We explore the use of the singular value decomposition (SVD) in image compression. We link the SVD and the multiresolution algorithms. In [22] it is derived a multiresolution representation of the SVD decomposition, and in [15] the SVD algorithm and Wavelets are linked, proposing a mixed algorithm which roughly consist on applying firstly a discrete Wavelet transform and secondly the SVD algori...

Variational Mode Decomposition (VMD) is widely used for inspection purposes. The initial parameters are usually set manually, which a limitation of this technique. In paper, method to automatically select these through combination Singular Value (SVD) and Improved-VMD (IVMD) proposed. VMD applied multiple times with varying K-value parameter. original signal its sub-signals arising from decompo...

BACKGROUND Recovery from stroke is presumed to be a function of a cerebral network. Chronic small vessel disease (SVD) has been shown to disrupt this network's integrity and has been proposed as a predictor of poor outcome after stroke. We studied this hypothesis in patients with acute ischemic stroke of the striatocapsular region, an area of pronounced cortical and subcortical connectivity. ...

BACKGROUND AND PURPOSE cerebral small vessel disease (SVD) is the most common cause of vascular dementia. Interest in the use of surrogate markers is increasing. The aims of this study were to determine if brain volume was different between patients with SVD and control subjects, whether it correlated with cognition in SVD, and whether changes in brain volume could be detected during prospectiv...

The singular value decomposition (SVD) is a popular matrix factorization that has been used widely in applications ever since an efficient algorithm for its computation was developed in the 1970s. In recent years, the SVD has become even more prominent due to a surge in applications and increased computational memory and speed. To illustrate the vitality of the SVD in data analysis, we highligh...

The hierarchical SVD provides a quasi-best low rank approximation of high dimensional data in the hierarchical Tucker framework. Similar to the SVD for matrices, it provides a fundamental but expensive tool for tensor computations. In the present work we examine generalizations of randomized matrix decomposition methods to higher order tensors in the framework of the hierarchical tensors repres...

Small vessel disease (SVD) is linked to cognitive impairment and dementia, yet little is known regarding functional activation in patients with SVD. Resting fMRI recordings suggest reduced connectivity in prefrontal, parietal and cingulate nodes and reciprocally increased connectivity in cerebellum, alterations which predicted neuropsychological test performance. Together with diffusion tensor ...