Active noise cancellation gets a boost: A novel diffusion-based approach in spline adaptive filters.

This study addresses the challenge of unwanted noise in signal processing, particularly for applications requiring high-fidelity audio like noise-canceling headphones. Current adaptive filters offer some noise reduction but struggle with specific noise profiles. We propose the enhanced adaptive filter and a distributed learning utilizing a novel diffusion-based framework that leverages spline adaptation.

Machine learning-enhanced band gaps prediction for low-symmetry double and layered perovskites.

Density functional theory (DFT) calculations are widely used for material property prediction, but their computational cost can hinder the discovery of novel perovskites. This work explores machine learning (ML) as a faster alternative for predicting band gaps in complex perovskites, focusing on low-symmetry double and layered structures. We employ Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and Extreme Gradient Boosting (XGBoost) to predict both direct and indirect band gaps.

Convergence and performance analysis of kernel regularized robust recursive least squares.

Kernel recursive least squares (KRLS) is very sensitive to non-Gaussian noise and hence, robust extensions are proposed using maximum correntropy criterion or generalized maximum correntropy. However, because of the complex form of the model, there is no theoretical analysis on the convergence of these filters. In this paper, we propose a new alternative: Kernel Regularized Robust RLS (KRLS).