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...

BACKGROUND AND PURPOSE Arterial stiffness, a risk factor of brain small vessel diseases (SVD), causes hemodynamic changes. Mechanical stresses, circumferential wall tension (WT), and shear stress (SS) may change with arterial stiffness and be related to SVD. We investigated the associations between mechanical stresses and arterial stiffness and SVD. METHODS A total of 1296 subjects without ap...

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 ...

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 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...

SVD and FFT are both the efficient tools for image analysis and face recognition. In this paper, we first study the role of SVD and FFT in both filed. Then the decomposition information from SVD and FFT are compared. Next, a new viewpoint that the singular value matrix contains the illumination information of the image is proposed and testified by the experiments based on the ORL face database ...

The Silicon Vertex Detector (SVD) is the main diagnostic tool in the ALPHA-experiment. It provides precise spatial and timing information of antiproton (antihydrogen) annihilation events (vertices), and most importantly, the SVD is capable of directly identifying and analysing single annihilation events, thereby forming the basis of ALPHA's analysis. This paper describes the ALPHA SVD and its u...

In this paper, signal vector based detection (SVD) algorithm for spatial modulation (SM) is modified to achieve a near maximum-likelihood (ML) performance and reduces the complexity compared to ML. First, the proposed low-complexity SVD (LC-SVD) algorithm orders the antenna index list based on the angle between the received vector y and the channel vector j h , and then it estimates symbol by c...

Singular value decomposition (SVD) analysis has important applications for time-dependent crystallographic data, extracting significant information. Herein, a successful application of SVD analysis of time-resolved powder diffraction data over the course of an in-situ photodimerization reaction of anthracene derivatives is introduced. SVD revealed significant results in the case of 9-methylanth...