The following volunteers have been named Editors-in-Chief of IEEE Signal Processing Society publications. The term for these Editors-in-Chief will run from 1 January 2021 through 31 December 2023.

IEEE SPS has built a streamlined mechanism for employers to add a job announcement by simply filling in a simple job opportunity submission Web form related to a particular TC field. To submit job announcements for a particular Technical Committee, the submission form can be found by visiting the page below and selecting a particular TC.

Each month, the Chapter Briefs Newsletter will feature an OU Analytics Tip that will be helpful to all who utilize the OU Analytics tool.  This month's Tip features Potential Chapters.

In an effort to continue our programs and services during this difficult time, the SPS Awards Board is now accepting nominations for all Society level awards, from paper awards to the major society awards. Nominations are due by 1 September 2020.

The coronavirus (COVID-19) pandemic has led to widespread morbidity and mortality. Several countries have been forced into prolonged lockdowns in a desperate attempt to control the pandemic by physical distancing. Such lockdowns have devastated livelihoods and put large numbers of people at grave risk of prolonged unemployment, poverty, and even starvation.

Dr. Yonina Eldar is a Professor in the Department of Mathematics and Computer Science, Weizmann Institute of Science, Rechovot, Israel. She was previously a Professor in the Department of Electrical Engineering at the Technion, where she held the Edwards Chair in Engineering.

Given the impossibility of travel during the COVID-19 crisis,  Computational Imaging TC is launching an SPS Webinar Series SPACE (Signal Processing And Computational imagE formation) as a regular bi-weekly online seminar series to reach out to the global computational imaging and signal processing community.

Deep neural networks achieve state-of-the-art performance in many domains in signal processing. The main practice is getting pairs of examples, input, and its desired output, and then training a network to produce the same outputs with the goal that it will learn how to generalize also to new unseen data, which is indeed  the case in many scenarios.