Transparent, robust watermarking schemes tend to insert invisible watermarks with the highest possible energy. Singular values, in singular value decomposition (SVD), are shown to be robust to intentional and unintentional attacks. However, none of the algorithms proposed in the literature considered the visual imperceptibility constraint imposed by the human visual models. In this paper, we in...

The singular value decomposition (SVD) is a very important tool for narrowband adaptive sensor array processing. It finds application in areas as diverse as high resolution direction finding, stabilised adaptive beamforming and blind signal separation [1,2]. The SVD decorrelates the signals received from an array of sensors by applying a unitary matrix of complex scalars which serves to modify ...

This paper proposes a multiresolution form of the singular value decomposition (SVD) and shows how it may be used for signal analysis and approximation. It is well-known that the SVD has optimal decorrelation and subrank approximation properties. The multiresolution form of SVD proposed here retains those properties, and moreover, has linear computational complexity. By using the multiresolutio...

We propose a constructive algorithm, called the tensor-based Kronecker product (KP) singular value decomposition (TKPSVD), that decomposes an arbitrary real matrix A into a finite sum of KP terms with an arbitrary number of d factors, namely A = ∑R j=1 σj A dj ⊗ · · · ⊗A1j . The algorithm relies on reshaping and permuting the original matrix into a d-way tensor, after which its tensor-train ran...

The ongoing work at the University of Ljubljana, oriented towards application of Singular Value Decomposition in Digital Signal Processing shall be presented in the following work. It has been shown by many authors that the SVD can be an effective tool in solving problems like signal detection, signal parameter estimation, signal separation, adaptive filtering and equalisation. It has been show...

In this paper we present a novel technique for wavelet-based corner detection using singular value decomposition (SVD). Here SVD facilitates the selection of global natural scale in discrete wavelet transform. We de®ne natural scale as the level associated with most prominent (dominant) eigenvalue. Eigenvector corresponding to dominant eigenvalue is considered as the optimal scale. The corners ...

It is possible for Singular Value Decomposition (SVD) watermark embedding to have a quasi-one-way functionality. While basic SVD embedding has the drawback of being non-reversible, an advanced SVD method which uses a reversible relation only works one-way. When embedding takes place, the decomposing matrix obtained from the original image differs from the one obtained from the embedded image. T...

The text retrieval method using Latent Semantic Indexing (LSI) with the truncated Singular Value Decomposition (SVD) has been intensively studied in recent years. The term-document matrices after SVD are full matrices, although the rank is reduced substantially. To reduce memory consumption, we examine some strategies to sparsify the truncated SVD matrices. After applying the sparsification str...

Plant structure is utilized for the simpli cation of system analysis and controller synthesis. For plants where the directionality is independent of frequency, the singular value decomposition (SVD) is used to decouple the system into nominally independent subsystems of lower dimension. In H2and H1-optimal control, the controller synthesis can thereafter be performed for each of these subsystem...

A novel robust digital image watermarking scheme based on singular value decomposition (SVD) and micro-genetic algorithm (micro-GA) is proposed in this paper. In an SVD-based watermarking scheme, the singular values of the cover image are modified to by considering multiple scaling factors to embed the watermark image. Determining the proper values of scaling factors is not an easy task. Here w...