Project Idea
This project investigates the creation of an intelligent enhancementmodule aimed at enhancing the quality of signals received by radio telescopes through AI-powered noise filtering. Constructed using a filter out noise and uncover faint celestial signals. To assess thRaspberry Pi, the system analyzes raw astronomical data instantly, utilizing machine learning particularly neural networks and SVM to e system, we utilized simulated radio signals containing Gaussian noise. ese results indicate that a budget-friendly, compact platform cThe findings indicated that neural networks outperformed SVM in improving signal clarity, realizing an average enhancement of 35% in signal to noise ratio, and did so with low latency on a Raspberry Pi 4. T han significantly improve the performance of radio telescopes. This study lays a hopeful foundation for upcoming experimental trials and real world applications in actual observational settings.
01
The Beginning
The idea started from the need to improve astronomical signal quality and remove Noise using AI instead of relying only on traditional methods.
02
The Idea
The project provides an AI-Powered Noise Filtering Module running on a Raspberry Pi to improve signals, achieving a 35% improvement in SNR using Neural Networks.
03
The Experience
We started with the concept of using Machine Learning for signal processing. We then developed the system on a Raspberry Pi 4 and tested it with Simulated Radio Signals containing Gaussian Noise. Neural Networks and SVM were compared, with Neural Networks showing better performance.
A visual tour documenting the manufacturing stages, laboratory testing, and live tracking experiments in the field.
Explore the projects and experiments that shape the Electrospace experience.
