Yanuar Rizki Pahlevi, MPCAS

​T​itle of master thesis: Deep Learning for Optical Tweezers: DeepCalib Implementation for Brownian Motion with Delayed Feedback

Password: 479414

Abs​tract:
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 andto measure the delayed feedback employed, using the trapped particle trajectory asan input. We demonstrate that this approach is outperforming variance methods inorder to calibrate stiffness, also outperforming approximation method to measure the delay in harmonic trap case.

Supervisor: Aykut Argun
Examiner: Giovanni Volpe
Opponent: Ivan Gentile Japiassu
Category Student project presentation
Location: Nexus 4030, meeting room, Kemigården 1, Fysik Origo
Starts: 09 June, 2022, 17:00
Ends: 09 June, 2022, 18:00

Page manager Published: Tue 31 May 2022.