Lei Cheng – författare

This book presents recent advances of Bayesian inference in structured tensor decompositions. It explains how Bayesian modeling and inference lead to tuning-free tensor decomposition algorithms, which achieve state-of-the-art performances in many applications, includingblind source separation;social network mining;image and video processing;array signal processing; and,wireless communications.The book begins with an introduction to the general topics of tensors and Bayesian theories. It then discusses probabilistic models of various structured tensor decompositions and their inference algorithms, with applications tailored for each tensor decomposition presented in the corresponding chapters. The book concludes by looking to the future, and areas where this research can be further developed.Bayesian Tensor Decomposition for Signal Processing and Machine Learning is suitable for postgraduates and researchers with interests in tensor data analytics and Bayesian methods.

This book presents recent advances of Bayesian inference in structured tensor decompositions. It explains how Bayesian modeling and inference lead to tuning-free tensor decomposition algorithms, which achieve state-of-the-art performances in many applications, includingblind source separation;social network mining;image and video processing;array signal processing; and,wireless communications.The book begins with an introduction to the general topics of tensors and Bayesian theories. It then discusses probabilistic models of various structured tensor decompositions and their inference algorithms, with applications tailored for each tensor decomposition presented in the corresponding chapters. The book concludes by looking to the future, and areas where this research can be further developed.Bayesian Tensor Decomposition for Signal Processing and Machine Learning is suitable for postgraduates and researchers with interests in tensor data analytics and Bayesian methods.

This book presents recent advances of Bayesian inference in structured tensor decompositions. It explains how Bayesian modeling and inference lead to tuning-free tensor decomposition algorithms, which achieve state-of-the-art performances in many applications, includingblind source separation;social network mining;image and video processing;array signal processing; and,wireless communications.The book begins with an introduction to the general topics of tensors and Bayesian theories. It then discusses probabilistic models of various structured tensor decompositions and their inference algorithms, with applications tailored for each tensor decomposition presented in the corresponding chapters. The book concludes by looking to the future, and areas where this research can be further developed.Bayesian Tensor Decomposition for Signal Processing and Machine Learning is suitable for postgraduates and researchers with interests in tensor data analytics and Bayesian methods.

This groundbreaking book presents the first comprehensive treatment of tensor signal processing for the critical challenges in modern MIMO communications and integrated sensing and communications (ISAC) systems. Moving decisively beyond matrix-based approaches, it demonstrates how tensor algebra and decompositions unlock unprecedented capabilities for modeling, estimation, and design.Key Features: Fundamentals to Frontiers: Provides a solid grounding in tensor algebra (CPD, Tucker, TTD) and systematically applies it to core MIMO problems like channel estimation, precoding, decoding, and radar beamforming/DOA estimation.Cutting-Edge System Coverage: Offers in-depth solutions for the most advanced architectures, including Millimeter-Wave Massive MIMO, XL-MIMO with Dynamic Metasurface Antennas, Movable-Antenna Systems, and Hybrid Analog-Digital MIMO-ISAC.Practical Tensor Methodologies: Details novel tensor-based algorithms for complex scenarios like wideband frequency-selective channels, time-varying channels, and unified channel/target parameter estimation in ISAC, supported by performance analysis and numerical results.Future-Proofing Technology: Explores tensor modeling for emerging paradigms such as Near-Field Communications/Localization, IRS-Assisted Systems, UAV Communications/Sensing, and Massive Access.Integrated Sensing and Communications (ISAC) Focus: Features dedicated chapters on unified tensor frameworks and channel-training aided sensing specifically for MIMO-ISAC, a cornerstone of 6G.Essential for researchers and engineers, this book establishes tensor processing as the indispensable mathematical foundation for designing, analyzing, and optimizing the ultra-complex wireless and sensing systems of today and tomorrow.

Applied Methods and Techniques for Mechatronic Systems brings together the relevant studies in mechatronic systems with the latest research from interdisciplinary theoretical studies, computational algorithm development and exemplary applications. Readers can easily tailor the techniques in this book to accommodate their ad hoc applications. The clear structure of each paper, background - motivation - quantitative development (equations) - case studies/illustration/tutorial (curve, table, etc.) is also helpful. It is mainly aimed at graduate students, professors and academic researchers in related fields, but it will also be helpful to engineers and scientists from industry.Lei Liu is a lecturer at Huazhong University of Science and Technology (HUST), China; Quanmin Zhu is a professor at University of the West of England, UK; Lei Cheng is an associate professor at Wuhan University of Science and Technology, China; Yongji Wang is a professor at HUST; Dongya Zhao is an associate professor at China University of Petroleum.