In this research work adopted the frequency domain watermarking scheme which is embedded using discrete wavelet transform (DWT) singular value decomposition (SVD) and High Boost Filtering (HF). By singular values factoring it represent smaller set of values and it can preserve constructive feature of an original image. After that, apply high boost filtering in decomposed in high frequency sub-b...

In this research work adopted the frequency domain watermarking scheme which is embedded using discrete wavelet transform (DWT) and singular value decomposition (SVD). By singular values factoring it represent smaller set of values and it can preserve constructive feature of an original image. After that, apply discrete wavelet transform in decomposed in low frequency sub-band on both images to...

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 ...

In this paper, a new method for non-blind image watermarking that is robust against affine transformation and ordinary image manipulation is presented. The suggested method presents a watermarking scheme based on redundant discrete wavelet transform and Singular Value Decomposition. After applying RDWT to both cover and watermark images, we apply SVD to the LL subbands of them. We then modify s...

Properties and potential applications of the block pulse response circulant matrix (PRCM) and its singular value decomposition (SVD) are investigated in relation to MIMO control and identification. The SVD of the PRCM is found to provide complete directional as well as frequency decomposition of a MIMO system in a real matrix form. Three examples were considered: design of MIMO FIR controller, ...

The purpose of this note is to give a specific form for Kruppa’s equations in terms of the Fundamental matrix. Kruppa’s equations can be written explicitly in terms of the singular value decomposition (SVD) of the

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...

We discuss an investigation into parallelizing the computation of a singular value decomposition (SVD). We break the process into three steps: bidiagonalization, computation of the singular values, and computation of the singular vectors. We discuss the algorithms, parallelism, implementation, and performance of each of these three steps. The original goal was to accomplish all three tasks usin...

this article presents a new subspace-based technique for reducing the noise ofsignals in time-series. in the proposed approach, the signal is initially representedas a data matrix. then using singular value decomposition (svd), noisy datamatrix is divided into signal subspace and noise subspace. in this subspace division,each derivative of the singular values with respect to rank order is used ...

Higher-order tensor decompositions are analogous to the familiar Singular Value Decomposition (SVD), but they transcend the limitations of matrices (second-order tensors). SVD is a powerful tool that has achieved impressive results in information retrieval, collaborative filtering, computational linguistics, computational vision, and other fields. However, SVD is limited to two-dimensional arra...