Pub Date : 2024-10-11DOI: 10.1109/MSP.2024.3448109
{"title":"Special Issue: Artificial Intelligence for Education: A Signal Processing Perspective","authors":"","doi":"10.1109/MSP.2024.3448109","DOIUrl":"https://doi.org/10.1109/MSP.2024.3448109","url":null,"abstract":"","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"41 4","pages":"8-8"},"PeriodicalIF":9.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10714906","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408689","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}
Pub Date : 2024-10-11DOI: 10.1109/MSP.2024.3448111
{"title":"Call for Papers Special Issue on The Mathematics of Deep Learning","authors":"","doi":"10.1109/MSP.2024.3448111","DOIUrl":"https://doi.org/10.1109/MSP.2024.3448111","url":null,"abstract":"","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"41 4","pages":"9-9"},"PeriodicalIF":9.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10714909","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408845","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}
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.
{"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}
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
提供从业人员和研究人员感兴趣的社会信息，包括新闻、评论或技术说明。
{"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}
Pub Date : 2024-10-11DOI: 10.1109/MSP.2024.3464888
{"title":"Join the SPS","authors":"","doi":"10.1109/MSP.2024.3464888","DOIUrl":"https://doi.org/10.1109/MSP.2024.3464888","url":null,"abstract":"","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"41 4","pages":"C3-C3"},"PeriodicalIF":9.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10714504","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142409049","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}
Pub Date : 2024-10-11DOI: 10.1109/MSP.2024.3385950
Tom Tirer;Raja Giryes;Se Young Chun;Yonina C. Eldar
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.
{"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}
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