POT : Python Optimal Transport

Fast EMD for Python: a wrapper for Pele and Werman's C++ implementation of the Earth Mover's Distance metric

Signal processing method and algorithm library

CEEMDAN_LSTM is a Python project for decomposition-integration forecasting models based on EMD methods and LSTM.

Digital signal analysis library for python. The library includes such methods of the signal analysis, signal processing and signal parameter estimation as ARMA-based techniques; subspace-based techniques; matrix-pencil-based methods; singular-spectrum analysis (SSA); dynamic-mode decomposition (DMD); empirical mode decomposition; variational mod…

This is a MATLAB project which can calculate EMD/EEMD

Go directly from an event storming to a working API: Event Markdown / Markup validation & NodeJS CQRS/ES application builder.

Multi-task model for named-entity recognition, relation extraction, entity mention detection and coreference resolution.

GammaRay: a graphical interface to GSLib and other geomodeling algorithms. *NEW* in Apr, 11th: Contact analysis.

Ensemble Empirical Mode Decomposition (EEMD) and Its Complete Variant (CEEMDAN)

EMD(Empirical Mode decomposition) light weight library, C/C++ language

Simulated annealing lifting for high girth QC-LDPC include ACE/EMD optimization. Make QC-LDPC from protograph (base matrix)

Progressive edge growth PEG for LDPC code and QC-LDPC construction C++, Python, Matlab PEG with ACE and Avoiding Generating Small Cycles

Example of Empirical Mode Decomposition algorithm

Pipeline for analysing and visualising CyTOF datasets, with a focus on PTM signalling networks.

Paper reproduction: Instantaneous 3D EEG Signal Analysis Based on Empirical Mode Decomposition and the Hilbert–Huang Transform Applied to Depth of Anaesthesia

Analysis scripts accompanying "Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics" https://doi.org/10.1101/2021.04.12.439547

Class to filter EEG artifacts using WPT, ICA and EMD

Variational Optimal Transportation

Diagnóstico de falla de rodamiento utilizando descomposición modal empírica y deep learning