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.

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. 

In accordance with the Bylaws of the IEEE Signal Processing Society, I am writing to solicit nominations for the Awards Board and the Nominations and Appointments (N&A) Committee. This year, the Society will be filling TWO positions on the N&A Committee for the term 2023-2024 and TWO positions on the Awards Board for the term 2023-2025. 

Recently, deep convolutional neural network (CNNs) have been widely used in Single Image Super-Resolution (SISR) and have obtained great success. However, most of the existing methods are limited to local receptive field and equal treatment of different types of information; 

IEEE SPS has built a streamlined mechanism for employers to add a job announcement by simply filling in a simple job opportunity submission Web form related to a particular TC field. To submit job announcements for a particular Technical Committee, the submission form can be found by visiting the page below and selecting a particular TC.

This dissertation develops Defense through AI-powered SYstem-scientific methods (DAISY) for high-confidence Cyber-Physical Systems (CPSs).  We start by designating five generations of Security Paradigms (SPs) that have evolved since the birth of the Internet.

Recent years have seen great strides being made in R&D of speech and language technologies. As these technologies continue to permeate our daily lives, they need to support diverse users and usage contexts, especially those with inputs that deviate from the mainstream.

Shrikanth (Shri) Narayanan is University Professor and Niki & C. L. Max Nikias Chair in Engineering at the University of Southern California, where he is Professor of Electrical & Computer Engineering, Computer Science, Linguistics, Psychology, Neuroscience, Pediatrics, and Otolaryngology-Head & Neck Surgery, Director of the Ming Hsieh Institute and Research Director of the Information Sciences Institute. 

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.

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.