In this talk, we discuss a new transform technique for solving fractional programming (FP), i.e., a family of optimization problems with ratio terms. The classic FP techniques such as the Charnes-Cooper and Dinkelbach’s methods typically deal with a single ratio, and in general, do not work for multiple ratios. 

Since the establishment of the IEEE BBDITM Signal Processing Society on December 30, 2020, we have successfully organized 54 events. There was a grand inauguration of the society chapter on February 4, 2021. And several other events were organized both offline as well as online by our branch chapter. 

The 2022 Multimedia Prize Paper Award Nomination Period is Open. Any paper published in T-MM in 2019, 2020 or 2021 is eligible. Judging shall be on the bases of originality, subject matter, timeliness, potential impact, and presentation quality.

Dr. Oriol Vinyals is a Principal Scientist at DeepMind, and a team lead of the Deep Learning group. His work focuses on Deep Learning and Artificial Intelligence. Prior to joining DeepMind, Oriol was part of the Google Brain team. He holds a Ph.D. in EECS from the University of California, Berkeley and is a recipient of the 2016 MIT TR35 innovator award. 

We develop algorithms to analyzing facial expression by learning from the data. Since local characters of muscle movements play an important role in distinguishing facial expression by machines, we explore the local characters of facial expressions by introducing the attention mechanism in both supervised and self-supervised supervised manners. Our methods is experimentally shown to be effective on facial expression recognition with occlusions and facial action unit detection.

As part of our ongoing video series to demonstrate the applications of signal processing in everyday life, the IEEE Signal Processing Society Computational Imaging Technical Committee has created a new video, Computational Imaging in Everyday Life.

Many important application domains generate distributed collections of heterogeneous local datasets. These local datasets are related via an intrinsic network structure that arises from domain-specific notions of similarity between local datasets. Networked federated learning aims at learning a tailored local model for each local dataset. 

If you want to test your circuit design being an electrical or electronics engineer, then you have chosen the right event. Test your design, simulation, and hardware implementation skills with real-time applications (in collaboration with IEEE Robotics and Automation Society). 

IEEE Transactions on Mobile Computing seeks applicants for the position of Editor-in-Chief. The EIC term is three years, nonrenewable. The Steering Committee of the Transactions on Mobile Computing invites applications, which should be sent to Cathy Martin by 1 March 2022.

We design a data-driven wavelet transform, called the empirical wavelet transform, which permits to extract very accurate time-frequency information from signals, or features from images.