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In recent years, there has been a significant motivation to develop software techniques for the noninvasive diagnosis of abnormal functioning of the vocal apparatus by studying the patients' voice signal. Learn about noninvasive pathology approaches for the prediction of healthy and affected voice signals from a recent Lecture Notes in the IEEE Signal Processing Magazine.

The IEEE Signal Processing Society (SPS) announced the 2010 Class of Distinguished Lecturers.  Five colleagues were honored and they are: Sheila Hemami (Cornell University, NY); Shrikanth Narayanan (University of Southern California, CA); Antonio Oretga (University of Southern California, CA); Venu Veeravalli (University of Illinois at Urbana-Champaign, Il); and Abdelhak Zoubir (Darmstadt University of Techno

The EURASIP and the IEEE Signal Processing Society Greek Chapter jointly organized a full-day event in October 2009 at the University of Athens: the inaugural "Greek Signal Processing Jam''. The event was envisioned as an opportunity to bring this community together, attract new talent to signal processing, and expose all to some of the latest developments in the broad area.

Synthetic attendance--immersive communications with people in a meeting happening afar--has been one of mankind's elusive dreams. The Exploratory DSP column of the IEEE Signal Processing Magazine (November 2009) discusses critical questions in immersive communications that are still awaiting resolution.

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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.

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.

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