Measurement of temperature and strain using Brillouin optical frequency-domain analysis method

2002 ◽  
Author(s):  
Vladimir A. Saetchnikov ◽  
Ellyn A. Chernyavskaya ◽  
Tatjana P. Yanukovich
Keyword(s):  
Frequency Domain ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Optical Frequency ◽  
Analysis Method

Frequency-domain analysis method for analyzing and improving the steady-state characteristics of microcantilever in tapping-mode atomic force microscopy

2018 ◽  
Vol 82 (1) ◽  
pp. 10701
Author(s):  
Xiaohui Gu ◽  
Lining Sun ◽  
Changhai Ru
Keyword(s):  
Steady State ◽  
Frequency Domain ◽  
Stability Margin ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
System Stability ◽  
Analysis Method ◽  
External Interference ◽  
Tapping Mode ◽  
The Stability

In tapping-mode AFM, the steady-state characteristics of microcantilever are extremely important to determine the AFM performance. Due to the external excitation signal and the tip-sample interactions, the solving process of microcantilever motion equation will become very complicated with the traditional time-domain analysis method. In this paper, we propose the novel frequency-domain analysis method to analyze and improve the steady-state characteristics of microcantilever. Compared with the previous methods, this new method has three prominent advantages. Firstly, the analytical expressions of amplitude and phase of cantilever system can be derived conveniently. Secondly, the stability of the cantilever system can be accurately determined and the stability margin can be obtained quantitatively in terms of the phase margin and the magnitude margin. Thirdly, on this basis, external control mechanism can be devised quickly and easily to guarantee the high stability of the cantilever system. With this novel method, we derive the frequency response curves and discuss the great influence of the intrinsic parameters on the system stability, which provides theoretical guidance for selecting samples to achieve better AFM images in the experiments. Moreover, we introduce a new external series correction method to significantly increase the stability margin. The results indicate that the cantilever system is no longer easily disturbed by external interference signals.

scholarly journals Time-Efficient Convolutional Neural Network-Assisted Brillouin Optical Frequency Domain Analysis

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2724
Author(s):  
Christos Karapanagiotis ◽  
Aleksander Wosniok ◽  
Konstantin Hicke ◽  
Katerina Krebber
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Frequency Domain ◽  
Curve Fitting ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Processing Method ◽  
Optical Frequency ◽  
Post Processing

To our knowledge, this is the first report on a machine-learning-assisted Brillouin optical frequency domain analysis (BOFDA) for time-efficient temperature measurements. We propose a convolutional neural network (CNN)-based signal post-processing method that, compared to the conventional Lorentzian curve fitting approach, facilitates temperature extraction. Due to its robustness against noise, it can enhance the performance of the system. The CNN-assisted BOFDA is expected to shorten the measurement time by more than nine times and open the way for applications, where faster monitoring is essential.

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