Software With an Improved Ability to Track a Large Number of Markers
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University of Pittsburgh researchers have developed an algorithm for use in marker-free drift tracking during high-throughput localization microscopy. This approach aims to compensate for sample drift during image acquisition using a fast and precise algorithm without the need for additional markers or optical components. These challenges have led many researchers to use marker-assisted approaches to improve precision.
University of Pittsburgh researchers have identified an ultra-fast high-density emitter localization algorithm designed for high throughput super resolution localization microscopy. This innovative algorithm significantly reduces data analysis time, achieving a throughput three orders of magnitude higher than conventional algorithms while maintaining comparable localization accuracy and emitter recall rate. FIG is a schematic diagram of a control system exemplifying an embodiment of the innovative algorithm embedded in super-resolution localization microscopy.
University of Pittsburgh researchers have developed a novel method for full-field, non-intrusive measurement of fluid velocity fields using Event-Based Cameras (EBC) combined with a proprietary algorithm. The developed algorithm processes this data using reduced order modeling and machine learning, reconstructing a dense flow field with high accuracy. The data obtained from EBC is processed using a novel algorithm that learns the inherent spatio-temporal correlation in the data, reconstructing a dense flow field.