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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...

Industrial control systems (ICSs) manage and monitor critical civil or military infrastructure, such as water treatment facilities, power plants, electricity grids, transportation systems, oil and gas refineries, and health care.

Individual feature articles and special issues are two major mechanisms of full-length tutorial surveys of IEEE Signal Processing Magazine (SPM). Since the May 2016 feature article cluster by Jane Wang et al. on brain signal analytics, SPM’s current and past editors-in-chief and their teams have been exploring a different way to complement this existing structure - a feature article cluster (or mini special issue) that allows for a set of three to five solicited articles on a current topic, instead of just one (feature article) or ten to 11 (a special issue).

For centuries, humans have been exploring the subsurface structure of planet Earth. Several Earth geophysical applications, such as mining, earthquake studies, and oil and gas exploration, have driven research that produced, over the years, ground-breaking theories and innovative technologies that image Earth’s subsurface. The pursuit is ongoing with an increasing desire to have higher-resolution subsurface models and images. Signal processing, data interpretation, and modeling have been the cornerstones of such innovations.

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