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pro vyhledávání: '"A. Tieng"'
Sleep staging is a challenging task, typically manually performed by sleep technologists based on electroencephalogram and other biosignals of patients taken during overnight sleep studies. Recent work aims to leverage automated algorithms to perform
Externí odkaz:
http://arxiv.org/abs/2411.07964
Alternating Diffusion (AD) is a commonly applied diffusion-based sensor fusion algorithm. While it has been successfully applied to various problems, its computational burden remains a limitation. Inspired by the landmark diffusion idea considered in
Externí odkaz:
http://arxiv.org/abs/2404.19649
Autor:
Jeong, Seonghyeon, Wu, Hau-Tieng
We present a theoretical foundation regarding the boundedness of the t-SNE algorithm. t-SNE employs gradient descent iteration with Kullback-Leibler (KL) divergence as the objective function, aiming to identify a set of points that closely resemble t
Externí odkaz:
http://arxiv.org/abs/2401.17675
Autor:
Wu, Hau-Tieng
In this manuscript, we propose an efficient manifold denoiser based on landmark diffusion and optimal shrinkage under the complicated high dimensional noise and compact manifold setup. It is flexible to handle several setups, including the high ambie
Externí odkaz:
http://arxiv.org/abs/2401.03921
Autor:
Shimizu, Riki, Wu, Hau-Tieng
Objective: Sleep spindles contain crucial brain dynamics information. We introduce the novel non-linear time-frequency analysis tool 'Concentration of Frequency and Time' (ConceFT) to create an interpretable automated algorithm for sleep spindle anno
Externí odkaz:
http://arxiv.org/abs/2310.18381
We introduce a novel ridge detection algorithm for time-frequency (TF) analysis, particularly tailored for intricate nonstationary time series encompassing multiple non-sinusoidal oscillatory components. The algorithm is rooted in the distinctive geo
Externí odkaz:
http://arxiv.org/abs/2309.06673
Dealing with time series with missing values, including those afflicted by low quality or over-saturation, presents a significant signal processing challenge. The task of recovering these missing values, known as imputation, has led to the developmen
Externí odkaz:
http://arxiv.org/abs/2309.04630
Objective: Breathing pattern variability (BPV), as a universal physiological feature, encodes rich health information. We aim to show that, a high-quality automatic sleep stage scoring based on a proper quantification of BPV extracting from the singl
Externí odkaz:
http://arxiv.org/abs/2306.02857
Autor:
Chen, Chih-Wei, Wu, Hau-Tieng
A common method for estimating the Hessian operator from random samples on a low-dimensional manifold involves locally fitting a quadratic polynomial. Although widely used, it is unclear if this estimator introduces bias, especially in complex manifo
Externí odkaz:
http://arxiv.org/abs/2303.12547
The moving average of the complex modulus of the analytic wavelet transform provides a robust time-scale representation for signals to small time shifts and deformation. In this work, we derive the Wiener chaos expansion of this representation for st
Externí odkaz:
http://arxiv.org/abs/2301.01540