The objective of the paper is to embed a watermark digital image using discrete wavelet transform. The utmost thrust is to satisfy robustness of watermarked image. For this, singular value decomposition (SVD) method is used. SVD slightly change the singular values of cover image but do not affect the visual perception of the cover image. Genetic algorithm is also used to optimize the result. PS...

In recent literature on digital image processing much attention is devoted to the singular value decomposition (SVD) of a matrix. Many authors refer to the Karhunen-Loeve transform (KLT) and principal components analysis (PCA) while treating the SVD. In this paper we give definitions of the three transforms and investigate their relationships. It is shown that in the context of multivariate sta...

In many practical direction-of-arrival (DOA) problems the number of sources and their directions from an antenna array do not remain stationary. Hence a practical DOA algorithm must be able to track changes with a minimal number of snapshots. In this paper we describe DOA algorithms, based on a new decomposition, that are not expensive to compute or diicult to update. The algorithms are compare...

Singular Value Decomposition (SVD) has recently emerged as a new paradigm for processing different types of images. SVD is an attractive algebraic transform for image processing applications. The paper proposes an experimental survey for the SVD as an efficient transform in image processing applications. Despite the well-known fact that SVD offers attractive properties in imaging, the exploring...

Singular value decomposition has been used in signal processing, image processing, principal component analysis, robotics and my other real time applications. These applications demand fast processing of large datasets. SVD needs large amount of computation. In this paper, we present the parallel implementation of Singular Value Decomposition in FGPA. SVD is implemented using two sided Jacobi a...

Protection of digital images from unauthorized access is the main purpose of this paper. A reliable approach to encrypt a digital image in spatial domain is presented here. Our algorithm is based on the singular value decomposition and one dimensional cellular automata. First, we calculate the singular value decomposition (SVD) of the original image in which the features of the image are extrac...

The singular value decomposition (SVD) is among the most ubiquitous matrix factorizations. Specifically, it is a cornerstone algorithm for data analysis, dimensionality reduction and data compression. However, despite modern computer power, massive datasets pose a computational challenge for traditional SVD algorithms. We present the R package rsvd, which enables the fast computation of the SVD...

In the previous lecture, we discussed the Singular Value Decomposition (SVD) of the term-document matrix D ∈ <m× n where n is the number of documents in the corpus and m is the number of terms in the vocabulary. With the help of SVD (which is unique up to sign if the singular values are distinct), we can decompose an m×n term-document matrix into three special smaller matrices. The result is fr...

Golub and Loan (1980) presented a numerically-stable TLS algorithm which utilizes the singular value decomposition (SVD). Subsequent refinements to the method predominantly use SVD, and much of the current literature emphasizes stabilization of the inverse and implicit model regularization by SVD truncation (Fierro et al., 1997). Because it is numerically intensive, however, the SVD generally p...