Deep-Learning-Based 3-D Surface Reconstruction—A Survey

Ultrareliable low-latency communication (URLLC) constitutes a key service class of the fifth generation (5G) and beyond cellular networks. Notably, designing and supporting URLLC pose a herculean task due to the fundamental need to identify and accurately characterize the underlying statistical models in which the system operates, e.g., interference statistics, channel conditions, and the behavior

In the last decade, deep learning (DL) has significantly impacted industry and science. Initially largely motivated by computer vision tasks in 2-D imagery, the focus has shifted toward 3-D data analysis. In particular, 3-D surface reconstruction, i.e., reconstructing a 3-D shape from sparse input, is of great interest to a large variety of application fields. DL-based approaches show promising quantitative

Training Spiking Neural Networks Using Lessons From Deep Learning by J. K. Eshraghian, M. Ward, E. O. Neftci, X. Wang, G. Lenz, G. Dwivedi, M. Bennamoun, D. S. Jeong, and W. D. Lu

Motivated by the advancing computational capacity of distributed end-user equipment (UE), as well as the increasing concerns about sharing private data, there has been considerable recent interest in machine learning (ML) and artificial intelligence (AI) that can be processed on distributed UEs. Specifically, in this paradigm, parts of an ML process are outsourced to multiple distributed UEs. Then

The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the future of deep learning might look like. This article serves as a tutorial and perspective showing how to apply the lessons learned from several decades of research in deep learning, gradient descent, backpropagation, and neuroscience

With the increasing penetration of renewable energy and flexible loads in smart grids, a more complicated power system with high uncertainty is gradually formed, which brings about great challenges to smart grid operations. Traditional optimization methods usually require accurate mathematical models and parameters and cannot deal well with the growing complexity and uncertainty. Fortunately, the widespread

This month’s articles touch upon policy and technological guidance for spectrum sharing about 100 GHz, Ga-based circuits and antennas, and microwave metalens antennas.

This month’s articles take a comprehensive look at the smart grid evolution through power system digitalization and marketization, renewable energy penetration, and electronic device integration.

The electromagnetic spectrum plays a fundamental role in the development of the digital society. It enables wireless communications (either between humans or machines) and sensing (for example, for Earth exploration, radio astronomy, imaging, and radars). While each of these uses benefits from a larger bandwidth, the spectrum is a finite resource. This introduces competing interests among the different

Recently, there has been growing interest in the use of metamaterial (MTM)-based lenses, also known as metalenses, as innovative antenna technology. Increasingly widespread applications of metalenses in modern microwave communication and sensing systems have been found, following the development of the first microwave artificial lens in the 1940s based on the concept of an artificial dielectric, which

Cyberization is the foundation of vehicle electrification and automation, requiring the deployment of ever-increasing on-board sensing, communication, and computing services. However, vehicle cyberization also introduces new cyber vulnerabilities. In this article, we discuss the opportunities and challenges of using the common 12/24V automotive batteries as sensors and actuators to provide vehicles

This article reviews the application and technology advancement of gallium (Ga)-based liquid metal (LM) in high-frequency circuits and antennas. It discusses the material properties of common LMs, the fluidic channels used to contain LM and their manufacturing techniques, and the actuation techniques, which are all critical for the design and implementation of LM-based devices. LM’s fluidic and pliable

This article examines the emerging challenges in modeling and analyzing the electric power system due to the widespread growth of variable renewable energy (VRE), particularly in the form of distributed energy resources (DERs), which are displacing traditional large power plants. Many of these resources are connected to the system through power electronic interfaces, also known as inverter-based resources

Summary form only. This month’s articles review cognitive dynamic systems, bottom-up and top-down approaches for neuromorphic computing, and knowledge-aware research for zero-shot and few-shot learning.

The field of design and design automation of micro/nano circuits and systems promotes interdisciplinary research spanning computer science, computer engineering, and electrical engineering. This field has created key technologies without which it would be impossible to achieve advances in information processing, which is an inseparable part of our everyday lives. For example, fundamental principles

While Moore’s law has driven exponential computing power expectations, its nearing end calls for new avenues for improving the overall system performance. One of these avenues is the exploration of alternative brain-inspired computing architectures that aim at achieving the flexibility and computational efficiency of biological neural processing systems. Within this context, neuromorphic engineering

Machine learning (ML), especially deep neural networks, has achieved great success, but many of them often rely on a number of labeled samples for supervision. As sufficient labeled training data are not always ready due to, e.g., continuously emerging prediction targets and costly sample annotation in real-world applications, ML with sample shortage is now being widely investigated. Among all these

The emergence of artificial emotional intelligence technology is revolutionizing the fields of computers and robotics, allowing for a new level of communication and understanding of human behavior that was once thought impossible. While recent advancements in deep learning have transformed the field of computer vision, automated understanding of evoked or expressed emotions in visual media remains

Presents a summary of articles in this issue of the publication. This month’s regular papers cover a broad range of topics including megahertz wireless power transfer and resistive neural hardware accelerators.

Deep neural networks (DNNs), as a subset of machine learning (ML) techniques, entail that real-world data can be learned, and decisions can be made in real time. However, their wide adoption is hindered by a number of software and hardware limitations. The existing general-purpose hardware platforms used to accelerate DNNs are facing new challenges associated with the growing amount of data and are

The field of cognitive dynamic systems (CDSs) is an emerging area of research, whereby engineering learns from neuroscience. Under this framework, engineering systems are configured in a manner that mimics the human brain and improves the utility and performance of traditional systems. In essence, a CDS builds on Fuster’s paradigm of cognition and is fulfilled with the presence of five cognitive processes:

Huge advancements have been made over the years in terms of modern image-sensing hardware and visual computing algorithms (e.g., computer vision, image processing, and computational photography). However, to this day, there still exists a current gap between the hardware and software design in an imaging system, which silos one research domain from another. Bridging this gap is the key to unlocking

As a power supply method with high spatial freedom, megahertz (MHz) wireless power transfer (WPT) has great potential in particular application fields. The role and importance of WPT are described from the perspective of interdisciplinary. This article starts with WPT systems’ performance at different frequencies, emphasizes the basic composition and working principle of MHz systems, and focuses on

This article was jointly produced by IEEE Spectrum and PROCEEDINGS OF THE IEEE with similar versions published in both publications.

The articles in this month’s issue provide insight into the most important powerelectronics- based technologies for energy transition.

Grid integration of renewable energy (REN) requires efficient and reliable power conversion stages, particularly with an increasing demand for high controllability and flexibility seen from the grid side. Underpinned by advanced control and information technologies, power electronics converters play an essential role in large-scale REN generation. However, the use of power converters has also exposed

Summary form only. The articles in this month’s issue offer insight into integrated wireless-communication systems at D-band frequencies, object detection technology, and radar-based monitoring of vital signs.

Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Such model-based methods utilize mathematical formulations that represent the underlying physics, prior information, and additional domain knowledge. Simple classical models are useful but sensitive to inaccuracies and may lead to poor performance when real systems display complex

The evolution of wireless communication points to increasing demands on throughput for data-intensive applications in modern society. Integrated millimeter-wave systems with electrical beam-steering capabilities are promising candidates for wireless technologies of the future and are currently the subject of widespread academic and commercial research. The $D$ -band, ranging from 110–170 GHz, offers

In the last years, substantial attention has been paid to the use of radar systems in health monitoring, due to the availability of both low-cost radar devices and computationally efficient algorithms for processing their measurements. In this article, a tutorial overview of radar-based monitoring of vital signs is provided. More specifically, we first focus on the available radar technologies and

In 1944, Von Neumann and Morgenstern published their seminal book titled Theory of Games and Economic Behavior, which was a groundbreaking book and created the interdisciplinary research field of game theory. Since then, game theory, as a study of strategic decision making, has found a wide range of applications in not only various disciplines but also our daily life. Generative adversarial networks

Ferroelectric transistors (FeFETs) based on doped hafnium oxide (HfO2) have received much attention due to their technological potential in terms of scalability, high-speed, and low-power operation. Unfortunately, however, HfO2-FeFETs also suffer from persistent reliability challenges, specifically affecting retention, endurance, and variability. A deep understanding of the reliability physics of HfO2-FeFETs

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Over the past two decades, we have seen a rapid technological evolution of object detection and its profound impact on the entire computer vision field. If we consider today’s object detection technique as a revolution driven by deep learning, then, back in the

Learning to reject is a special kind of self-awareness (the ability to know what you do not know), which is an essential factor for humans to become smarter. Although machine intelligence has become very accurate nowadays, it lacks such kind of self-awareness and usually acts as omniscient, resulting in overconfident errors. This article presents a comprehensive overview of this topic from three perspectives:

Nash equilibrium, as an essential strategic profile in game theory, is of both practical relevance and theoretical significance due to its wide penetration into various fields, such as smart grids, wireless communication networks, and networked mobile vehicles. In particular, distributed Nash equilibrium seeking strategies have recently attracted increasing attention because they show remarkable advantages

This month’s regular papers issue focuses on machine learning for emergency management, data-intensive computing, and efficient edge inference, a vital element of Edge-AI.

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For many decades, progress in computing hardware has been closely associated with CMOS logic density, performance, and cost. As such, slowdown in 2-D scaling, frequency saturation in CPUs, and increased cost of design and chip fabrication for advanced technology nodes since the early 2000s have led to concerns about how semiconductor technology may evolve in the future. However, the last two decades

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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As one of the most important infrastructures, the energy system covers electricity, heating, cooling, natural gas, oil, coal, hydrogen, etc. Energy security, energy equity, and environmental sustainability are the well-known energy trilemma. The energy system is the largest source of carbon emissions; therefore, the goal for carbon neutrality puts forward very high requirements. Renewable energy will

The article focuses on naval technical developments, across the period 1945–1970 in the United Kingdom, Canada, and the United States. It is a condensed version of this article originally prepared for the USNA McMullen Naval History Symposium in 2017 and is a collaboration between David Boslaugh, Peter Marland, and John Vardalas (Stevens Institute of Technology) who have previously written about the

Modern research in technology fields, such as electronics, distributed networks of sensing/functional nodes, and wireless and wearable devices, is relentlessly converging around wide application paradigms, such as Internet of Things (IoT) [1] and Internet of Everything (IoE) [2] — Table 1 , at the end of section, offers a full list of used acronyms. From a different perspective, recent advances in

Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted in breakthroughs in many areas. However, deploying these highly accurate models for data-driven, learned, automatic, and practical machine learning (ML) solutions to end-user applications remains challenging. DL algorithms are often computationally expensive, power-hungry, and require large memory to process complex

Emergency situations encompassing natural and human-made disasters, as well as their cascading effects, pose serious threats to society at large. Machine learning (ML) algorithms are highly suitable for handling the large volumes of spatiotemporal data that are generated during such situations. Hence, over the years, they have been utilized in emergency management to aid first responders and decision-makers

With the fast development of remote sensing platforms and sensors technology, change detection with heterogeneous remote sensing images (Hete-CD) has become an attractive topic in recent years and plays a vital role in land cover change detection for responding to natural disaster emergencies when homogeneous images are unavailable. Although Hete-CD has been developed for about three decades, and various

This article presents considerations toward an information and control architecture for future electric energy systems driven by massive changes resulting from the societal goals of decarbonization and electrification. This article describes the new requirements and challenges of an extended information and control architecture that needs to be addressed for continued reliable delivery of electricity

Optical communication systems form the backbone of today’s communication and information society. Approximately six billion kilometers of optical fiber are installed around the globe today, enough to wrap a string of glass as thin as a human hair around the globe about 150000 times, or 20 times from Earth to Sun and back. Over just a single such strand of optical fiber, cutting-edge optical communication

In order to overcome the capacity limitations of current lightwave systems based on the single-mode optical fiber, massively parallel transmission in the spatial domain [space-division multiplexing (SDM)] supported by extended parallelism in the frequency domain (ultrawideband (UWB) systems) must be used. This article reviews key aspects of parallel transmission systems as the only significant capacity

In accordance with European plans, dynamic controls are developed to allow for system-wide integration of 100% renewable energy sources (RESs) interfaced through power electronic converters while maintaining security. At the heart of the development is the concept of the grid-forming resource (GFR) which brings together both the technologies of renewable energy resource and grid-forming converter.

Integrated energy systems (IESs), in which various energy flows are interconnected and coordinated to release potential flexibility for more efficient and secure operation, have drawn increasing attention in recent years. In this article, an integrated energy management system (IEMS) that performs online analysis and optimization on coupling energy flows in an IES is comprehensively introduced. From

Electric vehicle (EV) charging infrastructure will play a critical role in decarbonization during the next decades, energizing a large share of the transportation sector. This will further increase the enabling role of power electronics converters as an energy transition technology in the widespread adoption of clean energy sources and their efficient use. However, this deep transformation comes with