The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
A new noise removal algorithm based on improved neural network, is applied to remove the impulse noise of the digital images. First of all, an improved neural network is used to detect the noise-pixels and distinguish it from noise-free pixels efficiently; Second, the noise-pixels are replaced further by the suitable pixel which has the most local similarity; Finally, the output is the combination...
In this paper, an improved neural network is applied to CCD noise removal in digital image. According to the characteristics of the nonlinear response function in the CCD camera, the denoising scheme is based on the adaptive window sizes and parameter of the filter. The improved NN both approaches to CCD PTC (photon transfer curve) and classifies it in nonlinearity and assigns the optimum coefficient...
This paper proposes a new narrowband active noise control (ANC) system where an ANFIS (adaptive network-based fuzzy inference system) is utilized as an adaptive controller. The ANFIS is a combination of a neural network and a fuzzy inference system. For the purpose of computational cost reduction, the nonlinear premise parameters in the ANFIS are fixed and only its linear consequent parameters are...
This paper presents a new method to estimate process noise covariances Q and observation noise covariances R of nonlinear time-varying system. Based on the analysis of key factor to estimate noise covariances Q and R for identification and prediction of nonlinear time-varying system using extended Kalman filter based on neural network. Then the paper extends the Mehra approach of noise statistics...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.