Results 241 to 250 of about 128,325 (262)

Compressive K-SVD

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
Dictionary learning algorithms design a dictionary that is specifically tailored to enable sparse representation of a given set of training signals. In turn, the increased sparsity of the signals with respect to this dictionary enables significantly improved performance in a variety of state-of-the-art signal processing tasks, e.g. compressive sensing.
Farhad Pourkamali Anaraki, Shannon M. Hughes   +1 more
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Information filtering using the Riemannian SVD (R-SVD)

1998
The Riemannian SVD (or R-SVD) is a recent nonlinear generalization of the SVD which has been used for specific applications in systems and control. This decomposition can be modified and used to formulate a filtering-based implementation of Latent Semantic Indexing (LSI) for conceptual information retrieval.
Eric P. Jiang, Michael W. Berry
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Laguerre-SVD reduced order modeling

IEEE 8th Topical Meeting on Electrical Performance of Electronic Packaging (Cat. No.99TH8412), 2000
A reduced-order modeling method based on a system description in terms of orthonormal Laguerre functions, together with a Krylov subspace decomposition technique is presented. The link with Pade approximation, the block Arnoldi process and singular value decomposition (SVD) leads to a simple and stable implementation of the algorithm. Novel features of
L. Knockaert, D. De Zutter
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PGD Versus SVD

2013
The issue of separability is of major importance when using the Proper Generalized Decomposition. Efficient computer implementations require the separated representation of model parameters, boundary conditions and/or source terms. This can be performed by applying the Singular Value Decomposition (SVD) or its multi-dimensional counterpart, the so ...
Francisco Chinesta, Roland Keunings, Adrien Leygue   +2 more
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Performance evaluation of RDWT-SVD and DWT-SVD watermarking schemes

AIP Conference Proceedings, 2016
Digital image watermarking protects content by embedding a signal (i.e., owner information) into the host image without noticeable degradation in visual quality. To develop any image watermarking scheme, there some important requirements should be achieved such as imperceptibly, robustness, capacity, security, and, etc.
Taha H. Rassem, Nasrin M. Makbol, Bee Ee Khoo   +2 more
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Performance Evaluation of Robust Watermarking Using DWT-SVD and RDWT-SVD

2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), 2019
DWT and RDWT are two prominent techniques for digital watermarking. The two key limitations associated with DWT method are: the shift variance property, and the size of original image gets decreased at every decomposition level. These limitations lead to a reduction in data payload offered by the watermarking system. RDWT succeeds in dealing with these
Poonam Kadian, Nidhi Arora, Shaifali M. Arora   +2 more
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SVD—based approximations of bivariate functions

2005 IEEE International Symposium on Circuits and Systems, 2005
A method to approximate functions of two variables is presented; it is suitable for hardware implementations based on digital or mixed signal architectures. Such a method is based on the properties of the singular value decomposition (SVD) of a matrix that stores the samples of the function to be approximated.
F. Bizzarri, PARODI, MAURO, STORACE, MARCO   +2 more
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SVD and DWT-SVD Domain Robust Watermarking using Differential Evolution Algorithm

2009
This study aims to develop two optimal watermarking techniques based on SVD and DWT-SVD domain for grey-scale images. The first one embeds the watermark by modifying the singular values of the host image with multiple SFs. In the second approach, a hybrid algorithm is proposed based on DWT and SVD.
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A general framework for SVD flows and joint SVD flows

2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2003
The paper presents a general framework for the development of continuous algorithms for SVD and joint SVD problems. The framework for SVD is derived based on gradient flows on unitary groups. Two previous examples of SVD flows discovered heuristically are derived systematically using the framework.
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2017
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