Electromagnetic Interference

Electromagnetic interference (EMI) poses a significant challenge to the reliable operation of electronic systems, particularly in sensitive applications like medical imaging and automotive electronics. Current research focuses on developing advanced EMI mitigation techniques, employing both traditional signal processing methods and increasingly sophisticated artificial intelligence approaches, such as deep learning and physics-informed neural networks (PINNs), including Kolmogorov-Arnold Networks (KANs). These efforts aim to improve the accuracy and efficiency of EMI suppression, leading to enhanced system performance and reduced energy consumption in various sectors. The development of automated EMI classification systems, using deep learning models trained on real-world data, is also a key area of progress, improving the consistency and speed of compliance testing.