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An Overview of Advancements in Multimotor Drives: Structural Diversity, Advanced Control, Specific Technical Challenges, and Solutions Proc. IEEE (IF 20.6) Pub Date : 2024-04-17 Chao Gong, Yunwei Ryan Li, Navid R. Zargari
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Scanning the Issue Proc. IEEE (IF 20.6) Pub Date : 2024-04-10
Gustav Fechner’s 1860 delineation of psychophysics, the measurement of sensation in relation to its stimulus, is widely considered to be the advent of modern psychological science. In psychophysics, a researcher parametrically varies some aspects of a stimulus and measures the resulting changes in a human subject’s experience of that stimulus; doing so gives insight to the determining relationship
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Future Special Issues/Special Sections of the Proceedings Proc. IEEE (IF 20.6) Pub Date : 2024-04-10
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Informing Machine Perception With Psychophysics Proc. IEEE (IF 20.6) Pub Date : 2024-04-10 Justin Dulay, Sonia Poltoratski, Till S. Hartmann, Samuel E. Anthony, Walter J. Scheirer
Gustav Fechner’s 1860 delineation of psychophysics, the measurement of sensation in relation to its stimulus, is widely considered to be the advent of modern psychological science. In psychophysics, a researcher parametrically varies some aspects of a stimulus and measures the resulting changes in a human subject’s experience of that stimulus; doing so gives insight into the determining relationship
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Internet-Based Social Engineering Psychology, Attacks, and Defenses: A Survey Proc. IEEE (IF 20.6) Pub Date : 2024-04-05 Theodore Tangie Longtchi, Rosana Montañez Rodriguez, Laith Al-Shawaf, Adham Atyabi, Shouhuai Xu
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When Robotics Meets Wireless Communications: An Introductory Tutorial Proc. IEEE (IF 20.6) Pub Date : 2024-04-01 Daniel Bonilla Licea, Mounir Ghogho, Martin Saska
The importance of ground mobile robots (MRs) and unmanned aerial vehicles (UAVs) within the research community, industry, and society is growing fast. Nowadays, many of these agents are equipped with communication systems that are, in some cases, essential to successfully achieve certain tasks. In this context, we have begun to witness the development of a new interdisciplinary research field at the
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Trustworthy Graph Neural Networks: Aspects, Methods, and Trends Proc. IEEE (IF 20.6) Pub Date : 2024-03-21 He Zhang, Bang Wu, Xingliang Yuan, Shirui Pan, Hanghang Tong, Jian Pei
Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications such as recommendation systems and question answering to cutting-edge technologies such as drug discovery in life sciences and n-body simulation in astrophysics. However, task performance is not the only requirement for GNNs. Performance-oriented
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In-Band Full-Duplex: The Physical Layer Proc. IEEE (IF 20.6) Pub Date : 2024-03-08 Besma Smida, Risto Wichman, Kenneth E. Kolodziej, Himal A. Suraweera, Taneli Riihonen, Ashutosh Sabharwal
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Future Special Issues/Special Sections of the Proceedings Proc. IEEE (IF 20.6) Pub Date : 2024-03-04
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Scanning the Issue Proc. IEEE (IF 20.6) Pub Date : 2024-03-04
A growing number of artificial intelligence (AI) academics can no longer find the means and resources to compete on a global scale. This is a somewhat recent phenomenon, but an accelerating one, with private actors investing enormous compute resources into cutting-edge AI research.
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Choose Your Weapon: Survival Strategies for Depressed AI Academics [Point of View] Proc. IEEE (IF 20.6) Pub Date : 2024-03-04 Julian Togelius, Georgios N. Yannakakis
As someone who does artificial intelligence (AI) research in a university, you develop a complicated relationship with the corporate AI research powerhouses, such as Google DeepMind, OpenAI, and Meta AI. Whenever you see one of these papers that train some kind of gigantic neural net model to do something you were not even sure a neural network could do, unquestionably pushing the state-of-the-art
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A State-of-the-Art Survey on Full-Duplex Network Design Proc. IEEE (IF 20.6) Pub Date : 2024-02-27 Yonghwi Kim, Hyung-Joo Moon, Hanju Yoo, Byoungnam Kim, Kai-Kit Wong, Chan-Byoung Chae
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Physical Layer Covert Communication in B5G Wireless Networks—its Research, Applications, and Challenges Proc. IEEE (IF 20.6) Pub Date : 2024-02-21 Yu’e Jiang, Liangmin Wang, Hsiao-Hwa Chen, Xuemin Shen
Physical layer covert communication is a crucial secure communication technology that enables a transmitter to convey information covertly to a recipient without being detected by adversaries. Unlike typical cryptography and physical layer security systems that concentrate on protecting the sent signal content, covert communications seek to conceal the existence of legitimate transmission. Thus, with
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Cloud-Native Computing: A Survey From the Perspective of Services Proc. IEEE (IF 20.6) Pub Date : 2024-02-12 Shuiguang Deng, Hailiang Zhao, Binbin Huang, Cheng Zhang, Feiyi Chen, Yinuo Deng, Jianwei Yin, Schahram Dustdar, Albert Y. Zomaya
The development of cloud computing delivery models inspires the emergence of cloud-native computing. Cloud-native computing, as the most influential development principle for web applications, has already attracted increasingly more attention in both industry and academia. Despite the momentum in the cloud-native industrial community, a clear research roadmap on this topic is still missing. As a contribution
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Scanning the Issue Proc. IEEE (IF 20.6) Pub Date : 2023-12-18
Traditionally, cloud platforms have been based on a single computing device type: central processing units (CPUs). One of the main reasons for this homogeneity of hardware resources has been cost efficiency—for years, cloud providers have reaped the benefits of the economies of scale by buying thousands of very similar types of servers. The homogeneity of servers has other advantages as well, for instance
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Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision Proc. IEEE (IF 20.6) Pub Date : 2023-12-12 Jinli Suo, Weihang Zhang, Jin Gong, Xin Yuan, David J. Brady, Qionghai Dai
Signal capture is at the forefront of perceiving and understanding the environment; thus, imaging plays a pivotal role in mobile vision. Recent unprecedented progress in artificial intelligence (AI) has shown great potential in the development of advanced mobile platforms with new imaging devices. Traditional imaging systems based on the “capturing images first and processing afterward” mechanism cannot
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A Comprehensive Survey on Distributed Training of Graph Neural Networks Proc. IEEE (IF 20.6) Pub Date : 2023-12-08 Haiyang Lin, Mingyu Yan, Xiaochun Ye, Dongrui Fan, Shirui Pan, Wenguang Chen, Yuan Xie
Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model in broad application fields for their effectiveness in learning over graphs. To scale GNN training up for large-scale and ever-growing graphs, the most promising solution is distributed training that distributes the workload of training across multiple computing nodes. At present, the volume of related research on
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Scanning the Issue Proc. IEEE (IF 20.6) Pub Date : 2023-11-20
Deep-Learning-Based 3-D Surface Reconstruction—A Survey
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Statistical Tools and Methodologies for Ultrareliable Low-Latency Communication—A Tutorial Proc. IEEE (IF 20.6) Pub Date : 2023-11-20 Onel L. A. López, Nurul H. Mahmood, Mohammad Shehab, Hirley Alves, Osmel Martínez Rosabal, Leatile Marata, Matti Latva-Aho
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
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A Visionary Look at the Security of Reconfigurable Cloud Computing Proc. IEEE (IF 20.6) Pub Date : 2023-11-21 Mirjana Stojilović, Kasper Rasmussen, Francesco Regazzoni, Mehdi B. Tahoori, Russell Tessier
Field-programmable gate arrays (FPGAs) have become critical components in many cloud computing platforms. These devices possess the fine-grained parallelism and specialization needed to accelerate applications ranging from machine learning to networking and signal processing, among many others. Unfortunately, fine-grained programmability also makes FPGAs a security risk. Here, we review the current
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Deep-Learning-Based 3-D Surface Reconstruction—A Survey Proc. IEEE (IF 20.6) Pub Date : 2023-10-30 Anis Farshian, Markus Götz, Gabriele Cavallaro, Charlotte Debus, Matthias Nießner, Jón Atli Benediktsson, Achim Streit
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
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Scanning the Issue Proc. IEEE (IF 20.6) Pub Date : 2023-09-14
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
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Trusted AI in Multiagent Systems: An Overview of Privacy and Security for Distributed Learning Proc. IEEE (IF 20.6) Pub Date : 2023-09-14 Chuan Ma, Jun Li, Kang Wei, Bo Liu, Ming Ding, Long Yuan, Zhu Han, H. Vincent Poor
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
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Training Spiking Neural Networks Using Lessons From Deep Learning Proc. IEEE (IF 20.6) Pub Date : 2023-09-06 Jason K. Eshraghian, Max Ward, Emre O. Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, Wei D. Lu
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
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Deep Reinforcement Learning for Smart Grid Operations: Algorithms, Applications, and Prospects Proc. IEEE (IF 20.6) Pub Date : 2023-09-05 Yuanzheng Li, Chaofan Yu, Mohammad Shahidehpour, Tao Yang, Zhigang Zeng, Tianyou Chai
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
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Scanning the Issue Proc. IEEE (IF 20.6) Pub Date : 2023-07-25
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.
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The Evolution of Smart Grids [Scanning the Issue] Proc. IEEE (IF 20.6) Pub Date : 2023-07-11 Chongqing Kang, Daniel Kirschen, Timothy C. Green
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.
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Coexistence and Spectrum Sharing Above 100 GHz Proc. IEEE (IF 20.6) Pub Date : 2023-07-03 Michele Polese, Xavier Cantos-Roman, Arjun Singh, Michael J. Marcus, Thomas J. Maccarone, Tommaso Melodia, Josep Miquel Jornet
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
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Microwave Metalens Antennas Proc. IEEE (IF 20.6) Pub Date : 2023-07-03 Zhi Ning Chen, Teng Li, Xianming Qing, Jin Shi, Shunli Li, Yuanyan Su, Wei E. I. Liu, Chunhua Xue, Qun Lou, Zhi Hao Jiang, Ruolei Xu, Peiqin Liu, Huiwen Sheng
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
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Rethink Physical Security: Protecting Vehicles via Battery-Enabled Sensing and Control [Point of View] Proc. IEEE (IF 20.6) Pub Date : 2023-06-27 Liang He, Kang G. Shin
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
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Circuits and Antennas Incorporating Gallium-Based Liquid Metal Proc. IEEE (IF 20.6) Pub Date : 2023-06-26 Yi-Wen Wu, Shaker Alkaraki, Shi-Yang Tang, Yi Wang, James R. Kelly
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
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Dynamic Performance Modeling and Analysis of Power Grids With High Levels of Stochastic and Power Electronic Interfaced Resources Proc. IEEE (IF 20.6) Pub Date : 2023-06-26 Jae-Kyeong Kim, Jiseong Kang, Jae Woong Shim, Heejin Kim, Jeonghoon Shin, Chongqing Kang, Kyeon Hur
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
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Scanning the Issue Proc. IEEE (IF 20.6) Pub Date : 2023-06-14
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.
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Micro/Nano Circuits and Systems Design and Design Automation: Challenges and Opportunities Proc. IEEE (IF 20.6) Pub Date : 2023-06-15 Gert Cauwenberghs, Jason Cong, X. Sharon Hu, Siddharth Joshi, Subhasish Mitra, Wolfgang Porod, H.-S. Philip Wong
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
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Bottom-Up and Top-Down Approaches for the Design of Neuromorphic Processing Systems: Tradeoffs and Synergies Between Natural and Artificial Intelligence Proc. IEEE (IF 20.6) Pub Date : 2023-06-05 Charlotte Frenkel, David Bol, Giacomo Indiveri
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
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Zero-Shot and Few-Shot Learning With Knowledge Graphs: A Comprehensive Survey Proc. IEEE (IF 20.6) Pub Date : 2023-06-05 Jiaoyan Chen, Yuxia Geng, Zhuo Chen, Jeff Z. Pan, Yuan He, Wen Zhang, Ian Horrocks, Huajun Chen
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
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Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion: Drawing Insights From Psychology, Engineering, and the Arts, This Article Provides a Comprehensive Overview of the Field of Emotion Analysis in Visual Media and Discusses the Latest Research, Systems, Challenges, Ethical Implications, and Potential Impact of Artificial Emotional Proc. IEEE (IF 20.6) Pub Date : 2023-05-23 James Z Wang,Sicheng Zhao,Chenyan Wu,Reginald B Adams,Michelle G Newman,Tal Shafir,Rachelle Tsachor
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
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Scanning the Issue Proc. IEEE (IF 20.6) Pub Date : 2023-05-16
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.
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Resistive Neural Hardware Accelerators Proc. IEEE (IF 20.6) Pub Date : 2023-05-16 Kamilya Smagulova, Mohammed E. Fouda, Fadi Kurdahi, Khaled N. Salama, Ahmed Eltawil
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
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Cognitive Dynamic Systems: A Review of Theory, Applications, and Recent Advances Proc. IEEE (IF 20.6) Pub Date : 2023-05-16 Waleed Hilal, S. Andrew Gadsden, John Yawney
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:
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Software-Defined Imaging: A Survey Proc. IEEE (IF 20.6) Pub Date : 2023-04-27 Suren Jayasuriya, Odrika Iqbal, Venkatesh Kodukula, Victor Torres, Robert Likamwa, Andreas Spanias
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
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Overview of Megahertz Wireless Power Transfer Proc. IEEE (IF 20.6) Pub Date : 2023-04-19 Yijie Wang, Zhan Sun, Yueshi Guan, Dianguo Xu
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
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Gallium Nitride Versus Silicon Carbide: Beyond the Switching Power Supply [Industry View] Proc. IEEE (IF 20.6) Pub Date : 2023-04-05 Umesh K. Mishra
This article was jointly produced by IEEE Spectrum and PROCEEDINGS OF THE IEEE with similar versions published in both publications.
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Energy Transition Technology: The Role of Power Electronics Proc. IEEE (IF 20.6) Pub Date : 2023-04-05 Jose Rodriguez, Frede Blaabjerg, Marian P. Kazmierkowski
The articles in this month’s issue provide insight into the most important powerelectronics- based technologies for energy transition.
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Power Electronics Technology for Large-Scale Renewable Energy Generation Proc. IEEE (IF 20.6) Pub Date : 2023-03-14 Frede Blaabjerg, Yongheng Yang, Katherine A. Kim, Jose Rodriguez
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
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Scanning the Issue Proc. IEEE (IF 20.6) Pub Date : 2023-03-07
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.
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Model-Based Deep Learning Proc. IEEE (IF 20.6) Pub Date : 2023-03-01 Nir Shlezinger, Jay Whang, Yonina C. Eldar, Alexandros G. Dimakis
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