ABSTRACT The use of distances between Time-Frequency Representations (TFRs) has recently led to a time-frequency formulation of the problem of non-stationary signals classification. In this paper, we propose a new method based upon the optimization of the TFR, remaining in the Cohen’s group. We show that a radially gaussian kernel and a Fisher-like contrast criterion provide improved classifica...

Measuring the diffusion properties of crossing fibers is very challenging due to the high number of model parameters involved and the intrinsically low SNR of Diffusion Weighted MR Images. Noise filtering aims at suppressing the noise while pertaining the data distribution. We propose an adaptive version of the Linear Minimum Mean Square Error (LMMSE) estimator to achieve this. Our filter appli...

Bone piays a pivotaE role in storing calciurri and phosphate in vertebrates. This tissue is maintained by the balance of bene formatien and bene resorption. Osteoblast-tineage c lls, consisting of osteoblasts, esteocytes and bone lining cells, are engaged in bone formation. Bene resorption is mediated by osteoclasts. Recent research revealed that receptor activator ofNF-KB (RANK)-RANK ligand (R...

Ž . Temporal complexity of early ventricular fibrillation VF is re-assessed through measuring the correlation dimension Ž . D , entropy K and high-dimensional component s from electrocardiogram ECG recordings using the Gaussian kernel 2 2 algorithm. Seven representative ECG traces of induced VF among 53 pig subjects are selected for analysis. VF is found to have 80–90% low-dimensional determini...

Gaussian kernel smoothing has been widely used in 3D whole brain imaging analysis as a way to increase signal-to-noise ratio. Gaussian kernel is isotropic in Euclidian space. However, data obtained on the convoluted brain cortex fails to be isotropic in the Euclidean sense. On the curved surface, a straight line between two points is not the shortest distance so one may incorrectly assign less ...

In this paper, we apply kernel PCA for speech enhancement and derive pre-image iterations for speech enhancement. Both methods make use of a Gaussian kernel. The kernel variance serves as tuning parameter that has to be adapted according to the SNR and the desired degree of de-noising. We develop a method to derive a suitable value for the kernel variance from a noise estimate to adapt pre-imag...

The main purpose of this paper is providing a systematic study and classification of non-scalar kernels for Reproducing Kernel Hilbert Spaces (RKHS), to be used in the analysis of deformation in shape spaces endowed with metrics induced by the action of groups of diffeomorphisms. After providing an introduction to matrix-valued kernels and their relevant differential properties, we explore exte...

The importance of the support vector machine and its applicability to a wide range of problems is well known. The strength of the support vector machine lies in its kernel. In our recent paper, we have shown how the Laplacian kernel overcomes some of the drawbacks of the Gaussian kernel. However this was not a total remedy for the shortcomings of the Gaussian kernel. In this paper, we design a ...

The importance of the support vector machine and its applicability to a wide range of problems is well known. The strength of the support vector machine lies in its kernel. In our recent paper, we have shown how the Laplacian kernel overcomes some of the drawbacks of the Gaussian kernel. However this was not a total remedy for the shortcomings of the Gaussian kernel. In this paper, we design a ...

The importance of the support vector machine and its applicability to a wide range of problems is well known. The strength of the support vector machine lies in its kernel. In our recent paper, we have shown how the Laplacian kernel overcomes some of the drawbacks of the Gaussian kernel. However this was not a total remedy for the shortcomings of the Gaussian kernel. In this paper, we design a ...