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Signing Off as Editor-in-Chief

Three years have gone by quickly. I started as the editor-in-chief (EIC) of IEEE Signal Processing Magazine (SPM) in January 2018. It coincided with other changes in my personal life that made the transition steeper than I had expected. Looking back, it is how I imagine the New Year’s polar bear plunge might be. Of course, three years of service is a tad bit longer than a few minutes of swimming in ridiculously cold water. 

Sampling Signals on Graphs: From Theory to Applications

The study of sampling signals on graphs, with the goal of building an analog of sampling for standard signals in the time and spatial domains, has attracted considerable attention recently. Beyond adding to the growing theory on graph signal processing (GSP), sampling on graphs has various promising applications. In this article, we review the current progress on sampling over graphs, focusing on theory and potential applications.

Localized Spectral Graph Filter Frames: A Unifying Framework, Survey of Design Considerations, and Numerical Comparison

A major line of work in graph signal processing [2] during the past 10 years has been to design new transform methods that account for the underlying graph structure to identify and exploit structure in data residing on a connected, weighted, undirected graph. The most common approach is to construct a dictionary of atoms (building block signals) and represent the graph signal of interest as a linear combination of these atoms. Such representations enable visual analysis of data, statistical analysis of data, and data compression, and they can also be leveraged as regularizers in machine learning and ill-posed inverse problems, such as inpainting, denoising, and classification.

A User Guide to Low-Pass Graph Signal Processing and Its Applications: Tools and Applications

The notion of graph filters can be used to define generative models for graph data. In fact, the data obtained from many examples of network dynamics may be viewed as the output of a graph filter. With this interpretation, classical signal processing tools, such as frequency analysis, have been successfully applied with analogous interpretation to graph data, generating new insights for data science. What follows is a user guide on a specific class of graph data, where the generating graph filters are low pass; i.e., the filter attenuates contents in the higher graph frequencies while retaining contents in the lower frequencies. Our choice is motivated by the prevalence of low-pass models in application domains such as social networks, financial markets, and power systems. 

Graph Signal Processing: Foundations and Emerging Directions

The articles in this special section focus on graph signal processing. Generically, the networks that sustain our societies can be understood as complex systems formed by multiple nodes, where global network behavior arises from local interactions between connected nodes. More succinctly, a network or a graph can be defined as a structure that encodes relationships between pairs of elements of a set. The simplicity of this definition drives the application of graphs and networks to a wide variety of disciplines, such as biology, medicine, psychology, sociology, economics, engineering, computer science, and so on.

Upcoming Webinar by Dr. Foad Sohabi: "Hybrid Digital and Analog Beamforming Design for Large-Scale Antenna Arrays"

The potentials of using millimeter-wave (mmWave) frequency for future wireless cellular communication systems have motivated the study of large-scale antenna arrays for achieving highly directional beamforming. However, the conventional fully digital beamforming methods, which require one radio frequency (RF) chain per antenna element, are not viable for large-scale antenna arrays due to the high cost and high power consumption of RF chain components in high frequencies.

IEEE SPS 2020 Members-at-Large and Regional Directors-at-Large Election Results

Three new Members-at-Large will take their seats on the IEEE Signal Processing Society Board of Governors beginning 1 January 2021 and will serve until 31 December 2023. Nine candidates competed for the three Member-at-Large positions. These successful candidates represent a broad spectrum of the IEEE Signal Processing Society.