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Section: Events
Date: October 17, 2017
Time: 11:00 AM ET (New York Time)
Title: Digital Signaling: Interacting Social Sensors and Social Networ
Registration | Full webinar details
Section: Publications & Resources
For our 50th anniversary in 1998, the IEEE Signal Processing Society (SPS) worked with the IEEE History Center to prepare a monograph outlining the history of the Society. In 2023, we will celebrate the 75th anniversary of the IEEE Signal Processing Society!
Section: Events
Publication: IEEE Signal Processing Magazine
White Paper Due: July 6, 2022
Publication Date: Mid 2023
CFP Document
Section: Publications & Resources
The IEEE Signal Processing Society is excited to announce we are partnering with the IEEE Humanitarian Activities Committee (HAC) to conduct a special Call for Proposals to support projects in SPS chapters that utilize Signal Processing technologies to address local community challenges.
Section: Events
Date: October 20, 2022
Chapter: Vietnam
Chapter Chair: Nguyen Ling Trung
Title: Data Fusion Through Matrix and Tensor Decompositions:
Overview of Solutions, Challenges, and Prospects
Chair: Rodrigo Andres Nayvares Cayo
Email: rodrigonayvares@ieee.org
Advisor: Arun Balodi
Email: drbalodi@gmail.com
Chair: Pragna Shekar
Email: pragnashekar31@gmail.com
Section: Publications & Resources
The Audio Engineering Society (AES), the IEEE Consumer Technology Society (CTSoc), and the IEEE Signal Processing Society (SPS) cordially invite you to a first-of-a-kind joint event discussing the state of the art perspectives in this rapidly evolving field.
Section: Publications & Resources
Publication: IEEE Signal Processing Magazine
The July issue of IEEE Signal Processing Magazine (SPM) is a special issue focused on “Explainability in Data Science: Interpretability, Reproducibility, and Replicability.” With increased enthusiasm for machine learning, it is a very timely topic, and I invite every IEEE Signal Processing Society (SPS) member to read these very instructive papers.
Section: Publications & Resources
Publication: IEEE Signal Processing Magazine
Most of the work we do in signal processing these days is data driven. The shift from the more traditional and model-driven approaches to those that are data driven has also underlined the importance of explainability of our solutions. Because most traditional signal processing approaches start with a number of modeling assumptions, they are comprehensible by the very nature of their construction.
