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10 years of news and resources for members of the IEEE Signal Processing Society

Education & Resources

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. 

This webinar, part of the new IEEE JSTSP webinar series on recent special issues (Sis), will overview the Joint Communication and Radar Sensing (JCR) for Emerging Applications. The webinar will start with a brief motivation of the area of JCR, followed by a summary of the technical papers that appear in the SI.

In this talk, we investigate the model-driven deep learning for multiple input-multiple output (MIMO) detection. In particular, the MIMO detector is specially designed by unfolding an iterative algorithm and adding some trainable parameters. 

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. 

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. 

X-ray security screening is widely utilized in aviation and transportation, and its importance has sparked interest in automated screening systems. The goal of this webinar is to explore computerized X-ray security imaging methods by classifying them into traditional machine learning and modern deep learning applications.

In his seminal paper, Dr. Ronald Mahler not only developed the Probability Hypothesis Density (PHD) filter, but also detailed the Random Finite Set (RFS) framework for multi-object systems. These complex dynamical systems, in which the number of objects and their states are unknown and vary randomly with time, have a wide range of applications from surveillance, computer vision, robotics to biomedical research.

Sound field estimation using a microphone array is a fundamental problem in acoustic signal processing, which has a wide variety of applications, such as visualization/auralization of an acoustic field, spatial audio reproduction using a loudspeaker array or headphones, and active noise cancellation in a spatial region.

As a popular signal modeling technique, sparse representation (SR) has achieved great success in image fusion during the last decade. However, due to the patch-based manner adopted in standard SR models, most existing SR-based image fusion methods suffer from two drawbacks, namely, limited ability in detail preservation and high sensitivity to mis-registration, while these two issues are of great concern in image fusion. 

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