Tensor decompositions for signal processing: Theory, advances, and applications

IF 3.6 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Neriman Tokcan , Shakir Showkat Sofi , Van Tien Pham , Clémence Prévost , Sofiane Kharbech , Baptiste Magnier , Thanh Phuong Nguyen , Yassine Zniyed , Lieven De Lathauwer
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引用次数: 0

Abstract

In the era of big data, rapid advancements in technology and data collection methods have led to the generation and accessibility of vast amounts of multi-modal, high-dimensional data across a diverse range of disciplines. Tensor methods have emerged as essential tools in signal processing, providing powerful frameworks to model and analyze such complex data effectively. This survey offers a comprehensive overview of tensor factorization techniques and their applications in key areas. We explore their role in remote sensing, focusing on tensor-based methods for analyzing hyperspectral and multispectral images, tackling challenges such as recovering super-resolution images and addressing spectral unmixing. In wireless communication, we examine tensor methods used for signal modulation in unsourced massive random access communication, which achieve strong performance in multi-antenna channel and signal modeling. We also discuss tensor applications in network compression, where they reduce the computational demands of deep neural networks, making them more feasible for edge devices. Additionally, we highlight the use of tensor methods in high-dimensional missing data completion problems, showcasing their versatility across various domains. Furthermore, we explore applications in image analysis and computer vision, where tensors are effectively utilized for motion and object tracking, 3D modeling, satellite image analysis, and medical imaging. By bridging theoretical advancements with practical applications, this survey aims to guide researchers in leveraging tensor methods to tackle emerging challenges in signal processing.
信号处理的张量分解:理论、进展和应用
在大数据时代,技术和数据收集方法的快速发展导致了大量跨不同学科的多模态、高维数据的生成和可访问性。张量方法已经成为信号处理中必不可少的工具,为有效地建模和分析此类复杂数据提供了强大的框架。本调查提供了张量分解技术及其在关键领域的应用的全面概述。我们探讨了它们在遥感中的作用,重点关注基于张量的方法来分析高光谱和多光谱图像,解决诸如恢复超分辨率图像和解决光谱分解等挑战。在无线通信中,我们研究了在无源大规模随机接入通信中用于信号调制的张量方法,该方法在多天线信道和信号建模中具有较强的性能。我们还讨论了张量在网络压缩中的应用,它们减少了深度神经网络的计算需求,使其更适用于边缘设备。此外,我们强调了张量方法在高维缺失数据补全问题中的使用,展示了它们在各个领域的多功能性。此外,我们探索图像分析和计算机视觉中的应用,其中张量被有效地用于运动和目标跟踪,3D建模,卫星图像分析和医学成像。通过将理论进步与实际应用相结合,本调查旨在指导研究人员利用张量方法来解决信号处理中出现的挑战。
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来源期刊
Signal Processing
Signal Processing 工程技术-工程:电子与电气
CiteScore
9.20
自引率
9.10%
发文量
309
审稿时长
41 days
期刊介绍: Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing. Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.
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