A Python library for signal decomposition algorithms

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…

A demo of using Hilbert-Huang Transform (HHT) for non-stationary and non-linear signal analysis.

Python library to Fetch & Analyze Stock Market data.

Stock Market Decision Support System using Deep Learning & Sentiment Analysis

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

Python implementation of time varying filter EMD

Parallel 1D Empirical Mode Decomposition (EMD) on GPU for processing multiple signals. For each IMF, it computes the upper and lower envelopes and identifies critical points (local maxima and minima).

This repository contains code reproducing an existing method to detect atrial fibrillation using empirical mode decomposition of signals. This was a lecture that I gave for graduate-level BioSignal Processing course.

Holo-Hilbert spectral analysis in python

Adaptive Multivariate Empirical Mode Decomposition

Analysis of Phonocardiogram signals using Empirical Mode Decomposition and Higher Order Statistics. This project was part of the course Advanced Signal Processing Techniques (ECE Department, AUTH, 8th semester).

Empirical Mode Decomposition filter to remove the baseline frequency in ECG signal

Empirical Mode Decomposition

Sensor selection for process monitoring based on deciphering acoustic emissions from different dynamics of the Laser Powder Bed Fusion process using Empirical Mode Decompositions and Interpretable Machine Learning

Implementation of the fabemd based exposure fusion algorithm in Qt/C++

Sentiment Index of China's Stock Market and its Causal Effect on Stock Indices

Development of the smoothing agregator that originates from the time series research project: https://github.com/lebedevaale/early_warning_model

Code, slides and raw data for talk presented at the satRday Conference in Belgrade on October 27, 2018 (https://belgrade2018.satrdays.org/).

Exemplo de aplicação do método EMD (empirical mode decomposition) apresentado na disciplina "Introdução à Ciência de Dados" pelo professor Rodrigo Mello em 2019, em São Carlos-SP