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The Latest News, Articles, and Events in Signal Processing

John M. Cioffi and H. Vincent Poor have been elected International Fellows of the Royal Academy of Engineering of the United Kingdom, two of only three engineers worldwide to be so elected this year.  They were inducted into the Academy at its New Fellows Dinner on 9 November 2009, in London, UK.

Ph.D. and post-doc positions are available to work on fluorescence microscopy related projects in joint research group with University of Texas at Dallas and UT Southwestern Medical Center at Dallas. The projects aim to develop novel imaging modalities, image processing and data analysis methods for fluorescence microscopy live cell experiments in particular single molecule detection.

Signal processing engineers usually use high level languages to develop advanced algorithms for new radars and to determine the optimal parameters for these algorithms. The long execution times due to computational complexity and/or very large data sets hinders an efficient engineering development workflow. Rapid prototyping tools such as parallel MATLAB can enable a software design workflow that helps the development of radar prototypes by providing interactivity and reducing the execution time of the algorithms under test.

The ability to learn about a stochastic process from noisy observations is fundamental to many applications. In order to track a dynamic process, typical knowledge representation is the state space model such as a linear Gauss Markov model, where efficient algorithms exist to perform state estimation under many different model assumptions. However, for meta level tracking, we are not only interested in the state estimation, but also temporal and structural classification of the process.

Polarimetric Synthetic Aperture Radar (POLSAR) data is an important source for many operational remote sensing applications. Segmentation and classification of image data are important tasks for POLSAR data analysis and interpretation, which often requires human interaction. In this thesis, spectral graph partitioning methodology is used to exploit both the polarimetric attributes of pixels, and the visual aspect of the image data through visual cues.

FACETS of CODING THEORY:  from ALGORITHMS to NETWORKS0911portrait_kotter

An exclusive article by Petar Djuric, SPS Vice President-Finance, on Society's finances and many efforts for bringing additional values to its members, including expanded access to SPS publications, enhanced digital publications, new conference events and member discount in registration, and travel support to students and distinguished lecturers.

The Laboratory for Information and Decision Systems (LIDS) at the Massachusetts Institute of Technology (MIT) is hosting the Paths Ahead in the Science of Information and Decision Systems symposium on 12-14 Novemeber 2009 in Cambridge, MA.  The symposium brings together leading researchers from all around the world who have been influential in shaping the vision of and leading the field of information

Lecture Notes on multiview point cloud regularization (MVPCR), a framework that unifies and generalizes several semisupervised kernel methods that are based on data-dependent regularization in reproducing kernel Hilbert spaces. Special cases of MVPCR include coregularized least squares (CoRLS), manifold regularization (MR), and graph-based SSL.

Ph.D. and post-doc positions are available to work on fluorescence microscopy related projects in joint research group with University of Texas at Dallas and UT Southwestern Medical Center at Dallas. The projects aim to develop novel imaging modalities, image processing and data analysis methods for fluorescence microscopy live cell experiments in particular single molecule detection.

by Roger Woods, John McAllister, Ying Yi, and Gaye Lightbody. Woods_FPGA
Published by Wiley. ISBN#978-0-470-03009-7.

"Best of the Web" focuses on web resources that are useful for understanding the features of digital signal processor (DSP) devices and using DSPs to build systems and applications.

JPEG XR is the newest image coding standard from the JPEG committee. It primarily targets the representation of continuous-tone still images such as photographic images, and achieves high image quality, on par with JPEG 2000, while requiring low computational resources and storage capacity.

from IEEE Member and Geographic Activities (MGA) Board

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