The IEEE Signal Processing Society is proud to announce the launch of our new on-demand interactive education short course now available on the IEEE Learning Network (ILN). This professionally produced video course, the first in a series, offers a practical approach to understanding and utilizing transformers in speech or computer vision projects.

Deep learning, in general, focuses on training a neural network from large labeled datasets. Yet, in many cases, there is value in training a network just from the input at hand. This is particularly relevant in many signal and image processing problems where training data are scarce and diversity is large on the one hand, and on the other, there is a lot of structure in the data that can be exploited. Using this information is the key to deep internal learning strategies, which may involve training a network from scratch using a single input or adapting an already trained network to a provided input example at inference time. This survey article aims at covering deep internal learning techniques that have been proposed in the past few years for these two important directions. While our main focus is on image processing problems, most of the approaches that we survey are derived for general signals (vectors with recurring patterns that can be distinguished from noise) and are therefore applicable to other modalities.

By “social learning,” in this article we refer to mechanisms for opinion formation and decision making over graphs and the study of how agents’ decisions evolve dynamically through interactions with neighbors and the environment. The study of social learning strategies is critical for at least two reasons. On one hand, it allows for a deeper understanding of the fundamental cognitive mechanisms that enable opinion formation over networks and the propagation of information or misinformation over them. On the other hand, these same learning strategies are effective for decision making by networked agents under challenging conditions, such as highly dynamic environments, nonstationary models and data, untruthful or malicious agents, sparsely connected graphs, and constrained communication. The article presents a unifying framework that covers several cases of interest, such as single-agent Bayesian learning, multiagent non-Bayesian learning, adaptive social learning, social machine learning, partial information sharing, influence discovery, and many others. The presentation highlights important limitations of the traditional social learning strategies. One limitation is the inability to track well drifting conditions. Traditional approaches lead to stubborn agents, which resist new states of information and are slow to react to changes in the environment, like an opinion that changes over time. Another limitation of the traditional strategies is that they assume perfect knowledge of the data models, which is seldom available in practice. The article illustrates recent advances that address these issues. We show how to endow multiagent networks with adaptation abilities and how to build social machine learning solutions that learn the necessary models directly from the data. These are fundamental steps toward the construction of socially intelligent networks, capable of exploiting cooperation and diversity across the agents to guarantee reliable learning performance under nonstationary, heterogeneous, and uncertain environments.

The IEEE Signal Processing Society is the world's premier association for signal processing engineers and industry professionals, serving its more than 23,000 members with highly ranked publications, world class conferences, professional development resources, job opportunities, and more. Half-year pricing in effect until 15 August 2024! Network with other professionals through SPS conferences, workshops, Technical Committees, Special Interest Groups, and local events curated by more than 270 worldwide SPS Chapters. Access members-only discounts on SPS publications and conferences. Gain eligibility to aply for travel grants to SPS conferences and workshops, including ICASSP and ICIP.

In the first issue of 2024, we introduced the new lead editorial team of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Signal Processing Magazine</i> (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SPM</i>), composed of our four area editors. Their terms started with mine this January, and they oversee the Society e-newsletter and the three main components of our magazine: feature articles, special issues, and columns and forum articles. As a team, we have undertaken a complete revision of the specifications for all article types and the information we provide our authors. We also revised the templates for all article types along with proposals and white papers, and all are included within the IEEE Author Center’s template selector <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref>.

In this article, we present a powerful unifying framework for widely linear (WL) adaptive filters building on the concept of geometric algebra (GA), including recently proposed complex-valued (CV), quaternion-valued, and GA WL adaptive filters (WLAFs). We also consider and review WL adaptive filtering methods that feature robustness against impulsive noise, noisy input measurements, partial coefficient updates, subband structures, censoring, and composite structures under the unified framework. Furthermore, we propose innovative WL adaptive filtering algorithms for functional link polynomial (FLP) nonlinear filters, infinite-impulse response (IIR) systems, and kernel-based nonlinear system identification, showcasing the advantages of the unified framework. The article also investigates the relationship among WLAFs, graph filters, and Cayley–Dickson (CD)-valued adaptive filters, offering new insights into how the unified framework can be extended to graph signals and CD numbers. Finally, the article motivates future work on WL adaptive filtering based on GA and its special cases.

the Professional Group on Audio of the IRE was formed, establishing what would become the IEEE society structure we know today. 75 years later, this group -now the IEEE Signal Processing Society -is the technical home to nearly 20,000 passionate, dedicated professionals and a bastion of innovation, collaboration, and leadership.