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 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...

The purpose of this paper is to present a largely self-contained proof of the singular value decomposition (SVD), and to explore its application to the low rank approximation problem. We begin by proving background concepts used throughout the paper. We then develop the SVD by way of the polar decomposition. Finally, we show that the SVD can be used to achieve the best low rank approximation of...

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 ...

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...

The authors discuss the importance of using the singular value decomposition (SVD) in computing the capacity of multiple input multiple output (MIMO) and in estimation the channel gain from the transmitter to the receiver. Examples that show how the SVD simplifies computing the MIMO channel capacity are discussed. Numerical results that show what factors determine the performance of using SVD i...

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...

The eigenface method is a technique that has been widely used for facial recognition algorithms. The method relies on the use of a one-dimensional Singular Value Decomposition (SVD), but recently it has been argued that a two dimensional SVD would be more effective. In this study, both methods were applied to a handwritten character recognition problem. The two methods yielded similar accuracy ...

Six components of sporaviridin (SVD) were successfully isolated by HPLC using methano-1 M ammonium chloride as the mobile phase. Each component possesses expectedly antimicrobial activity. To ensure the structural relationship between the intact SVD and N-acetylated sporaviridins (N-Ac-SVD), they were converted to the corresponding of N-acetates by acetylation in methanol, whose structures had ...