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}
Pub Date : 2025-06-19DOI: 10.1109/JPROC.2025.3574102
Sukwoong Choi;William S. Moses;Neil Thompson
In the children’s story of the Tortoise and the Hare, the speedier Hare is outpaced by a Tortoise with other advantages (diligence). An analogous contest is happening in computing, between a Quantum Tortoise and a Classical Hare. Here, the Classical Hare’s speed advantage is literal—classical computers run faster than quantum ones. Like his namesake, the Quantum Tortoise is slower, but also has an advantage—in this case, the ability to run algorithms that are unavailable to classical computers. When this algorithmic advantage is substantial enough, the Quantum Tortoise can beat the Classical Hare and solve a problem faster. This article analyzes when the Quantum Tortoise will beat the Classical Hare—and when it will not.
{"title":"The Quantum Tortoise and the Classical Hare: When Will Quantum Computers Outpace Classical Ones and When Will They Be Left Behind?","authors":"Sukwoong Choi;William S. Moses;Neil Thompson","doi":"10.1109/JPROC.2025.3574102","DOIUrl":"10.1109/JPROC.2025.3574102","url":null,"abstract":"In the children’s story of the Tortoise and the Hare, the speedier Hare is outpaced by a Tortoise with other advantages (diligence). An analogous contest is happening in computing, between a Quantum Tortoise and a Classical Hare. Here, the Classical Hare’s speed advantage is literal—classical computers run faster than quantum ones. Like his namesake, the Quantum Tortoise is slower, but also has an advantage—in this case, the ability to run algorithms that are unavailable to classical computers. When this algorithmic advantage is substantial enough, the Quantum Tortoise can beat the Classical Hare and solve a problem faster. This article analyzes when the Quantum Tortoise will beat the Classical Hare—and when it will not.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 2","pages":"113-124"},"PeriodicalIF":23.2,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11045206","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144328521","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 : 2025-06-11DOI: 10.1109/jproc.2025.3570483
Matthias Althoff, Sebastian Maierhofer, Gerald Würsching, Yuanfei Lin, Florian Lercher, Roland Stolz
{"title":"No More Traffic Tickets: A Tutorial to Ensure Traffic-Rule Compliance of Automated Vehicles","authors":"Matthias Althoff, Sebastian Maierhofer, Gerald Würsching, Yuanfei Lin, Florian Lercher, Roland Stolz","doi":"10.1109/jproc.2025.3570483","DOIUrl":"https://doi.org/10.1109/jproc.2025.3570483","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"12 1","pages":""},"PeriodicalIF":20.6,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144268680","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}
In today’s competitive and fast-evolving business environment, it is critical for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of big data and artificial intelligence (AI) techniques has revolutionized human resource management (HRM). The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which, in turn, delivers intelligence for real-time decision-making and effective talent management for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for HRM, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of HRM. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios at different levels: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.
{"title":"A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics","authors":"Chuan Qin;Le Zhang;Yihang Cheng;Rui Zha;Dazhong Shen;Qi Zhang;Xi Chen;Ying Sun;Chen Zhu;Hengshu Zhu;Hui Xiong","doi":"10.1109/JPROC.2025.3572744","DOIUrl":"10.1109/JPROC.2025.3572744","url":null,"abstract":"In today’s competitive and fast-evolving business environment, it is critical for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of big data and artificial intelligence (AI) techniques has revolutionized human resource management (HRM). The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which, in turn, delivers intelligence for real-time decision-making and effective talent management for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for HRM, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of HRM. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios at different levels: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 2","pages":"125-171"},"PeriodicalIF":23.2,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144237076","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 : 2025-04-25DOI: 10.1109/JPROC.2025.3564448
{"title":"2022-2024 Index Proceedings of the IEEE Vol. 110-112","authors":"","doi":"10.1109/JPROC.2025.3564448","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3564448","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 12","pages":"1-53"},"PeriodicalIF":23.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10976630","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875253","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 : 2025-04-22DOI: 10.1109/JPROC.2025.3554938
Gianluca Setti
{"title":"Editorial—A Time for Reflection","authors":"Gianluca Setti","doi":"10.1109/JPROC.2025.3554938","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3554938","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 12","pages":"1758-1760"},"PeriodicalIF":23.2,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10972350","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860783","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 : 2025-04-22DOI: 10.1109/JPROC.2025.3559609
{"title":"IEEE Connects You to a Universe of Information","authors":"","doi":"10.1109/JPROC.2025.3559609","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3559609","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 12","pages":"1852-1852"},"PeriodicalIF":23.2,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10972344","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860867","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 : 2025-04-22DOI: 10.1109/JPROC.2025.3549207
{"title":"Future Special Issues/Special Sections of the Proceedings","authors":"","doi":"10.1109/JPROC.2025.3549207","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3549207","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"112 12","pages":"1851-1851"},"PeriodicalIF":23.2,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10972366","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860866","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}
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