Pub Date : 2025-07-17DOI: 10.1109/JPROC.2025.3580187
{"title":"Future Special Issues/Special Sections of the Proceedings","authors":"","doi":"10.1109/JPROC.2025.3580187","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3580187","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 2","pages":"208-208"},"PeriodicalIF":23.2,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11082636","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-17DOI: 10.1109/JPROC.2025.3581362
Summary form only: Abstracts of articles presented in this issue of the publication.
仅以摘要形式提供：本刊发表的文章摘要。
{"title":"Scanning the Issue","authors":"","doi":"10.1109/JPROC.2025.3581362","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3581362","url":null,"abstract":"Summary form only: Abstracts of articles presented in this issue of the publication.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 2","pages":"110-112"},"PeriodicalIF":23.2,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11082593","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-17DOI: 10.1109/JPROC.2025.3580191
{"title":"Proceedings of the IEEE: Stay Informed. Become Inspired.","authors":"","doi":"10.1109/JPROC.2025.3580191","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3580191","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 2","pages":"C4-C4"},"PeriodicalIF":23.2,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11082590","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The electromyography (EMG) signal is particularly useful in monitoring muscle activity, and it can be acquired noninvasively on the skin surface. Thanks to these key characteristics, EMG-based human–machine interfaces (HMIs) for prosthetic myocontrol, as well as gesture recognition, are becoming widespread. A key challenge in this context is to design embedded systems to process EMG signals and generate motor commands with miniaturized, unobtrusive, and low-power devices, reliably and in real time, at a relatively low cost to provide continuous monitoring without causing stigma or discomfort. This article presents an in-depth review of the current status and future research challenges in systems and circuits for EMG acquisition and processing. We start by illustrating the sensor interfaces and acquisition systems required for signal analysis to provide efficient and effective ways of understanding the signal and its nature. We, then, focus on conventional state-of-the-art (SoA) EMG gesture recognition algorithms as well as novel architectures that tackle EMG processing challenges, i.e., hyperdimensional computing (HDC), blind source separation (BSS), and spiking neural networks (SNNs). Finally, we discuss open challenges, such as EMG variability, natural control, and efficient computation, to bring the myocontrol completely out of the laboratory, filling the gap between research prototypes and real-world applications.
{"title":"EMG Acquisition and Processing for Hand Movement Decoding on Embedded Systems: State of the Art and Challenges","authors":"Simone Benatti;Elisa Donati;Ali Moin;Marcello Zanghieri;Mattia Orlandi;Alessio Burrello;Fiorenzo Artoni;Silvestro Micera;Luca Benini;Jan M. Rabaey","doi":"10.1109/JPROC.2025.3581995","DOIUrl":"10.1109/JPROC.2025.3581995","url":null,"abstract":"The electromyography (EMG) signal is particularly useful in monitoring muscle activity, and it can be acquired noninvasively on the skin surface. Thanks to these key characteristics, EMG-based human–machine interfaces (HMIs) for prosthetic myocontrol, as well as gesture recognition, are becoming widespread. A key challenge in this context is to design embedded systems to process EMG signals and generate motor commands with miniaturized, unobtrusive, and low-power devices, reliably and in real time, at a relatively low cost to provide continuous monitoring without causing stigma or discomfort. This article presents an in-depth review of the current status and future research challenges in systems and circuits for EMG acquisition and processing. We start by illustrating the sensor interfaces and acquisition systems required for signal analysis to provide efficient and effective ways of understanding the signal and its nature. We, then, focus on conventional state-of-the-art (SoA) EMG gesture recognition algorithms as well as novel architectures that tackle EMG processing challenges, i.e., hyperdimensional computing (HDC), blind source separation (BSS), and spiking neural networks (SNNs). Finally, we discuss open challenges, such as EMG variability, natural control, and efficient computation, to bring the myocontrol completely out of the laboratory, filling the gap between research prototypes and real-world applications.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 3","pages":"256-286"},"PeriodicalIF":23.2,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144629904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-10DOI: 10.1109/JPROC.2025.3584656
Tong Su;Tong Wu;Junbo Zhao;Anna Scaglione;Le Xie
Given the availability of more comprehensive measurement data in modern power systems, reinforcement learning (RL) has gained significant interest in operation and control. Conventional RL relies on trial-and-error interactions with the environment and reward feedback, which often leads to exploring unsafe operating regions and executing unsafe actions, especially when deployed in real-world power systems. To address these challenges, safe RL has been proposed to optimize operational objectives while ensuring safety constraints are met, keeping actions and states within safe regions throughout both training and deployment. Rather than relying solely on manually designed penalty terms for unsafe actions, as is common in conventional RL, safe RL methods reviewed here primarily leverage advanced and proactive mechanisms. These include techniques such as Lagrangian relaxation, safety layers, and theoretical guarantees like Lyapunov functions to rigorously enforce safety boundaries. This article provides a comprehensive review of safe RL methods and their applications across various power system operations and control domains, including security control, real-time operation, operational planning, and emerging areas. It summarizes existing safe RL techniques, evaluates their performance, analyzes suitable deployment scenarios, and examines algorithm benchmarks and application environments. This article also highlights real-world implementation cases and identifies critical challenges such as scalability in large-scale systems and robustness under uncertainty, providing potential solutions and outlining future directions to advance the reliable integration and deployment of safe RL in modern power systems.
{"title":"A Review of Safe Reinforcement Learning Methods for Modern Power Systems","authors":"Tong Su;Tong Wu;Junbo Zhao;Anna Scaglione;Le Xie","doi":"10.1109/JPROC.2025.3584656","DOIUrl":"10.1109/JPROC.2025.3584656","url":null,"abstract":"Given the availability of more comprehensive measurement data in modern power systems, reinforcement learning (RL) has gained significant interest in operation and control. Conventional RL relies on trial-and-error interactions with the environment and reward feedback, which often leads to exploring unsafe operating regions and executing unsafe actions, especially when deployed in real-world power systems. To address these challenges, safe RL has been proposed to optimize operational objectives while ensuring safety constraints are met, keeping actions and states within safe regions throughout both training and deployment. Rather than relying solely on manually designed penalty terms for unsafe actions, as is common in conventional RL, safe RL methods reviewed here primarily leverage advanced and proactive mechanisms. These include techniques such as Lagrangian relaxation, safety layers, and theoretical guarantees like Lyapunov functions to rigorously enforce safety boundaries. This article provides a comprehensive review of safe RL methods and their applications across various power system operations and control domains, including security control, real-time operation, operational planning, and emerging areas. It summarizes existing safe RL techniques, evaluates their performance, analyzes suitable deployment scenarios, and examines algorithm benchmarks and application environments. This article also highlights real-world implementation cases and identifies critical challenges such as scalability in large-scale systems and robustness under uncertainty, providing potential solutions and outlining future directions to advance the reliable integration and deployment of safe RL in modern power systems.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 3","pages":"213-255"},"PeriodicalIF":23.2,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144603017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-08DOI: 10.1109/jproc.2025.3583997
Christopher Ruf, Scott Gleason
{"title":"Spaceborne GNSS-R Bistatic Radar Remote Sensing, CYGNSS, and Future Missions","authors":"Christopher Ruf, Scott Gleason","doi":"10.1109/jproc.2025.3583997","DOIUrl":"https://doi.org/10.1109/jproc.2025.3583997","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"51 1","pages":""},"PeriodicalIF":20.6,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144603057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-01DOI: 10.1109/JPROC.2025.3577229
Peter Adam Hoeher;Yang Leng;Rongwu Zhu;Marco Liserre
This article provides a systematic overview of the basics of talkative power conversion (TPC). TPC is an emerging technique for simultaneous information and power transmission, in which data modulation is integrated into a switched-mode power converter. The data sequence is embedded in the ripple voltage, which is superimposing the output voltage of the converter. In contrast to conventional power line communication (PLC), TPC can be used universally, not only in grid applications. Aspects of power electronics (PE) and digital communication are presented in a structured form, including new perspectives such as multiple-input multiple-output (MIMO) techniques applied to TPC, adaptive modulation and channel coding, and advanced receiver design with adaptive channel and load estimation. The new aspects aim to mitigate the inherent shortcomings of TPC.
{"title":"Talkative Power Conversion: A Tutorial","authors":"Peter Adam Hoeher;Yang Leng;Rongwu Zhu;Marco Liserre","doi":"10.1109/JPROC.2025.3577229","DOIUrl":"10.1109/JPROC.2025.3577229","url":null,"abstract":"This article provides a systematic overview of the basics of talkative power conversion (TPC). TPC is an emerging technique for simultaneous information and power transmission, in which data modulation is integrated into a switched-mode power converter. The data sequence is embedded in the ripple voltage, which is superimposing the output voltage of the converter. In contrast to conventional power line communication (PLC), TPC can be used universally, not only in grid applications. Aspects of power electronics (PE) and digital communication are presented in a structured form, including new perspectives such as multiple-input multiple-output (MIMO) techniques applied to TPC, adaptive modulation and channel coding, and advanced receiver design with adaptive channel and load estimation. The new aspects aim to mitigate the inherent shortcomings of TPC.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 4","pages":"344-369"},"PeriodicalIF":25.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144533229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep neural networks (DNNs) are indispensable in various artificial intelligence (AI) applications. However, their inherent complexity presents significant challenges, particularly when deploying them on resource-constrained devices. To overcome these hurdles, academia and industry are actively seeking ways to accelerate and optimize DNN implementations. A significant area of research revolves around discovering more effective methods to represent the enormous data volumes processed by DNNs. Traditional number systems (NSs) have proven nonoptimal for this task, prompting extensive exploration into alternative and bespoke systems for DNNs. This survey aims to comprehensively discuss various NSs utilized to efficiently represent DNN data. These systems are categorized mainly based on their impact on DNN performance and hardware implementation. This survey offers an overview of these categorized NSs and delves into different subsystems within each, outlining their effect on DNN performance and hardware design. Furthermore, these systems are compared quantitatively and qualitatively concerning their expected quantization error, memory utilization, and computational requirements. This survey also emphasizes the challenges linked with each system and the diverse proposed solutions to address them. Insights into the utilization of these NSs for sophisticated DNNs are also presented in this survey. Readers will acquire a deeper understanding of the importance of efficient NSs for DNNs, explore commonly used systems, comprehend the tradeoffs between these systems, delve into design considerations influencing their impact on DNN performance, and discover recent trends and potential research avenues in this field.
{"title":"A Survey and Comparative Analysis of Number Systems for Deep Neural Networks","authors":"Ghada Alsuhli;Vasilis Sakellariou;Hani Saleh;Mahmoud Al-Qutayri;Baker Mohammad;Thanos Stouraitis","doi":"10.1109/JPROC.2025.3578756","DOIUrl":"10.1109/JPROC.2025.3578756","url":null,"abstract":"Deep neural networks (DNNs) are indispensable in various artificial intelligence (AI) applications. However, their inherent complexity presents significant challenges, particularly when deploying them on resource-constrained devices. To overcome these hurdles, academia and industry are actively seeking ways to accelerate and optimize DNN implementations. A significant area of research revolves around discovering more effective methods to represent the enormous data volumes processed by DNNs. Traditional number systems (NSs) have proven nonoptimal for this task, prompting extensive exploration into alternative and bespoke systems for DNNs. This survey aims to comprehensively discuss various NSs utilized to efficiently represent DNN data. These systems are categorized mainly based on their impact on DNN performance and hardware implementation. This survey offers an overview of these categorized NSs and delves into different subsystems within each, outlining their effect on DNN performance and hardware design. Furthermore, these systems are compared quantitatively and qualitatively concerning their expected quantization error, memory utilization, and computational requirements. This survey also emphasizes the challenges linked with each system and the diverse proposed solutions to address them. Insights into the utilization of these NSs for sophisticated DNNs are also presented in this survey. Readers will acquire a deeper understanding of the importance of efficient NSs for DNNs, explore commonly used systems, comprehend the tradeoffs between these systems, delve into design considerations influencing their impact on DNN performance, and discover recent trends and potential research avenues in this field.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 2","pages":"172-207"},"PeriodicalIF":23.2,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11053145","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144500786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-23DOI: 10.1109/JPROC.2025.3576367
Junke Wang;Zhenxin Li;Chao Zhang;Jingjing Chen;Zuxuan Wu;Larry S. Davis;Yu-Gang Jiang
Online media data, in the form of images and videos, are becoming mainstream communication channels. However, recent advances in deep learning (DL), particularly deep generative models, open the doors for producing perceptually convincing images and videos at a low cost, which not only poses a serious threat to the trustworthiness of digital information but also has severe societal implications. This motivates a growing interest in research in media tampering detection (TD), i.e., using DL techniques to examine whether media data have been maliciously manipulated. Depending on the content of the targeted images, media forgery could be divided into image tampering and Deepfake techniques. The former typically moves or erases the visual elements in ordinary images, while the latter manipulates the expressions and even the identity of human faces. Accordingly, the means of defense include image TD and Deepfake detection (DFD), which share a wide variety of properties. In this article, we provide a comprehensive review of the current media TD approaches and discuss the challenges and trends in this field for future research.
{"title":"Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection","authors":"Junke Wang;Zhenxin Li;Chao Zhang;Jingjing Chen;Zuxuan Wu;Larry S. Davis;Yu-Gang Jiang","doi":"10.1109/JPROC.2025.3576367","DOIUrl":"10.1109/JPROC.2025.3576367","url":null,"abstract":"Online media data, in the form of images and videos, are becoming mainstream communication channels. However, recent advances in deep learning (DL), particularly deep generative models, open the doors for producing perceptually convincing images and videos at a low cost, which not only poses a serious threat to the trustworthiness of digital information but also has severe societal implications. This motivates a growing interest in research in media tampering detection (TD), i.e., using DL techniques to examine whether media data have been maliciously manipulated. Depending on the content of the targeted images, media forgery could be divided into image tampering and Deepfake techniques. The former typically moves or erases the visual elements in ordinary images, while the latter manipulates the expressions and even the identity of human faces. Accordingly, the means of defense include image TD and Deepfake detection (DFD), which share a wide variety of properties. In this article, we provide a comprehensive review of the current media TD approaches and discuss the challenges and trends in this field for future research.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 3","pages":"287-311"},"PeriodicalIF":23.2,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144370742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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