The IEEE Signal Processing Society Kolkata Chapter has been selected as the recipient of the 2024 Chapter of the Year Award!

The Signal Processing Society (SPS) has 12 Technical Committees that support a broad selection of signal processing-related activities defined by the scope of the Society.

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) conducts webinars presented by professionals in the field of signal processing and related technologies on an ongoing basis. Webinars are also hosted periodically by SPS Technical Committees, and other network of communities. Visit the Upcoming Events page to see a list of our upcoming webinars and join us! Visit the SPS BLOG for more related content on a regular basis!

Mérouane Debbah is  Professor at  Khalifa University of Science and Technology in Abu Dhabi and founding Director of the KU 6G Research Center. He is a frequent keynote speaker at international events in the field of telecommunication and AI. 

An occlusion-aware model for gait video processing uses SMPL-based human mesh models and machine learning to achieve superior recognition in challenging surveillance videos.

We propose a novel low-level feature similarity (FSIM) induced FR IQA metric, namely, FSIM. FSIM can measure image quality automatically and consistently with human perception.

The IEEE Signal Processing Society invites nominations for the position of Editor-in-Chief for the following journals: IEEE Open Journal of Signal Processing, IEEE Journal of Selected Topics in Signal Processing, and IEEE Signal Processing Letters for a 3-year term starting 1 January 2026.

Electromagnetic inverse scattering problems (ISPs) are crucial in noninvasive imaging but challenging due to nonlinearity and computational costs. This blog explores machine learning-based ISP solvers with physics-guided loss functions, emphasizing the role of near-field priors and multiple-scattering effects. Numerical experiments highlight the advantages and limitations of these approaches.