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January 2024
Statistical Principles of Time Reversal
Time reversal is a physical principle well known for its deterministic focusing effect. Recently discovered statistical effects show that the time reversal focusing spot is not a point but has a Bessel power distribution. This finding offers accurate and reliable speed estimation indoors, where multipaths are abundant, with mostly nonline-of-sight (NLOS) conditions, and enable various indoor applications, such as wireless sensing and tracking. No known techniques can thrive in such scenarios. In essence, time reversal is an effective tool that embraces multipaths as virtual sensors with hundreds of thousands of degrees of freedom for our utilization.
Signal Processing at 75: More Dynamic and Pervasive Than Ever
The year 2023 marked the 75th anniversary of the IEEE Signal Processing Society (SPS), which was founded in 1948 as the “Professional Group on Audio” of the Institute of Radio Engineers (IRE), becoming the first IEEE Society. (The IRE, founded in 1912 with a focus on radio and then electronics, together with the American Institute of Electrical Engineers, founded in 1884 with an emphasis on power and utilities, were united in 1963 to form IEEE.)
An Exciting Juncture for Signal Processing Research: On Building Bridges, Challenges, and Opportunities
A warm greeting to the signal processing community as I start my term as the editor-in-chief of IEEE Signal Processing Magazine ( SPM ). I hope to be worthy of the confidence invested in me and to be able to follow successfully in Christian Jutten’s footsteps.
Going for Sustainable Conferences
The research landscape is evolving very dynamically. This column reflects on it from a conference viewpoint and focuses on the importance of creating a more sustainable culture for the conference portfolio that the IEEE Signal Processing Society (SPS) offers. Among the different considerations, the role that virtual conferences can play is highlighted.
November 2023
A Signal Processing Interpretation of Noise-Reduction Convolutional Neural Networks: Exploring the mathematical formulation of encoding-decoding CNNs
Encoding-decoding convolutional neural networks (CNNs) play a central role in data-driven noise reduction and can be found within numerous deep learning algorithms. However, the development of these CNN architectures is often done in an ad hoc fashion and theoretical underpinnings for important design choices are generally lacking. Up to now, there have been different existing relevant works that have striven to explain the internal operation of these CNNs. Still, these ideas are either scattered and/or may require significant expertise to be accessible for a bigger audience.
