SICD (Sistema Integral de Control Distribuido)

RAY-DEEP Cosmic Ray Detection using Deep Learning

Project Description:

The goal of RAY-DEEP is to develop an experiment based on the detection of cosmic rays, focusing on muons, photons, and electrons, using a real-time FPGA target. This is achieved through image classification techniques using Deep Learning models implemented in System on Chip (SoC + FPGA) for Edge Computing processing, and validated on high-altitude balloons.

Project Objectives:

As a result of the project, an electronic system will be developed that incorporates a camera and AI models capable of identifying the ionization to which the earth is subjected, transforming the results into an impact on the scientific, industrial, and social communities. The developed technology and the obtained image sources will strengthen existing public databases (images) in different environments and altitudes, allowing the scientific community to continue researching and, at the same time, supporting aerial systems, autonomous vehicles, and crews by providing information about non-visible conditions to which they are exposed

Specific technical objectives:

Develop an artificial intelligence system applied to cosmic ray detection to identify risk in real-time, and ensure that this system can be integrated into a device.
Highlight the relevance of this experiment concerning the possible uses of Artificial Intelligence, through various techniques, in the field of minimizing cosmic risks and opening the door to new developments in the future that can generate high-value services.
Development of models and the implementation of algorithms.
Analyze the impact of the experiment in the field of developing solutions based on Artificial Intelligence, which can also be significant, as the chosen technique, based on neuromorphic technology, represents an applied innovation that will serve as a reference for other potential uses.