vibration

A dirt cheap solution to enable haptic feedback on trackers in VRChat and Resonite

Educational Vibration programs. Intended for undergraduate and early graduate students.

Standards-conformant acoustics library for Python: measurement and metrology, building, environmental and room acoustics, psychoacoustics, electroacoustics, vibration, underwater and aircraft/rotorcraft noise. Every method is implemented from the standard and validated against reference data, with teaching-style docs (formerly PyOctaveBand).

A Structural Dynamics Python Library

Harmonic balance solvers

Vibration Controller targetting Multiple-Input-Multiple-Output (MIMO) and Combined Environments Control

dynamic analysis of structural vibrations.

This repository will simulate an IoT-based predictive maintenance system designed to monitor industrial equipment through sensors. It will include data ingestion, processing, and machine learning components to predict potential failures, optimizing maintenance schedules and reducing downtime.

Calculation tool for loudspeaker design

solve the dynamics of laminated plates using a modified Ritz approach (https://doi.org/10.1016/j.compstruct.2022.116504)

High-precision physics engine dedicated to the study of standing waves and visualization of its normal modes.

Home of the Prometheus Alarm Clock Project

Event-based Estimation of Periodic Phenomena Rate using Correlation in 3D - Kolář, J., Špetlík, R., Matas, J. (2024), In Proceedings of the 17th International Conference on Machine Vision (ICMV 2024)

Code to run on a Raspberry PI zero w to make it act as a keyboard, then maps Bluetooth controller inputs to keyboard keys to make it work on RG350

API for Access to the BAE T1A Hawk Starboard wing and Full Structure Modal Tests

🧠 [Signal] Fault-specific vibration signal generation using WGAN-CGAN

Motor vibration analysis using order tracking

This repo is OBSOLETE! see https://github.com/3ll3d00d/qvibe-analyser and https://github.com/3ll3d00d/qvibe-recorder instead

End-to-End Industrial AI-driven condition monitoring system for early fault detection in rotating machinery using Unsupervised LSTM Autoencoders. Detects failures 72h in advance. Early fault detection with 100% accuracy on high-frequency sensor data (NASA IMS Dataset).