Results 191 to 200 of about 31,823 (308)

Physics-based foundation for empirical mode decomposition

, 2009
McFarland, DM, Tsakirtzis, S, Vakakis, AF, Bergman, LA, Lee, YS   +4 more
core   +1 more source

Redefining LiF as a Nanostructured Building Block for Interphase Engineering in Lithium Metal Batteries

Advanced Science, EarlyView.
LiF‐rich solid electrolyte interphases in lithium metal batteries are redefined as controllable nanostructured building blocks rather than homogeneous protective films. This review shows how LiF distribution, grain boundaries, and heterogeneous interfaces govern electron blocking and Li ion transport, and compares fluorinated electrolytes, artificial ...
Gwangsik Kim, JinHyeok Cha
wiley   +1 more source

Automatic differentiation of voluntary and tremulous motion using ensemble empirical mode decomposition and convolutional Bi-directional LSTM. [PDF]

Sci Rep
Ling JQ, Bakibillah ASM, Chan PY, Nurzaman SG, Tan CP.   +4 more
europepmc   +1 more source

A New Method of Identifying Characteristic Points in the Impedance Cardiography Signal Based on Empirical Mode Decomposition. [PDF]

Sensors (Basel), 2023
Trybek P, Sobotnicka E, Wawrzkiewicz-Jałowiecka A, Machura Ł, Feige D, Sobotnicki A, Richter-Laskowska M.   +6 more
europepmc   +1 more source

Empirical Mode Decomposition Based Time-Frequency Attributes

, 1999
This paper describes a new technique, called the empirical mode decomposition (EMD), that allows the decomposition of one-dimensional signals into intrinsic oscillatory modes.
Ivan Magrin-Chagnolleau And, Ivan Magrin-chagnolleau, Richard G. Baraniuk   +2 more
core  

A Phase‐Resolved Geometric Deep Learning Framework Maps Structural Determinants of Disease‐Associated Protein Aggregation and Guides Suppressor Design

Advanced Science, EarlyView.
SKALE 2.0 maps disease‐associated protein aggregation as a phase‐resolved structural process, linking mutation‐induced geometric perturbations to nucleation, elongation, and suppressor design. Across neurodegenerative proteins, the framework reveals cryptic aggregation vulnerabilities, separates phase‐concordant and phase‐switching mutations, and ...
Jia Shen Sio, Wei Xuan Wilson Loo, Yan Shan Loo, Wen Xin Tan, Hui Xuan Lim, Huitao Liu, Chen Seng Ng   +6 more
wiley   +1 more source

Improving the performance of daily pan evaporation (Ev_p) prediction using the ensemble empirical mode decomposition combined with deep learning models. [PDF]

Sci Rep
Kayhomayoon Z, Arya Azar N, Ghordoyee Milan S, Berndtsson R, Kianmehr P.   +4 more
europepmc   +1 more source

An Empirical Mode Decomposition Fuzzy Forecast Model for COVID-19. [PDF]

Neural Process Lett, 2022
Chen BL, Shen YY, Zhu GC, Yu YT, Ji M.
europepmc   +1 more source

Condition‐Associated Pattern Extraction and Recovery From Multi‐Condition Single‐Cell RNA‐seq Data With CAPER

Advanced Science, EarlyView.
Decoupling biological signals from unwanted variation in multi‑condition single‑cell RNA sequencing data remains challenging. CAPER disentangles condition‑associated biological effects from sample heterogeneity through matrix factorization, producing interpretable latent factors and a batch‑corrected expression matrix.
Ye Li, Jin Ning, An Wang, Minxi Shi, Yuanze Chen, Guoliang Liu, Shiquan Sun   +6 more
wiley   +1 more source

Cancelable biometric authentication leveraging empirical mode decomposition and quaternion representations for IoT security. [PDF]

Sci Rep
Nasr M, Brzostowski K, Piórkowski A, Abd El-Samie FE.   +3 more
europepmc   +1 more source