AI-Assisted Identification of State and Type of Flat-Panel Monitors in the Presence of EM Noise

We present a method for identification of type and state (on/off) of flat-panel monitors, based on artificial neural networks and measurements of electromagnetic emanations in the presence of real-life noise, including other monitors in the vicinity. The proposed approach can identify monitor type and state with 99% accuracy (from a set of twelve). Instead of a single network, we use an ensemble of independently trained multilayer perceptron networks, since each training yields a different network. Further, we present an approach for automatic detection of significant frequency subranges of measured emanations, which eliminates less relevant inputs to neural networks and leads to reduction of their topological complexity, accelerates the training, minimizes the required set of experimental data and provides an overall insight into the spectral characteristics of emanations.