In this work we consider algorithms based on the Singular Value Decomposition (SVD) to approximate Lyapunov and Exponential Dichotomy spectra of dynamical systems. We review existing contributions, and propose new algorithms of the continuous SVD method. We present implementation details for the continuous SVD method, and illustrate on several examples the behavior of continuous (and also discr...

The leading mode of the singular value decomposition (SVD) geopotential height (GPH) and boundary layer structure index (BLSI).

The scaled total least square (STLS) problem, introduced by B.D. Rao in 1997, unifies both the total least square (TLS) and the least square (LS) problems. The STLS problems can be solved by the singular value decomposition (SVD). In this paper, we give a rank-revealing two-sided orthogonal decomposition method for solving the STLS problem. An error analysis is presented. Our numerical experime...

In this paper a new approach for speech enhancement is presented. The proposed algorithm is based on singular value decomposition (SVD) and wavelet transform. A model of contaminant noise is estimated by using SVD in the recommended method and then, using of noise estimation determines thresholding value. Needlessness of silence frame in order to estimate the noise model is an advantage of sugg...

This paper presents a watermarking technique for fingerprint images using the DWT-SVD (Discrete WaveletTransform -Singular Value Decomposition). The watermarking image is embedded and extracted to calculated the PSNR (Peak Signal to Noise Ratio) and NC (Normalized cross correlation) value. Watermark Embedding Technique or the algorithm should be imperceptible i.e. embedding watermark should not...

An efficient Singular Value Decomposition (SVD) algorithm is an important tool for distributed and streaming computation in big data problems. It is observed that update of singular vectors of a rank-1 perturbed matrix is similar to a Cauchy matrix-vector product. With this observation, in this paper, we present an efficient method for updating Singular Value Decomposition of rank1 perturbed ma...

In this paper we propose new blur identi cation algorithm based on singular value decomposition (SVD) of degraded image. An unknown space-invariant point-spread function (PSF) is also decomposed using SVD. Magnitude functions of PSF singular vectors (left and right) are identi ed using averaged spectra of corresponding singular vectors of degraded image. Phase functions of PSF singular vectors ...

Consider the Singular Value Decomposition (SVD) of a two-parameter function A(x), x ∈ Ω ⊂ R2, where Ω is simply connected and compact, with boundary Γ. No matter how differentiable the function A is (even analytic), in general the singular values lose all smoothness at points where they coalesce. In this work, we propose and implement algorithms which locate points in Ω where the singular value...

This paper addresses the problem of image representation based on a sparse decomposition over a learned dictionary. We propose an improved matching pursuit algorithm for Multiple Measurement Vectors (MMV) and an adaptive algorithm for dictionary learning based on multi-Singular Value Decomposition (SVD), and combine them for image representation. Compared with the traditional K-SVD and orthogon...

We present a practical and efficient means to compute the singular value decomposition (svd) of a quaternion matrix A based on bidiagonalization of A to a real bidiagonal matrix B using quaternionic Householder transformations. Computation of the svd of B using an existing subroutine library such as lapack provides the singular values of A. The singular vectors of A are obtained trivially from ...