Earthquake detection and analysis in Python.
Learn how to use PyCBC to analyze gravitational-wave data and do parameter inference.
In this project, I implement an enhanced active contour method that uses discrete wavelet transform for energy minimization to increase the accuracy.
Real-Time implementation of EQcorrscan methods.
A mock data study for 3G correlated confusion noise
StochasticA is a textbook / website for an “Introduction to Stochastic Signal Processing”. Materials for this website can be found here. Be sure to read the README.md document if you want to know more about the implementation.
Bat like sonar sensor that can track multiple targets and estimate angle of arrival using chirps and cross correlation in near real time.
Find point sources in sky maps using matched filtering.
Procesamiento de imagenes usando tecnica Gaussian-Matched Filters
DiallelX is a CPU-oriented modern fortran program to approximate Network Cross-Correlation coefficients (NCCs) among multiple continuous records and template waveforms observed at multiple seismic stations. The results, relatively less accurate but sufficient to find new seismic events, are obtained several-fold faster than a conventional scheme.
26th place solution for Kaggle LANL Earthquake Prediction competition
This repository includes LMS-Adaptive algorithm implementation
Optimized Codes to run Matched Filtering on GW Data using Particle Swarm Optimization
Completed solutions, analyses, and documentation for the Gravitational Wave Open Data Workshop 2026, covering the full gravitational-wave data analysis pipeline from data-access and waveform generation to matched filtering, Bayesian parameter estimation, event detection, and continuous-wave searches using Python, PyCBC, GWpy, Bilby, and LALSuite.
Performs automatic segmentation of hard exudates using the opencv library
Signal processing and parameter estimation of GW190521 — a real LIGO gravitational-wave event — using matched filtering, PSD estimation, CBC and burst waveform models, and Bayesian inference.
Python learning project exploring acoustic signal analysis, noise, multipath echoes and matched-filter delay detection.
A digital communication project that aims to explore matched filters.
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