Displaying similar documents to “A noise of new type and its generalized functionals”

State estimation under non-Gaussian Lévy noise: A modified Kalman filtering method

Xu Sun, Jinqiao Duan, Xiaofan Li, Xiangjun Wang (2015)

Banach Center Publications

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The Kalman filter is extensively used for state estimation for linear systems under Gaussian noise. When non-Gaussian Lévy noise is present, the conventional Kalman filter may fail to be effective due to the fact that the non-Gaussian Lévy noise may have infinite variance. A modified Kalman filter for linear systems with non-Gaussian Lévy noise is devised. It works effectively with reasonable computational cost. Simulation results are presented to illustrate this non-Gaussian filtering...

Accent Recognition for Noisy Audio Signals

Ma, Zichen, Fokoue, Ernest (2014)

Serdica Journal of Computing

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It is well established that accent recognition can be as accurate as up to 95% when the signals are noise-free, using feature extraction techniques such as mel-frequency cepstral coefficients and binary classifiers such as discriminant analysis, support vector machine and k-nearest neighbors. In this paper, we demonstrate that the predictive performance can be reduced by as much as 15% when the signals are noisy. Specifically, in this paper we perturb the signals with different levels...

Application of the adaptive center-weighted vector median framework for the enhancement of cDNA microarray images

Rastislav Lukac, Bogdan Smołka (2003)

International Journal of Applied Mathematics and Computer Science

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In this paper a novel method of noise reduction in color images is presented. The new technique is capable of attenuating both impulsive and Gaussian noise, while preserving and even enhancing the sharpness of the image edges. Extensive simulations reveal that the new method outperforms significantly the standard techniques widely used in multivariate signal processing. In this work we apply the new noise reduction method for the enhancement of the images of the so called gene chips....