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News and Resources for Members of the IEEE Signal Processing Society

Technical Committee News

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.

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.

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.

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.

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.

In this talk, we will discuss a panoramic view of digital forensics in the last 10 years and how it needed to evolve from basic computer vision and simple natural language processing techniques to powerful AI-driven methods to deal with the signs of the new age. 

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.

Standards are an important component of today’s commercial marketplace for a variety of advanced technology products.  A frequently cited example of a successful commercial standard is the development of the IEEE 802.11 Wi-Fi standard used to connect billions of wireless devices to local area networks.  

Multimedia contents are deeply intertwined with our lives, and, as a consequence, they've become an invaluable asset also for investigative and evidentiary use. However, there are still numerous open challenges for law enforcement agencies when it comes to acquire, authenticate, enhance, and analyze images and videos for forensic use. 

The intent of this webinar is to demonstrate the optimality of splines for the resolution of inverse problems in imaging and the design of deep neural networks. To that end, I first present a representer theorem that states that the extremal points of a broad class of linear inverse problems with a generalized total-variation constraint are adaptive splines whose type is linked to the underlying regularization operator. 

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