Nita Patel is a Senior Director, Engineering at L3Harris Technologies. L3Harris is an agile, global aerospace and defense technology innovator, delivering end-to-end solutions that meet customers' mission-critical needs.

Chetan Arora is an Associate Professor with Computer Science Department at IIT Delhi. He received Ph.D. in Computer Science and B.Tech in Electrical Engineering, both from IIT Delhi in 2012 and 1999 respectively. He did his Post Doctoral Research with Prof. Shmuel Peleg from 2012-2014 in Hebrew University, Israel.  Prior to joining IIT Delhi, he was an Assistant Professor with IIIT Delhi.

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.

It is my pleasure to announce that the IEEE Signal Processing Society (SPS) annual election will commence on 17 August! This year, all eligible SPS Members will now vote for the Regional Directors-at-Large for Regions 1-6 and 8 (term 1 January 2021 through 31 December 2022), and Members-at-Large (term 1 January 2021 through 31 December 2023) of the IEEE Signal Processing Society Board of Governors (BoG).

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.

The Signal Processing Society (SPS) has 13 Technical Committees that support a broad selection of signal processing-related activities defined by the scope of the Society.

As a reminder, most of the SPS publications have eliminated month-based issues and moved to a volume-only, continuous pagination model. This allows for rapid dissemination of content for our journals and now, articles are posted to their respective journals on IEEEXplore® nearly every day! 

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.