10.31.2025
IEEE Signal Processing Society Annual Election Opens on 17 October
The IEEE Signal Processing Society annual election will commence on 17 October, and your vote is more important than ever! Ballots must be received… Read more
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IEEE SPS Events
SA-TWG Webinar: Seeing Beyond the Blur: Imaging Black Holes with Increasingly Strong Assumptions
Date: 10-December-2025
Time: 1:30 PM ET (New York Time)
Presenter: Dr. Katherine L. (Katie) Bouman
10 Dec
2025 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
Dates: 14-17 December, 2025
Location: Dominican Republic
Call for Papers Deadline: July 15, 2025
14 Dec
Call for Proposals: IEEE Spoken Language Technology Workshop (SLT 2028)
Intent to Bid Submission Deadline: 15 December 2025
15 Dec
Call for Proposals:IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2028)
Intent to Bid Submission Deadline: 15 December 2025
15 Dec
Call for Proposals: IEEE International Workshop on Multimedia Signal Processing (MMSP 2028)
Intent to Bid Submission Deadline: 15 December 2025
15 Dec
Call for Proposals: Machine Learning for Signal Processing (MLSP 2028)
Intent to Bid Submission Deadline: 15 December 2025
15 Dec
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Deadline Extended to Submit Proposals for 2026 Member-Driven Initiatives
Deadline has been extened to submit your proposal today to be considered for funding for events being held during April-September 2026 Read more
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Introducing Industry Signals – By Industry, For Industry
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SPS on X
🌍 The EHT is a global network of telescopes working together to photograph black holes. Recovering an image from this data means solving an ill-posed inverse problem—a puzzle with infinite possible solutions. 🔗Upcoming events at https://t.co/HoBOSLbzvq @klbouman @caltech
🔭 For decades, scientists theorized that a black hole would cast a “shadow” against hot gas. But imaging it requires an Earth-sized radio telescope. Enter: the Event Horizon Telescope (EHT). 🔗Upcoming events at https://t.co/2bi3byZelk. @klbouman @caltech
📸 This talk explores how @klbouman produced the first images of Sagittarius A\*, and how machine learning is helping us sharpen our view—mapping its dynamic environment in 3D. 🔗Upcoming events at https://t.co/lDz6VSSUpi @Caltech
