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Call for Papers Special Issue on The Mathematics of Deep Learning 深度学习数学》特刊征稿启事
IF 9.4 1区 工程技术
IEEE Signal Processing Magazine Pub Date : 2024-10-11 DOI: 10.1109/MSP.2024.3448111
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引用次数: 0
SPS Resource Center SPS 资源中心
IF 9.4 1区 工程技术
IEEE Signal Processing Magazine Pub Date : 2024-10-11 DOI: 10.1109/MSP.2024.3468769
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引用次数: 0
The Future of Bionic Limbs: The untapped synergy of signal processing, control, and wireless connectivity 仿生肢体的未来：尚未开发的信号处理、控制和无线连接的协同作用
IF 9.4 1区 工程技术
IEEE Signal Processing Magazine Pub Date : 2024-10-11 DOI: 10.1109/MSP.2024.3401403
Federico Chiariotti;Pranav Mamidanna;Suraj Suman;Čedomir Stefanović;Dario Farina;Petar Popovski;Strahinja Došen
{"title":"The Future of Bionic Limbs: The untapped synergy of signal processing, control, and wireless connectivity","authors":"Federico Chiariotti;Pranav Mamidanna;Suraj Suman;Čedomir Stefanović;Dario Farina;Petar Popovski;Strahinja Došen","doi":"10.1109/MSP.2024.3401403","DOIUrl":"https://doi.org/10.1109/MSP.2024.3401403","url":null,"abstract":"The flexibility and dexterity of human limbs rely on the processing of a vast quantity of signals within the sensory-motor networks in the brain and spinal cord, distilled into stimuli that govern the commands and movements. Hence, the use of assistive devices, such as robotic limbs or exoskeletons, is critically dependent on the processing of a large number of heterogeneous signals to mimic natural movements. This article provides a panoramic overview of the three paradigms for the control of bionic limbs based on mechatronic technology. Two of them have already been established in the literature, while the third one, advocated by this article, is an emerging approach, enabled by the latest developments in connectivity and computation. In the first paradigm, the bionic limbs rely on conventional control and are directly reconnected to the human sensory-motor system, which requires a large signal processing bandwidth. The second paradigm is based on semiautonomous limbs, endowed with context-aware processing and certain decision capability. Following the advances in wireless connectivity and cloud/edge processing, this article introduces a third paradigm of connected limbs.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"41 4","pages":"58-75"},"PeriodicalIF":9.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Ophthalmic Biomarker Detection: Highlights From the IEEE Video and Image Processing Cup 2023 Student Competition [SP Competitions] 眼科生物标记检测：2023 年 IEEE "视频和图像处理杯 "学生竞赛亮点 [SP 竞赛］
IF 9.4 1区 工程技术
IEEE Signal Processing Magazine Pub Date : 2024-10-11 DOI: 10.1109/MSP.2024.3405667
Ghassan AlRegib;Mohit Prabhushankar;Kiran Kokilepersaud;Prithwijit Chowdhury;Zoe Fowler;Stephanie Trejo Corona;Lucas A. Thomaz;Angshul Majumdar
{"title":"Ophthalmic Biomarker Detection: Highlights From the IEEE Video and Image Processing Cup 2023 Student Competition [SP Competitions]","authors":"Ghassan AlRegib;Mohit Prabhushankar;Kiran Kokilepersaud;Prithwijit Chowdhury;Zoe Fowler;Stephanie Trejo Corona;Lucas A. Thomaz;Angshul Majumdar","doi":"10.1109/MSP.2024.3405667","DOIUrl":"https://doi.org/10.1109/MSP.2024.3405667","url":null,"abstract":"Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"41 4","pages":"96-104"},"PeriodicalIF":9.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10714509","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142409014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Join the SPS 加入 SPS
IF 9.4 1区 工程技术
IEEE Signal Processing Magazine Pub Date : 2024-10-11 DOI: 10.1109/MSP.2024.3464888
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引用次数: 0
Deep Internal Learning: Deep learning from a single input 深度内部学习从单一输入进行深度学习
IF 9.4 1区 工程技术
IEEE Signal Processing Magazine Pub Date : 2024-10-11 DOI: 10.1109/MSP.2024.3385950
Tom Tirer;Raja Giryes;Se Young Chun;Yonina C. Eldar
{"title":"Deep Internal Learning: Deep learning from a single input","authors":"Tom Tirer;Raja Giryes;Se Young Chun;Yonina C. Eldar","doi":"10.1109/MSP.2024.3385950","DOIUrl":"https://doi.org/10.1109/MSP.2024.3385950","url":null,"abstract":"Deep learning, in general, focuses on training a neural network from large labeled datasets. Yet, in many cases, there is value in training a network just from the input at hand. This is particularly relevant in many signal and image processing problems where training data are scarce and diversity is large on the one hand, and on the other, there is a lot of structure in the data that can be exploited. Using this information is the key to deep internal learning strategies, which may involve training a network from scratch using a single input or adapting an already trained network to a provided input example at inference time. This survey article aims at covering deep internal learning techniques that have been proposed in the past few years for these two important directions. While our main focus is on image processing problems, most of the approaches that we survey are derived for general signals (vectors with recurring patterns that can be distinguished from noise) and are therefore applicable to other modalities.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"41 4","pages":"40-57"},"PeriodicalIF":9.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
IEEE Connecting IEEE 连接
IF 9.4 1区 工程技术
IEEE Signal Processing Magazine Pub Date : 2024-10-11 DOI: 10.1109/MSP.2024.3468788
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引用次数: 0
IEEE Feedback IEEE 反馈
IF 9.4 1区 工程技术
IEEE Signal Processing Magazine Pub Date : 2024-10-11 DOI: 10.1109/MSP.2024.3470088
{"title":"IEEE Feedback","authors":"","doi":"10.1109/MSP.2024.3470088","DOIUrl":"https://doi.org/10.1109/MSP.2024.3470088","url":null,"abstract":"","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"41 4","pages":"6-6"},"PeriodicalIF":9.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10714510","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Socially Intelligent Networks: A framework for decision making over graphs 社会智能网络：图上决策框架
IF 9.4 1区 工程技术
IEEE Signal Processing Magazine Pub Date : 2024-10-11 DOI: 10.1109/MSP.2024.3431168
Virginia Bordignon;Vincenzo Matta;Ali H. Sayed
{"title":"Socially Intelligent Networks: A framework for decision making over graphs","authors":"Virginia Bordignon;Vincenzo Matta;Ali H. Sayed","doi":"10.1109/MSP.2024.3431168","DOIUrl":"https://doi.org/10.1109/MSP.2024.3431168","url":null,"abstract":"By “social learning,” in this article we refer to mechanisms for opinion formation and decision making over graphs and the study of how agents’ decisions evolve dynamically through interactions with neighbors and the environment. The study of social learning strategies is critical for at least two reasons. On one hand, it allows for a deeper understanding of the fundamental cognitive mechanisms that enable opinion formation over networks and the propagation of information or misinformation over them. On the other hand, these same learning strategies are effective for decision making by networked agents under challenging conditions, such as highly dynamic environments, nonstationary models and data, untruthful or malicious agents, sparsely connected graphs, and constrained communication. The article presents a unifying framework that covers several cases of interest, such as single-agent Bayesian learning, multiagent non-Bayesian learning, adaptive social learning, social machine learning, partial information sharing, influence discovery, and many others. The presentation highlights important limitations of the traditional social learning strategies. One limitation is the inability to track well drifting conditions. Traditional approaches lead to stubborn agents, which resist new states of information and are slow to react to changes in the environment, like an opinion that changes over time. Another limitation of the traditional strategies is that they assume perfect knowledge of the data models, which is seldom available in practice. The article illustrates recent advances that address these issues. We show how to endow multiagent networks with adaptation abilities and how to build social machine learning solutions that learn the necessary models directly from the data. These are fundamental steps toward the construction of socially intelligent networks, capable of exploiting cooperation and diversity across the agents to guarantee reliable learning performance under nonstationary, heterogeneous, and uncertain environments.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"41 4","pages":"20-39"},"PeriodicalIF":9.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
ICIP 2024 ICIP 2024
IF 9.4 1区 工程技术
IEEE Signal Processing Magazine Pub Date : 2024-10-11 DOI: 10.1109/MSP.2024.3464928
{"title":"ICIP 2024","authors":"","doi":"10.1109/MSP.2024.3464928","DOIUrl":"https://doi.org/10.1109/MSP.2024.3464928","url":null,"abstract":"","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"41 4","pages":"89-89"},"PeriodicalIF":9.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10714919","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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