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Blind Identification and Separation of Complex-valued Signals

consider the blind identification and source separation problem in the complex-domain, where the available statistical properties are richer and;

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Blind Equalization and System Identification

common characteristics of these algorithms as well as learning from their different perspectives. Blind Equalization and System Identification;

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Supervised Learning with Complex-valued Neural Networks

varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial;

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Nonlinear Blind Source Separation and Blind Mixture Identification

specific signal processing and blind source separation communities.;

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Statistical Signal Processing of Complex-Valued Data

Complex-valued random signals are embedded in the very fabric of science and engineering, yet the usual assumptions made about their;

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New Signal Processing Research

signals such as ECG, radar signals, and many others. Processing of such signals includes storage and reconstruction, separation of information;

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Singular Spectrum Analysis of Biomedical Signals

effective approach for single channel signal analysis, and its bivariate, multivariate, tensor based, complex-valued, quaternion-valued and robust;

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Singular Spectrum Analysis of Biomedical Signals

effective approach for single channel signal analysis, and its bivariate, multivariate, tensor based, complex-valued, quaternion-valued and robust;

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Complex Valued Nonlinear Adaptive Filters

This book was written in response to the growing demand for a text that provides a unified treatment of linear and nonlinear complex valued;

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Source Separation and Machine Learning

Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS;

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Nonlinear Source Separation

we have little knowledge about the sources and about the mixture process, we speak of blind source separation. Linear blind source separation;

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Handbook of Blind Source Separation

, this handbook provides the definitive reference on Blind Source Separation, giving a broad and comprehensive description of all the core;

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Blind Source Separation

This book provides readers a complete and self-contained set of knowledge about dependent source separation, including the latest;

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Advances in Modern Blind Signal Separation Algorithms

in adverse environments. One such prominent statistical signal processing technique is blind signal separation (BSS). BSS was first introduced;

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Adaptive Blind Signal And Image Processing

efficient algorithms for blind source separation, Independent, Principal, Minor Component Analysis, and Multichannel Blind Deconvolution (MBD) and;

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Latent Variable Analysis and Signal Separation

fusion, and related topics; advances in nonlinear blind source separation; sparse and low rank modeling for acoustic signal processing.;

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Unsupervised Adaptive Filtering

, presenting a thorough and integrated treatment of the two major classes of algorithms used in the field, namely, blind signal separation and blind;

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Blind Source Separation

A systematic exploration of both classic and contemporary algorithms in blind source separation with practical case studies The book;

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Recent Developments in the Economics of Information

. It traces the unfolding of the fertile ambiguity and ambivalence of the notion of information with the identification and eventual separation;

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Speech and Audio Signal Processing

identification, and similarity estimation. * Audio Source Separation, including multi-microphone beamforming, blind source separation, and the perception;

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Quaternion Fourier Transforms for Signal and Image Processing

results pertaining to Quaternion Fourier Transforms. QFT is a central component of processing color images and complex valued signals. The book s;

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Blind Identification of Structured Dynamic Systems

This book is intended for researchers active in the field of (blind) system identification and aims to provide new identification ideas;

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Proportionate-type Normalized Least Mean Square Algorithms

times. PtNLMS algorithms are extended from real-valued signals to complex-valued signals. The computational complexity of the presented;

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Complex-valued Neural Networks

-ever book on complex-valued neural networks, the most active scientists at the forefront of the field describe theories and applications from;

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Convex and Set-Valued Analysis

-valued analysis. In the first part, properties of convex sets, the theory of separation, convex functions and their differentiability, properties;

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Combined Parametric-Nonparametric Identification of Block-Oriented Systems

are based on the decomposition of the complex system identification task into simpler local sub-problems by using non-parametric (kernel or;

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Representations of Multiple-Valued Logic Functions

representations of multiple-valued logic functions, including functional expressions, spectral representations on finite Abelian groups, and their;

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