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Section: Publications & Resources

A new era of pervasive data generation is enabled by emerging sensing modalities and will pose new challenges to signal processing, data science, and robotics. For example, underwater robotic technology enables the development of advanced networks for underwater localization and mapping, and emerging aerial robotic technology enables the development of advanced networks for wide area localization and mapping. 

Section: Publications & Resources

The First TReNDS Neuroimaging Competition is LIVE! The competition features the largest normative dataset of its kind and will push the limits of current predictive technologies to help determine the translational value of the provided features for future clinical use. Find more about this unique opportunity here.

Section: Publications & Resources

The global COVID 19 pandemic has changed everything. Universities, research institutes, companies and other organizations will be operating differently from now, that is, their business models will change drastically. Even when everything goes back to normal, the “new normal” will not resemble our “old normal.” 

Welcome! Ahmed H. Tewfik Message from the IEEE Signal Processing Society President  

Section: Publications & Resources

The IEEE Signal Processing Society invites nominations for the positions of the Chair, Women in Signal Processing Committee and Chair, Young Professionals Committee. The term for the two positions is three years (1 January 2021-31 December 2023). Nominations must be received no later than 10 July 2020.

Section: Publications & Resources

The ALASKA 2 Steganalysis Challenge is now open!  The goal of the competition is to push the community towards image steganalysis, “into the wild” facing two main challenges: 1) The very high heterogeneity of image "sources", with widely different type of content, processing operations, sensor noises, etc. and 2) Designing machine learning method for detection with low false positive, ideally that could be guaranteed.  The entry deadline is 13 July 2020 at 11:59 PM (UTC). 

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

The field of machine learning has undergone radical transformations during the last decade. These transformations, which have been fueled by our ability to collect and generate tremendous volumes of training data and leverage massive amounts of low-cost computing power, have led to an explosion in research activity in the field by academic and industrial researchers.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

I am writing this column on the first official day of spring while “sheltering in place” in Northern California. In these uncertain times, we are all experiencing the anxiety that comes from an unpredictable situation that we do not control; the shock of seeing, perhaps for the first time, all of the shelves in grocery stores empty; and the stress of working, living, and sleeping in the same place.

Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

Batch training of machine learning models based on neural networks is well established, whereas, to date, streaming methods are largely based on linear models. To go beyond linear in the online setting, nonparametric methods are of interest due to their universality and ability to stably incorporate new information via convexity or Bayes's rule.