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Section: Publications & Resources
Publication: IEEE Signal Processing Magazine
A computational experiment is deemed reproducible if the same data and methods are available to replicate quantitative results by any independent researcher, anywhere and at any time, granted that they have the required computing power. Such computational reproducibility is a growing challenge that has been extensively studied among computational researchers as well as within the signal processing and machine learning research community.
Section: Events
Date: 10 March 2023
Chapter: Gujarat
Chapter Chair: Chirag Paunwala
Title: Wearable Sensor Signal Processing and Data Analytics for Health Applications
Section: Events
Date: 8 March 2023
Time: 7:00 PM ET (New York Time)
Title: Power Scaling of Uplink Massive MIMO Systems with Arbitrary-Rank Channel Means
Section: Publications & Resources
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!
Section: Events
Date: 30 March 2023
Time: 8:00 AM PST | 5:00 PM (CEST)
Presenter(s): Arash Ajoudani
Section: Events
Date: 26 April 2023
Time: 11:00 AM ET (New York Time)
Presenter(s): Dr. Preeti Kumari, Dr. Nitin Jonathan Myers, Dr. Robert W. Heath, Jr.
Section: Events
Date: 29 March 2023
Time: 12:30 PM ET (New York Time)
Presenter(s): Dr. Mauricio Delbracio
Section: Events
Date: 13 March 2023
Time: 1:30 PM ET (New York Time)
Presenter(s): Dr. Donald Williamson
Section: Events
Date: 12 April 2023
Time: 9:30 AM ET (New York Time)
Presenter(s): Dr. Pan Gao, Dr. Alijosa Smolic
Section: Publications & Resources
Publication: IEEE Signal Processing Magazine
The compressive sensing (CS) scheme exploits many fewer measurements than suggested by the Nyquist–Shannon sampling theorem to accurately reconstruct images, which has attracted considerable attention in the computational imaging community. While classic image CS schemes employ sparsity using analytical transforms or bases, the learning-based approaches have become increasingly popular in recent years. Such methods can effectively model the structure of image patches by optimizing their sparse representations or learning deep neural networks while preserving the known or modeled sensing process.
