Introduces novel zero-phase filter matrix designs and three new sparsity-assisted models (SASD, SAPR, SASDPR) that combine LTI filters, sparse derivatives, and wavelets for simultaneous denoising and pattern recognition, demonstrated on sleep EEG.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Bayesian particle filtering with Rao-Blackwellisation for joint kinematic state and latent intent estimation using virtual leader models on simulated and radar data.
citing papers explorer
-
Sparsity-Assisted Signal Denoising and Pattern Recognition in Time-Series Data
Introduces novel zero-phase filter matrix designs and three new sparsity-assisted models (SASD, SAPR, SASDPR) that combine LTI filters, sparse derivatives, and wavelets for simultaneous denoising and pattern recognition, demonstrated on sleep EEG.