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During the last few decades, the number of seniors over the age of 60 has increased significantly. A recent study from the United Nations has shown that the number of people aged 65 years or over will increase from 727 million in 2020 to 1.5 billion by 2050. Consequently, the proportion of the global population aged 65 years or over will increase from 9.3% in 2020 to 16% in 2050. 
Signal processing (SP) is at the very heart of our digital lives, owing to its role as the pivotal enabling technology for advancement across multiple disciplines. Its prominence in modern data science has created a necessity to supply industry, government labs, and academia with graduates who possess relevant SP expertise and are well equipped to deal with the manifold challenges in current and future applications.
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.

The articles in this special section focus on nonconvex optimization for signal processing and machine learning. Optimization is now widely recognized as an indispensable tool in signal processing (SP) and machine learning (ML). Indeed, many of the advances in these fields rely crucially on the formulation of suitable optimization models and deployment of efficient numerical optimization algorithms. In the early 2000s, there was a heavy focus on the use of convex optimization techniques to tackle SP and ML applications.

The articles in this special section were focused on the current state of the art as well as emerging trends in the design, development, and deployment of sensing and perception technologies for autonomous and automated driving. Such technologies include camera, ultrasound, Global Navigation Satellite System-, lidar-, and radar-based platforms integrat ing signa l processing components to process the acquired data and extract information to be used for recognition, navigation, and situational awareness.

The field of machine learning has undergone radical transformations during the last decade. These transformations, which have been fueled by our ability to collect and generate tremendous volumes of training data and leverage massive amounts of low-cost computing power, have led to an explosion in research activity in the field by academic and industrial researchers.

The articles in this special section focus on computational magnetic resonance imaging (MRI) using compressed sensing applications. Presents recent developments in computational MRI. These developments are pushing the frontier of computational imaging beyond CS. Similar to CS, most of these algorithms rely on image representation in one form or another. 

Since its inception in the early 1970s [1], magnetic resonance imaging (MRI) has revolutionized radiology and medicine. Apart from high-quality data acquisition, image reconstruction is an important step to guarantee high image quality in MRI.

IEEE Life Fellow James J. Spilker, Jr., Global Positioning Systems (GPS) pioneer, philanthropist, and entrepreneur died on 24 September at the age of 86. Lives around the world are better every day thanks to Prof. Spilker’s passion and dedication to his work. Prof. Spilker’s early childhood was marked by difficulties.

The success of artificial neural networks (ANNs) in carrying out various specialized cognitive tasks has brought renewed efforts to apply machine learning (ML) tools for economic, commercial, and societal aims, while also raising expectations regarding the advent of an artificial “general intelligence” [1][2][3]. Recent highly publicized examples of ML breakthroughs include the ANN-based algorithm AlphaGo...

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