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We are currently in the middle of a second quantum revolution where the rules discovered a century ago to understand the quantum world are being applied to develop new quantum technologies [1] . Yet, most of the understanding of quantum physics is developed from the orthodox (also known as the Copenhagen) interpretation of the quantum phenomena [2] , [3] . However, the orthodox theory has important

Land transportation over the past two centuries has experienced astonishing advancement. Up until the 1860s, it took more than six months to get from the East Coast to the West Coast of the United States. Today, it may take only three days by automobile. We are even considering flying cars and there are air-taxi startup companies that have announced going public [1] . Vehicle propulsion electrification

Electric and hybrid electric vehicles (EV/HEV) are promising solutions for fossil fuel conservation and pollution reduction for a safe environment and sustainable transportation. The design of these energy-efficient powertrains requires optimization of components, systems, and controls. Controls entail battery management, fuel consumption, driver performance demand emissions, and management strategy

This article presents the state-of-the-art electric vehicle (EV) charging technologies that benefit from the wide bandgap (WBG) devices, which is regarded as the most significant revolution in the power electronics industry in the past few decades. First, the recent WBG device technology evolution, performance comparison, and reliability issues are introduced. Then, topology development, efficiency

The idea of electric propulsion for transportation is not new; indeed, the first cars, nearly 200 years ago, were electric. However, our dependence on fossil fuels over the last 100 years is now being questioned, and as a global society, we are moving toward more-electric transportation solutions. Electric propulsion of aircraft is part of this trend, either all-electric or through a large variety

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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We are currently experiencing the dawn of what is known as the fourth industrial revolution. At the center of this historical happening, as one of the key enabling technologies, lies a discipline that deals with data and whose goal is to extract information and related knowledge that is hidden in it, in order to make predictions and, subsequently, take decisions. Machine learning (ML) is the name that

The two fields of machine learning and graphical causality arose and are developed separately. However, there is, now, cross-pollination and increasing interest in both fields to benefit from the advances of the other. In this article, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying

Tropical geometry is a relatively recent field in mathematics and computer science, combining elements of algebraic geometry and polyhedral geometry. The scalar arithmetic of its analytic part preexisted in the form of max-plus and min-plus semiring arithmetic used in finite automata, nonlinear image processing, convex analysis, nonlinear control, optimization, and idempotent mathematics. Tropical

Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing

Tensors, or multidimensional arrays, are data structures that can naturally represent visual data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic spaces and high-order interactions, tensors have a long history of applications in a wide span of computer vision problems. With the advent of the deep learning paradigm shift in computer vision, tensors have become

Deep artificial neural networks apply principles of the brain’s information processing that led to breakthroughs in machine learning spanning many problem domains. Neuromorphic computing aims to take this a step further to chips more directly inspired by the form and function of biological neural circuits, so they can process new knowledge, adapt, behave, and learn in real time at low power levels

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 threat of a large-scale electromagnetic event having a negative impact on the electric grid is real. Whether human-caused, via the detonation of a nuclear device, or natural, via a high-intensity burst of solar radiation, our historic experience with these phenomena indicates that as a global community, we should be prepared for such events and know how to mitigate their impacts. Current studies

The first three industrial revolutions came about as a result of mechanization, electricity, and information technology (IT), respectively. Now, the introduction of the Internet of Things and Services into the manufacturing environment is fostering a 4th industrial revolution (Industry 4.0), where the smart optimization and computerization of all the actors and phases of the manufacturing process,

One of the key targets of Industry 4.0 and digital production, in general, is the support of faster, cleaner, and increasingly customizable manufacturing processes. Additive manufacturing (AM) is a natural fit in this context, as it offers the possibility to produce complex parts without the design constraints of traditional manufacturing routes, typically reducing both material waste and time to market

In recent years, the introduction and exploitation of innovative information technologies in industrial contexts have led to the continuous growth of digital shop floor environments. The new Industry 4.0 model allows smart factories to become very advanced IT industries, generating an ever-increasing amount of valuable data. As a consequence, the necessity of powerful and reliable software architectures

For smart manufacturing, an automated robotic assembly system built upon an autoprogramming environment is necessary to reduce setup time and cost for robots that are engaged in frequent task reassignment. This article presents an approach to the autoprogramming of robotic assembly tasks with minimal human assistance. The approach integrates “robotic learning of assembly tasks from observation” and

Intelligent algorithms and learning are the basis for evolving smart cyber–physical production systems (CPPSs) and Industrie 4.0. A smart image detection algorithm shall be added to reduce downtime due to the extended operation of glass bottles in a yogurt producing plant. To support the engineers in doing so, a comprehensive domain-specific language (DSL), DSL4hDNCS, is introduced, enabling rapid

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

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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As one of the key communication scenarios in the fifth generation (5G) and also the sixth generation (6G) of mobile communication networks, ultrareliable and low-latency communications (URLLC) will be central for the development of various emerging applications such as industry automation, tactile internet, augmented/virtual reality, and more. In order to support such mission-critical applications

As one of the key communication scenarios in the fifth-generation and also the sixth-generation (6G) mobile communication networks, ultrareliable and low-latency communications (URLLCs) will be central for the development of various emerging mission-critical applications. State-of-the-art mobile communication systems do not fulfill the end-to-end delay and overall reliability requirements of URLLCs

With the broader and highly successful usage of machine learning (ML) in industry and the sciences, there has been a growing demand for explainable artificial intelligence (XAI). Interpretability and explanation methods for gaining a better understanding of the problem-solving abilities and strategies of nonlinear ML, in particular, deep neural networks, are, therefore, receiving increased attention

This article presents a comprehensive, multidisciplinary overview of the development of powertrain technologies for energy-efficient heavy-duty earthmoving machines. The heavy-duty earthmoving equipment industry has been among the biggest contributors to emissions globally. However, due to high power demand and multidisciplinary powertrain structures, improving the energy efficiency of heavy-duty mobile

Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication systems and beyond. By imbuing intelligence into the network edge, edge nodes can proactively carry out decision-making and, thereby, react to local environmental changes and disturbances while experiencing zero communication latency. To achieve this goal, it is essential to cater for high ML inference accuracy

Graph neural networks (GNNs) are information processing architectures for signals supported on graphs. They are presented here as generalizations of convolutional neural networks (CNNs) in which individual layers contain banks of graph convolutional filters instead of banks of classical convolutional filters. Otherwise, GNNs operate as CNNs. Filters are composed of pointwise nonlinearities and stacked

The increasing need to slow down climate change for environmental protection demands further advancements toward regenerative energy and sustainable mobility. While individual mobility applications are assumed to be satisfied with improving battery electric vehicles (BEVs), the growing sector of freight transport and heavy-duty applications requires alternative solutions to meet the requirements of

Driven by massive amounts of data and important advances in computational resources, new deep learning systems have achieved outstanding results in a large spectrum of applications. Nevertheless, our current theoretical understanding of the mathematical foundations of deep learning lags far behind its empirical success. However, the field of adversarial robustness has recently become one of the main

Deep learning approaches to anomaly detection (AD) have recently improved the state of the art in detection performance on complex data sets, such as large collections of images or text. These results have sparked a renewed interest in the AD problem and led to the introduction of a great variety of new methods. With the emergence of numerous such methods, including approaches based on generative models

The generative adversarial network (GAN) framework has emerged as a powerful tool for various image and video synthesis tasks, allowing the synthesis of visual content in an unconditional or input-conditional manner. It has enabled the generation of high-resolution photorealistic images and videos, a task that was challenging or impossible with prior methods. It has also led to the creation of many

The machine recognition of speech spoken at a distance from the microphones, known as far-field automatic speech recognition (ASR), has received a significant increase in attention in science and industry, which caused or was caused by an equally significant improvement in recognition accuracy. Meanwhile, it has entered the consumer market with digital home assistants with a spoken language interface

Memories occupy most of the silicon area in nowadays’ system-on-chips and contribute to a significant part of system power consumption. Though widely used, nonvolatile Flash memories still suffer from several drawbacks. Magnetic random access memories (MRAMs) have the potential to mitigate most of the Flash shortcomings. Moreover, it is predicted that they could be used for DRAM and SRAM replacement

Index modulation (IM) provides a novel way for the transmission of additional data bits via the indices of the available transmit entities compared with classical communication schemes. This study examines the flexible utilization of existing IM techniques in a comprehensive manner to satisfy the challenging and diverse requirements of 5G and beyond services. After spatial modulation (SM), which transmits

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Vehicular traffic has evolved as a crucial means for the transport of persons and goods, and its importance for the economic and social life of modern society cannot be overemphasized. On the other hand, recurrent vehicular traffic congestion, which appears on a daily basis, particularly in metropolitan areas, around the globe, has been a (increasingly) serious, in fact threatening, problem that calls

In this month’s Point-of-View article, the authors propose a novel paradigm for vehicular traffic in the era of connected and automated vehicles (CAVs), which includes two combined principles: lane-free traffic and vehicle nudging; the latter implying that vehicles may be “pushing” from a distance (using communication or sensors) other vehicles in front of them. This traffic paradigm features several

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Rechargeable lithium batteries (RLBs), including lithium-ion batteries (LIBs), are accelerating the electrification of transportation and grid energy storage. This transformation of the transportation and energy sector could bring more clean energy into our energy security. The RLB technology is growing rapidly in these sectors due to substantial cost reductions and mobility needs. Yet, the durability