SPS Webinars

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.

MIMO communication remains an important technology for wireless communication systems. In this tutorial, we revisit classical signal processing models for MIMO wireless communications. We consider how those models may be updated as MIMO systems go to higher carrier frequencies, broader bandwidths and new kinds of array architectures.

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. 

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems have become very popular for sensing and wireless communications beyond 5G. While the abundant spectrum available at the mmWave frequency bands enables higher cellular data rates and precise positioning, links at mmWave frequencies are very sensitive to blockages and have significantly higher path loss.

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. 

The use of wireless frequencies above 100 GHz has attracted considerable interest for both massive bandwidth communication links and very high resolution RADAR and sensing. Systems in these frequencies have unique characteristics in terms of device nonlinearities, MIMO architectures and radio propagation that in turn present significant design challenges.

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.