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The Latest News, Articles, and Events in Signal Processing

November 18, 2019
Time: 11:00 AM ET (New York Time)
Topic: Network Topology Inference from Spectral Templates

December 10, 2019
Time: 9:00 AM ET (New York Time)
Topic: Toward Efficient and Flexible CNN-based Denoising in Photography
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January 21, 2020
Time: 12:00 PM ET (New York Time)
Topic: FemtoPixel: Lensless Imaging with Compressive Ultrafast Sensing
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February 25, 2020
Time: 8:00 AM ET (New York Time)
Topic: Enabling Identity-Based Integrity Auditing and Data Sharing With
Sensitive Information Hiding for Secure Cloud Storage
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April 20, 2020
Time: 9:00 AM ET (New York Time)
Topic: Deep Learning on Graphs and Manifolds: Going Beyond Euclidean Data
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May 28, 2020
Time: 2:00 PM ET (New York Time)
Topic: Distributed Localization and Tracking of Mobile Networks
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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.

April 20, 2022
Time: 9:00 AM ET (New York Time)
Topic: Cognitive Radar Systems: the road to reality
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May 17, 2022
Time: 10:30 AM ET (New York Time)
Topic: Nonconvex Optimization Meets Low-Rank Matrix Factorization
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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. 

IEEE Signal Processing Magazine
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.
IEEE Signal Processing Magazine
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.
IEEE Signal Processing Magazine
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.
IEEE Signal Processing Magazine
“Science without conscience is only ruin of the soul” said François Rabelais. This centuries-old quote still resonates, today maybe louder than ever. I began to write this editorial at the end of February when Russian tanks and soldiers invaded Ukraine and waves of bombers began dropping their bombs on Ukrainian cities, targeting civilian buildings, hospitals, and schools. This dramatic event was a shock to Europeans, since most of them have lived in relative peace for more than 70 years.
IEEE Signal Processing Magazine
While I am writing this column, the Russia–Ukraine war is raging. As bombings, destruction, and human suffering flood the daily news, I deeply feel the pain of our Ukrainian colleagues, those who have friends and family in the affected areas, those who had to put their studies and careers on hold to fight for their survival. I also acknowledge the agony of those around the world who are watching the developments in horror, trying to comprehend why such insanity was necessary.

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