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Section: Events
Date: 16 January 2023
Chapter: Italy Chapter
Chapter Chair: Enrico Magli
Title: Convolutional Neural Networks Demystified: A Matched Filtering
Approach to Fully Interpretable CNNs
Section: Our Story
Florida Atlantic University
Boca Raton, FL, USA
Section: Professional Development
The IEEE SPS Education Program provides learning resources (live or on-demand), for members and nonmembers alike, tailored for professional growth and development in the fields of interest of signal processing.
Section: Events
Date: 12-13 September 2023
Location: Edinburgh, UK
Section: Events
Date: June 20-22, 2023
Location: Ghent, Belgium
Section: Events
Date: 31 October-3 November 2023
Location: Taipei, Taiwan
Section: Publications & Resources
Publication: IEEE Signal Processing Magazine
First, I would like to wish you and your loved ones a nice new year filled with health and happiness. The last few years have been challenging for various reasons: the COVID-19 pandemic, climatic events, and the war in Ukraine, to name a few. It seems impossible to be able to stop the megalomania and madness of some human beings.
Section: Publications & Resources
Publication: IEEE Signal Processing Magazine
May the year 2023 bring everyone closer to the fulfilment of their dreams. We have left behind a year marked with successes on multiple fronts, including health and technology as well as a year filled with proud people risking their lives for freedom. In particular, two big movements have captured our hearts: the fierce resistance of Ukranian people against the Russian invasion of their country, and the prodemocracy uprising in Iran with women in the lead.
Section: Publications & Resources
Publication: IEEE Signal Processing Magazine
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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.
