SPS Feed

The Latest News, Articles, and Events in Signal Processing

SPS Webinar: Dr. Wangmeng Zuo

Date: December 10, 2019
Time: 9:00 AM ET (New York Time)
Title: Toward Efficient and Flexible CNN-based Denoising in Photography
Registration | Full webinar details

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webinars

SPS Webinar Series

Events

SPS Webinar

11 May 2022

SPS Webinar: Dr. Guy Satat

Date: January 21, 2020
Time: 12:00 PM ET (New York Time)
Title: FemtoPixel: Lensless Imaging with Compressive Ultrafast Sensing
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11 May 2022

SPS Webinar: Wenting Shen and Jiankun Hu

Date: February 25, 2020
Time: 8:00 AM ET (New York Time)
Title: Enabling Identity-Based Integrity Auditing and Data Sharing With
Sensitive Information Hiding for Secure Cloud Storage

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11 May 2022

SPS Webinar: Dr. Michael Bronstein

Date: April 20, 2020
Time: 9:00 AM ET (New York Time)
Title: Deep Learning on Graphs and Manifolds: Going Beyond Euclidean Data
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11 May 2022

SPS Webinar: Dr. Florian Meyer

Date: May 28, 2020
Time: 2:00 PM ET (New York Time)
Title: Distributed Localization and Tracking of Mobile Networks
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11 May 2022

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.

11 May 2022

SAM TC Webinar: Maria Sabrina Greco

Date: April 20, 2022
Time: 9:00 AM ET (New York Time)
Title: Cognitive Radar Systems: the road to reality
Registration | Full webinar details

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11 May 2022

SPS Webinar: Dr. Yuejie Chi

Date: May 17, 2022
Time: 10:30 AM ET (New York Time)
Title: Nonconvex Optimization Meets Low-Rank Matrix Factorization
Registration | Full webinar details

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11 May 2022

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.

10 May 2022

2022 IEEE SPS Cycle 1 School on 2022 IEEE SPS Seasonal School on AI in Healthcare

June 6-10, 2022
Registeration Deadline: May 29, 2022
Location: India

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10 May 2022

2022 IEEE SPS Seasonal school on Video Surveillance and Monitoring

May 26-27, 2022
Registeration Deadline: May 20, 2022
Location: Bahawalpur, Pakistan

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10 May 2022

2022 Joint IEEE SPS and EURASIP Summer School on Defining 6G: Theory, Applications, and Enabling Technologies

Date: August 29-September 1, 2022
Registeration Deadline: N/A
Location: Sweden

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Seasonal Schools

EURASIP

Enabling Technologies

Theory

Summer School

09 May 2022

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

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.

09 May 2022

Federated Learning: A signal processing perspective

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.

09 May 2022

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

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.

09 May 2022

Ethical Dilemmas in the Sciences

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.

09 May 2022

On Dual-Use Information Technology

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.

09 May 2022

SPS Webinar: 17 May 2022, presented by Dr. Yuejie Chi

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.