Digital image watermarking is proposed using lifting wavelet transform and singular value decomposition for copyright protection and authentication. In this paper, lifting wavelet transform (LWT) transforms the image into subbands. The subband having energy greater than computed ‘Q’ value is selected for watermark embedding. Singular value decomposition (SVD) matrix is derived for this subband ...

SVD and FFT are both the efficient tools for image analysis and face recognition. In this paper, we first study the role of SVD and FFT in both filed. Then the decomposition information from SVD and FFT are compared. Next, a new viewpoint that the singular value matrix contains the illumination information of the image is proposed and testified by the experiments based on the ORL face database ...

We discuss techniques for Model-Independent Analysis (MIA) of a beamline using correlation matrices of physical variables and Singular Value Decomposition (SVD) of a beamline BPM matrix. The beamline matrix is formed from BPM readings for a large number of pulses. The method has been applied to the Linear Accelerator of the SLAC Linear Collider (SLC). submitted to Sixth European Particle Accele...

The singular value decomposition (SVD) is a popular matrix factorization that has been used widely in applications ever since an efficient algorithm for its computation was developed in the 1970s. In recent years, the SVD has become even more prominent due to a surge in applications and increased computational memory and speed. To illustrate the vitality of the SVD in data analysis, we highligh...

Singular-value decomposition (SVD)-based multiple-input multiple-output (MIMO) systems have attracted a lot of attention in the wireless community. However, applying SVD to frequency-selective MIMO channels results in unequally weighted single-input single-output (SISO) channels requiring complex resource allocation techniques for optimizing the channel performance. Therefore, a different appro...

Singular value decomposition (SVD) analysis has important applications for time-dependent crystallographic data, extracting significant information. Herein, a successful application of SVD analysis of time-resolved powder diffraction data over the course of an in-situ photodimerization reaction of anthracene derivatives is introduced. SVD revealed significant results in the case of 9-methylanth...

The authors discuss the importance of using the singular value decomposition (SVD) in computing the capacity of multiple input multiple output (MIMO) and in estimation the channel gain from the transmitter to the receiver. Examples that show how the SVD simplifies computing the MIMO channel capacity are discussed. Numerical results that show what factors determine the performance of using SVD i...

This paper proposes a neural network approach based on Error Back Propagation (EBP) for classification of different eye images. To reduce the complexity of layered neural network the dimensions of input vectors are optimized using Singular Value Decomposition (SVD). The main objective of this work is to prove usefulness of SVD to form a compact set of features for classification by EBP algorith...

This paper proposes a novel voice activity detector (VAD) based on singular value decomposition (SVD). The spectro-temporal characteristics of background noise region can be easily analyzed by SVD. The proposed method naturally drops hangover algorithm from VAD. Moreover, it adaptively changes the decision threshold by employing the most dominant singular value of the observation matrix in the ...