Abnormal Signal

Abnormal signal detection and analysis encompass diverse research areas focused on identifying and interpreting deviations from expected patterns in various data types, ranging from acoustic and biological signals to neuroimaging data and digital communications. Current research emphasizes the development and application of machine learning models, including neural networks (e.g., ResNets, LSTMs, and transformers), graph convolutional networks, and generative models, to improve signal detection accuracy, robustness, and interpretability. These advancements have significant implications for diverse fields, enabling improved diagnostics in healthcare, enhanced safety in autonomous systems, and more efficient data processing in various scientific and engineering applications.