On a multiscale approach for filter efficiency simulations

Component Approach to Distributed Multiscale Simulations

a new approach to credibility premium for zero-inflated poisson models for panel data

هدف اصلی از این تحقیق به دست آوردن و مقایسه حق بیمه باورمندی در مدل های شمارشی گزارش نشده برای داده های طولی می باشد. در این تحقیق حق بیمه های پبش گویی بر اساس توابع ضرر مربع خطا و نمایی محاسبه شده و با هم مقایسه می شود. تمایل به گرفتن پاداش و جایزه یکی از دلایل مهم برای گزارش ندادن تصادفات می باشد و افراد برای استفاده از تخفیف اغلب از گزارش تصادفات با هزینه پائین خودداری می کنند، در این تحقیق ...

Deconvolution filter design for fractal signal transmission systems: a multiscale Kalman filter bank approach

In case the point to be interpolated is at the middle of two sampled points, the additional calculations reduce to the values given by Table II(c). As can be seen from Table II, there is a tradeoff between the number of operations and accuracy of interpolation. If a definite intermediate value is to be calculated, the values of the kernel for that specific point may be kept in memory as a look-...

Multiscale Rician Approach to Gabor Filter Design for Texture Segmentation

Gabor filters have been applied successfully to the segmentation of textured images. Previous investigators have used banks of Gabor filters, where the filter parameters were predetermined ad hoc and not necessarily optimized for a particular task. Other investigators have proposed using filters tuned to dominant components in the FFT of constituent textures. More recent work presented a Gabor ...

A multiscale polynomial filter for adaptive smoothing

The effectiveness of Savitzky–Golay type symmetric polynomial smoothers is known to be strongly dependent on the window size. Many authors note that selection of the appropriate window size is essential for achieving the correct trade-off between noise reduction and avoiding the introduction of bias. However, it is often overlooked that, in the case of non-stationary signals, the optimal window...