## What is/are Nonlinear Blind?

Nonlinear Blind - As nonlinear blind source separation is inherently hard, MoVi-Fi innovatively employs deep contrastive learning to tackle the problem; this self-supervised method requires no ground truth in training, and it exploits contrastive signal features to distinguish vital signs from body movements.^{[1]}Results from nonlinear Blinder-Oaxaca decompositions highlight that most of these gaps remain unexplained by differences in observed characteristics and may be due instead to unobserved behavioural and psychological traits and to cultural and social norms about gender roles in financial decision-making.

^{[2]}This paper proposes a nonlinear blind equalization algorithm and its application framework based on clustering algorithm and amplifier characteristics in the constellation symbol point domain aims to eliminate these effects.

^{[3]}The existing nonlinear blind source separation methods of multi-fault completely rely on matrix decomposition, however matrix decomposition needs to meet strict constraints to ensure the uniqueness of the decomposition.

^{[4]}This article proposes a nonlinear blind scatterer separation method to retrieve the phase centers of the layovered scatterers, avoiding the computationally expensive tomographic inversion.

^{[5]}In this paper, we consider the problem of nonlinear blind compressed sensing, i.

^{[6]}To accurately estimate source signals from their post-nonlinear mixtures, a post-nonlinear blind source separation (PNLBSS) method with kurtosis constraints is proposed based on augmented Lagrangian particle swarm optimization (PSO).

^{[7]}In the context of nonlinear Blind Source Separation (BSS), the Post-Nonlinear (PNL) model is of great importance due to its suitability for practical nonlinear problems.

^{[8]}Here, we propose a method for nonlinear blind separation of highly correlated components spectra from a single 1H NMR mixture spectra.

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## nonlinear blind source

As nonlinear blind source separation is inherently hard, MoVi-Fi innovatively employs deep contrastive learning to tackle the problem; this self-supervised method requires no ground truth in training, and it exploits contrastive signal features to distinguish vital signs from body movements.^{[1]}The existing nonlinear blind source separation methods of multi-fault completely rely on matrix decomposition, however matrix decomposition needs to meet strict constraints to ensure the uniqueness of the decomposition.

^{[2]}To accurately estimate source signals from their post-nonlinear mixtures, a post-nonlinear blind source separation (PNLBSS) method with kurtosis constraints is proposed based on augmented Lagrangian particle swarm optimization (PSO).

^{[3]}In the context of nonlinear Blind Source Separation (BSS), the Post-Nonlinear (PNL) model is of great importance due to its suitability for practical nonlinear problems.

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