SPS Webinars

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 Principal Component Analysis (PCA) is considered to be a quintessential data preprocessing tool in many machine learning applications. But the high dimensionality and massive scale of data in several of these applications means the traditional centralized PCA solutions are fast becoming irrelevant for them. 

The Principal Component Analysis (PCA) is considered to be a quintessential data preprocessing tool in many machine learning applications. But the high dimensionality and massive scale of data in several of these applications means the traditional centralized PCA solutions are fast becoming irrelevant for them. 

Privacy issues and communication costs are both major concerns in distributed optimization in networks. There is often a tradeoff between them because encryption methods used for privacy-preservation often introduce significant communication overhead.

In past decades, conventional communication primarily focused on how to accurately and effectively transmit symbols, which is categorized as the first level of communications by Shannon and Weaver. With the developments of cellular communication systems, the achieved transmission rate is gradually approaching to the Shannon limit. 

As the standardization of 5G gradually solidifies, researchers are speculating what 6G will be. One common theme is that radio sensing functionality would be integrated into 6G networks in a low-cost and fast manner. 

Practically anyone can now generate a realistic-looking deepfake video. Many methods to detect deepfakes were recently proposed by the research community. However, it is still unclear how realistic deepfake videos are for an average person and whether the algorithms are significantly better than humans at detecting them.

Neuro-imaging is safe and non-invasive, which is widely used in the early diagnosis and prediction of Alzheimer's disease, Parkinson's disease, Autism and other brain diseases. With the rapid development of intelligent computing technology, it is possible to use neuro-imaging data for intelligent diagnosis of brain diseases.

Over the past fifteen years, “cognition” has emerged as an enabling technology for incorporating learning and adaptivity on both transmit and receive to optimize or make more robust the radar performance in dynamic environments.The term ‘cognitive radar’ was introduced for the first time by Dr. Simon Haykin in 2006, but the foundations of the cognitive systems date back several decades to research on knowledge-aided signal processing, and adaptive radar design.

Substantial progress has been made recently on developing provably accurate and efficient algorithms for low-rank matrix factorization via nonconvex optimization. While conventional wisdom often takes a dim view of nonconvex optimization algorithms due to their susceptibility to spurious local minima, simple iterative methods such as gradient descent have been remarkably successful in practice.