Results 61 to 70 of about 8,576 (212)

Wavelet‐Based Hurst Exponent Estimation

WIREs Computational Statistics, Volume 18, Issue 3, September 2026.
The review explores how wavelet‐based methods for estimating the Hurst parameters have developed from their theoretical roots to real‐world applications in fields like biology, engineering, and telecommunications. The review aims to highlight key techniques, compare their strengths and limitations, and point out challenges that still need to be ...
Dixon Vimalajeewa, Fabrizio Ruggeri, Brani Vidakovic   +2 more
wiley   +1 more source

Débruitage fréquentiel de signaux par EMD [PDF]

, 2013
Dans cet article, nous proposons un nouveau schéma de débruitage des signaux basé sur la décomposition modale empirique associée à une analyse fréquentielle.
DARE, Delphine, DARE-EMZIVAT, Delphine, BOUDRAA, Abdel-Ouahab, KOMATY, Ali   +3 more
core  

Multi‐Modal AI Approach in Depression Detection and Treatment: A Systematic Review of Last Decade

WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 3, September 2026.
Overview of multimodal approaches for depression detection and treatment. ABSTRACT Depression is a common and devastating mental health illness with serious personal and societal consequences. Despite advancing treatment techniques, there are still hurdles in the effective diagnosis and treatment of depression, such as prompt diagnosis, personalized ...
Smith K. Khare, Esmaeil S. Nadimi, U. Rajendra Acharya, Victoria Blanes‐Vidal   +3 more
wiley   +1 more source

The flow chart of the modified detrended fluctuation analysis with the empirical mode decomposition (EMD-DFA). [PDF]

, 2019
The flow chart of the modified detrended fluctuation analysis with the empirical mode decomposition (EMD-DFA).
Shouwei Yue (4423150), Ke Li (106849), Wei Wang (17594), Na Wei (122650), Yunling Xiao (8162568)   +4 more
core   +1 more source

Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors

Mathematics, 2019
Empirical mode decomposition (EMD)-based methods are powerful digital signal processing techniques because they do not need a priori information of the target signal due to their intrinsic adaptive behavior.
Martin Valtierra-Rodriguez, Juan Pablo Amezquita-Sanchez, Arturo Garcia-Perez, David Camarena-Martinez   +3 more
doaj   +1 more source

Toward Using Equation Discovery to Generate Parameterizations of Biogeochemical Processes

Geophysical Research Letters, Volume 53, Issue 12, 28 June 2026.
Abstract Equation discovery methods, such as symbolic regression, show great promise to generate parameterizations of biogeochemical processes in an objective data‐driven manner, yet remain untested in ocean biogeochemistry. Here, we apply symbolic regression to a state‐of‐the‐art ocean biogeochemical model, using it as a surrogate data set to ...
Chengwang Wang, B. B. Cael, Alessandro Tagliabue   +2 more
wiley   +1 more source

An Improved EMD Method Based on Utilizing Certain Inflection Points in the Construction of Envelope Curves

Archives of Acoustics, 2023
The empirical mode decomposition (EMD) algorithm is widely used as an adaptive time-frequency analysis method to decompose nonlinear and non-stationary signals into sets of intrinsic mode functions (IMFs).
Mohsen KAFIL, Kaveh DARABI, Saeed ZIAEI-RAD   +2 more
doaj   +1 more source

Global Observational Comparison of Large‐Scale Traveling Atmospheric and Ionospheric Disturbances During the May 2024 Geomagnetic Storm

Geophysical Research Letters, Volume 53, Issue 12, 28 June 2026.
Abstract During the geomagnetic storm on 10 May 2024, neutral density measurements from 14 Tianmu, Swarm, and GRACE‐FO satellites at ∼510 km altitude, combined with total electron content (TEC) observations, enabled the first global observational comparison of large‐scale traveling atmospheric and ionospheric disturbances (LSTADs/TIDs) via snapshots ...
Xiaolong Wei, Guoying Jiang, Yajun Zhu, Yongping Li, Jiangzhao Ai, Tingwei Han, Huijun Le, Aibin Zhang, Jiyao Xu   +8 more
wiley   +1 more source

A Multi-Stage Decomposition and Hybrid Statistical Framework for Time Series Forecasting

Axioms
Modeling and forecasting nonstationary and nonlinear economic time series remain fundamentally challenging due to structural breaks, volatility clustering, and noise contamination that distort the intrinsic stochastic structure.
Swera Zeb Abbasi, Mahmoud M. Abdelwahab, Imam Hussain, Moiz Qureshi, Moeeba Rind, Paulo Canas Rodrigues, Ijaz Hussain, Mohamed A. Abdelkawy   +7 more
doaj   +1 more source

Forecasting Electricity Market Risk Using Empirical Mode Decomposition (EMD)—Based Multiscale Methodology [PDF]

Energies, 2016
The electricity market has experienced an increasing level of deregulation and reform over the years. There is an increasing level of electricity price fluctuation, uncertainty, and risk exposure in the marketplace. Traditional risk measurement models based on the homogeneous and efficient market assumption no longer suffice, facing the increasing ...
Kaijian He, Hongqian Wang, Jiangze Du, Yingchao Zou   +3 more
openaire   +2 more sources