A Framework for AI-Powered Neutralization of Environmental Interferences in Measurement Processes

Accurate measurements are essential for understanding, comparing, and controlling phenomena around us. However, measurement accuracy is often compromised by interfering factors such as instrument quality, calibration, operator skill, and environmental conditions (e.g., vibrations, electromagnetic fields, or weather). While issues like instrument quality and operator training can often be addressed, mitigating complex environmental interferences poses a greater challenge. Traditional approaches struggle to model the intricate effects of such interferences, making AI a promising alternative due to its ability to leverage large datasets to capture complex patterns and relationships. In this paper, we propose AIM, a software framework designed to support the development of AI-powered solutions for neutralizing environmental interferences during measurement processes. AIM provides a modular architecture and generic building blocks to seamlessly integrate base measurement instruments, auxiliary sensors, and AI models, facilitating the creation of tailored solutions for accurate measurements.