The accuracy and interpretation of results obtained by Diffusion Tensor Imaging (DTI) are largely influenced by several experimental parameter settings. In Voxel-Based (VB) analysis images are smoothed, in order to improve their Signal to Noise Ratio (SNR) and to reduce the impact of normalization and artifacts. This is a critical step and care must be taken so that directional information and ...

Speech recognition using articulatory features estimated using Acoustic-to-Articulatory Inversion (AAI) is considered. A recently proposed sparse smoothing approach is used to postprocess the estimates from Gaussian Mixture Model (GMM) based AAI using MinimumMean Squared Error (MMSE) criterion. It is well known that low-pass smoothing as post-processing improves the AAI performance. Sparse smoo...

The quadratically convergent algorithms for training SVM with smoothing methods are discussed in this paper. By smoothing the objective function of an SVM formulation, Lee and Mangasarian [Comput Optim Appl 20(1):5-22, 2001] presented one such algorithm called SSVM and proved that the error bound between the new smooth problem and the original one was O(1/p) for large positive smoothing paramet...

This paper discusses the issues to consider when selecting congestion control scheme for an integrated ground radio and satellite network engaging primarily in transmitting multi-media traac, and then it proceeds to discuss eeects (both positive and negative) of smoothing rendered on the input traac as a congestion control scheme. We chose traac shaping by smoothing rendered on the input traac ...

A parametric smoothing model is developed to quantitatively describe the smoothing action of polishing tools that use visco-elastic materials. These materials flow to conform to the aspheric shape of the workpieces, yet behave as a rigid solid for short duration caused by tool motion over surface irregularities. The smoothing effect naturally corrects the mid-to-high frequency errors on the wor...

Smoothing or filtering of data is first preprocessing step for noise suppression in many applications involving data analysis. Moving average is the most popular method of smoothing the data, generalization of this led to the development of Savitzky-Golay filter. Many window smoothing methods were developed by convolving the data with different window functions for different applications; most ...

The size of the Gaussian kernel defines the "width" of the curve which determines in turn how much the data is smoothed. The width is not expressed in terms of the standard deviation ?, as customary in statistics, but with the Full Width at Half Maximum (FWHM). In this case the FWHM would be 2.35: The maximum of this curve is y = 0.4 at x = 0. The half maximum is y = 0.2 at x = -1.175 and at x ...

The size of the Gaussian kernel defines the "width" of the curve which determines in turn how much the data is smoothed. The width is not expressed in terms of the standard deviation ?, as customary in statistics, but with the Full Width at Half Maximum (FWHM). In this case the FWHM would be 2.35: The maximum of this curve is y = 0.4 at x = 0. The half maximum is y = 0.2 at x = -1.175 and at x ...

The size of the Gaussian kernel defines the "width" of the curve which determines in turn how much the data is smoothed. The width is not expressed in terms of the standard deviation ?, as customary in statistics, but with the Full Width at Half Maximum (FWHM). In this case the FWHM would be 2.35: The maximum of this curve is y = 0.4 at x = 0. The half maximum is y = 0.2 at x = -1.175 and at x ...