IEEE Signal Processing Magazine最新文献_第7页

Hypercomplex Signal Processing in Digital Twin of the Ocean: Theory and application [Hypercomplex Signal and Image Processing] 海洋数字双胞胎中的超复杂信号处理：理论与应用 [超复杂信号与图像处理］

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IEEE Signal Processing Magazine Pub Date : 2024-08-20 DOI: 10.1109/MSP.2024.3389496

Zhaoyuan Yu;Dongshuang Li;Pei Du;Wen Luo;Kit Ian Kou;Uzair Aslam Bhatti;Werner Benger;Guonian Lv;Linwang Yuan

{"title":"Hypercomplex Signal Processing in Digital Twin of the Ocean: Theory and application [Hypercomplex Signal and Image Processing]","authors":"Zhaoyuan Yu;Dongshuang Li;Pei Du;Wen Luo;Kit Ian Kou;Uzair Aslam Bhatti;Werner Benger;Guonian Lv;Linwang Yuan","doi":"10.1109/MSP.2024.3389496","DOIUrl":"https://doi.org/10.1109/MSP.2024.3389496","url":null,"abstract":"The digital twin of the ocean (DTO) is a groundbreaking concept that uses interactive simulations to improve decision-making and promote sustainability in earth science. The DTO effectively combines ocean observations, artificial intelligence (AI), advanced modeling, and high-performance computing to unite digital replicas, forecasting, and what-if scenario simulations of the ocean systems. However, there are several challenges to overcome in achieving the DTO’s objectives, including the integration of heterogeneous data with multiple coordinate systems, multidimensional data analysis, feature extraction, high-fidelity scene modeling, and interactive virtual–real feedback. Hypercomplex signal processing offers a promising solution to these challenges, and this study provides a comprehensive overview of its application in DTO development. We investigate a range of techniques, including geometric algebra, quaternion signal processing, Clifford signal processing, and hypercomplex machine learning, as the theoretical foundation for hypercomplex signal processing in the DTO. We also review the various application aspects of the DTO that can benefit from hypercomplex signal processing, such as data representation and information fusion, feature extraction and pattern recognition, and intelligent process simulation and forecasting, as well as visualization and interactive virtual–real feedback. Our research demonstrates that hypercomplex signal processing provides innovative solutions for DTO advancement and resolving scientific challenges in oceanography and broader earth science.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"41 3","pages":"33-48"},"PeriodicalIF":9.4,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013263","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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Understanding Vector-Valued Neural Networks and Their Relationship With Real and Hypercomplex-Valued Neural Networks: Incorporating intercorrelation between features into neural networks [Hypercomplex Signal and Image Processing] 理解矢量值神经网络及其与实值和超复值神经网络的关系：将特征之间的相互关系纳入神经网络[超复杂信号和图像处理］

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IEEE Signal Processing Magazine Pub Date : 2024-08-20 DOI: 10.1109/MSP.2024.3401621

Marcos Eduardo Valle

{"title":"Understanding Vector-Valued Neural Networks and Their Relationship With Real and Hypercomplex-Valued Neural Networks: Incorporating intercorrelation between features into neural networks [Hypercomplex Signal and Image Processing]","authors":"Marcos Eduardo Valle","doi":"10.1109/MSP.2024.3401621","DOIUrl":"https://doi.org/10.1109/MSP.2024.3401621","url":null,"abstract":"Despite the many successful applications of deep learning models for multidimensional signal and image processing, most traditional neural networks process data represented by (multidimensional) arrays of real numbers. The intercorrelation between feature channels is usually expected to be learned from the training data, requiring numerous parameters and careful training. In contrast, vector-valued neural networks (referred to as \u0000<italic>V-nets</i>\u0000) are conceived to process arrays of vectors and naturally consider the intercorrelation between feature channels. Consequently, they usually have fewer parameters and often undergo more robust training than traditional neural networks. This article aims to present a broad framework for V-nets. In this context, hypercomplex-valued neural networks are regarded as vector-valued models with additional algebraic properties. Furthermore, this article explains the relationship between vector-valued and traditional neural networks. To be precise, a V-net can be obtained by placing restrictions on a real-valued model to consider the intercorrelation between feature channels. Finally, I show how V-nets, including hypercomplex-valued neural networks, can be implemented in current deep learning libraries as real-valued networks.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"41 3","pages":"49-58"},"PeriodicalIF":9.4,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013265","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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ICIP 2024 ICIP 2024

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IEEE Signal Processing Magazine Pub Date : 2024-08-20 DOI: 10.1109/MSP.2024.3439828

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New Society Editors-in-Chief Named for 2025 [Society News] 2025 年新任学会主编已任命 [学会新闻］

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IEEE Signal Processing Magazine Pub Date : 2024-08-20 DOI: 10.1109/MSP.2024.3415268

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IEEE Signal Processing Magazine Pub Date : 2024-08-20 DOI: 10.1109/MSP.2024.3437464

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Meeting the Challenges of a Growing ICASSP: Highlights from ICASSP 2024 [Conference Highlights] 迎接不断发展的国际科学理事会的挑战：国际科学理事会2024年会议要点[会议要点］

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IEEE Signal Processing Magazine Pub Date : 2024-08-20 DOI: 10.1109/MSP.2024.3420491

Hanseok Ko;Monson Hayes;John Hansen

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IEEE Signal Processing Magazine Pub Date : 2024-08-20 DOI: 10.1109/MSP.2024.3420048

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Hypercomplex Signal and Image Processing: Part 2 [From the Guest Editors] 超复杂信号和图像处理：第 2 部分 [特邀编辑寄语］

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IEEE Signal Processing Magazine Pub Date : 2024-08-20 DOI: 10.1109/MSP.2024.3415248

Nektarios A. Valous;Eckhard Hitzer;Salvatore Vitabile;Swanhild Bernstein;Carlile Lavor;Derek Abbott;Maria Elena Luna-Elizarrarás;Wilder Lopes

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IEEE Signal Processing Magazine Pub Date : 2024-08-20 DOI: 10.1109/MSP.2024.3439908

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IEEE Signal Processing Magazine Pub Date : 2024-08-20 DOI: 10.1109/MSP.2024.3439895

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