Search Results for "arduino data acquisition"

Ubix stands for Universal Business Intelligence Computing System. Ubix Linux is an open-source, Debian-based Linux distribution geared towards data acquisition, transformation, analysis and presentation. Ubix Linux purpose is to offer a tiny but versatile datalab. Ubix Linux is easily accessible, resource-efficient and completely portable on a simple USB key. Ubix Linux is a perfect toolset for learning data analysis and artificial intelligence basics on small to medium datasets. ...

fastMRI is a large-scale collaborative research project by Facebook AI Research (FAIR) and NYU Langone Health that explores how deep learning can accelerate magnetic resonance imaging (MRI) acquisition without compromising image quality. By enabling reconstruction of high-fidelity MR images from significantly fewer measurements, fastMRI aims to make MRI scanning faster, cheaper, and more accessible in clinical settings. The repository provides an open-source PyTorch framework with data loaders, subsampling utilities, reconstruction models, and evaluation metrics, supporting both research reproducibility and practical experimentation. ...

This repo contains the code for the O'Reilly Media, Inc. book "Hands-on Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data" by Ankur A. Patel. Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to the holy grail in AI research, the so-called general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot...

The Hydroponic Automation Platform Initiative (HAPI) develops and provides hardware and software components for automating food production using hydroponic, aquaponics, and precision agriculture techniques. High-yield production in urban settings is one of the primary goals. Artifacts include hardware design (mainly Arduino-based), firmware, management software and reporting modules.

A three-step approach towards experimental brain-computer-interfaces, based on the OCZ nia device for EEG-data acquisition and artificial neural networks for signal-interpretation.