00000nam a2200000 i 4500 UP-99946688289984444 Buklod 20240807093338.0 m |o d | ta 240807s2021 mau u eng 9783030649777 (eBook) 18082995 DLC DENGII rda eng TK 5102.9 D48 2021eb Deville, Yannick 1964- author. Nonlinear blind source separation and blind mixture identification methods for bilinear, linear-quadratic and polynomial mixtures Yannick Deville, Leonardo Tomazeli Duarte, Shahram Hosseini. First edition. Cham, Switzerland Springer [2021] 1 online resource. text rdacontent computer rdamedia online resource rdacarrier SpringerBriefs in Electrical and Computer Engineering IP based subscription, on campus and remote access Access via Electronic Resources of the UPD Engineering Library and University Library websites. This book provides a detailed survey of the methods that were recently developed to handle advanced versions of the blind source separation problem, which involve several types of nonlinear mixtures. Another attractive feature of the book is that it is based on a coherent framework. More precisely, the authors first present a general procedure for developing blind source separation methods. Then, all reported methods are defined with respect to this procedure. This allows the reader not only to more easily follow the description of each method but also to see how these methods relate to one another. The coherence of this book also results from the fact that the same notations are used throughout the chapters for the quantities (source signals and so on) that are used in various methods. Finally, among the quite varied types of processing methods that are presented in this book, a significant part of this description is dedicated to methods based on artificial neural networks, especially recurrent ones, which are currently of high interest to the data analysis and machine learning community in general, beyond the more specific signal processing and blind source separation communities. Presents advanced configurations of the blind source separation problem, involving bilinear, linear-quadratic and polynomial mixing models; Provides a detailed and coherent description of the methods reported in the literature for handling these types of mixing phenomena; Focuses on complex configurations involving nonlinear mixing transforms. Electronic reproduction Cham, Switzerland Springer 2021 Available via World Wide Web through SpringerLink. Signal processing. Image processing. Speech processing systems. Tomazeli Duarte, Leonardo author. Hosseini, Shahram author. Available for University of the Philippines System via SpringerLink. Click here to access https://doi.org/10.1007/978-3-030-64977-7 Also available remotely for the University of the Philippines System via SpringerLink. Click here to access thru EZproxy https://link-springer-com.ezproxy.engglib.upd.edu.ph/book/10.1007/978-3-030-64977-7 FO UPD DENG-II TK 5102.9 D48 2021eb Electronic Resource