that are characterized by their unknown Probability Density functions. Under the assumption that each random parameter and the noise term are independent and are identically distributed sequece, a simple mathematical relationship is established between the measured output Probability Density function of the system and the unknown Probability Density functions of the random parameters and noise term. The mement generating function in Probability theory has been used to transfer the multiple convolution integration into a simple algebraic operation. An identification algorithm is then established that estimates these unknown Probability Density functions of the parameters and the noise term by using the measured output Probability Density functions and the system input.
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Robust control of the output Probability Density functions for multivariable stochastic systems
Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171), 1Co-Authors: Hong WangAbstract:This paper presents two robust solutions to the control of the output Probability Density function for multi-input and multi-output stochastic systems, where the purpose of control input design is to minimise the difference between the Probability Density function of the system output and a given one. The Probability Density function of the system output is approximated by a B-spline neural network with all its weights dynamically related to the control input. The measured Probability Density function of the system output is directly used to construct two robust control algorithms which are insensitive to the unknown input. The stability of the closed loop system are proved under certain conditions. An illustrative example is included to demonstrate the use of the developed control algorithms and desired results have been obtained.
