Smart decimation method applied to real-time monitoring

Real-time signal monitoring in high-data-rate environments, such as fusion energy experiments, requires efficient data reduction techniques to ensure timely and accurate visualization. Traditional decimation methods, like the widely used “1 of N,” select points uniformly without considering the signal’s intrinsic characteristics. This approach often results in poor similarity between the decimated and original signals, particularly for high acquisition rate data. This work introduces a novel intelligent decimation method tailored for one-dimensional time-evolving signals. The proposed method dynamically analyzes the signal in real-time to identify regions of high informational content and adaptively determines the most suitable decimation points. By prioritizing signal richness and distributing points more precisely, this method achieves superior fidelity compared to classical decimation, while maintaining or surpassing decimation efficiency. Experimental validation using TJ-II data demonstrates significant improvements in signal similarity, highlighting the potential of intelligent decimation for advancing real-time monitoring in data-intensive scientific environments.