Singular value decomposition
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2013 32nd Chinese Control Conference (CCC)
The Chinese Control Conference (CCC) is an annual international conference initiated by the Technical Committee on Control Theory (TCCT), Chinese Association of Automation (CAA). It provides a forum for scientists and engineers over the world to present their new theoretical results and techniques in the field of systems and control. The conference consists of plenary talks, panel discussions, invited sessions, pre-conference workshops, oral sessions and poster sessions etc. for academic exchanges.
Bioinformatics, Computational Biology, Biomedical Engineering
Information, automation, and related areas.
Submitted papers can cover any relevant aspect of Computer Science and Engineering, but they should present a clear research contribution written in a clear and concise manner, with experimental evidence or theoretical developments and proofs that support the claims of the paper. The topics covered include (but are not limited) to Computer Architectures and Digital Systems, Algorithms, Theory, Software Engineering, Data Engineering, Computational Intelligence, System Security, Computer Systems and Netwo
ICIECS2010 will be held on December 25-26, 2010 in Wuhan, China. This conference will bring together the top researchers from Asian Pacific nations, North America, Europe and around the world to exchange their research results and address open issues in Surveying and Mapping, Artificial Intelligence and Its Application, Image Processing and Digital Signal Processing, Computer Modeling and Simulation, and Software Engineering.
Guohui He; Junying Gan Intelligent Multimedia, Video and Speech Processing, 2004. Proceedings of 2004 International Symposium on, 2004
A method for singular value feature extraction of a face image is presented, and its model is established, which includes singular value decomposition (SVD), singular value dimension compression (SVDC), singular value vector standardization (SVVS), and singular value vector arrangement (SVVA). SVDC solves the problems that the information of a singular value feature is redundant and calculation data is huge; SVVS ...
Shuyue Chen; Jun Feng Information and Automation (ICIA), 2010 IEEE International Conference on, 2010
Singular value decomposition technique is widely employed in feature analysis due to its strong capability of feature expression. Aiming at detection of fabric defects, this paper gives an approach for the fabric defects extraction in an image based on the theories of singular value decomposition, and proposes the corresponding algorithm. Firstly, singular value decomposition is performed on sub-image of the ...
Demirel, Hasan; Anbarjafari, G.; Jahromi, M.N.S. Computer and Information Sciences, 2008. ISCIS '08. 23rd International Symposium on, 2008
In this paper, a novel image equalization technique which is based on singular value decomposition (SVD) is proposed. The singular value matrix represents the intensity information of the given image and any change on the singular values change the intensity of the input image. The proposed technique converts the image into the SVD domain and after normalizing the singular value ...
Shidaganti, G.I.; Chickerur, S. Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on, 2013
Various prominent feature points of human faces play a critical role for designing a system that can perform facial recognition. Involvement of facial expression as precision parameter exponentially increases the challenges in developing a face recognition system. There exist abundant volume of studies in the area of face recognition, but majority of the techniques are either computationally not viable or ...
Lin-Ping Song; Shu-yi Zhang Image Processing, IEEE Transactions on, 1999
A reconstruction method is given for seismic transmission traveltime tomography. The method is implemented via the combinations of singular value decomposition, appropriate weighting matrices, and variable regularization parameter. The problem is scaled through the weighting matrices so that the singular spectrum is normalized. Matching the normalized singular values, a regularization parameter varies within the interval [0, 1], and linearly increases ...
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Perahia, Eldad IEEE Std 802.11n-2009 Standard with IEEE 802.11n Physical Layer eLearning Tutorial - Bundle, 2010
The IEEE 802.11n standard will enable a new class of consumer and enterprise products utilizing wireless LAN connectivity that is ten times faster than is feasible with the current IEEE802.11a/b/g standards. This tutorial will provide a comprehensive overview of the Physical Layer (PHY) technology in the p802.11n draft standard. The course will begin with an overview of the applications, environments, ...
Perahia, Eldad IEEE 802.11N Physical Layer, 2008
The IEEE802.11n standard will enable a new class of consumer and enterprise products utilizing wireless LAN connectivity that is ten times faster than is feasible with the current IEEE802.11a/b/g standards. This tutorial will provide a comprehensive overview of the Physical Layer (PHY) technology in the p802.11n draft standard. The course will begin with an overview of the applications, environments, channel ...
No standards are currently tagged "Singular value decomposition"
Signal-processing aspects of image processing, imaging systems, and image scanning, display, and printing. Includes theory, algorithms, and architectures for image coding, filtering, enhancement, restoration, segmentation, and motion estimation; image formation in tomography, radar, sonar, geophysics, astronomy, microscopy, and crystallography; image scanning, digital half-toning and display, andcolor reproduction.
The technology of transmission, recording, reproduction, processing, and measurement of speech; other audio-frequency waves and other signals by digital, electronic, electrical, acoustic, mechanical, and optical means; the components and systems to accomplish these and related aims; and the environmental, psychological, and physiological factors of thesetechnologies.