IEEE Signal Processing Magazine最新文献_第3页

Special Issue on Accelerating Brain Discovery Through Data Science and Neurotechnology 通过数据科学和神经技术加速大脑发现特刊

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IEEE Signal Processing Magazine Pub Date : 2024-10-11 DOI: 10.1109/MSP.2024.3448099

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Efficient Deconvolution With the Discrete Fourier Transform 利用离散傅里叶变换进行高效解卷积

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IEEE Signal Processing Magazine Pub Date : 2024-10-11 DOI: 10.1109/MSP.2024.3440568

Alan V. Oppenheim;Ronald W. Schafer;James Ward

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How to Design a Cheap Music Detection System Using a Simple Multilayer Perceptron With Temporal Integration 如何利用具有时态整合功能的简单多层感知器设计廉价音乐检测系统

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IEEE Signal Processing Magazine Pub Date : 2024-10-11 DOI: 10.1109/MSP.2024.3424578

Zafar Rafii;Erling Wold;Richard Boulderstone

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Join SPS 加入 SPS

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IEEE Signal Processing Magazine Pub Date : 2024-08-20 DOI: 10.1109/MSP.2024.3439894

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An Invitation to Hypercomplex Phase Retrieval: Theory and applications [Hypercomplex Signal and Image Processing] 超复杂相位检索邀请函：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.2024.3394153

Roman Jacome;Kumar Vijay Mishra;Brian M. Sadler;Henry Arguello

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Augmented Statistics of Quaternion Random Variables: A lynchpin of quaternion learning machines [Hypercomplex Signal and Image Processing] 四元数随机变量的增强统计：四元数学习机的关键 [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.2024.3384178

Clive Cheong Took;Sayed Pouria Talebi;Rosa Maria Fernandez Alcala;Danilo P. Mandic

{"title":"Augmented Statistics of Quaternion Random Variables: A lynchpin of quaternion learning machines [Hypercomplex Signal and Image Processing]","authors":"Clive Cheong Took;Sayed Pouria Talebi;Rosa Maria Fernandez Alcala;Danilo P. Mandic","doi":"10.1109/MSP.2024.3384178","DOIUrl":"https://doi.org/10.1109/MSP.2024.3384178","url":null,"abstract":"Learning machines for vector sensor data are naturally developed in the quaternion domain and are underpinned by quaternion statistics. To this end, we revisit the “augmented” representation basis for discrete quaternion random variables (RVs) \u0000<inline-formula><tex-math>${bf{q}}^{a}[n]$</tex-math></inline-formula>\u0000, i.e., \u0000<inline-formula><tex-math>${[}{bf{q}}{[}{n}{]};{bf{q}}^{imath}{[}{n}{]};{bf{q}}^{jmath}{[}{n}{]}{bf{q}}^{kappa}{[}{n}{]]}$</tex-math></inline-formula>\u0000, and demonstrate its pivotal role in the treatment of the generality of quaternion RVs. This is achieved by a rigorous consideration of the augmented quaternion RV and by involving the additional second-order statistics, besides the traditional covariance \u0000<inline-formula><tex-math>$E{{bf{q}}mathbf{[}{n}mathbf{]}{bf{q}}^{{*}}mathbf{[}{n}mathbf{]}}$</tex-math></inline-formula>\u0000 \u0000<xref>[1]</xref>\u0000. To illuminate the usefulness of quaternions, we consider their most well-known application—3D orientation—and offer an account of augmented statistics for purely imaginary (pure) quaternions. The quaternion statistics presented here can be exploited in the analysis of existing and the development of novel statistical machine learning methods, hence acting as a lynchpin for quaternion learning machines.","PeriodicalId":13246,"journal":{"name":"IEEE Signal Processing Magazine","volume":"41 3","pages":"72-87"},"PeriodicalIF":9.4,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013274","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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Volunteer Power Through Noisy Gradients and Self-Organization: What About Pruning? [From the Editor] 通过噪声梯度和自组织实现志愿力量：如何修剪？[编辑的话］

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IEEE Signal Processing Magazine Pub Date : 2024-08-20 DOI: 10.1109/MSP.2024.3429689

Tülay Adali

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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］

IF 9.4 1区工程技术

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］

IF 9.4 1区工程技术

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

IF 9.4 1区工程技术

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

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