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Seminar Announcement Friday, October 31, 2008 Prof. Gaurav Sharma
Multi-media watermarking applications pose multiple constraints that must be simultaneously satisfied: sufficient watermarking strength, imperceptibility of embedding distortions, robustness to signal processing operations being the common requirements. In addition, applications often require multiple watermarks for different purposes: for example, for copyright protection, traitor tracing, and authentication. Traditional methods have addressed these scenarios through ad hoc modifications of the two main watermarking methods: spread-spectrum embedding and quantization index modulation. In this talk, we present a novel set theoretic methodology for watermarking that effectively addresses both of these scenarios in a common and principled framework. We present applications of the methodology to both spread-spectrum and quantization-index-modulation watermarks and demonstrate its effectiveness. Professional Biography: Gaurav Sharma is an associate professor at the
Electrical and Computer Engineering Department at the University of Rochester
and is the Director for the Center for Electronic Imaging Systems (CEIS), a
New York state funded center located at the University of Rochester. He
received the PhD degree in Electrical and Computer engineering from --- Seminar Announcement Friday, November 7, 2008 Prof. Nikos Sidiropoulos
Matrix algebra plays an important role in modern signal processing. In many applications, the information-bearing signal lies in a subspace, while the parameters of interest correspond to a particular basis of this subspace. Whereas the signal subspace can often be reliably estimated from measured data, the particular basis of interest cannot be identified without additional problem-specific structure. This is due to rotational indeterminacy - non-uniqueness of low-rank matrix decomposition. The situation is very different for three-or higher-way arrays, i.e., data 'boxes' indexed by three or more independent variables, for which low-rank decomposition is unique under certain conditions. This talk will be a guided tour of theory and algorithms for analyzing data boxes, with emphasis on communications and array processing applications. Professional Biography: Nikos
Sidiropoulos received the Diploma in Electrical Engineering from the
Aristotelian University of Thessaloniki, Greece, and M.Sc. and Ph.D. degrees
in Electrical Engineering from the University of Maryland at College Park, in
1988, 1990, and 1992, respectively. He has served as Assistant Professor at
the University of Virginia, and Associate Professor at the University of
Minnesota. Since 2002, he is Professor in the Technical University of Crete,
Chania, Greece. His current research interests are primarily in signal processing
for communications, cross-layer resource allocation in wireless networks,
convex approximation, and multi-way analysis. He has served as Associate
Editor for IEEE Transactions on Signal Processing and IEEE Signal Processing
Letters. Dr. Sidiropoulos received the IEEE Signal Processing Society Best
Paper Award in 2001 and in 2007. He is a Distinguished Lecturer of the IEEE
Signal Processing Society for 2008-2009.
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