BACKGROUND AND PURPOSE Cerebral small vessel disease (SVD) is associated with cognitive impairment. This may be because of decreased microstructural integrity and microvascular perfusion, but data on these relationships are scarce. We determined the relationship between cognition and microvascular perfusion and microstructural integrity in SVD patients, using intravoxel incoherent motion imagin...

the speech enhancement techniques are often employed to improve the quality and intelligibility of the noisy speech signals. this paper discusses a novel technique for speech enhancement which is based on singular value decomposition. this implementation utilizes a genetic algorithm based optimization method for reducing the effects of environmental noises from the singular vectors as well as t...

BACKGROUND AND PURPOSE It is unknown whether changes in cerebral small vessel disease (SVD) are limited to the brain or part of a generalized vascular disorder. METHODS We examined the sublingual microcirculation of 10 healthy controls, 10 patients with large vessel disease, and 8 with SVD, with side-stream dark field imaging. We analyzed 146 video fragments masked to the origin of the videos...

OBJECTIVE To characterize brain network connectivity impairment in cerebral small-vessel disease (SVD) and its relationship with MRI disease markers and cognitive impairment. METHODS A cross-sectional design applied graph-based efficiency analysis to deterministic diffusion tensor tractography data from 115 patients with lacunar infarction and leukoaraiosis and 50 healthy individuals. Structu...

In this paper we propose novel methods for compression and recovery of multilinear data under limited sampling. We exploit the recently proposed tensorSingular Value Decomposition (t-SVD)[1], which is a group theoretic framework for tensor decomposition. In contrast to popular existing tensor decomposition techniques such as higher-order SVD (HOSVD), t-SVD has optimality properties similar to t...

With the development of data mining technologies, privacy protection has become a challenge for data mining applications in many fields. To solve this problem, many privacy-preserving data mining methods have been proposed. One important type of such methods is based on Singular Value Decomposition (SVD). The SVD-based method provides perturbed data instead of original data, and users extract o...

We propose new algorithms for singular value decomposition (SVD) of very large-scale matrices based on a low-rank tensor approximation technique called the tensor train (TT) format. The proposed algorithms can compute several dominant singular values and corresponding singular vectors for large-scale structured matrices given in a TT format. The computational complexity of the proposed methods ...

In this paper, the performance of SVD and Schur decomposition is evaluated and compared for image copyright protection applications. The watermark image is embedded in the cover image by using Quantization Index Modulus Modulation (QIMM) and Quantization Index Modulation (QIM). Watermark image is embedded in the D matrix of Schur decomposition and Singular Value Decomposition (SVD). Watermarkin...

Spectral embedding based on the Singular Value Decomposition (SVD) is a widely used “preprocessing” step in many learning tasks, typically leading to dimensionality reduction by projecting onto a number of dominant singular vectors and rescaling the coordinate axes (by a predefined function of the singular value). However, the number of such vectors required to capture problem structure grows w...