ResIOT FluidAI
A Smart System for Fluid Analysis in Dynamic, Ecological, and Multisectoral Contexts through Innovative Artificial Intelligence and Machine Learning Models
The ResIOT FluidAI project, co-funded by the European Regional Development Fund (FESR 2021-2027 n.11693 – 30/07/2024), aims to develop an innovative software system based on Artificial Intelligence designed for advanced and automated fluid analysis. Leveraging cutting-edge technologies, the system enables detailed analysis of liquids, identifying microorganisms, bacteria, and solid residues, even in dynamic contexts where fluids are in motion within natural or artificial conduits. This project addresses the needs of various strategic sectors, including healthcare, water management, the food industry, the chemical industry, and environmental management. The system provides a critical contribution to improving the planning, monitoring, and treatment of water resources, supporting essential activities such as potable water quality control, wastewater analysis, and the reduction of water waste.
Project Goals
The project aims to develop a comprehensive and autonomous software system capable of:
. Accurately analyzing fluids to identify microorganisms and residues through real-time processing of high-resolution images.
. Generating advanced reports and forecasts based on the collected data, providing valuable tools for continuous monitoring and resource optimization.
. Integrating environmental data such as weather conditions and geographic location to enhance the accuracy and effectiveness of the analyses.
Thanks to these features, the system will deliver precise and reliable information, essential for reducing environmental impact, ensuring water quality, and optimizing water consumption.
A Project for Innovation
The system is designed to be highly performant and flexible, leveraging modern technologies such as On-Edge computation, which allows data to be processed directly on the analysis device. This approach ensures fast and accurate results, even with limited hardware resources. Additionally, the software is equipped with self-learning capabilities, continuously improving its accuracy by processing the data collected over time.
With these features, the project represents a significant step forward toward smarter, more sustainable, and efficient water resource management, offering tangible benefits for the environment, public health, and industry.
