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Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Kalman–Yakubovich–Popov (KYP). The very historical heritage of our field is at war. Do we need a new proof?

This issue of IEEE Control Systems includes a special issue on distributed games over networks. Guest Editors Lacra Pavel and Yiguang Hong have assembled four articles for this special issue. The first article, by Tao Li, Guanze Peng, Quanyan Zhu, and Tamer Bas¸ar, provides a systematic treatment of game-theoretic learning methods and their application in network problems, which is key for understanding

It is an honor and a challenge to be asked by Thomas Parisini to write a president’s invited column for IEEE Control Systems. It represents a big responsibility as well, especially as I am going to give an overview of a technology that might be perceived as controversial (mainly because it has been around for many years but continues to have a significant impact on our society).

Reports on the CS society Technical Committee Outstanding Student Paper Prize.

This article presents experimental results of a transonic wind-tunnel test that demonstrates the use of generalized predictive control for flutter suppression for a subsonic wind-tunnel wing model. The generalized predictive control algorithm is based on the minimization of a suitable cost function over finite costing and control horizons. The cost function minimizes not only the sum of the mean square

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In this issue of IEEE Control Systems, we speak with Necmiye Ozay, associate professor of electrical engineering and computer science at the University of Michigan, Ann Arbor; Giuseppe Notarstefano, professor and director of automation engineering at the Alma Mater Studiorum Università di Bologna; and Sergio Grammatico, an associate professor at the Delft University of Technology.

Q. How did your education and early career lead to your initial and continuing interest in the control field?

Q. How did your education and early career lead to your initial and continuing interest in the control field?

Q. How did your education and early career lead to your initial and continuing interest in the control field?

This special issue provides an introduction to game-theoretic decision making in multiagents over networks—a research area that has exploded in the systems and control community over the past few years. There has been an almost exponentially growing interest in the application of game-theoretic concepts and tools in control, multiagent systems, and networks. This is partly due to their wide application:

Multiagent decision making over networks has recently attracted an exponentially growing number of researchers from the systems and control community. The area has gained increasing momentum in engineering, social sciences, economics, urban science, and artificial intelligence as it serves as a prevalent framework for studying large and complex systems and has been widely applied to many problems,

Game theory, which studies the cooperation and conflict among multiple rational decision makers, called players, can be utilized to analyze a large class of engineering systems (for example, wireless communication networks and smart grids). A game usually consists of three components: the players; the players’ actions; and their objective functions, which the players try to either maximize (in which

Generalized games model interactions between a set of selfish decision makers, called players or agents, where both the objective function and the feasible decision set of each player may depend on strategies of the competitors. Such games arise, for example, when agents compete for a share of some common but limited resources. For instance, consider a set of vehicles sharing the road, set of radio

Decision making under uncertainty has been studied extensively over the last 70 years, if not earlier. In the field of optimization, models for two-stage, stochastic, linear programming, presented by Dantzig [1] and Beale [2], are often viewed as the basis for the subsequent development of the field of stochastic optimization. This subfield of optimization now encompasses a breadth of models that can

Norbert Wiener, one of the heroes in the field of modeling and data analysis portrayed in the book, once said, “To live effectively, is to live with adequate information.” This book provides insight on how that adequate information may be gathered through data and modeling.

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The papers in this special section focus on increasing interests in the development and implementation of advanced artificial intelligence (AI) and machine learning (ML) methods for tackling the reliability and system health prognostics challenges in various industrial applications.

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Welcome to the June 2022 issue of IEEE Geoscience and Remote Sensing Magazine (GRSM)! Our theme for this issue is “Building Upon a Legacy of Remote Sensing to Advance Our Future.” Supporting this theme, the 12 features in this issue cover instruments and techniques that build upon the long heritage of societal benefits from remote sensing as well as introduce some new and innovative ideas taking the

On behalf of the IEEE Geoscience and Remote Sensing Society (GRSS), I would like to invite you to participate in IGARSS 2022, our annual International Geoscience and Remote Sensing Symposium. This year’s symposium will be held in Kuala Lumpur, Malaysia, from 17 to 22 July using a hybrid conference format. The theme for this year’s meeting will focus on “Preserving Our Heritage, Enabling Our Future

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In the fields of image restoration and image fusion, model- and data-driven methods are the two representative frameworks. However, both approaches have their respective advantages and disadvantages. Model-driven techniques consider the imaging mechanism, which is deterministic and theoretically reasonable; however, they cannot easily model complicated nonlinear problems. Data-driven schemes have a

The Surface Water Ocean Topography (SWOT) mission, jointly developed by NASA and French Space Agency (CNES) with contributions from the Canadian and U.K. space agencies, and planned for launch in 2022, is designed to provide a spatially distributed and high-frequency measurement of water elevation data for the hydrology and oceanography communities for the first time [1], [2]. By virtue of its novel

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Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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The July issue of IEEE Signal Processing Magazine (SPM) is a special issue focused on “Explainability in Data Science: Interpretability, Reproducibility, and Replicability.” With increased enthusiasm for machine learning, it is a very timely topic, and I invite every IEEE Signal Processing Society (SPS) member to read these very instructive papers.

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Most of the work we do in signal processing these days is data driven. The shift from the more traditional and model-driven approaches to those that are data driven has also underlined the importance of explainability of our solutions. Because most traditional signal processing approaches start with a number of modeling assumptions, they are comprehensible by the very nature of their construction.

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Data-driven solutions are playing an increasingly important role in numerous practical problems across multiple disciplines. The shift from the traditional model-driven approaches to those that are data driven naturally emphasizes the importance of the explainability of solutions, as, in this case, the connection to a physical model is often not obvious. Explainability is a broad umbrella and includes

In many modern data science problems, data are represented by a graph (network), e.g., social, biological, and communication networks. Over the past decade, numerous signal processing and machine learning (ML) algorithms have been introduced for analyzing graph structured data. With the growth of interest in graphs and graph-based learning tasks in a variety of applications, there is a need to explore

In addition to the impressive predictive power of machine learning (ML) models, more recently, explanation methods have emerged that enable an interpretation of complex nonlinear learning models, such as deep neural networks. Gaining a better understanding is especially important, e.g., for safety-critical ML applications or medical diagnostics and so on. Although such explainable artificial intelligence

In this article, we present a leap-forward expansion to the study of explainability in neural networks by considering explanations as answers to abstract reasoning-based questions. With P as the prediction from a neural network, these questions are “Why P?”, “What if not P?”, and “Why P, rather than Q?” for a given contrast prediction Q. The answers to these questions are observed correlations, counterfactuals

The rise in deep neural networks (DNNs) has led to increased interest in explaining their predictions. While many methods for this exist, there is currently no consensus on how to evaluate them. On the other hand, robustness is a popular topic for deep learning (DL) research; however, it has been hardly talked about in explainability until very recently.

Creativity is often thought of as the pinnacle of human achievement, but artificial intelligence (AI) is now starting to play a central role in creative processes, whether autonomously or in collaboration with people. Widespread deployment is now pushing for explanations on how creative AI is working, whether to engender trust, enable action, provide a basis for evaluation, or for intrinsic reasons

The accumulation and integration of electronic health records have opened up opportunities for their new uses over the course of a patient’s care. In artificial intelligence (AI), natural language processing (NLP) methods that can extract important information from clinical documents are gaining success. For clinicians to consider using automatically extracted information in their decision making,

Predictive modeling of neuroimaging data (predictive neuroimaging) for evaluating individual differences in various behavioral phenotypes and clinical outcomes is of growing interest. However, the field is experiencing challenges regarding the interpretability of results. Approaches to defining the specific contribution of functional connections, regions, and networks in prediction models are urgently

Time series data correspond to observations of phenomena that are recorded over time [1]. Such data are encountered regularly in a wide range of applications, such as speech and music recognition, monitoring health and medical diagnosis, financial analysis, motion tracking, and shape identification, to name a few. With such a diversity of applications and the large variations in their characteristics

Exponential functions are essential in many areas of science and engineering. Fast and efficient computing of such functions in multiple floating-point formats is a complex task for all classes of processing units—general purpose CPUs, digital signal processors, GPUs, intelligent processing units, and tensor processors (TPU)—developed recently for neural computing and deep machine learning.

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