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IEEE SPS SAM TC Webinar: 20 April 2022, by Maria Sabrina Greco

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.

Joint IEEE SPS and EURASIP Summer School on Defining 6G: Theory, Applications, and Enabling Technologies

With 5G being a reality, what will 6G be? Extensive discussions have been initiated around possible paradigm shifts and new technologies for 6G in recent years. With this summer school, we aim at providing a comprehensive roadmap about fundamental theory, emerging applications, and possible enabling technologies for 6G. 

Self-Supervised Representation Learning: Introduction, advances, and challenges

Self-supervised representation learning (SSRL) methods aim to provide powerful, deep feature learning without the requirement of large annotated data sets, thus alleviating the annotation bottleneck-one of the main barriers to the practical deployment of deep learning today. These techniques have advanced rapidly in recent years, with their efficacy approaching and sometimes surpassing fully supervised pretraining alternatives across a variety of data modalities, including image, video, sound, text, and graphs.

Federated Learning: A signal processing perspective

The dramatic success of deep learning is largely due to the availability of data. Data samples are often acquired on edge devices, such as smartphones, vehicles, and sensors, and in some cases cannot be shared due to privacy considerations. Federated learning is an emerging machine learning paradigm for training models across multiple edge devices holding local data sets, without explicitly exchanging the data. Learning in a federated manner differs from conventional centralized machine learning and poses several core unique challenges and requirements, which are closely related to classical problems studied in the areas of signal processing and communications.

Fire, Water, and Signal Processing: Researchers are turning to signal processing to help them address challenges posed by two of the planet’s fundamental forces

Fire and water, two of nature’s basic forces, are each capable of sustaining or destroying life and property. Research projects in California and Hawaii are, respectively, helping displaced families cope with devasting wildfires, and investigating a way to increase water supply availability on isolated islands. Both projects are relying on signal processing to help them meet their goals.