Deep Learning for Optical Tweezers DeepCalib Implementation for Brownian Motion with Delayed Feedback
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Examensarbete för masterexamen
Modellbyggare
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Sammanfattning
Brownian motion with delayed feedback, theoretically studied to take control of
Brownian particle movement’s direction. One can use optical tweezers to implement
delayed feedback. Calibrating optical tweezers with delay implemented is not an
easy job. In this study, Deep learning technique using Long Short Term Memory
(LSTM) layer as main composition of the model to calibrate the trap stiffness and to
measure the delayed feedback employed, using the trapped particle trajectory as an
input. We demonstrate that this approach is outperforming approximation method
in order to calibrate stiffness and to measure the delay in harmonic trap case.
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Ämne/nyckelord
Deep Learning, Optical Tweezers, Optical Tweezers, Delayed Feedback