Doppler Velocity

Doppler velocity, the change in frequency of a wave due to the relative motion between source and observer, is a crucial measurement across diverse scientific fields. Current research focuses on improving the accuracy and efficiency of Doppler velocity measurements and analysis, employing techniques like deep learning (including convolutional neural networks and recurrent neural networks) and novel algorithms such as Doppler Iterative Closest Point (DICP) to address challenges such as noise reduction, signal quality assessment, and artifact removal in applications ranging from medical imaging to autonomous driving. These advancements are significantly impacting various fields, enabling more precise measurements in areas like fetal monitoring, cardiovascular analysis, and robotic surgery, as well as enhancing the capabilities of autonomous systems through improved object detection and localization.