March 2021

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March, 2021
SPM March 2021

Volume 38 | Issue 2

Ten years ago, the world marveled at the ability of social media technology to assist an entire region in its pursuit of democracy. As I write this column days after the U.S. Presidential Inauguration, the world this time is overwhelmingly appalled by the role that same technology played in a violent attempt to overturn democracy. Those who decried the shutdown of access to social media desperately implemented by authoritarian regimes applauded similar restrictions implemented by tech companies in a quest to forestall additional violence.
This past summer, Prof. Robert Heath Jr. IEEE Signal Processing Magazine’s (SPM’s) former editor-in-chief, stressed to me how important it is to include a strong team of scientists on the magazine’s editorial board. It is especially important for area editors and members of the senior editorial board, but also associate editors for columns and forums as well as the e-Newsletter.
Many problems in signal processing [e.g., filter bank design, independent component analysis (ICA), beamforming design, and neural network training] can be formulated as optimization over groups of transformations that depend continuously on real parameters (Lie groups). Such problems are usually tackled in two ways: using a constrained optimization procedure or using some parameterization to transform them into unconstrained problems.
The old adage "you are what you wear" is taking on an entirely new meaning as smart watches, fitness trackers, and a rapidly expanding array of other wearable devices flood onto the market, enabling users to monitor their exercise progress, retrieve critical health data, and accomplish a wide range of other useful and informative tasks.
Deep neural networks provide unprecedented performance gains in many real-world problems in signal and image processing. Despite these gains, the future development and practical deployment of deep networks are hindered by their black-box nature, i.e., a lack of interpretability and the need for very large training sets. 

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