The singular value decomposition (SVD) is a fundamental matrix decomposition in linear algebra. It is widely applied in many modern techniques, for example, highdimensional data visualization, dimension reduction, data mining, latent semantic analysis, and so forth. Although the SVD plays an essential role in these fields, its apparent weakness is the order three computational cost. This order ...

Diabetes patients have more than double the risk of ischemic stroke compared with non-diabetic individuals, and its neuroimaging characteristics have important clinical implications. To understand the pathophysiology of ischemic stroke in diabetes, it is important to focus not only on the stroke subtype, but also on the size and location of the occlusive vessels. Specifically, ischemic stroke i...

This thesis presents a systolic algorithm for the SVD of arbitrary complex matrices, based on the cyclic Jacobi method with \parallel ordering". As a basic step in the algorithm, a two-step, two-sided unitary transformation scheme is employed to diagonalize a complex 2 2 matrix. The transformations are tailored to the use of CORDIC (COordinate Rotation Digital Computer) algorithms for high spee...

Finding a way to effectively suppress speckle in SAR images has great significance. K-means singular value decomposition (K-SVD) has shown great potential in SAR image de-noising. However, the traditional K-SVD is sensitive to the position and phase of the characteristics in the image, and the de-noised image by K-SVD has lost some detailed information of the original image. In this paper, we p...

The purpose of this study was to analyze the outcomes and complications between stroke subtypes after intravenous thrombolysis. A total of 471 patients with acute ischemic stroke after intravenous thrombolysis from January 2007 to April 2014 were enrolled and classified according to the Trial of Org 10172 in Acute Stroke Treatment. A multivariate logistic regression model was used to evaluate t...

An ultra-wideband (UWB) synthetic aperture radar (SAR) simulation technique that employs physical and statistical models is developed and presented. This joint physics/statistics based technique generates images that have many of the “blob-like” and “spiky” clutter characteristics of UWB radar data in forested regions while avoiding the intensive computations required for the implementation of ...

Singular Value Decomposition (SVD) is a powerful tool for multivariate analysis. However, independent computation of the SVD for each sample taken from a bandlimited matrix random process will result in singular value sample paths whose tangled evolution is not consistent with the structure of the underlying random process. The solution to this problem is developed as follows: (i) a SVD with re...

BACKGROUND Cognitive impairment is common in patients with cerebral small vessel disease, but is not well detected using common cognitive screening tests which have been primarily devised for cortical dementias. We developed the Brief Memory and Executive Test (BMET); a rapid screening measure sensitive to the impaired executive function and processing speed characteristic of small vessel disea...

In this paper the implementation of the SVD{updating algorithm using orthonormal { rotations is presented. An orthonormal {rotation is a rotation by an angle of a given set of {rotation angles (e.g. the angles i = arctan2 ?i) which are choosen such that the rotation can be implemented by a small amount of shift{add operations. A version of the SVD{updating algorithm is used where all computatio...

Cerebral small vessel disease (SVD) is an important cause of stroke, cognitive decline and vascular dementia (VaD). It is associated with diffuse white matter abnormalities and small deep cerebral ischemic infarcts. The molecular mechanisms involved in the development and progression of SVD are unclear. As hypertension is a major risk factor for developing SVD, Spontaneously Hypertensive Rats (...