This paper presents a DWT-DCT-SVD based hybrid watermarking method for color images. Robustness is achieved by applying DCT to specific wavelet sub-bands and then factorizing each quadrant of frequency sub-band using singular value decomposition. Watermark is embedded in host image by modifying singular values of host image. Performance of this technique is then compared by replacing DCT by Wal...

basically the Watermarking process is use for hiding secret information for labeling digital picture. This paper presents a method of watermark embedding and extracting which is based on SVD (singular value decomposition) and Arnold Transform. The SVD is a linear algebra technique used for diagonalizable matrices and it transfer of convert most of the signal energy into very few singular values...

In this paper, we propose a strategy to improve the forecasting of traffic accidents in Concepción, Chile. The forecasting strategy consists of four stages: embedding, decomposition, estimation and recomposition. At the first stage, the Hankel matrix is used to embed the original time series. At the second stage, the Singular Value Decomposition (SVD) technique is applied. SVD extracts the sing...

Erikkson showed that singular value decomposition(SVD) of flattenings determined a partition of a phylogenetic tree to be a split. In this paper, based on his work, we develop new statistically consistent algorithms fit for grid computing to construct a phylogenetic tree by computing SVD of flattenings with the small fixed number of rows.

Computing the Singular Value Decomposition (SVD) of 3× 3 matrices is commonplace in 3D computational mechanics and computer graphics applications. We present a C++ implementation of implicit symmetric QR SVD with Wilkinson shift. The method is fast and robust in both float and double precisions. We also perform a benchmark test to study the performance compared to other popular algorithms.

Complex symmetric matrices arise from many applications, such as chemical exchange in nuclear magnetic resonance and power systems. Singular value decomposition (SVD) reveals a great deal of properties of a matrix. A complex symmetric matrix has a symmetric SVD (SSVD), also called Takagi Factorization, which exploits the symmetry [3]. Let A be a complex symmetric matrix, its Takagi factorizatio...

In this paper, we propose a novel way to include unsupervised feature selection methods in probabilistic taxonomy learning models. We leverage on the computation of logistic regression to exploit unsupervised feature selection of singular value decomposition (SVD). Experiments show that this way of using SVD for feature selection positively affects perfor-

In this paper, we propose a novel way to include unsupervised feature selection methods in probabilistic taxonomy learning models. We leverage on the computation of logistic regression to exploit unsupervised feature selection of singular value decomposition (SVD). Experiments show that this way of using SVD for feature selection positively affects performances.

Abstract We compare two common methods for detecting functional connectivity: thresholding correlations and Singular Value Decomposition (SVD). We find that thresholding correlations is better at detecting focal regions of correlated voxels, whereas SVD is better at detecting extensive regions of correlated voxels. We apply these results to resting state networks in an fMRI data set, and to loo...

We compare two common methods for detecting functional connectivity: thresholding correlations and singular value decomposition (SVD). We find that thresholding correlations are better at detecting focal regions of correlated voxels, whereas SVD is better at detecting extensive regions of correlated voxels. We apply these results to resting state networks in an fMRI dataset to look for connecti...