Signal Quality

Signal quality assessment focuses on evaluating the reliability and usability of data from various sources, aiming to improve data analysis and decision-making across diverse fields. Current research emphasizes the development and application of machine learning models, including convolutional neural networks, transformers, and autoencoders, to automatically identify and mitigate signal degradation from noise, interference, or artifacts. These advancements are crucial for enhancing the accuracy and reliability of applications ranging from medical diagnostics (e.g., fetal heart monitoring, ECG analysis) to autonomous vehicle navigation and industrial predictive maintenance, where data quality directly impacts system performance and safety.